├── src ├── util │ ├── __init__.py │ ├── constants.py │ ├── util.py │ └── ud_list.py ├── h01_data │ ├── __init__.py │ ├── model │ │ ├── __init__.py │ │ ├── albert_per_word.py │ │ ├── roberta_per_word.py │ │ └── bert_per_word.py │ ├── processor │ │ ├── __init__.py │ │ ├── albert.py │ │ ├── roberta.py │ │ ├── fasttext.py │ │ ├── ud.py │ │ └── bert.py │ └── process.py ├── h02_learn │ ├── __init__.py │ ├── model │ │ ├── __init__.py │ │ ├── data_parallel.py │ │ ├── base.py │ │ ├── categoric.py │ │ ├── linear.py │ │ └── mlp.py │ ├── dataset │ │ ├── pos_tag.py │ │ ├── dep_label.py │ │ ├── parse.py │ │ ├── __init__.py │ │ └── base.py │ └── train_info.py └── h03_analysis │ └── plot_all.sh ├── activate.sh ├── checkpoints ├── dep_label │ ├── basque │ │ └── mlp │ │ │ ├── bert │ │ │ ├── finished.txt │ │ │ └── all_results.tsv │ │ │ ├── fast │ │ │ ├── finished.txt │ │ │ └── all_results.tsv │ │ │ └── random │ │ │ ├── finished.txt │ │ │ └── all_results.tsv │ ├── english │ │ └── mlp │ │ │ ├── albert │ │ │ ├── finished.txt │ │ │ └── all_results.tsv │ │ │ ├── bert │ │ │ ├── finished.txt │ │ │ └── all_results.tsv │ │ │ ├── fast │ │ │ ├── finished.txt │ │ │ └── all_results.tsv │ │ │ ├── random │ │ │ ├── finished.txt │ │ │ └── all_results.tsv │ │ │ └── roberta │ │ │ ├── finished.txt │ │ │ └── all_results.tsv │ ├── marathi │ │ └── mlp │ │ │ ├── bert │ │ │ ├── finished.txt │ │ │ └── all_results.tsv │ │ │ ├── fast │ │ │ ├── finished.txt │ │ │ └── all_results.tsv │ │ │ └── random │ │ │ ├── finished.txt │ │ │ └── all_results.tsv │ └── turkish │ │ └── mlp │ │ ├── bert │ │ ├── finished.txt │ │ └── all_results.tsv │ │ ├── fast │ │ ├── finished.txt │ │ └── all_results.tsv │ │ └── random │ │ ├── finished.txt │ │ └── all_results.tsv └── pos_tag │ ├── basque │ └── mlp │ │ ├── bert │ │ ├── finished.txt │ │ └── all_results.tsv │ │ ├── fast │ │ ├── finished.txt │ │ └── all_results.tsv │ │ └── random │ │ ├── finished.txt │ │ └── all_results.tsv │ ├── english │ └── mlp │ │ ├── bert │ │ ├── finished.txt │ │ └── all_results.tsv │ │ ├── fast │ │ ├── finished.txt │ │ └── all_results.tsv │ │ ├── albert │ │ ├── finished.txt │ │ └── all_results.tsv │ │ ├── random │ │ ├── finished.txt │ │ └── all_results.tsv │ │ └── roberta │ │ ├── finished.txt │ │ └── all_results.tsv │ ├── marathi │ └── mlp │ │ ├── bert │ │ ├── finished.txt │ │ └── all_results.tsv │ │ ├── fast │ │ ├── finished.txt │ │ └── all_results.tsv │ │ └── random │ │ ├── finished.txt │ │ └── all_results.tsv │ └── turkish │ └── mlp │ ├── bert │ ├── finished.txt │ └── all_results.tsv │ ├── fast │ ├── finished.txt │ └── all_results.tsv │ └── random │ ├── finished.txt │ └── all_results.tsv ├── results └── plots │ ├── explanation.pdf │ ├── pos_tag__basque.pdf │ ├── pos_tag__english.pdf │ ├── pos_tag__marathi.pdf │ ├── pos_tag__turkish.pdf │ ├── dep_label__basque.pdf │ ├── dep_label__english.pdf │ ├── dep_label__marathi.pdf │ └── dep_label__turkish.pdf ├── environment.yml ├── .circleci └── config.yml ├── README.md ├── .gitignore └── Makefile /src/util/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /src/h01_data/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /src/h02_learn/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /activate.sh: -------------------------------------------------------------------------------- 1 | conda activate bayesian-mi -------------------------------------------------------------------------------- /checkpoints/dep_label/basque/mlp/bert/finished.txt: -------------------------------------------------------------------------------- 1 | done training 2 | -------------------------------------------------------------------------------- /checkpoints/dep_label/basque/mlp/fast/finished.txt: -------------------------------------------------------------------------------- 1 | done training 2 | -------------------------------------------------------------------------------- /checkpoints/pos_tag/basque/mlp/bert/finished.txt: -------------------------------------------------------------------------------- 1 | done training 2 | -------------------------------------------------------------------------------- /checkpoints/pos_tag/basque/mlp/fast/finished.txt: -------------------------------------------------------------------------------- 1 | done training 2 | -------------------------------------------------------------------------------- /checkpoints/pos_tag/basque/mlp/random/finished.txt: -------------------------------------------------------------------------------- 1 | done training 2 | -------------------------------------------------------------------------------- /checkpoints/pos_tag/english/mlp/bert/finished.txt: -------------------------------------------------------------------------------- 1 | done training 2 | -------------------------------------------------------------------------------- /checkpoints/pos_tag/english/mlp/fast/finished.txt: -------------------------------------------------------------------------------- 1 | done training 2 | -------------------------------------------------------------------------------- /checkpoints/pos_tag/marathi/mlp/bert/finished.txt: -------------------------------------------------------------------------------- 1 | done training 2 | -------------------------------------------------------------------------------- /checkpoints/pos_tag/marathi/mlp/fast/finished.txt: -------------------------------------------------------------------------------- 1 | done training 2 | -------------------------------------------------------------------------------- /checkpoints/pos_tag/turkish/mlp/bert/finished.txt: -------------------------------------------------------------------------------- 1 | done training 2 | -------------------------------------------------------------------------------- /checkpoints/pos_tag/turkish/mlp/fast/finished.txt: -------------------------------------------------------------------------------- 1 | done training 2 | -------------------------------------------------------------------------------- /checkpoints/dep_label/basque/mlp/random/finished.txt: -------------------------------------------------------------------------------- 1 | done training 2 | -------------------------------------------------------------------------------- /checkpoints/dep_label/english/mlp/albert/finished.txt: -------------------------------------------------------------------------------- 1 | done training 2 | -------------------------------------------------------------------------------- /checkpoints/dep_label/english/mlp/bert/finished.txt: -------------------------------------------------------------------------------- 1 | done training 2 | -------------------------------------------------------------------------------- /checkpoints/dep_label/english/mlp/fast/finished.txt: -------------------------------------------------------------------------------- 1 | done training 2 | -------------------------------------------------------------------------------- /checkpoints/dep_label/english/mlp/random/finished.txt: -------------------------------------------------------------------------------- 1 | done training 2 | -------------------------------------------------------------------------------- /checkpoints/dep_label/english/mlp/roberta/finished.txt: -------------------------------------------------------------------------------- 1 | done training 2 | -------------------------------------------------------------------------------- /checkpoints/dep_label/marathi/mlp/bert/finished.txt: -------------------------------------------------------------------------------- 1 | done training 2 | -------------------------------------------------------------------------------- /checkpoints/dep_label/marathi/mlp/fast/finished.txt: -------------------------------------------------------------------------------- 1 | done training 2 | -------------------------------------------------------------------------------- /checkpoints/dep_label/marathi/mlp/random/finished.txt: -------------------------------------------------------------------------------- 1 | done training 2 | -------------------------------------------------------------------------------- /checkpoints/dep_label/turkish/mlp/bert/finished.txt: -------------------------------------------------------------------------------- 1 | done training 2 | -------------------------------------------------------------------------------- /checkpoints/dep_label/turkish/mlp/fast/finished.txt: -------------------------------------------------------------------------------- 1 | done training 2 | -------------------------------------------------------------------------------- /checkpoints/dep_label/turkish/mlp/random/finished.txt: -------------------------------------------------------------------------------- 1 | done training 2 | -------------------------------------------------------------------------------- /checkpoints/pos_tag/english/mlp/albert/finished.txt: -------------------------------------------------------------------------------- 1 | done training 2 | -------------------------------------------------------------------------------- /checkpoints/pos_tag/english/mlp/random/finished.txt: -------------------------------------------------------------------------------- 1 | done training 2 | -------------------------------------------------------------------------------- /checkpoints/pos_tag/english/mlp/roberta/finished.txt: -------------------------------------------------------------------------------- 1 | done training 2 | -------------------------------------------------------------------------------- /checkpoints/pos_tag/marathi/mlp/random/finished.txt: -------------------------------------------------------------------------------- 1 | done training 2 | -------------------------------------------------------------------------------- /checkpoints/pos_tag/turkish/mlp/random/finished.txt: -------------------------------------------------------------------------------- 1 | done training 2 | -------------------------------------------------------------------------------- /results/plots/explanation.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rycolab/bayesian-mi/HEAD/results/plots/explanation.pdf -------------------------------------------------------------------------------- /results/plots/pos_tag__basque.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rycolab/bayesian-mi/HEAD/results/plots/pos_tag__basque.pdf -------------------------------------------------------------------------------- /results/plots/pos_tag__english.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rycolab/bayesian-mi/HEAD/results/plots/pos_tag__english.pdf -------------------------------------------------------------------------------- /results/plots/pos_tag__marathi.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rycolab/bayesian-mi/HEAD/results/plots/pos_tag__marathi.pdf -------------------------------------------------------------------------------- /results/plots/pos_tag__turkish.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rycolab/bayesian-mi/HEAD/results/plots/pos_tag__turkish.pdf -------------------------------------------------------------------------------- /results/plots/dep_label__basque.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rycolab/bayesian-mi/HEAD/results/plots/dep_label__basque.pdf -------------------------------------------------------------------------------- /results/plots/dep_label__english.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rycolab/bayesian-mi/HEAD/results/plots/dep_label__english.pdf -------------------------------------------------------------------------------- /results/plots/dep_label__marathi.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rycolab/bayesian-mi/HEAD/results/plots/dep_label__marathi.pdf -------------------------------------------------------------------------------- /results/plots/dep_label__turkish.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rycolab/bayesian-mi/HEAD/results/plots/dep_label__turkish.pdf -------------------------------------------------------------------------------- /src/h01_data/model/__init__.py: -------------------------------------------------------------------------------- 1 | from .bert_per_word import BertPerWordModel 2 | from .albert_per_word import AlbertPerWordModel 3 | from .roberta_per_word import RobertaPerWordModel 4 | -------------------------------------------------------------------------------- /src/h01_data/processor/__init__.py: -------------------------------------------------------------------------------- 1 | from .ud import UdProcessor 2 | from .bert import BertProcessor 3 | from .albert import AlbertProcessor 4 | from .roberta import RobertaProcessor 5 | from .fasttext import FasttextProcessor 6 | -------------------------------------------------------------------------------- /src/h02_learn/model/__init__.py: -------------------------------------------------------------------------------- 1 | from .mlp import MLP 2 | from .linear import Linear 3 | from .data_parallel import TransparentDataParallel 4 | from .parser import LinearParser, MLPParser 5 | from .categoric import Categoric 6 | -------------------------------------------------------------------------------- /src/h03_analysis/plot_all.sh: -------------------------------------------------------------------------------- 1 | 2 | for task in 'pos_tag' 'dep_label' 3 | do 4 | for lang in 'english' 'marathi' 'turkish' 'basque' 5 | do 6 | python src/h03_analysis/plot_pareto.py --task ${task} --language ${lang} 7 | done 8 | done 9 | -------------------------------------------------------------------------------- /src/h01_data/model/albert_per_word.py: -------------------------------------------------------------------------------- 1 | from transformers import AlbertModel 2 | 3 | from .bert_per_word import BertPerWordModel 4 | 5 | 6 | class AlbertPerWordModel(BertPerWordModel): 7 | @staticmethod 8 | def get_bert(bert_option): 9 | model = AlbertModel.from_pretrained(bert_option) 10 | return model 11 | -------------------------------------------------------------------------------- /src/h01_data/model/roberta_per_word.py: -------------------------------------------------------------------------------- 1 | from transformers import RobertaModel 2 | 3 | from .bert_per_word import BertPerWordModel 4 | 5 | 6 | class RobertaPerWordModel(BertPerWordModel): 7 | @staticmethod 8 | def get_bert(bert_option): 9 | model = RobertaModel.from_pretrained(bert_option) 10 | return model 11 | -------------------------------------------------------------------------------- /environment.yml: -------------------------------------------------------------------------------- 1 | name: bayesian-mi 2 | channels: 3 | - defaults 4 | - conda-forge 5 | - pytorch 6 | dependencies: 7 | - python=3.7 8 | - numpy 9 | - pandas 10 | - scikit-learn 11 | - tqdm 12 | - matplotlib 13 | - pylint=2.4.4 14 | - pip 15 | - pip: 16 | - seaborn 17 | - ipdb 18 | - conllu 19 | - adjusttext 20 | - statsmodels 21 | -------------------------------------------------------------------------------- /src/util/constants.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | 4 | device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') 5 | 6 | 7 | USED_LANGUAGES = ['english', 'basque', 'marathi', 'turkish'] 8 | 9 | LANGUAGE_CODES = { 10 | 'english': 'en', 11 | 'czech': 'cs', 12 | 'basque': 'eu', 13 | 'finnish': 'fi', 14 | 'turkish': 'tr', 15 | 'arabic': 'ar', 16 | 'japanese': 'ja', 17 | 'tamil': 'ta', 18 | 'korean': 'ko', 19 | 'marathi': 'mr', 20 | 'urdu': 'ur', 21 | 'telugu': 'te', 22 | 'indonesian': 'id', 23 | } 24 | -------------------------------------------------------------------------------- /src/h01_data/processor/albert.py: -------------------------------------------------------------------------------- 1 | from transformers import AlbertTokenizer 2 | 3 | from h01_data.model import AlbertPerWordModel 4 | from util import constants 5 | from .bert import BertProcessor 6 | 7 | 8 | class AlbertProcessor(BertProcessor): 9 | # pylint: disable=arguments-differ 10 | albert_name = 'albert-base-v2' 11 | name = 'albert' 12 | 13 | def __init__(self): 14 | super().__init__() 15 | self.bert_tokenizer = AlbertTokenizer.from_pretrained(self.albert_name) 16 | self.bert_model = AlbertPerWordModel(self.albert_name).to(device=constants.device) 17 | self.bert_model.eval() 18 | 19 | self.pad_id = self.bert_tokenizer.convert_tokens_to_ids('[PAD]') 20 | -------------------------------------------------------------------------------- /src/h01_data/processor/roberta.py: -------------------------------------------------------------------------------- 1 | from transformers import RobertaTokenizer 2 | 3 | from h01_data.model import RobertaPerWordModel 4 | from util import constants 5 | from .bert import BertProcessor 6 | 7 | 8 | class RobertaProcessor(BertProcessor): 9 | # pylint: disable=arguments-differ 10 | roberta_name = 'roberta-base' 11 | name = 'roberta' 12 | 13 | def __init__(self): 14 | super().__init__() 15 | print('roberta') 16 | self.bert_tokenizer = RobertaTokenizer.from_pretrained(self.roberta_name) 17 | self.bert_model = RobertaPerWordModel(self.roberta_name).to(device=constants.device) 18 | self.bert_model.eval() 19 | 20 | self.pad_id = self.bert_tokenizer.convert_tokens_to_ids('[PAD]') 21 | -------------------------------------------------------------------------------- /src/h02_learn/model/data_parallel.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | 3 | 4 | class TransparentDataParallel(nn.DataParallel): 5 | 6 | def set_best(self, *args, **kwargs): 7 | return self.module.set_best(*args, **kwargs) 8 | 9 | def recover_best(self, *args, **kwargs): 10 | return self.module.recover_best(*args, **kwargs) 11 | 12 | def save(self, *args, **kwargs): 13 | return self.module.save(*args, **kwargs) 14 | 15 | def train_batch(self, *args, **kwargs): 16 | return self.module.train_batch(*args, **kwargs) 17 | 18 | def eval_batch(self, *args, **kwargs): 19 | return self.module.eval_batch(*args, **kwargs) 20 | 21 | def get_norm(self, *args, **kwargs): 22 | return self.module.get_norm(*args, **kwargs) 23 | 24 | def get_rank(self, *args, **kwargs): 25 | return self.module.get_rank(*args, **kwargs) 26 | -------------------------------------------------------------------------------- /src/h02_learn/dataset/pos_tag.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | # import pandas as pd 3 | # import torch 4 | # from torch.utils.data import Dataset 5 | 6 | # from h01_data.process import get_data_file_base as get_file_names 7 | from .base import BaseDataset 8 | # from util import util 9 | 10 | 11 | class PosTagDataset(BaseDataset): 12 | name = 'pos_tag' 13 | 14 | def load_index(self, x_raw, words=None): 15 | self.x, self.words = self.factorize(x_raw, words) 16 | self.n_words = len(self.words) 17 | 18 | @staticmethod 19 | def _load_data(iterator): 20 | x_raw, y_raw = [], [] 21 | for sentence_ud, sentence_tokens in iterator(): 22 | for i, token in enumerate(sentence_ud): 23 | pos_tag = token['pos'] 24 | 25 | if pos_tag in {"_", "X"}: 26 | continue 27 | 28 | x_raw += [sentence_tokens[i]] 29 | y_raw += [pos_tag] 30 | 31 | x_raw = np.array(x_raw) 32 | y_raw = np.array(y_raw) 33 | 34 | return x_raw, y_raw 35 | -------------------------------------------------------------------------------- /src/util/util.py: -------------------------------------------------------------------------------- 1 | import os 2 | import io 3 | import csv 4 | import shutil 5 | import pathlib 6 | import pickle 7 | import random 8 | import numpy as np 9 | import torch 10 | 11 | 12 | def config(seed): 13 | random.seed(seed) 14 | np.random.seed(seed) 15 | torch.manual_seed(seed) 16 | 17 | 18 | def write_csv(filename, results): 19 | with io.open(filename, 'w', encoding='utf8') as f: 20 | writer = csv.writer(f, delimiter=',') 21 | writer.writerows(results) 22 | 23 | 24 | def write_data(filename, data): 25 | with open(filename, "wb") as f: 26 | pickle.dump(data, f) 27 | 28 | 29 | def read_data(filename): 30 | with open(filename, "rb") as f: 31 | data = pickle.load(f) 32 | return data 33 | 34 | 35 | def rmdir_if_exists(fdir): 36 | if os.path.exists(fdir): 37 | shutil.rmtree(fdir) 38 | 39 | 40 | def file_len(fname): 41 | if not os.path.isfile(fname): 42 | return 0 43 | 44 | i = 0 45 | with open(fname, 'r') as f: 46 | for i, _ in enumerate(f): 47 | pass 48 | return i + 1 49 | 50 | 51 | def mkdir(folder): 52 | pathlib.Path(folder).mkdir(parents=True, exist_ok=True) 53 | -------------------------------------------------------------------------------- /src/h02_learn/model/base.py: -------------------------------------------------------------------------------- 1 | from abc import ABC, abstractmethod 2 | import copy 3 | import torch 4 | import torch.nn as nn 5 | 6 | from util import constants 7 | 8 | 9 | class BaseModel(nn.Module, ABC): 10 | # pylint: disable=abstract-method 11 | name = 'base' 12 | ignore_index = -1 13 | 14 | def __init__(self): 15 | super().__init__() 16 | 17 | self.best_state_dict = None 18 | 19 | def set_best(self): 20 | self.best_state_dict = copy.deepcopy(self.state_dict()) 21 | 22 | def recover_best(self): 23 | self.load_state_dict(self.best_state_dict) 24 | 25 | def save(self, path): 26 | fname = self.get_name(path) 27 | torch.save({ 28 | 'kwargs': self.get_args(), 29 | 'model_state_dict': self.state_dict(), 30 | }, fname) 31 | 32 | @abstractmethod 33 | def get_args(self): 34 | pass 35 | 36 | @classmethod 37 | def load(cls, path): 38 | checkpoints = cls.load_checkpoint(path) 39 | model = cls(**checkpoints['kwargs']) 40 | model.load_state_dict(checkpoints['model_state_dict']) 41 | return model 42 | 43 | @classmethod 44 | def load_checkpoint(cls, path): 45 | fname = cls.get_name(path) 46 | return torch.load(fname, map_location=constants.device) 47 | 48 | @classmethod 49 | def get_name(cls, path): 50 | return '%s/model.tch' % (path) 51 | -------------------------------------------------------------------------------- /src/h02_learn/dataset/dep_label.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import torch 3 | 4 | from .pos_tag import PosTagDataset 5 | 6 | 7 | class DepLabelDataset(PosTagDataset): 8 | name = 'dep_label' 9 | 10 | def load_index(self, x_raw, words=None): 11 | if words is None: 12 | words = [] 13 | 14 | new_words = sorted(list(set(np.unique(x_raw)) - set(words))) 15 | if new_words: 16 | words = np.concatenate([words, new_words]) 17 | 18 | words_dict = {word: i for i, word in enumerate(words)} 19 | x = np.array([[words_dict[token] for token in tokens] for tokens in x_raw]) 20 | 21 | self.x = torch.from_numpy(x) 22 | self.words = words 23 | 24 | self.n_words = len(words) 25 | 26 | def _load_data(self, iterator): 27 | x_raw, y_raw = [], [] 28 | for sentence_ud, sentence_tokens in iterator(): 29 | for i, token in enumerate(sentence_ud): 30 | head = token['head'] 31 | rel = token['rel'] 32 | 33 | if rel in {"_", "root"}: 34 | continue 35 | 36 | x_raw_tail = sentence_tokens[i] 37 | x_raw_head = sentence_tokens[head - 1] 38 | 39 | x_raw += [[x_raw_tail, x_raw_head]] 40 | y_raw += [rel] 41 | 42 | x_raw = np.array(x_raw) 43 | y_raw = np.array(y_raw) 44 | 45 | if len(x_raw.shape) == 3: 46 | x_raw = x_raw.reshape(x_raw.shape[0], -1) # pylint: disable=E1136 # pylint/issues/3139 47 | 48 | return x_raw, y_raw 49 | -------------------------------------------------------------------------------- /src/h01_data/processor/fasttext.py: -------------------------------------------------------------------------------- 1 | import os 2 | import fasttext 3 | import fasttext.util 4 | 5 | from util import constants 6 | from util import util 7 | from .ud import UdProcessor 8 | 9 | 10 | class FasttextProcessor(UdProcessor): 11 | def __init__(self, language): 12 | super().__init__() 13 | self.fasttext_model = self.load_model(language) 14 | 15 | @staticmethod 16 | def load_model(language): 17 | lang = constants.LANGUAGE_CODES[language] 18 | ft_path = 'data/fasttext' 19 | ft_fname = os.path.join(ft_path, 'cc.%s.300.bin' % lang) 20 | if not os.path.exists(ft_fname): 21 | print("Downloading fasttext model") 22 | temp_fname = fasttext.util.download_model(lang, if_exists='ignore') 23 | util.mkdir(ft_path) 24 | os.rename(temp_fname, ft_fname) 25 | os.rename(temp_fname + '.gz', ft_fname + '.gz') 26 | 27 | print("Loading fasttext model") 28 | return fasttext.load_model(ft_fname) 29 | 30 | def process_file(self, ud_file, output_file, **kwargs): 31 | print("Processing file {}".format(ud_file)) 32 | 33 | print("PHASE ONE: reading file and tokenizing") 34 | tokens, _ = self.tokenize(ud_file) 35 | 36 | print("PHASE FOUR: getting fasttext embeddings") 37 | fast_embeddings = self.get_embeddings(tokens) 38 | 39 | util.write_data(output_file % 'fast', fast_embeddings) 40 | 41 | print("Completed {}".format(ud_file)) 42 | 43 | def get_embeddings(self, words): 44 | embeddings = [[self.fasttext_model[word] for word in sentence]for sentence in words] 45 | return embeddings 46 | -------------------------------------------------------------------------------- /src/h02_learn/train_info.py: -------------------------------------------------------------------------------- 1 | 2 | class TrainInfo: 3 | batch_id = 0 4 | running_loss = [] 5 | best_loss = float('inf') 6 | best_batch = 0 7 | eps = 0.02 8 | 9 | def __init__(self, pbar, wait_iterations, eval_batches): 10 | self.pbar = pbar 11 | self.wait_iterations = wait_iterations 12 | self.eval_batches = eval_batches 13 | 14 | @property 15 | def finish(self): 16 | return (self.batch_id - self.best_batch) >= self.wait_iterations 17 | 18 | @property 19 | def eval(self): 20 | return (self.batch_id % self.eval_batches) == 0 21 | 22 | @property 23 | def max_epochs(self): 24 | return self.best_batch + self.wait_iterations 25 | 26 | @property 27 | def avg_loss(self): 28 | return sum(self.running_loss) / len(self.running_loss) 29 | 30 | def new_batch(self, loss): 31 | self.batch_id += 1 32 | self.pbar.update(1) 33 | self.running_loss += [loss] 34 | 35 | def is_best(self, dev_results): 36 | dev_loss = dev_results['loss'] 37 | if dev_loss < self.best_loss - self.eps: 38 | self.best_loss = dev_loss 39 | self.best_batch = self.batch_id 40 | self.pbar.total = self.max_epochs 41 | return True 42 | 43 | return False 44 | 45 | def reset_loss(self): 46 | self.running_loss = [] 47 | 48 | def print_progress(self, dev_results): 49 | dev_loss = dev_results['loss'] 50 | dev_acc = dev_results['acc'] 51 | self.pbar.set_description( 52 | 'Training loss: %.4f Dev loss: %.4f acc: %.4f' % 53 | (self.avg_loss, dev_loss, dev_acc)) 54 | self.reset_loss() 55 | -------------------------------------------------------------------------------- /src/h01_data/processor/ud.py: -------------------------------------------------------------------------------- 1 | from conllu import parse_incr 2 | 3 | from util import util 4 | 5 | 6 | class UdProcessor: 7 | def __init__(self): 8 | self.max_tokens = 100 9 | 10 | def process_file(self, ud_file, output_file, **kwargs): 11 | # pylint: disable=unused-argument 12 | print("Processing file {}".format(ud_file)) 13 | 14 | print("PHASE ONE: reading file and tokenizing") 15 | tokens, ud_data = self.tokenize(ud_file) 16 | save_data = list(zip(ud_data, tokens)) 17 | 18 | # Pickle, compress, and save 19 | util.write_data(output_file % 'ud', save_data) 20 | 21 | def tokenize(self, file_name): 22 | all_ud_tokens = [] 23 | all_ud_data = [] 24 | 25 | count_del, count_total = 0, 0 26 | 27 | # Initialise all the trees and embeddings 28 | with open(file_name, "r", encoding="utf-8") as file: 29 | for token_list in parse_incr(file): 30 | 31 | ud_tokens = [] 32 | ud_data = [] 33 | 34 | for item in token_list: 35 | ud_tokens.append(item['form']) 36 | ud_data.append({ 37 | 'word': item['form'], 38 | 'pos': item['upostag'], 39 | 'head': item['head'], 40 | 'rel': item['deprel'], 41 | }) 42 | 43 | # If there are more than max_tokens tokens skip the sentence 44 | if len(ud_tokens) <= self.max_tokens: 45 | all_ud_tokens.append(ud_tokens) 46 | all_ud_data.append(ud_data) 47 | else: 48 | count_del += 1 49 | count_total += 1 50 | 51 | if count_del > 0: 52 | print('\n\n\tWarning!Removed %d (of %d) long sentences\n\n' % (count_del, count_total)) 53 | return all_ud_tokens, all_ud_data 54 | -------------------------------------------------------------------------------- /.circleci/config.yml: -------------------------------------------------------------------------------- 1 | # Python CircleCI 2.0 configuration file 2 | # 3 | # Check https://circleci.com/docs/2.0/language-python/ for more details 4 | # 5 | version: 2 6 | jobs: 7 | build: 8 | docker: 9 | # specify the version you desire here 10 | # use `-browsers` prefix for selenium tests, e.g. `3.7.6-browsers` 11 | # - image: ashander/miniconda3gcc 12 | - image: continuumio/miniconda3 13 | # continuumio/miniconda3 14 | 15 | # Specify service dependencies here if necessary 16 | # CircleCI maintains a library of pre-built images 17 | # documented at https://circleci.com/docs/2.0/circleci-images/ 18 | # - image: circleci/postgres:9.4 19 | 20 | working_directory: ~/repo 21 | 22 | steps: 23 | - checkout 24 | 25 | # Download and cache dependencies 26 | - restore_cache: 27 | keys: 28 | - v1.1-dependencies-{{ checksum "environment.yml" }} 29 | 30 | - run: 31 | name: install dependencies 32 | command: | 33 | ENVS=$(conda env list | awk '{print $1}' ) 34 | echo $ENVS 35 | if ! [[ $ENVS = *"bayesian-mi"* ]]; then 36 | source /opt/conda/etc/profile.d/conda.sh 37 | apt-get update --fix-missing 38 | apt-get install -y gcc g++ 39 | conda update -y -n base -c defaults conda 40 | conda env create -f environment.yml 41 | conda activate bayesian-mi 42 | conda install -y pytorch torchvision cpuonly -c pytorch 43 | pip install transformers 44 | pip install git+https://github.com/facebookresearch/fastText 45 | else 46 | echo "Conda env already installed" 47 | fi; 48 | 49 | - save_cache: 50 | key: v1.1-dependencies-{{ checksum "environment.yml" }} 51 | paths: 52 | - /opt/conda 53 | 54 | - run: 55 | name: run linter 56 | command: | 57 | source /opt/conda/etc/profile.d/conda.sh 58 | conda activate bayesian-mi 59 | pylint src/ --rcfile .pylintrc 60 | 61 | - store_artifacts: 62 | path: test-reports 63 | destination: test-reports 64 | -------------------------------------------------------------------------------- /src/h01_data/model/bert_per_word.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | from transformers import BertModel 4 | 5 | from util import constants 6 | 7 | 8 | class BertPerWordModel(nn.Module): 9 | # pylint: disable=arguments-differ 10 | 11 | def __init__(self, bert_option): 12 | super().__init__() 13 | self.bert = self.get_bert(bert_option) 14 | 15 | @staticmethod 16 | def get_bert(bert_option): 17 | model = BertModel.from_pretrained(bert_option) 18 | return model 19 | 20 | def forward(self, x, attention_mask, mappings): 21 | outputs = self.bert(x, attention_mask=attention_mask) 22 | last_layer = outputs[0] 23 | return self.from_bpe_to_word(last_layer, mappings) 24 | 25 | def from_bpe_to_word(self, output, mappings): 26 | batch_size = output.size(0) 27 | longest_token_sent = mappings.size(1) 28 | 29 | hidden_states = output[:, 1:-1] 30 | embedding_size = output.size(-1) 31 | 32 | hidden_states_per_token = torch.zeros( 33 | (batch_size, longest_token_sent, embedding_size)).to(device=constants.device) 34 | mask_start = torch.zeros(batch_size).long().to(device=constants.device) 35 | 36 | for mask_pos in range(0, longest_token_sent): 37 | mask_sizes = mappings[:, mask_pos] 38 | 39 | hidden_states_per_token[:, mask_pos] = \ 40 | self.sum_bpe_embeddings(hidden_states, mask_start, mask_sizes) 41 | 42 | mask_start += mask_sizes 43 | 44 | return hidden_states_per_token 45 | 46 | @staticmethod 47 | def sum_bpe_embeddings(hidden_states, mask_start, mask_sizes): 48 | mask_idxs = [] 49 | for i, (sent_start, sent_size) in enumerate(zip(mask_start, mask_sizes)): 50 | mask_idxs += [(i, sent_start.item() + x) for x in range(sent_size)] 51 | mask_idxs = list(zip(*mask_idxs)) 52 | 53 | hidden_states_temp = \ 54 | torch.zeros_like(hidden_states).float().to(device=constants.device) 55 | hidden_states_temp[mask_idxs] = hidden_states[mask_idxs] 56 | 57 | embedding_size = hidden_states.size(-1) 58 | return hidden_states_temp.sum(dim=1) / \ 59 | mask_sizes.unsqueeze(-1).repeat(1, embedding_size).float() 60 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # bayesian-mi 2 | 3 | This code accompanies the paper "Bayesian Framework for Information-Theoretic Probing" published in EMNLP 2021. 4 | 5 | 6 | ## Install Dependencies 7 | 8 | Create a conda environment with 9 | ```bash 10 | $ conda env create -f environment.yml 11 | ``` 12 | Then activate the environment and install your appropriate version of [PyTorch](https://pytorch.org/get-started/locally/). 13 | ```bash 14 | $ conda install -y pytorch torchvision cudatoolkit=10.1 -c pytorch 15 | $ # conda install pytorch torchvision cpuonly -c pytorch 16 | $ pip install transformers 17 | ``` 18 | Install the newest version of fastText: 19 | ```bash 20 | $ pip install git+https://github.com/facebookresearch/fastText 21 | ``` 22 | 23 | ## Download and parse universal dependencies (UD) data 24 | 25 | You can easily download UD data with the following command 26 | ```bash 27 | $ make get_ud 28 | ``` 29 | 30 | You can then get the embeddings for it with command 31 | ```bash 32 | $ make process LANGUAGE= REPRESENTATION= 33 | ``` 34 | 35 | This repository has the option of using representations: onehot; random; bert; albert; and roberta. 36 | As languages, you should be able to experiment on: 'en' (english); 'cs' (czech); 'eu' (basque); 'fi' (finnish); 'tr' (turkish); 'ar' (arabic); 'ja' (japanese); 'ta' (tamil); 'ko' (korean); 'mr' (marathi); 'ur' (urdu); 'te' (telugu); 'id' (indonesian). 37 | If you wanna experiment on other languages, add the appropriate language code to `src/util/constants.py` and the ud path to `src/util/ud_list.py`. 38 | 39 | 40 | ## Train your models 41 | 42 | You can train your models using random search with the command 43 | ```bash 44 | $ make train LANGUAGE= REPRESENTATION= TASK= MODEL= 45 | ``` 46 | There are three tasks available in this repository: pos_tag; dep_label; and parse. 47 | The model studied in this paper was: 'mlp'. 48 | 49 | 50 | ## Compile results 51 | 52 | After training the models for all languages, you can compiled results with the command 53 | ```bash 54 | $ make get_results TASK= 55 | ``` 56 | 57 | ## Plot results 58 | 59 | Finally, you can plot the pareto curves in the paper with the command 60 | ```bash 61 | $ python src/h03_analysis/plot_pareto.py --task --language 62 | ``` 63 | 64 | 65 | ## Extra Information 66 | 67 | #### Contact 68 | 69 | To ask questions or report problems, please open an [issue](https://github.com/rycolab/bayesian-mi/issues). 70 | -------------------------------------------------------------------------------- /src/h02_learn/model/categoric.py: -------------------------------------------------------------------------------- 1 | import math 2 | import torch 3 | import torch.nn as nn 4 | 5 | from .base import BaseModel 6 | 7 | 8 | class Categoric(BaseModel): 9 | # pylint: disable=too-many-instance-attributes,arguments-differ 10 | 11 | name = 'categoric' 12 | 13 | def __init__(self, task, n_classes=100): 14 | # pylint: disable=too-many-arguments 15 | super().__init__() 16 | 17 | self.task = task 18 | self.n_classes = n_classes 19 | self.alpha = 2 20 | 21 | self.probs = nn.Parameter(torch.Tensor(self.n_classes)) 22 | self.log_probs = nn.Parameter(torch.Tensor(self.n_classes)) 23 | self.count = nn.Parameter( 24 | torch.LongTensor(self.n_classes).zero_(), 25 | requires_grad=False) 26 | 27 | self.criterion = nn.NLLLoss(ignore_index=self.ignore_index) 28 | 29 | def fit(self, trainloader): 30 | with torch.no_grad(): 31 | for _, y in trainloader: 32 | self.fit_batch(_, y) 33 | 34 | def fit_batch(self, _, y): 35 | for char in y.unique(): 36 | if char == self.ignore_index: 37 | continue 38 | self.count[char] += (y == char).sum() 39 | 40 | self.probs[:] = \ 41 | (self.count.float() + self.alpha) / (self.count.sum() + self.alpha * self.n_classes) 42 | self.log_probs[:] = torch.log(self.probs) 43 | 44 | def forward(self, x): 45 | batch_size = x.shape[0] 46 | if self.task == 'parse': 47 | max_len = x.shape[1] 48 | y_hat = self.log_probs[:max_len] \ 49 | .reshape(1, 1, -1) \ 50 | .repeat(batch_size, max_len, 1) 51 | else: 52 | y_hat = self.log_probs \ 53 | .reshape(1, -1) \ 54 | .repeat(batch_size, 1) 55 | 56 | return y_hat 57 | 58 | def eval_batch(self, data, target): 59 | mlp_out = self(data) 60 | loss = self.criterion(mlp_out, target) / math.log(2) 61 | accuracy = (mlp_out.argmax(dim=-1) == target).float().detach().sum() 62 | loss = loss.item() * data.shape[0] 63 | 64 | return loss, accuracy 65 | 66 | def get_args(self): 67 | return { 68 | 'n_classes': self.n_classes, 69 | 'task': self.task, 70 | } 71 | 72 | @staticmethod 73 | def print_param_names(): 74 | return [ 75 | 'n_classes', 'task' 76 | ] 77 | 78 | def print_params(self): 79 | return [ 80 | self.n_classes, self.task 81 | ] 82 | -------------------------------------------------------------------------------- /src/h02_learn/dataset/parse.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | # import pandas as pd 3 | import torch 4 | # from torch.utils.data import Dataset 5 | 6 | # from h01_data.process import get_data_file_base as get_file_names 7 | from .base import BaseDataset 8 | # from util import util 9 | 10 | 11 | class ParseDataset(BaseDataset): 12 | name = 'parse' 13 | 14 | def _process_index(self, classes, words): 15 | x_raw, y_raw = self.load_data(self.iterate_index) 16 | 17 | self.load_index(x_raw, words=words) 18 | self.load_classes(y_raw, classes=classes) 19 | 20 | def _process(self, classes): 21 | x_raw, y_raw = self.load_data(self.iterate_embeddings) 22 | 23 | self.load_embeddings(x_raw) 24 | self.load_classes(y_raw, classes=classes) 25 | 26 | def _load_data(self, iterator): 27 | x_raw, y_raw = [], [] 28 | for sentence_ud, sentence_tokens in iterator(): 29 | # Add root 30 | if isinstance(sentence_tokens[0], np.ndarray): 31 | x_sentence = [np.zeros(sentence_tokens[0].shape)] 32 | else: 33 | x_sentence = [''] 34 | 35 | y_sentence = [-1] 36 | 37 | for i, token in enumerate(sentence_ud): 38 | head = token['head'] 39 | 40 | if head is None: 41 | continue 42 | 43 | x_sentence += [sentence_tokens[i]] 44 | y_sentence += [head] 45 | 46 | x_raw += [np.array(x_sentence)] 47 | y_raw += [np.array(y_sentence)] 48 | 49 | return x_raw, y_raw 50 | 51 | def load_index(self, x_raw, words=None): 52 | if words is None: 53 | words = [] 54 | all_words = {token for sentence in x_raw for token in sentence} 55 | new_words = sorted(list(all_words - set(words))) 56 | if new_words: 57 | words = np.concatenate([words, new_words]) 58 | 59 | words_dict = {word: i for i, word in enumerate(words)} 60 | x = [np.array([[words_dict[token]] for token in tokens]) for tokens in x_raw] 61 | 62 | self.x = [torch.from_numpy(sentence) for sentence in x] 63 | self.words = words 64 | 65 | self.n_words = len(words) 66 | 67 | def load_embeddings(self, x_raw): 68 | self.assert_size(x_raw) 69 | self.x = [torch.from_numpy(x) for x in x_raw] 70 | 71 | def assert_size(self, x): 72 | assert x[0].shape[-1] == self.embedding_size 73 | 74 | def load_classes(self, y_raw, classes=None): 75 | self.y = [torch.from_numpy(y) for y in y_raw] 76 | 77 | self.classes = None 78 | self.n_classes = 0 79 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | 131 | # VsCode 132 | .vscode/ 133 | 134 | # Project specific 135 | data/ 136 | checkpoints/*/*/*/*/* 137 | !checkpoints/*/*/*/*/all_results.tsv 138 | !checkpoints/*/*/*/*/finished.txt 139 | -------------------------------------------------------------------------------- /Makefile: -------------------------------------------------------------------------------- 1 | LANGUAGE := marathi 2 | TASK := pos_tag 3 | REPRESENTATION := random 4 | MODEL := mlp 5 | DATA_DIR := ./data 6 | CHECKPOINT_DIR := ./checkpoints 7 | 8 | REPRESENTATIONS_CONTEXTUAL := bert albert roberta 9 | REPRESENTATIONS_UD := onehot random 10 | 11 | DATA_PROCESS := $(if $(filter-out $(REPRESENTATION), random),$(REPRESENTATION),ud) 12 | DATA_PROCESS := $(if $(filter-out $(REPRESENTATION), onehot),$(DATA_PROCESS),ud) 13 | 14 | UD_DIR_BASE := $(DATA_DIR)/ud 15 | 16 | UDURL := https://lindat.mff.cuni.cz/repository/xmlui/bitstream/handle/11234/1-3226/ud-treebanks-v2.6.tgz 17 | 18 | UD_DIR := $(UD_DIR_BASE)/ud-treebanks-v2.6 19 | UD_FILE := $(UD_DIR_BASE)/ud-treebanks-v2.6.tgz 20 | 21 | PROCESSED_DIR_BASE := $(DATA_DIR)/processed/ 22 | PROCESSED_DIR := $(PROCESSED_DIR_BASE)/$(LANGUAGE) 23 | PROCESSED_FILE_UD := $(PROCESSED_DIR)/train--ud.pickle.bz2 24 | PROCESSED_FILE := $(PROCESSED_DIR)/test--$(DATA_PROCESS).pickle.bz2 25 | 26 | TRAIN_DIR := $(CHECKPOINT_DIR)/$(TASK)/$(LANGUAGE) 27 | TRAIN_MODEL := $(TRAIN_DIR)/$(MODEL)/$(REPRESENTATION)/finished.txt 28 | 29 | COMPILED_RESULTS := results/compiled_$(TASK).tsv 30 | 31 | # ifeq ($(REPRESENTATION), bert) 32 | ifneq ($(filter $(REPRESENTATION),$(REPRESENTATIONS_UD)),) 33 | all: get_ud process train 34 | echo "Finished everything" 35 | else 36 | all: get_ud process_ud process train 37 | echo "Finished everything" 38 | endif 39 | 40 | get_results: $(COMPILED_RESULTS) 41 | 42 | train: $(TRAIN_MODEL) 43 | 44 | process: $(PROCESSED_FILE) 45 | 46 | process_ud: $(PROCESSED_FILE_UD) 47 | 48 | get_ud: $(UD_DIR) 49 | 50 | $(COMPILED_RESULTS): 51 | python -u src/h03_analysis/compile_results.py --checkpoint-path $(CHECKPOINT_DIR) --task $(TASK) 52 | 53 | $(TRAIN_MODEL): 54 | echo "Train " $(REPRESENTATION) " representation" 55 | python -u src/h02_learn/random_search.py --language $(LANGUAGE) --data-path $(PROCESSED_DIR_BASE) --representation $(REPRESENTATION) --checkpoint-path $(CHECKPOINT_DIR) --task $(TASK) --model $(MODEL) 56 | # python src/h02_learn/train.py --language $(LANGUAGE) --data-path $(PROCESSED_DIR_BASE) --representation $(REPRESENTATION) --checkpoint-path $(CHECKPOINT_DIR) --task $(TASK) --model $(MODEL) --ndata 10 57 | 58 | # Preprocess data 59 | $(PROCESSED_FILE): 60 | echo "Process language in ud " $(LANGUAGE) 61 | python src/h01_data/process.py --language $(LANGUAGE) --ud-path $(UD_DIR) --output-path $(PROCESSED_DIR_BASE) --representation $(DATA_PROCESS) 62 | 63 | # Preprocess ud base data 64 | $(PROCESSED_FILE_UD): $(UD_DIR) 65 | echo "Process language in ud " $(LANGUAGE) 66 | python src/h01_data/process.py --language $(LANGUAGE) --ud-path $(UD_DIR) --output-path $(PROCESSED_DIR_BASE) --representation ud 67 | 68 | # Get Universal Dependencies data 69 | $(UD_DIR): 70 | echo "Get ud data" 71 | mkdir -p $(UD_DIR_BASE) 72 | wget -P $(UD_DIR_BASE) $(UDURL) 73 | tar -xvzf $(UD_FILE) -C $(UD_DIR_BASE) 74 | -------------------------------------------------------------------------------- /src/h02_learn/dataset/__init__.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch.utils.data import DataLoader 3 | 4 | from util import constants 5 | from util import util 6 | from .pos_tag import PosTagDataset 7 | from .dep_label import DepLabelDataset 8 | from .parse import ParseDataset 9 | 10 | 11 | def batch_generator(task): 12 | def generate_batch(batch): 13 | x = torch.cat([item[0].unsqueeze(0) for item in batch], dim=0) 14 | y = torch.cat([item[1].unsqueeze(0) for item in batch], dim=0) 15 | 16 | x, y = x.to(device=constants.device), y.to(device=constants.device) 17 | return (x, y) 18 | 19 | if task in ['pos_tag', 'dep_label']: 20 | return generate_batch 21 | 22 | def pad_batch(batch): 23 | batch_size = len(batch) 24 | max_length = max([len(sentence[0]) for sentence in batch]) 25 | shape = batch[0][0].shape[-1] 26 | 27 | x = torch.ones(batch_size, max_length, shape) * -1 28 | y = torch.ones(batch_size, max_length).long() * -1 29 | 30 | for i, sentence in enumerate(batch): 31 | sent_len = len(sentence[0]) 32 | x[i, :sent_len] = sentence[0] 33 | y[i, :sent_len] = sentence[1] 34 | 35 | if shape == 1: 36 | x = x.squeeze(-1).long() 37 | x[x == -1] = 0 38 | 39 | x, y = x.to(device=constants.device), y.to(device=constants.device) 40 | return (x, y) 41 | 42 | if task in ['parse']: 43 | return pad_batch 44 | 45 | raise ValueError('Invalid task for batch generation') 46 | 47 | 48 | def get_data_cls(task): 49 | if task == 'pos_tag': 50 | return PosTagDataset 51 | if task == 'dep_label': 52 | return DepLabelDataset 53 | if task == 'parse': 54 | return ParseDataset 55 | 56 | raise ValueError('Invalid task %s' % task) 57 | 58 | 59 | def get_data_loader(dataset_cls, task, data_path, language, representations, embedding_size, 60 | mode, batch_size, shuffle, classes=None, words=None, max_instances=None): 61 | # pylint: disable=too-many-arguments 62 | trainset = dataset_cls(data_path, language, representations, embedding_size, 63 | mode, classes=classes, words=words, 64 | max_instances=max_instances) 65 | trainloader = DataLoader(trainset, batch_size=batch_size, shuffle=shuffle, 66 | collate_fn=batch_generator(task)) 67 | return trainloader, trainset.classes, trainset.words 68 | 69 | 70 | def get_data_loaders(data_path, task, language, representations, embedding_size, 71 | batch_size, max_instances=None): 72 | dataset_cls = get_data_cls(task) 73 | 74 | trainloader, classes, words = get_data_loader( 75 | dataset_cls, task, data_path, language, representations, embedding_size, 76 | 'train', batch_size=batch_size, shuffle=True, max_instances=max_instances) 77 | devloader, classes, words = get_data_loader( 78 | dataset_cls, task, data_path, language, representations, embedding_size, 79 | 'dev', batch_size=batch_size, shuffle=False, classes=classes, words=words) 80 | testloader, classes, words = get_data_loader( 81 | dataset_cls, task, data_path, language, representations, embedding_size, 82 | 'test', batch_size=batch_size, shuffle=False, classes=classes, words=words) 83 | return trainloader, devloader, testloader, \ 84 | testloader.dataset.n_classes, testloader.dataset.n_words 85 | -------------------------------------------------------------------------------- /src/h01_data/process.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | import argparse 4 | import torch 5 | 6 | sys.path.insert(1, os.path.join(sys.path[0], '..')) 7 | from h01_data.processor import \ 8 | UdProcessor, FasttextProcessor, \ 9 | BertProcessor, AlbertProcessor, RobertaProcessor 10 | from util import util 11 | from util.ud_list import UD_LIST 12 | 13 | 14 | def get_args(): 15 | parser = argparse.ArgumentParser() 16 | parser.add_argument("-m", "--batch-size", 17 | help="The size of the mini batches", 18 | default=8, 19 | required=False, 20 | type=int) 21 | parser.add_argument("--language", 22 | help="The language to use", 23 | required=True, 24 | type=str) 25 | parser.add_argument("--representation", type=str, required=True) 26 | parser.add_argument("--ud-path", 27 | help="The path to raw ud data", 28 | default='data/ud/ud-treebanks-v2.5/', 29 | required=False, 30 | type=str) 31 | parser.add_argument("--output-path", 32 | help="The path to save processed data", 33 | default='data/processed/', 34 | required=False, 35 | type=str) 36 | args = parser.parse_args() 37 | print(args) 38 | 39 | return args 40 | 41 | 42 | def get_ud_file_base(ud_path, language): 43 | return os.path.join(ud_path, UD_LIST[language]) 44 | 45 | 46 | def get_data_file_base(output_path, language): 47 | output_path = os.path.join(output_path, language) 48 | util.mkdir(output_path) 49 | return os.path.join(output_path, '%s--%s.pickle.bz2') 50 | 51 | 52 | def get_data_processor(representation, args): 53 | if representation in ['ud', 'random', 'onehot']: 54 | processor = UdProcessor() 55 | elif representation == 'bert': 56 | processor = BertProcessor() 57 | elif representation == 'albert': 58 | processor = AlbertProcessor() 59 | elif representation == 'roberta': 60 | processor = RobertaProcessor() 61 | elif representation == 'fast': 62 | processor = FasttextProcessor(args.language) 63 | else: 64 | raise ValueError('Invalid representation %s' % representation) 65 | 66 | return processor 67 | 68 | 69 | def process(language, ud_path, batch_size, output_path, representation, args): 70 | print("Loading data processor") 71 | processor = get_data_processor(representation, args) 72 | 73 | print("Precessing language %s" % language) 74 | ud_file_base = get_ud_file_base(ud_path, language) 75 | output_file_base = get_data_file_base(output_path, language) 76 | 77 | for mode in ['train', 'dev', 'test']: 78 | ud_file = ud_file_base % mode 79 | output_file = output_file_base % (mode, '%s') 80 | processor.process_file(ud_file, output_file, batch_size=batch_size) 81 | 82 | print("Process finished") 83 | 84 | 85 | def main(): 86 | args = get_args() 87 | 88 | batch_size = args.batch_size 89 | language = args.language 90 | ud_path = args.ud_path 91 | output_path = args.output_path 92 | # args.bert_name = 93 | 94 | with torch.no_grad(): 95 | process(language, ud_path, batch_size, output_path, args.representation, args) 96 | 97 | 98 | if __name__ == "__main__": 99 | main() 100 | -------------------------------------------------------------------------------- /src/h02_learn/model/linear.py: -------------------------------------------------------------------------------- 1 | import math 2 | import numpy as np 3 | import torch 4 | import torch.nn as nn 5 | 6 | from .base import BaseModel 7 | 8 | 9 | class Linear(BaseModel): 10 | # pylint: disable=too-many-instance-attributes,arguments-differ 11 | 12 | name = 'linear' 13 | 14 | def __init__(self, task, embedding_size=768, n_classes=3, alpha=0.0, 15 | dropout=0.1, representation=None, n_words=None): 16 | super().__init__() 17 | 18 | # Save things to the model here 19 | self.alpha = alpha 20 | self.dropout_p = dropout 21 | self.embedding_size = embedding_size 22 | self.n_classes = n_classes 23 | self.representation = representation 24 | self.n_words = n_words 25 | self.task = task 26 | 27 | if self.representation in ['onehot', 'random']: 28 | self.build_embeddings(n_words, embedding_size) 29 | 30 | self.linear = nn.Linear(embedding_size, n_classes) 31 | self.dropout = nn.Dropout(dropout) 32 | 33 | self.criterion = nn.CrossEntropyLoss() 34 | 35 | def build_embeddings(self, n_words, embedding_size): 36 | if self.task == 'dep_label': 37 | self.embedding_size = int(embedding_size / 2) * 2 38 | self.embedding = nn.Embedding(n_words, int(embedding_size / 2)) 39 | else: 40 | self.embedding = nn.Embedding(n_words, embedding_size) 41 | 42 | if self.representation == 'random': 43 | self.embedding.weight.requires_grad = False 44 | 45 | def forward(self, x, eps=1e-5): 46 | if self.representation in ['onehot', 'random']: 47 | x = self.get_embeddings(x) 48 | x = x / (x.norm(p=2, dim=-1, keepdim=True) + eps) 49 | 50 | x_emb = self.dropout(x) 51 | logits = self.linear(x_emb) 52 | return logits 53 | 54 | def get_embeddings(self, x): 55 | x_emb = self.embedding(x) 56 | if len(x.shape) > 1: 57 | x_emb = x_emb.reshape(x.shape[0], -1) 58 | 59 | return x_emb 60 | 61 | def train_batch(self, data, target, optimizer): 62 | optimizer.zero_grad() 63 | mlp_out = self(data) 64 | loss = self.get_loss(mlp_out, target) 65 | loss.backward() 66 | optimizer.step() 67 | 68 | return loss.item() 69 | 70 | def eval_batch(self, data, target): 71 | mlp_out = self(data) 72 | loss = self.criterion(mlp_out, target) / math.log(2) 73 | accuracy = (mlp_out.argmax(dim=-1) == target).float().sum() 74 | loss = loss.item() * data.shape[0] 75 | 76 | return loss, accuracy.item() 77 | 78 | def get_loss(self, predicted, target): 79 | entropy = self.criterion(predicted, target) / math.log(2) 80 | penalty = self.get_norm() 81 | return entropy + self.alpha * penalty 82 | 83 | def get_norm(self): 84 | ext_matrix = torch.cat([self.linear.weight, self.linear.bias.unsqueeze(-1)], dim=1) 85 | penalty = torch.norm(ext_matrix, p='nuc') 86 | 87 | return penalty 88 | 89 | def get_args(self): 90 | return { 91 | 'alpha': self.alpha, 92 | 'embedding_size': self.embedding_size, 93 | 'dropout': self.dropout_p, 94 | 'n_classes': self.n_classes, 95 | 'representation': self.representation, 96 | 'n_words': self.n_words, 97 | 'task': self.task, 98 | } 99 | 100 | @staticmethod 101 | def print_param_names(): 102 | return [ 103 | 'alpha', 'embedding_size', 'dropout', 104 | 'n_classes', 'representation', 'n_words', 105 | ] 106 | 107 | def print_params(self): 108 | return [ 109 | self.alpha, self.embedding_size, self.dropout_p, 110 | self.n_classes, self.representation, self.n_words 111 | ] 112 | -------------------------------------------------------------------------------- /src/h02_learn/dataset/base.py: -------------------------------------------------------------------------------- 1 | from abc import ABC, abstractmethod 2 | import numpy as np 3 | import pandas as pd 4 | import torch 5 | from torch.utils.data import Dataset 6 | 7 | from h01_data.process import get_data_file_base as get_file_names 8 | from util import util 9 | 10 | 11 | class BaseDataset(Dataset, ABC): 12 | # pylint: disable=too-many-instance-attributes 13 | 14 | def __init__(self, data_path, language, representation, embedding_size, 15 | mode, classes=None, words=None, max_instances=None): 16 | self.data_path = data_path 17 | self.language = language 18 | self.mode = mode 19 | self.representation = representation 20 | self.embedding_size = embedding_size 21 | self.max_instances = max_instances 22 | 23 | self.input_name_base = get_file_names(data_path, language) 24 | self.process(classes, words) 25 | 26 | assert len(self.x) == len(self.y) 27 | self.n_instances = len(self.x) 28 | 29 | def process(self, classes, words): 30 | if self.representation not in ['onehot', 'random']: 31 | self._process(classes) 32 | self.words = words 33 | self.n_words = None 34 | else: 35 | self._process_index(classes, words) 36 | 37 | def _process_index(self, classes, words): 38 | x_raw, y_raw = self.load_data(self.iterate_index) 39 | 40 | self.load_index(x_raw, words=words) 41 | self.load_classes(y_raw, classes=classes) 42 | 43 | def _process(self, classes): 44 | x_raw, y_raw = self.load_data(self.iterate_embeddings) 45 | 46 | self.load_embeddings(x_raw) 47 | self.load_classes(y_raw, classes=classes) 48 | 49 | def load_data(self, iterator): 50 | x_raw, y_raw = self._load_data(iterator) 51 | if self.max_instances is not None: 52 | x_raw = x_raw[:self.max_instances] 53 | y_raw = y_raw[:self.max_instances] 54 | return x_raw, y_raw 55 | 56 | @abstractmethod 57 | def _load_data(self, iterator): 58 | pass 59 | 60 | def iterate_index(self): 61 | data_ud = util.read_data(self.input_name_base % (self.mode, 'ud')) 62 | 63 | for (sentence_ud, words) in data_ud: 64 | yield sentence_ud, np.array(words) 65 | 66 | def iterate_embeddings(self): 67 | data_ud = util.read_data(self.input_name_base % (self.mode, 'ud')) 68 | data_embeddings = util.read_data(self.input_name_base % (self.mode, self.representation)) 69 | 70 | for (sentence_ud, _), sentence_emb in zip(data_ud, data_embeddings): 71 | yield sentence_ud, sentence_emb 72 | 73 | def load_embeddings(self, x_raw): 74 | self.assert_size(x_raw) 75 | self.x = torch.from_numpy(x_raw) 76 | 77 | def assert_size(self, x): 78 | assert len(x[0]) == self.embedding_size 79 | 80 | @abstractmethod 81 | def load_index(self, x_raw, words=None): 82 | pass 83 | 84 | def load_classes(self, y_raw, classes=None): 85 | self.y, self.classes = self.factorize(y_raw, classes) 86 | self.n_classes = self.classes.shape[0] 87 | 88 | def factorize(self, data_raw, classes=None): 89 | if self.mode != 'train': 90 | assert classes is not None 91 | 92 | if classes is None: 93 | data, classes = pd.factorize(data_raw, sort=True) 94 | else: 95 | new_classes = set(data_raw) - set(classes) 96 | if new_classes: 97 | classes = np.concatenate([classes, list(new_classes)]) 98 | 99 | classes_dict = {pos_class: i for i, pos_class in enumerate(classes)} 100 | data = np.array([classes_dict[token] for token in data_raw]) 101 | 102 | return torch.from_numpy(data), classes 103 | 104 | def __len__(self): 105 | return self.n_instances 106 | 107 | def __getitem__(self, index): 108 | return (self.x[index], self.y[index]) 109 | -------------------------------------------------------------------------------- /src/h02_learn/model/mlp.py: -------------------------------------------------------------------------------- 1 | import math 2 | import torch 3 | import torch.nn as nn 4 | 5 | from .base import BaseModel 6 | 7 | 8 | class MLP(BaseModel): 9 | # pylint: disable=too-many-instance-attributes,arguments-differ 10 | 11 | name = 'mlp' 12 | 13 | def __init__(self, task, embedding_size=768, n_classes=3, hidden_size=5, 14 | nlayers=1, dropout=0.1, representation=None, n_words=None): 15 | # pylint: disable=too-many-arguments 16 | super().__init__() 17 | 18 | # Save things to the model here 19 | self.dropout_p = dropout 20 | self.embedding_size = embedding_size 21 | self.hidden_size = hidden_size 22 | self.nlayers = nlayers 23 | self.n_classes = n_classes 24 | self.representation = representation 25 | self.n_words = n_words 26 | self.task = task 27 | 28 | if self.representation in ['onehot', 'random']: 29 | self.build_embeddings(n_words, embedding_size) 30 | 31 | self.mlp = self.build_mlp() 32 | self.out = nn.Linear(self.final_hidden_size, n_classes) 33 | self.dropout = nn.Dropout(dropout) 34 | 35 | self.criterion = nn.CrossEntropyLoss() 36 | 37 | def build_embeddings(self, n_words, embedding_size): 38 | if self.task == 'dep_label': 39 | self.embedding_size = int(embedding_size / 2) * 2 40 | self.embedding = nn.Embedding(n_words, int(embedding_size / 2)) 41 | else: 42 | self.embedding = nn.Embedding(n_words, embedding_size) 43 | 44 | if self.representation == 'random': 45 | self.embedding.weight.requires_grad = False 46 | 47 | def build_mlp(self): 48 | if self.nlayers == 0: 49 | self.final_hidden_size = self.embedding_size 50 | return nn.Identity() 51 | 52 | src_size = self.embedding_size 53 | tgt_size = self.hidden_size 54 | mlp = [] 55 | for _ in range(self.nlayers): 56 | mlp += [nn.Linear(src_size, tgt_size)] 57 | mlp += [nn.ReLU()] 58 | mlp += [nn.Dropout(self.dropout_p)] 59 | src_size, tgt_size = tgt_size, int(tgt_size / 2) 60 | self.final_hidden_size = src_size 61 | return nn.Sequential(*mlp) 62 | 63 | def forward(self, x): 64 | if self.representation in ['onehot', 'random']: 65 | x = self.get_embeddings(x) 66 | 67 | x_emb = self.dropout(x) 68 | x = self.mlp(x_emb) 69 | logits = self.out(x) 70 | return logits 71 | 72 | def get_embeddings(self, x): 73 | x_emb = self.embedding(x) 74 | if len(x.shape) > 1: 75 | x_emb = x_emb.reshape(x.shape[0], -1) 76 | 77 | return x_emb 78 | 79 | def train_batch(self, data, target, optimizer): 80 | optimizer.zero_grad() 81 | mlp_out = self(data) 82 | loss = self.criterion(mlp_out, target) 83 | loss.backward() 84 | optimizer.step() 85 | 86 | return loss.item() / math.log(2) 87 | 88 | def eval_batch(self, data, target): 89 | mlp_out = self(data) 90 | loss = self.criterion(mlp_out, target) / math.log(2) 91 | accuracy = (mlp_out.argmax(dim=-1) == target).float().detach().sum() 92 | loss = loss.item() * data.shape[0] 93 | 94 | return loss, accuracy 95 | 96 | @staticmethod 97 | def get_norm(): 98 | return torch.Tensor([0]) 99 | 100 | def get_args(self): 101 | return { 102 | 'nlayers': self.nlayers, 103 | 'hidden_size': self.hidden_size, 104 | 'embedding_size': self.embedding_size, 105 | 'dropout': self.dropout_p, 106 | 'n_classes': self.n_classes, 107 | 'representation': self.representation, 108 | 'n_words': self.n_words, 109 | 'task': self.task, 110 | } 111 | 112 | @staticmethod 113 | def print_param_names(): 114 | return [ 115 | 'n_layers', 'hidden_size', 'embedding_size', 'dropout', 116 | 'n_classes', 'representation', 'n_words', 117 | ] 118 | 119 | def print_params(self): 120 | return [ 121 | self.nlayers, self.hidden_size, self.embedding_size, self.dropout_p, 122 | self.n_classes, self.representation, self.n_words 123 | ] 124 | -------------------------------------------------------------------------------- /src/util/ud_list.py: -------------------------------------------------------------------------------- 1 | UD_LIST = { 2 | 'english': 'UD_English-EWT/en_ewt-ud-%s.conllu', 3 | 'czech': 'UD_Czech-PDT/cs_pdt-ud-%s.conllu', 4 | 'basque': 'UD_Basque-BDT/eu_bdt-ud-%s.conllu', 5 | 'finnish': 'UD_Finnish-TDT/fi_tdt-ud-%s.conllu', 6 | 'turkish': 'UD_Turkish-IMST/tr_imst-ud-%s.conllu', 7 | 'arabic': 'UD_Arabic-NYUAD/ar_nyuad-ud-%s.conllu', 8 | 'japanese': 'UD_Japanese-BCCWJ/ja_bccwj-ud-%s.conllu', 9 | 'tamil': 'UD_Tamil-TTB/ta_ttb-ud-%s.conllu', 10 | 'korean': 'UD_Korean-Kaist/ko_kaist-ud-%s.conllu', 11 | 'marathi': 'UD_Marathi-UFAL/mr_ufal-ud-%s.conllu', 12 | 'urdu': 'UD_Urdu-UDTB/ur_udtb-ud-%s.conllu', 13 | 'telugu': 'UD_Telugu-MTG/te_mtg-ud-%s.conllu', 14 | 'indonesian': 'UD_Indonesian-GSD/id_gsd-ud-%s.conllu', 15 | 16 | # 'af': 'UD_Afrikaans-AfriBooms/af_afribooms-ud-%s.conllu', 17 | # 'be': 'UD_Belarusian-HSE/be_hse-ud-%s.conllu', 18 | # 'bg': 'UD_Bulgarian-BTB/bg_btb-ud-%s.conllu', 19 | # 'ca': 'UD_Catalan-AnCora/ca_ancora-ud-%s.conllu', 20 | # 'cu': 'UD_Old_Church_Slavonic-PROIEL/cu_proiel-ud-%s.conllu', 21 | # 'da': 'UD_Danish-DDT/da_ddt-ud-%s.conllu', 22 | # 'de1': 'UD_German-GSD/de_gsd-ud-%s.conllu', 23 | # 'de2': 'UD_German-HDT/de_hdt-ud-%s.conllu', 24 | # 'es1': 'UD_Spanish-GSD/es_gsd-ud-%s.conllu', 25 | # 'es2': 'UD_Spanish-AnCora/es_ancora-ud-%s.conllu', 26 | # 'et1': 'UD_Estonian-EDT/et_edt-ud-%s.conllu', 27 | # 'el': 'UD_Greek-GDT/el_gdt-ud-%s.conllu', 28 | # 'fa': 'UD_Persian-Seraji/fa_seraji-ud-%s.conllu', 29 | # 'fr1': 'UD_French-Sequoia/fr_sequoia-ud-%s.conllu', 30 | # 'fr2': 'UD_French-Spoken/fr_spoken-ud-%s.conllu', 31 | # 'fr3': 'UD_French-FTB/fr_ftb-ud-%s.conllu', 32 | # 'fr4': 'UD_French-GSD/fr_gsd-ud-%s.conllu', 33 | # 'fr5': 'UD_French-ParTUT/fr_partut-ud-%s.conllu', 34 | # 'ga': 'UD_Irish-IDT/ga_idt-ud-%s.conllu', 35 | # 'gd': 'UD_Scottish_Gaelic-ARCOSG/gd_arcosg-ud-%s.conllu', 36 | # 'gl1': 'UD_Galician-CTG/gl_ctg-ud-%s.conllu', 37 | # 'got': 'UD_Gothic-PROIEL/got_proiel-ud-%s.conllu', 38 | # 'he': 'UD_Hebrew-HTB/he_htb-ud-%s.conllu', 39 | # 'hi': 'UD_Hindi-HDTB/hi_hdtb-ud-%s.conllu', 40 | # 'hr': 'UD_Croatian-SET/hr_set-ud-%s.conllu', 41 | # 'hu': 'UD_Hungarian-Szeged/hu_szeged-ud-%s.conllu', 42 | # 'hy': 'UD_Armenian-ArmTDP/hy_armtdp-ud-%s.conllu', 43 | # 'la1': 'UD_Latin-ITTB/la_ittb-ud-%s.conllu', 44 | # 'la3': 'UD_Latin-PROIEL/la_proiel-ud-%s.conllu', 45 | # 'lt1': 'UD_Lithuanian-ALKSNIS/lt_alksnis-ud-%s.conllu', 46 | # 'lt2': 'UD_Lithuanian-HSE/lt_hse-ud-%s.conllu', 47 | # 'lv': 'UD_Latvian-LVTB/lv_lvtb-ud-%s.conllu', 48 | # 'lzh': 'UD_Classical_Chinese-Kyoto/lzh_kyoto-ud-%s.conllu', 49 | # 'mt': 'UD_Maltese-MUDT/mt_mudt-ud-%s.conllu', 50 | # 'it1': 'UD_Italian-ISDT/it_isdt-ud-%s.conllu', 51 | # 'it2': 'UD_Italian-ParTUT/it_partut-ud-%s.conllu', 52 | # 'it3': 'UD_Italian-PoSTWITA/it_postwita-ud-%s.conllu', 53 | # 'it4': 'UD_Italian-TWITTIRO/it_twittiro-ud-%s.conllu', 54 | # 'it5': 'UD_Italian-VIT/it_vit-ud-%s.conllu', 55 | # 'nl1': 'UD_Dutch-Alpino/nl_alpino-ud-%s.conllu', 56 | # 'nl2': 'UD_Dutch-LassySmall/nl_lassysmall-ud-%s.conllu', 57 | # 'no1': 'UD_Norwegian-Bokmaal/no_bokmaal-ud-%s.conllu', 58 | # 'no2': 'UD_Norwegian-NynorskLIA/no_nynorsklia-ud-%s.conllu', 59 | # 'no3': 'UD_Norwegian-Nynorsk/no_nynorsk-ud-%s.conllu', 60 | # 'orv': 'UD_Old_Russian-TOROT/orv_torot-ud-%s.conllu', 61 | # 'pl1': 'UD_Polish-LFG/pl_lfg-ud-%s.conllu', 62 | # 'pl2': 'UD_Polish-PDB/pl_pdb-ud-%s.conllu', 63 | # 'pt1': 'UD_Portuguese-Bosque/pt_bosque-ud-%s.conllu', 64 | # 'pt2': 'UD_Portuguese-GSD/pt_gsd-ud-%s.conllu', 65 | # 'qhe': 'UD_Hindi_English-HIENCS/qhe_hiencs-ud-%s.conllu', 66 | # 'ro1': 'UD_Romanian-Nonstandard/ro_nonstandard-ud-%s.conllu', 67 | # 'ro2': 'UD_Romanian-RRT/ro_rrt-ud-%s.conllu', 68 | # 'ru1': 'UD_Russian-GSD/ru_gsd-ud-%s.conllu', 69 | # 'ru2': 'UD_Russian-SynTagRus/ru_syntagrus-ud-%s.conllu', 70 | # 'ru3': 'UD_Russian-Taiga/ru_taiga-ud-%s.conllu', 71 | # 'sk': 'UD_Slovak-SNK/sk_snk-ud-%s.conllu', 72 | # 'sl1': 'UD_Slovenian-SSJ/sl_ssj-ud-%s.conllu', 73 | # 'sr': 'UD_Serbian-SET/sr_set-ud-%s.conllu', 74 | # 'sv1': 'UD_Swedish-LinES/sv_lines-ud-%s.conllu', 75 | # 'sv2': 'UD_Swedish-Talbanken/sv_talbanken-ud-%s.conllu', 76 | # 'swl': 'UD_Swedish_Sign_Language-SSLC/swl_sslc-ud-%s.conllu', 77 | # 'ug': 'UD_Uyghur-UDT/ug_udt-ud-%s.conllu', 78 | # 'uk': 'UD_Ukrainian-IU/uk_iu-ud-%s.conllu', 79 | # 'vi': 'UD_Vietnamese-VTB/vi_vtb-ud-%s.conllu', 80 | # 'wo': 'UD_Wolof-WTB/wo_wtb-ud-%s.conllu', 81 | # 'zh1': 'UD_Chinese-GSDSimp/zh_gsdsimp-ud-%s.conllu', 82 | # 'zh2': 'UD_Chinese-GSD/zh_gsd-ud-%s.conllu', 83 | # 'cop': 'UD_Coptic-Scriptorium/cop_scriptorium-ud-%s.conllu', 84 | # 'fro': 'UD_Old_French-SRCMF/fro_srcmf-ud-%s.conllu', 85 | # 'grc1': 'UD_Ancient_Greek-Perseus/grc_perseus-ud-%s.conllu', 86 | # 'grc2': 'UD_Ancient_Greek-PROIEL/grc_proiel-ud-%s.conllu', 87 | } 88 | -------------------------------------------------------------------------------- /src/h01_data/processor/bert.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import torch 3 | from transformers import BertTokenizer 4 | 5 | from h01_data.model import BertPerWordModel 6 | from util import constants 7 | from util import util 8 | from .ud import UdProcessor 9 | 10 | 11 | class BertProcessor(UdProcessor): 12 | # pylint: disable=arguments-differ 13 | bert_name = 'bert-base-multilingual-cased' 14 | name = 'bert' 15 | 16 | def __init__(self): 17 | super().__init__() 18 | self.bert_tokenizer = BertTokenizer.from_pretrained(self.bert_name) 19 | self.bert_model = BertPerWordModel(self.bert_name).to(device=constants.device) 20 | self.bert_model.eval() 21 | 22 | self.pad_id = self.bert_tokenizer.convert_tokens_to_ids('[PAD]') 23 | 24 | def process_file(self, ud_file, output_file, batch_size=8): 25 | print("Processing file {}".format(ud_file)) 26 | 27 | print("PHASE ONE: reading file and tokenizing") 28 | all_bert_tokens, all_bert2target_map = self.tokenize(ud_file) 29 | 30 | print("PHASE TWO: padding, batching, and embedding for bert") 31 | all_bert_embeddings = self.embed_bert(all_bert_tokens, all_bert2target_map, batch_size) 32 | 33 | util.write_data(output_file % self.name, all_bert_embeddings) 34 | 35 | print("Completed {}".format(ud_file)) 36 | 37 | def tokenize(self, file_name): 38 | all_ud_tokens, _ = super().tokenize(file_name) 39 | 40 | all_bert_tokens = [] 41 | all_bert2target_map = [] 42 | 43 | # Initialise all the trees and embeddings 44 | for ud_tokens in all_ud_tokens: 45 | 46 | # Tokenize the sentence 47 | ud2bert_mapping = [] 48 | bert_tokens = [] 49 | for token in self.iterate_sentence(ud_tokens): 50 | bert_decomposition = self.bert_tokenizer.tokenize(token) 51 | if len(bert_decomposition) == 0: 52 | bert_decomposition = ['[UNK]'] 53 | 54 | bert_tokens += bert_decomposition 55 | ud2bert_mapping.append(len(bert_decomposition)) 56 | 57 | all_bert2target_map.append(ud2bert_mapping) 58 | all_bert_tokens.append(bert_tokens) 59 | 60 | return all_bert_tokens, all_bert2target_map 61 | 62 | @staticmethod 63 | def iterate_sentence(tokens): 64 | return tokens 65 | 66 | def embed_bert(self, all_bert_tokens, all_mappings, batch_size): 67 | all_bert_embeddings = [] 68 | 69 | batch_num = 0 70 | for batch_start in range(0, len(all_bert_tokens), batch_size): 71 | 72 | batch_num += 1 73 | if batch_num % 10 == 0: 74 | print("Processing batch {} to embeddings".format(batch_num)) 75 | 76 | # Get the batch 77 | batch_end = batch_start + batch_size 78 | batch = all_bert_tokens[batch_start:batch_end] 79 | batch_map = all_mappings[batch_start:batch_end] 80 | 81 | all_bert_embeddings += self.embed_batch(batch, batch_map) 82 | 83 | return all_bert_embeddings 84 | 85 | def embed_batch(self, batch, batch_map): 86 | input_ids, attention_mask, mappings, lengths = \ 87 | self.get_batch_tensors(batch, batch_map) 88 | 89 | with torch.no_grad(): 90 | embeddings = self.bert_model(input_ids, attention_mask, mappings) 91 | 92 | last_hidden_states = [ 93 | x[:lengths[i]] 94 | for i, x in enumerate(embeddings.cpu().numpy()) 95 | ] 96 | 97 | return last_hidden_states 98 | 99 | def get_batch_tensors(self, batch, batch_map): 100 | lengths_bert = [(len(sentence) + 2) for sentence in batch] # +2 for CLS/SEP 101 | longest_sent_bert = max(lengths_bert) 102 | lengths_orig = [(len(sentence)) for sentence in batch_map] 103 | longest_sent_orig = max(lengths_orig) 104 | 105 | # Pad it & build up attention mask 106 | input_ids = np.ones((len(batch), longest_sent_bert)) * self.pad_id 107 | attention_mask = np.zeros((len(batch), longest_sent_bert)) 108 | mappings = np.ones((len(batch), longest_sent_orig)) * -1 109 | 110 | for i, sentence in enumerate(batch): 111 | sentence_len = lengths_bert[i] 112 | 113 | input_ids[i, :sentence_len] = self.get_sentence_ids(sentence) 114 | # Mask is 1 for tokens that are NOT MASKED, 0 for MASKED tokens. 115 | attention_mask[i, :sentence_len] = 1 116 | mappings[i, :len(batch_map[i])] = batch_map[i] 117 | 118 | # Move data to torch and cuda 119 | input_ids = torch.LongTensor(input_ids).to(device=constants.device) 120 | attention_mask = torch.LongTensor(attention_mask).to(device=constants.device) 121 | mappings = torch.LongTensor(mappings).to(device=constants.device) 122 | 123 | return input_ids, attention_mask, mappings, lengths_orig 124 | 125 | def get_sentence_ids(self, sentence): 126 | return self.bert_tokenizer.convert_tokens_to_ids( 127 | ["[CLS]"] + sentence + ["[SEP]"]) 128 | -------------------------------------------------------------------------------- /checkpoints/dep_label/marathi/mlp/random/all_results.tsv: -------------------------------------------------------------------------------- 1 | run,ndata,hidden_size,nlayers,dropout,embedding_size,train_loss,dev_loss,test_loss,train_acc,dev_acc,test_acc,base_train_loss,base_dev_loss,base_test_loss,base_train_acc,base_dev_acc,base_test_acc 2 | 0,1,776,0,0.09,272,4.4374,5.0031,5.0160,1.0000,0.0330,0.0110,0.0002,5.1952,5.2121,1.0000,0.0431,0.0219 3 | 1,2,433,2,0.27,145,4.4594,5.0073,5.0236,0.5000,0.0305,0.0247,0.0000,6.2244,6.4261,1.0000,0.0305,0.0137 4 | 2,3,310,1,0.38,182,4.4811,4.8835,4.8850,0.3333,0.0305,0.0247,0.0003,4.4709,4.5596,1.0000,0.1421,0.1370 5 | 3,4,300,1,0.42,205,4.5025,4.8336,4.8278,0.2500,0.0305,0.0247,0.0003,4.3605,4.4866,1.0000,0.1777,0.1644 6 | 4,5,1009,2,0.39,134,4.3575,4.8410,4.8443,0.4000,0.0330,0.0110,0.0000,8.0052,7.9995,1.0000,0.1777,0.1507 7 | 5,6,657,2,0.41000000000000003,245,4.2676,4.8491,4.8549,0.3333,0.0305,0.0247,0.0000,6.5424,6.6197,1.0000,0.1726,0.1342 8 | 6,7,455,2,0.23,133,4.2090,4.7663,4.7616,0.2857,0.0305,0.0247,0.0000,6.1490,6.2309,1.0000,0.1447,0.1370 9 | 7,8,103,2,0.09,108,4.2737,4.7598,4.7690,0.2500,0.0305,0.0247,0.0012,4.4012,4.5353,1.0000,0.2107,0.1863 10 | 8,9,203,2,0.14,93,4.1747,4.7690,4.7854,0.3333,0.0330,0.0110,0.0004,4.4568,4.4929,1.0000,0.2030,0.2055 11 | 9,11,150,1,0.31,212,4.2165,4.7471,4.7654,0.2727,0.0330,0.0110,0.0009,4.3010,4.5321,1.0000,0.1980,0.1671 12 | 10,12,72,0,0.15,103,4.1561,4.6861,4.6963,0.2500,0.0330,0.0110,0.0704,4.6707,4.8571,1.0000,0.1701,0.1288 13 | 11,14,467,2,0.27,113,4.2660,4.6790,4.6825,0.2143,0.0330,0.0110,0.0002,5.6655,5.9893,1.0000,0.2360,0.1753 14 | 12,16,377,2,0.5,99,4.3925,4.7393,4.7435,0.1875,0.0330,0.0110,0.0033,5.1721,5.4133,1.0000,0.2081,0.1836 15 | 13,18,436,0,0.44,148,4.3772,4.7625,4.7737,0.1667,0.0330,0.0110,0.0757,5.0154,5.4181,1.0000,0.1599,0.1123 16 | 14,21,382,0,0.1,165,4.3646,4.7062,4.7195,0.1905,0.2487,0.2740,0.0445,4.9788,5.2357,1.0000,0.1878,0.1425 17 | 15,24,326,2,0.5,60,4.3296,4.6071,4.6114,0.2083,0.2487,0.2740,0.0345,4.9146,5.1547,1.0000,0.2741,0.2192 18 | 16,28,326,2,0.49,84,4.2033,4.5234,4.5179,0.2500,0.2487,0.2740,0.0123,4.2534,4.6202,1.0000,0.3452,0.3370 19 | 17,32,247,2,0.44,238,4.2458,4.5138,4.4997,0.2500,0.2487,0.2740,0.0011,4.1804,4.5525,1.0000,0.3325,0.3479 20 | 18,37,179,2,0.05,122,4.1790,4.4622,4.4412,0.2703,0.2487,0.2740,0.0020,4.2228,4.6161,1.0000,0.3629,0.3397 21 | 19,42,137,2,0.02,144,4.0892,4.4246,4.3924,0.2857,0.2487,0.2740,0.0033,4.1503,4.3657,1.0000,0.3147,0.3288 22 | 20,48,786,2,0.23,79,4.1342,4.4286,4.3914,0.2708,0.2487,0.2740,0.0002,5.6252,6.0432,1.0000,0.3680,0.3534 23 | 21,55,410,1,0.31,159,4.1233,4.4021,4.3493,0.2727,0.2487,0.2740,0.0036,3.7947,4.1147,1.0000,0.3629,0.3397 24 | 22,64,388,1,0.37,154,4.0053,4.3658,4.3046,0.2969,0.2487,0.2740,0.0052,3.8345,3.9208,1.0000,0.3325,0.3616 25 | 23,73,38,2,0.25,148,3.9980,4.3618,4.2779,0.3014,0.2487,0.2740,0.8695,3.9565,3.9266,0.8767,0.2868,0.3342 26 | 24,84,467,1,0.41000000000000003,115,4.0758,4.2896,4.1989,0.2857,0.2487,0.2740,0.1064,3.7424,3.3861,0.9643,0.4061,0.4767 27 | 25,96,176,0,0.09,124,4.0173,4.2568,4.1543,0.2812,0.2487,0.2740,0.2969,3.7631,3.6096,0.9688,0.3731,0.4027 28 | 26,110,57,2,0.02,240,4.0382,4.2388,4.1315,0.2727,0.2487,0.2740,0.1365,3.6339,3.4179,0.9636,0.4315,0.4767 29 | 27,127,968,2,0.15,50,4.1292,4.2393,4.1208,0.2520,0.2487,0.2740,0.1421,5.0743,4.7446,0.9606,0.4416,0.4986 30 | 28,145,268,0,0.01,235,4.1498,4.2395,4.1009,0.2414,0.2487,0.2740,0.2058,3.6545,3.5099,0.9655,0.4112,0.4493 31 | 29,167,270,0,0.3,144,4.0879,4.2167,4.0717,0.2455,0.2487,0.2740,0.5054,3.5425,3.3535,0.9581,0.4365,0.4849 32 | 30,191,596,2,0.24,188,4.0106,4.1871,4.0220,0.2513,0.2487,0.2740,0.1542,3.9290,3.6819,0.9581,0.4746,0.5178 33 | 31,220,694,0,0.49,59,4.0266,4.2016,4.0370,0.2409,0.2487,0.2740,1.1056,3.6791,3.3949,0.8727,0.4188,0.4603 34 | 32,252,837,0,0.48,176,4.0366,4.1944,4.0328,0.2302,0.2487,0.2740,0.6397,3.4853,3.4005,0.9246,0.4543,0.4548 35 | 33,289,487,2,0.4,75,3.9961,4.1964,4.0249,0.2249,0.2487,0.2740,0.2838,3.5466,3.3258,0.9550,0.4873,0.4932 36 | 34,332,418,0,0.11,210,3.9897,4.2003,4.0292,0.2289,0.2487,0.2740,0.4929,3.4218,3.3091,0.9398,0.4848,0.4932 37 | 35,381,407,0,0.05,85,3.9836,4.2061,4.0231,0.2283,0.2487,0.2740,0.7886,3.7255,3.4832,0.9055,0.4365,0.4548 38 | 36,437,657,0,0.0,102,3.9974,4.2004,4.0092,0.2174,0.2487,0.2740,1.2085,3.6313,3.8004,0.8741,0.4264,0.4110 39 | 37,502,442,1,0.22,225,3.9793,4.1892,3.9959,0.2171,0.2487,0.2740,0.2002,3.1391,2.8154,0.9562,0.5635,0.5562 40 | 38,576,398,1,0.37,187,3.9598,4.1822,3.9721,0.2188,0.2487,0.2740,0.2580,3.1112,2.8410,0.9497,0.5381,0.5479 41 | 39,661,369,2,0.04,195,3.9901,4.1659,3.9650,0.2179,0.2487,0.2740,0.2078,3.5777,3.6041,0.9501,0.5330,0.5534 42 | 40,759,75,2,0.43,230,3.9682,4.1421,3.9582,0.2200,0.2487,0.2740,0.8799,2.7311,2.6623,0.8564,0.5609,0.5589 43 | 41,871,179,1,0.18,187,3.9513,4.1177,3.9351,0.2216,0.2487,0.2740,0.3567,2.8805,2.7845,0.9414,0.5508,0.5562 44 | 42,1000,442,0,0.09,112,3.9746,4.1177,3.9411,0.2190,0.2487,0.2740,1.0321,3.4531,3.3558,0.8520,0.4670,0.4932 45 | 43,1148,111,0,0.14,188,3.9840,4.1300,3.9501,0.2160,0.2487,0.2740,1.1491,3.1844,3.1757,0.8406,0.4822,0.5014 46 | 44,1317,242,0,0.37,94,3.9993,4.1272,3.9343,0.2118,0.2487,0.2740,1.3273,3.2590,3.1172,0.7897,0.5051,0.5288 47 | 45,1512,107,0,0.35000000000000003,182,3.9966,4.1277,3.9274,0.2077,0.2487,0.2740,0.8727,3.0434,2.9574,0.8730,0.5102,0.5260 48 | 46,1735,150,0,0.11,215,4.0109,4.1308,3.9178,0.2063,0.2487,0.2740,1.1864,3.2388,3.1748,0.8225,0.4822,0.5068 49 | 47,1992,38,2,0.41000000000000003,292,4.0226,4.1086,3.8963,0.2058,0.2487,0.2740,1.5301,2.5284,2.3680,0.7214,0.5558,0.5836 50 | 48,2286,248,2,0.23,98,4.0374,4.0905,3.8772,0.2047,0.2487,0.2740,1.7483,2.6303,2.4342,0.6579,0.5482,0.5534 51 | 49,2624,670,1,0.29,67,4.0281,4.0866,3.8676,0.2092,0.2487,0.2740,1.3633,2.5978,2.3491,0.7572,0.5635,0.5918 52 | -------------------------------------------------------------------------------- /checkpoints/pos_tag/marathi/mlp/random/all_results.tsv: -------------------------------------------------------------------------------- 1 | run,ndata,hidden_size,nlayers,dropout,embedding_size,train_loss,dev_loss,test_loss,train_acc,dev_acc,test_acc,base_train_loss,base_dev_loss,base_test_loss,base_train_acc,base_dev_acc,base_test_acc 2 | 0,1,776,0,0.09,272,3.3692,3.9369,3.9485,1.0000,0.0295,0.0097,0.0035,3.8225,3.8674,1.0000,0.1477,0.1238 3 | 1,2,433,2,0.27,145,3.4150,3.9388,3.9574,0.5000,0.0750,0.0631,0.0000,6.1240,6.3882,1.0000,0.0864,0.0534 4 | 2,3,310,1,0.38,182,3.4594,3.8383,3.8939,0.3333,0.0750,0.0631,0.0007,3.9349,4.1515,1.0000,0.1091,0.0607 5 | 3,4,300,1,0.42,205,3.5025,3.7511,3.7950,0.2500,0.0750,0.0631,0.0010,3.3829,3.5439,1.0000,0.2682,0.2427 6 | 4,5,1009,2,0.39,134,3.5443,3.7610,3.7729,0.2000,0.0750,0.0631,0.0000,6.4557,6.9269,1.0000,0.2614,0.1990 7 | 5,6,657,2,0.41000000000000003,245,3.4466,3.7894,3.8095,0.3333,0.0295,0.0097,0.0000,4.5870,4.7862,1.0000,0.2273,0.2451 8 | 6,7,455,2,0.23,133,3.3873,3.7261,3.7725,0.2857,0.0295,0.0097,0.0001,4.4882,4.7643,1.0000,0.3318,0.2549 9 | 7,8,103,2,0.09,108,3.3517,3.7335,3.7848,0.2500,0.0750,0.0631,0.0023,3.4262,3.5787,1.0000,0.2955,0.2160 10 | 8,9,203,2,0.14,93,3.3315,3.6785,3.7215,0.2222,0.0750,0.0631,0.0005,3.8942,4.1549,1.0000,0.2977,0.2136 11 | 9,11,150,1,0.31,212,3.3452,3.7212,3.7707,0.2727,0.0295,0.0097,0.0025,3.3164,3.5158,1.0000,0.2568,0.1869 12 | 10,12,72,0,0.15,103,3.3005,3.6762,3.7460,0.2500,0.0295,0.0097,0.1166,4.1196,4.1581,1.0000,0.1500,0.1383 13 | 11,14,467,2,0.27,113,3.3123,3.6689,3.7604,0.2857,0.2477,0.1845,0.0001,4.8357,5.1352,1.0000,0.2545,0.1942 14 | 12,16,377,2,0.5,99,3.2284,3.6062,3.7053,0.3125,0.2477,0.1845,0.0021,4.5737,4.8851,1.0000,0.2136,0.1869 15 | 13,19,436,0,0.44,148,3.2628,3.6070,3.6858,0.3158,0.2477,0.1845,0.1300,3.8420,3.9997,1.0000,0.1727,0.1311 16 | 14,22,382,0,0.1,165,3.2534,3.5070,3.5812,0.3182,0.2477,0.1845,0.0749,3.6503,3.7723,1.0000,0.1886,0.1917 17 | 15,25,326,2,0.5,60,3.2925,3.4958,3.5842,0.3200,0.2477,0.1845,0.1286,3.4361,3.8423,0.9600,0.3068,0.2451 18 | 16,29,326,2,0.49,84,3.2668,3.4356,3.5035,0.3103,0.2477,0.1845,0.0815,3.2975,3.9087,0.9655,0.3136,0.2597 19 | 17,33,247,2,0.44,238,3.1988,3.3715,3.4346,0.3030,0.2477,0.1845,0.0636,2.8608,3.0833,0.9697,0.4523,0.4612 20 | 18,38,179,2,0.05,122,3.1643,3.3323,3.3669,0.2632,0.2477,0.1845,0.0549,2.8952,3.3991,0.9737,0.4136,0.3422 21 | 19,44,137,2,0.02,144,3.1723,3.3167,3.3429,0.2500,0.2477,0.1845,0.0487,2.9968,3.2616,0.9773,0.4068,0.3908 22 | 20,50,786,2,0.23,79,3.0956,3.2986,3.3360,0.2600,0.2477,0.1845,0.0409,4.1711,4.6301,0.9800,0.4523,0.4296 23 | 21,58,410,1,0.31,159,3.1162,3.3154,3.3330,0.2414,0.2477,0.1845,0.0413,2.8520,2.9621,0.9828,0.4455,0.4587 24 | 22,67,388,1,0.37,154,3.1033,3.3082,3.3010,0.2537,0.2227,0.2427,0.0394,2.9081,3.0721,0.9851,0.4068,0.4369 25 | 23,77,38,2,0.25,148,3.0437,3.2968,3.2921,0.2727,0.2227,0.2427,0.7890,2.6540,2.6172,0.9091,0.4205,0.4320 26 | 24,88,467,1,0.41000000000000003,115,3.0919,3.2944,3.2658,0.2500,0.2227,0.2427,0.1112,2.6094,2.5825,0.9659,0.4909,0.4806 27 | 25,102,176,0,0.09,124,3.0914,3.2847,3.2428,0.2549,0.2227,0.2427,0.8296,2.8917,2.8989,0.9314,0.4000,0.4272 28 | 26,117,57,2,0.02,240,3.0558,3.2519,3.2185,0.2479,0.2227,0.2427,0.2309,2.5351,2.4395,0.9402,0.4705,0.4733 29 | 27,135,968,2,0.15,50,3.0885,3.2537,3.2054,0.2296,0.2227,0.2427,0.1813,4.6636,3.9507,0.9407,0.5045,0.5437 30 | 28,155,268,0,0.01,235,3.0558,3.2311,3.1870,0.2194,0.2227,0.2427,0.5252,2.8573,2.6292,0.9161,0.4205,0.4733 31 | 29,179,270,0,0.3,144,3.0118,3.2186,3.1788,0.2235,0.2227,0.2427,1.2351,2.8324,2.6641,0.8492,0.4205,0.4709 32 | 30,206,596,2,0.24,188,2.9772,3.2160,3.1773,0.2233,0.2227,0.2427,0.2423,3.2574,2.9413,0.9175,0.5159,0.5558 33 | 31,237,694,0,0.49,59,2.9738,3.2121,3.1799,0.2236,0.2477,0.1845,0.8080,2.4539,2.5145,0.8270,0.4932,0.5146 34 | 32,273,837,0,0.48,176,2.9691,3.2264,3.1965,0.2271,0.2477,0.1845,0.8248,2.5317,2.5222,0.8828,0.4636,0.4903 35 | 33,314,487,2,0.4,75,2.9932,3.2352,3.2092,0.2293,0.2477,0.1845,0.3927,2.2955,2.3385,0.9140,0.5932,0.5801 36 | 34,362,418,0,0.11,210,3.0319,3.2361,3.2057,0.2155,0.2477,0.1845,1.2363,2.7533,2.5826,0.8039,0.4477,0.4830 37 | 35,417,407,0,0.05,85,3.0302,3.2475,3.2061,0.2086,0.2477,0.1845,1.5266,2.6637,2.7666,0.7170,0.4727,0.4951 38 | 36,480,657,0,0.0,102,3.0222,3.2486,3.2161,0.2229,0.2477,0.1845,1.9523,2.7133,2.7943,0.6333,0.4659,0.4539 39 | 37,552,442,1,0.22,225,3.0221,3.2661,3.2291,0.2192,0.2477,0.1845,0.3687,2.1065,1.9660,0.8986,0.5682,0.6141 40 | 38,636,398,1,0.37,187,3.0647,3.2704,3.2278,0.2091,0.2477,0.1845,0.4801,1.9608,1.9524,0.8852,0.5932,0.5922 41 | 39,732,369,2,0.04,195,3.0756,3.2638,3.2329,0.2172,0.2477,0.1845,0.3992,2.6671,2.9958,0.8825,0.6091,0.6141 42 | 40,843,75,2,0.43,230,3.0729,3.2547,3.2399,0.2289,0.2477,0.1845,0.8144,1.8385,1.7684,0.8292,0.5977,0.6262 43 | 41,971,179,1,0.18,187,3.0462,3.2546,3.2412,0.2317,0.2477,0.1845,0.5911,1.8261,1.8172,0.8774,0.6182,0.6578 44 | 42,1118,442,0,0.09,112,3.0578,3.2625,3.2406,0.2272,0.2477,0.1845,1.6805,2.5168,2.3378,0.6699,0.5205,0.5558 45 | 43,1287,111,0,0.14,188,3.0335,3.2698,3.2511,0.2300,0.2477,0.1845,1.3605,2.3976,2.4858,0.7537,0.5114,0.5437 46 | 44,1481,242,0,0.37,94,3.0191,3.2807,3.2624,0.2350,0.2477,0.1845,1.2763,2.1328,2.0643,0.6840,0.5523,0.5728 47 | 45,1706,107,0,0.35000000000000003,182,3.0191,3.2819,3.2689,0.2386,0.2477,0.1845,0.9553,1.9267,1.9452,0.7837,0.5909,0.6262 48 | 46,1964,150,0,0.11,215,3.0296,3.2919,3.2768,0.2378,0.2477,0.1845,1.7117,2.4049,2.4003,0.6777,0.5568,0.5194 49 | 47,2261,38,2,0.41000000000000003,292,3.0503,3.2770,3.2586,0.2375,0.2477,0.1845,1.2148,1.9609,1.8908,0.7138,0.6114,0.6165 50 | 48,2603,248,2,0.23,98,3.1370,3.2087,3.1817,0.2240,0.2477,0.1845,1.1150,1.7785,1.6743,0.7484,0.6000,0.6408 51 | 49,2997,670,1,0.29,67,3.1705,3.2029,3.1709,0.2149,0.2477,0.1845,0.8087,1.5390,1.4166,0.8248,0.6591,0.6990 52 | -------------------------------------------------------------------------------- /checkpoints/dep_label/marathi/mlp/bert/all_results.tsv: -------------------------------------------------------------------------------- 1 | run,ndata,hidden_size,nlayers,dropout,embedding_size,train_loss,dev_loss,test_loss,train_acc,dev_acc,test_acc,base_train_loss,base_dev_loss,base_test_loss,base_train_acc,base_dev_acc,base_test_acc 2 | 0,1,776,0,0.09,768,4.4374,5.0031,5.0160,1.0000,0.0330,0.0110,0.0002,9.2038,9.1856,1.0000,0.0330,0.0110 3 | 1,2,433,2,0.27,768,4.4594,5.0073,5.0236,0.5000,0.0305,0.0247,0.0000,18.4869,18.5420,1.0000,0.0584,0.0247 4 | 2,3,310,1,0.38,768,4.4811,4.8835,4.8850,0.3333,0.0305,0.0247,0.0032,8.8967,8.6507,1.0000,0.3046,0.2877 5 | 3,4,300,1,0.42,768,4.5025,4.8336,4.8278,0.2500,0.0305,0.0247,0.0000,8.2774,7.9813,1.0000,0.3173,0.3315 6 | 4,5,1009,2,0.39,768,4.3575,4.8410,4.8443,0.4000,0.0330,0.0110,0.0001,8.7494,8.5061,1.0000,0.3122,0.3068 7 | 5,6,657,2,0.41000000000000003,768,4.2676,4.8491,4.8549,0.3333,0.0305,0.0247,0.0001,8.9293,8.7587,1.0000,0.3249,0.3096 8 | 6,7,455,2,0.23,768,4.2090,4.7663,4.7616,0.2857,0.0305,0.0247,0.0004,8.9843,8.5495,1.0000,0.3198,0.2986 9 | 7,8,103,2,0.09,768,4.2737,4.7598,4.7690,0.2500,0.0305,0.0247,0.0064,5.8324,5.8568,1.0000,0.3122,0.3096 10 | 8,9,203,2,0.14,768,4.1747,4.7690,4.7854,0.3333,0.0330,0.0110,0.0017,7.0970,7.1057,1.0000,0.2868,0.2877 11 | 9,11,150,1,0.31,768,4.2165,4.7471,4.7654,0.2727,0.0330,0.0110,0.0073,6.3567,6.6068,1.0000,0.3376,0.3233 12 | 10,12,72,0,0.15,768,4.1561,4.6861,4.6963,0.2500,0.0330,0.0110,0.0044,6.2820,6.3348,1.0000,0.3756,0.3781 13 | 11,14,467,2,0.27,768,4.2660,4.6790,4.6825,0.2143,0.0330,0.0110,0.0005,7.0537,7.0967,1.0000,0.3528,0.3589 14 | 12,16,377,2,0.5,768,4.3925,4.7393,4.7435,0.1875,0.0330,0.0110,0.0066,6.0116,6.0101,1.0000,0.3350,0.3342 15 | 13,18,436,0,0.44,768,4.3772,4.7625,4.7737,0.1667,0.0330,0.0110,0.0120,5.7782,5.8237,1.0000,0.3503,0.3233 16 | 14,21,382,0,0.1,768,4.3646,4.7062,4.7195,0.1905,0.2487,0.2740,0.0385,6.0606,6.2197,1.0000,0.3401,0.3342 17 | 15,24,326,2,0.5,768,4.3296,4.6071,4.6114,0.2083,0.2487,0.2740,0.0099,4.8913,5.0767,1.0000,0.4645,0.4466 18 | 16,28,326,2,0.49,768,4.2033,4.5234,4.5179,0.2500,0.2487,0.2740,0.0072,5.6984,5.9421,1.0000,0.4721,0.4301 19 | 17,32,247,2,0.44,768,4.2458,4.5138,4.4997,0.2500,0.2487,0.2740,0.0140,5.0225,5.2702,1.0000,0.4873,0.4685 20 | 18,37,179,2,0.05,768,4.1790,4.4622,4.4412,0.2703,0.2487,0.2740,0.0031,6.1856,6.5954,1.0000,0.4645,0.4438 21 | 19,42,137,2,0.02,768,4.0892,4.4246,4.3924,0.2857,0.2487,0.2740,0.0060,5.8632,6.1050,1.0000,0.4543,0.4466 22 | 20,48,786,2,0.23,768,4.1342,4.4286,4.3914,0.2708,0.2487,0.2740,0.0004,7.1207,7.7745,1.0000,0.4848,0.4712 23 | 21,55,410,1,0.31,768,4.1233,4.4021,4.3493,0.2727,0.2487,0.2740,0.0041,5.2295,5.4837,1.0000,0.4391,0.4356 24 | 22,64,388,1,0.37,768,4.0053,4.3658,4.3046,0.2969,0.2487,0.2740,0.0060,4.6647,4.9664,1.0000,0.4975,0.4904 25 | 23,73,38,2,0.25,768,3.9980,4.3618,4.2779,0.3014,0.2487,0.2740,0.9855,3.6533,3.6070,0.8630,0.3604,0.3726 26 | 24,84,467,1,0.41000000000000003,768,4.0758,4.2896,4.1989,0.2857,0.2487,0.2740,0.0022,3.4805,3.3927,1.0000,0.5508,0.5507 27 | 25,96,176,0,0.09,768,4.0173,4.2568,4.1543,0.2812,0.2487,0.2740,0.0809,3.2736,3.0847,1.0000,0.5228,0.5452 28 | 26,110,57,2,0.02,768,4.0382,4.2388,4.1315,0.2727,0.2487,0.2740,0.0620,3.1407,3.1554,1.0000,0.5355,0.5260 29 | 27,127,968,2,0.15,768,4.1292,4.2393,4.1208,0.2520,0.2487,0.2740,0.0028,4.2260,4.2764,1.0000,0.5508,0.5479 30 | 28,145,268,0,0.01,768,4.1498,4.2395,4.1009,0.2414,0.2487,0.2740,0.0920,3.0896,2.7724,1.0000,0.5812,0.5726 31 | 29,167,270,0,0.3,768,4.0879,4.2167,4.0717,0.2455,0.2487,0.2740,0.1353,3.0617,2.7190,1.0000,0.5711,0.5808 32 | 30,191,596,2,0.24,768,4.0106,4.1871,4.0220,0.2513,0.2487,0.2740,0.0084,3.8406,3.3016,1.0000,0.5838,0.6438 33 | 31,220,694,0,0.49,768,4.0266,4.2016,4.0370,0.2409,0.2487,0.2740,0.2237,2.9125,2.4856,1.0000,0.5787,0.5945 34 | 32,252,837,0,0.48,768,4.0366,4.1944,4.0328,0.2302,0.2487,0.2740,0.2383,2.7572,2.4181,1.0000,0.5888,0.6356 35 | 33,289,487,2,0.4,768,3.9961,4.1964,4.0249,0.2249,0.2487,0.2740,0.0270,2.9288,2.7815,1.0000,0.6472,0.6329 36 | 34,332,418,0,0.11,768,3.9897,4.2003,4.0292,0.2289,0.2487,0.2740,0.2070,2.7912,2.4532,1.0000,0.6168,0.6247 37 | 35,381,407,0,0.05,768,3.9836,4.2061,4.0231,0.2283,0.2487,0.2740,0.1945,2.7807,2.4530,1.0000,0.6193,0.6384 38 | 36,437,657,0,0.0,768,3.9974,4.2004,4.0092,0.2174,0.2487,0.2740,0.2240,2.7057,2.3671,1.0000,0.6294,0.6438 39 | 37,502,442,1,0.22,768,3.9793,4.1892,3.9959,0.2171,0.2487,0.2740,0.0216,2.8852,2.6458,0.9980,0.6599,0.6603 40 | 38,576,398,1,0.37,768,3.9598,4.1822,3.9721,0.2188,0.2487,0.2740,0.0449,2.6738,2.3171,0.9983,0.6853,0.6603 41 | 39,661,369,2,0.04,768,3.9901,4.1659,3.9650,0.2179,0.2487,0.2740,0.0125,3.0422,2.9923,0.9985,0.6599,0.6767 42 | 40,759,75,2,0.43,768,3.9682,4.1421,3.9582,0.2200,0.2487,0.2740,0.6658,2.1051,1.8611,0.8946,0.6497,0.6986 43 | 41,871,179,1,0.18,768,3.9513,4.1177,3.9351,0.2216,0.2487,0.2740,0.1143,1.9810,1.8879,1.0000,0.6827,0.6986 44 | 42,1000,442,0,0.09,768,3.9746,4.1177,3.9411,0.2190,0.2487,0.2740,0.4040,1.9749,1.9317,0.9800,0.6751,0.7014 45 | 43,1148,111,0,0.14,768,3.9840,4.1300,3.9501,0.2160,0.2487,0.2740,0.4470,1.9531,1.9146,0.9625,0.6751,0.7014 46 | 44,1317,242,0,0.37,768,3.9993,4.1272,3.9343,0.2118,0.2487,0.2740,0.5345,1.9279,1.7887,0.9431,0.6777,0.7205 47 | 45,1512,107,0,0.35000000000000003,768,3.9966,4.1277,3.9274,0.2077,0.2487,0.2740,0.5677,1.8302,1.7131,0.9385,0.6904,0.7178 48 | 46,1735,150,0,0.11,768,4.0109,4.1308,3.9178,0.2063,0.2487,0.2740,0.5502,1.8181,1.6989,0.9395,0.6802,0.7233 49 | 47,1992,38,2,0.41000000000000003,768,4.0226,4.1086,3.8963,0.2058,0.2487,0.2740,1.1669,1.9195,1.6622,0.7776,0.6421,0.6849 50 | 48,2286,248,2,0.23,768,4.0374,4.0905,3.8772,0.2047,0.2487,0.2740,0.2432,1.5649,1.4813,0.9668,0.7259,0.7233 51 | 49,2624,670,1,0.29,768,4.0281,4.0866,3.8676,0.2092,0.2487,0.2740,0.2731,1.4707,1.4411,0.9630,0.7360,0.7507 52 | -------------------------------------------------------------------------------- /checkpoints/dep_label/marathi/mlp/fast/all_results.tsv: -------------------------------------------------------------------------------- 1 | run,ndata,hidden_size,nlayers,dropout,embedding_size,train_loss,dev_loss,test_loss,train_acc,dev_acc,test_acc,base_train_loss,base_dev_loss,base_test_loss,base_train_acc,base_dev_acc,base_test_acc 2 | 0,1,776,0,0.09,300,4.4374,5.0031,5.0160,1.0000,0.0330,0.0110,0.3092,5.0660,5.1126,1.0000,0.0330,0.0137 3 | 1,2,433,2,0.27,300,4.4594,5.0073,5.0236,0.5000,0.0305,0.0247,0.0003,8.5200,9.0912,1.0000,0.0508,0.0356 4 | 2,3,310,1,0.38,300,4.4811,4.8835,4.8850,0.3333,0.0305,0.0247,0.0394,4.6337,4.7003,1.0000,0.2868,0.2986 5 | 3,4,300,1,0.42,300,4.5025,4.8336,4.8278,0.2500,0.0305,0.0247,0.0525,4.3633,4.3880,1.0000,0.3731,0.3726 6 | 4,5,1009,2,0.39,300,4.3575,4.8410,4.8443,0.4000,0.0330,0.0110,0.0006,8.4465,8.7367,1.0000,0.3731,0.3479 7 | 5,6,657,2,0.41000000000000003,300,4.2676,4.8491,4.8549,0.3333,0.0305,0.0247,0.0038,6.8593,6.9968,1.0000,0.3553,0.3342 8 | 6,7,455,2,0.23,300,4.2090,4.7663,4.7616,0.2857,0.0305,0.0247,0.0025,7.5789,7.7411,1.0000,0.3452,0.3562 9 | 7,8,103,2,0.09,300,4.2737,4.7598,4.7690,0.2500,0.0305,0.0247,0.2923,5.1240,5.2028,1.0000,0.3020,0.3233 10 | 8,9,203,2,0.14,300,4.1747,4.7690,4.7854,0.3333,0.0330,0.0110,0.0909,6.3899,6.5257,1.0000,0.3477,0.3589 11 | 9,11,150,1,0.31,300,4.2165,4.7471,4.7654,0.2727,0.0330,0.0110,0.4735,4.4847,4.5919,1.0000,0.3528,0.3616 12 | 10,12,72,0,0.15,300,4.1561,4.6861,4.6963,0.2500,0.0330,0.0110,0.6163,4.1397,4.1323,1.0000,0.3401,0.3589 13 | 11,14,467,2,0.27,300,4.2660,4.6790,4.6825,0.2143,0.0330,0.0110,0.0501,8.8290,9.3499,1.0000,0.3756,0.3808 14 | 12,16,377,2,0.5,300,4.3925,4.7393,4.7435,0.1875,0.0330,0.0110,0.3889,6.9836,7.3435,0.9375,0.3629,0.3644 15 | 13,18,436,0,0.44,300,4.3772,4.7625,4.7737,0.1667,0.0330,0.0110,0.5363,4.1137,4.0943,1.0000,0.3299,0.3534 16 | 14,21,382,0,0.1,300,4.3646,4.7062,4.7195,0.1905,0.2487,0.2740,0.9360,4.5233,4.5155,1.0000,0.3147,0.3233 17 | 15,24,326,2,0.5,300,4.3296,4.6071,4.6114,0.2083,0.2487,0.2740,0.3836,4.4717,4.6668,1.0000,0.4289,0.4438 18 | 16,28,326,2,0.49,300,4.2033,4.5234,4.5179,0.2500,0.2487,0.2740,0.3450,4.6627,5.0156,0.9643,0.4365,0.4521 19 | 17,32,247,2,0.44,300,4.2458,4.5138,4.4997,0.2500,0.2487,0.2740,0.4894,4.6662,5.0078,1.0000,0.4264,0.4603 20 | 18,37,179,2,0.05,300,4.1790,4.4622,4.4412,0.2703,0.2487,0.2740,0.1309,4.8710,4.9576,1.0000,0.4213,0.4466 21 | 19,42,137,2,0.02,300,4.0892,4.4246,4.3924,0.2857,0.2487,0.2740,0.2535,4.6060,4.7713,1.0000,0.4442,0.4685 22 | 20,48,786,2,0.23,300,4.1342,4.4286,4.3914,0.2708,0.2487,0.2740,0.0084,6.5472,7.0749,1.0000,0.4340,0.4685 23 | 21,55,410,1,0.31,300,4.1233,4.4021,4.3493,0.2727,0.2487,0.2740,0.1959,3.9620,3.9993,1.0000,0.4289,0.4767 24 | 22,64,388,1,0.37,300,4.0053,4.3658,4.3046,0.2969,0.2487,0.2740,0.2927,3.8585,3.8228,1.0000,0.4289,0.4795 25 | 23,73,38,2,0.25,300,3.9980,4.3618,4.2779,0.3014,0.2487,0.2740,2.4498,3.9496,3.7530,0.4795,0.3350,0.3644 26 | 24,84,467,1,0.41000000000000003,300,4.0758,4.2896,4.1989,0.2857,0.2487,0.2740,0.4686,3.5873,3.1364,0.9524,0.4721,0.5233 27 | 25,96,176,0,0.09,300,4.0173,4.2568,4.1543,0.2812,0.2487,0.2740,0.7179,3.3466,2.9326,0.9688,0.4289,0.4712 28 | 26,110,57,2,0.02,300,4.0382,4.2388,4.1315,0.2727,0.2487,0.2740,1.6505,3.0939,2.7862,0.6545,0.4518,0.4521 29 | 27,127,968,2,0.15,300,4.1292,4.2393,4.1208,0.2520,0.2487,0.2740,0.1558,4.7628,4.5351,0.9606,0.4721,0.5479 30 | 28,145,268,0,0.01,300,4.1498,4.2395,4.1009,0.2414,0.2487,0.2740,0.8698,3.2385,2.7894,0.9655,0.4518,0.5726 31 | 29,167,270,0,0.3,300,4.0879,4.2167,4.0717,0.2455,0.2487,0.2740,0.9100,3.2273,2.7617,0.9521,0.4645,0.5507 32 | 30,191,596,2,0.24,300,4.0106,4.1871,4.0220,0.2513,0.2487,0.2740,0.4022,3.8016,3.3897,0.9267,0.4772,0.5616 33 | 31,220,694,0,0.49,300,4.0266,4.2016,4.0370,0.2409,0.2487,0.2740,1.0449,3.1243,2.6701,0.9318,0.4645,0.5616 34 | 32,252,837,0,0.48,300,4.0366,4.1944,4.0328,0.2302,0.2487,0.2740,0.7636,2.9560,2.5298,0.9365,0.4772,0.5808 35 | 33,289,487,2,0.4,300,3.9961,4.1964,4.0249,0.2249,0.2487,0.2740,0.9311,3.0733,2.7432,0.8304,0.5000,0.5726 36 | 34,332,418,0,0.11,300,3.9897,4.2003,4.0292,0.2289,0.2487,0.2740,0.8004,2.8660,2.4261,0.9187,0.5406,0.6082 37 | 35,381,407,0,0.05,300,3.9836,4.2061,4.0231,0.2283,0.2487,0.2740,0.7738,2.8642,2.4242,0.9239,0.5457,0.6000 38 | 36,437,657,0,0.0,300,3.9974,4.2004,4.0092,0.2174,0.2487,0.2740,0.7789,2.8563,2.3782,0.9268,0.5305,0.6137 39 | 37,502,442,1,0.22,300,3.9793,4.1892,3.9959,0.2171,0.2487,0.2740,1.1772,2.9036,2.4082,0.7988,0.5482,0.6000 40 | 38,576,398,1,0.37,300,3.9598,4.1822,3.9721,0.2188,0.2487,0.2740,0.5931,2.7696,2.3165,0.9080,0.5711,0.6137 41 | 39,661,369,2,0.04,300,3.9901,4.1659,3.9650,0.2179,0.2487,0.2740,0.9490,2.6685,2.5456,0.8351,0.5635,0.6219 42 | 40,759,75,2,0.43,300,3.9682,4.1421,3.9582,0.2200,0.2487,0.2740,1.3353,2.5548,2.2685,0.7365,0.5914,0.6329 43 | 41,871,179,1,0.18,300,3.9513,4.1177,3.9351,0.2216,0.2487,0.2740,1.0117,2.4522,2.1480,0.8427,0.5964,0.6356 44 | 42,1000,442,0,0.09,300,3.9746,4.1177,3.9411,0.2190,0.2487,0.2740,0.8613,2.3412,2.0755,0.8900,0.5888,0.6438 45 | 43,1148,111,0,0.14,300,3.9840,4.1300,3.9501,0.2160,0.2487,0.2740,0.8558,2.3180,2.0579,0.8920,0.6041,0.6712 46 | 44,1317,242,0,0.37,300,3.9993,4.1272,3.9343,0.2118,0.2487,0.2740,0.9231,2.2765,2.0042,0.8709,0.6117,0.6877 47 | 45,1512,107,0,0.35000000000000003,300,3.9966,4.1277,3.9274,0.2077,0.2487,0.2740,0.8626,2.1615,1.9281,0.8697,0.6244,0.6877 48 | 46,1735,150,0,0.11,300,4.0109,4.1308,3.9178,0.2063,0.2487,0.2740,0.7601,2.1423,1.9289,0.8847,0.6091,0.6795 49 | 47,1992,38,2,0.41000000000000003,300,4.0226,4.1086,3.8963,0.2058,0.2487,0.2740,1.3051,2.3303,1.9553,0.7510,0.5838,0.6411 50 | 48,2286,248,2,0.23,300,4.0374,4.0905,3.8772,0.2047,0.2487,0.2740,0.8524,2.0223,1.7951,0.8298,0.6244,0.6877 51 | 49,2624,670,1,0.29,300,4.0281,4.0866,3.8676,0.2092,0.2487,0.2740,0.7753,1.8801,1.6324,0.8529,0.6497,0.7041 52 | -------------------------------------------------------------------------------- /checkpoints/pos_tag/marathi/mlp/bert/all_results.tsv: -------------------------------------------------------------------------------- 1 | run,ndata,hidden_size,nlayers,dropout,embedding_size,train_loss,dev_loss,test_loss,train_acc,dev_acc,test_acc,base_train_loss,base_dev_loss,base_test_loss,base_train_acc,base_dev_acc,base_test_acc 2 | 0,1,776,0,0.09,768,3.3692,3.9369,3.9485,1.0000,0.0295,0.0097,0.0040,6.5632,6.6107,1.0000,0.0295,0.0097 3 | 1,2,433,2,0.27,768,3.4150,3.9388,3.9574,0.5000,0.0750,0.0631,0.0000,15.4518,15.8284,1.0000,0.0886,0.0631 4 | 2,3,310,1,0.38,768,3.4594,3.8383,3.8939,0.3333,0.0750,0.0631,0.0014,7.7375,8.5666,1.0000,0.2659,0.1553 5 | 3,4,300,1,0.42,768,3.5025,3.7511,3.7950,0.2500,0.0750,0.0631,0.0033,5.1700,5.8708,1.0000,0.4205,0.3252 6 | 4,5,1009,2,0.39,768,3.5443,3.7610,3.7729,0.2000,0.0750,0.0631,0.0000,8.8682,9.3004,1.0000,0.4045,0.3762 7 | 5,6,657,2,0.41000000000000003,768,3.4466,3.7894,3.8095,0.3333,0.0295,0.0097,0.0002,6.6100,6.8720,1.0000,0.3750,0.3471 8 | 6,7,455,2,0.23,768,3.3873,3.7261,3.7725,0.2857,0.0295,0.0097,0.0001,7.0868,7.3699,1.0000,0.4682,0.4102 9 | 7,8,103,2,0.09,768,3.3517,3.7335,3.7848,0.2500,0.0750,0.0631,0.0053,4.5580,4.8421,1.0000,0.4909,0.4369 10 | 8,9,203,2,0.14,768,3.3315,3.6785,3.7215,0.2222,0.0750,0.0631,0.0009,5.4460,5.6538,1.0000,0.4614,0.4175 11 | 9,11,150,1,0.31,768,3.3452,3.7212,3.7707,0.2727,0.0295,0.0097,0.0065,4.1864,4.4475,1.0000,0.4636,0.4102 12 | 10,12,72,0,0.15,768,3.3005,3.6762,3.7460,0.2500,0.0295,0.0097,0.0259,3.8056,4.1297,1.0000,0.5000,0.4369 13 | 11,14,467,2,0.27,768,3.3123,3.6689,3.7604,0.2857,0.2477,0.1845,0.0002,7.2602,7.8429,1.0000,0.4750,0.3835 14 | 12,16,377,2,0.5,768,3.2284,3.6062,3.7053,0.3125,0.2477,0.1845,0.0012,5.2768,5.7190,1.0000,0.4523,0.4223 15 | 13,19,436,0,0.44,768,3.2628,3.6070,3.6858,0.3158,0.2477,0.1845,0.0966,3.7906,4.0098,1.0000,0.4864,0.4005 16 | 14,22,382,0,0.1,768,3.2534,3.5070,3.5812,0.3182,0.2477,0.1845,0.0685,3.2023,3.3856,1.0000,0.4750,0.4078 17 | 15,25,326,2,0.5,768,3.2925,3.4958,3.5842,0.3200,0.2477,0.1845,0.0035,3.0101,3.6454,1.0000,0.5818,0.5121 18 | 16,29,326,2,0.49,768,3.2668,3.4356,3.5035,0.3103,0.2477,0.1845,0.0026,2.8796,3.3023,1.0000,0.6068,0.5728 19 | 17,33,247,2,0.44,768,3.1988,3.3715,3.4346,0.3030,0.2477,0.1845,0.0040,2.7282,3.1156,1.0000,0.6727,0.6189 20 | 18,38,179,2,0.05,768,3.1643,3.3323,3.3669,0.2632,0.2477,0.1845,0.0014,3.0817,3.2621,1.0000,0.6386,0.6238 21 | 19,44,137,2,0.02,768,3.1723,3.3167,3.3429,0.2500,0.2477,0.1845,0.0028,2.5018,2.6684,1.0000,0.6705,0.6359 22 | 20,50,786,2,0.23,768,3.0956,3.2986,3.3360,0.2600,0.2477,0.1845,0.0000,3.7217,3.9907,1.0000,0.6818,0.6481 23 | 21,58,410,1,0.31,768,3.1162,3.3154,3.3330,0.2414,0.2477,0.1845,0.0029,2.5393,2.6517,1.0000,0.6659,0.6650 24 | 22,67,388,1,0.37,768,3.1033,3.3082,3.3010,0.2537,0.2227,0.2427,0.0039,2.6237,2.7132,1.0000,0.6455,0.6481 25 | 23,77,38,2,0.25,768,3.0437,3.2968,3.2921,0.2727,0.2227,0.2427,0.4578,2.2006,2.1627,0.9481,0.5523,0.5704 26 | 24,88,467,1,0.41000000000000003,768,3.0919,3.2944,3.2658,0.2500,0.2227,0.2427,0.0044,2.2825,2.1173,1.0000,0.6682,0.7015 27 | 25,102,176,0,0.09,768,3.0914,3.2847,3.2428,0.2549,0.2227,0.2427,0.1769,1.8098,1.6327,1.0000,0.6909,0.6917 28 | 26,117,57,2,0.02,768,3.0558,3.2519,3.2185,0.2479,0.2227,0.2427,0.0599,1.9006,1.7206,0.9915,0.6750,0.7039 29 | 27,135,968,2,0.15,768,3.0885,3.2537,3.2054,0.2296,0.2227,0.2427,0.0001,3.4394,3.1446,1.0000,0.6727,0.7282 30 | 28,155,268,0,0.01,768,3.0558,3.2311,3.1870,0.2194,0.2227,0.2427,0.2091,1.7229,1.5547,0.9935,0.6886,0.7087 31 | 29,179,270,0,0.3,768,3.0118,3.2186,3.1788,0.2235,0.2227,0.2427,0.2950,1.7694,1.5756,0.9832,0.7023,0.7282 32 | 30,206,596,2,0.24,768,2.9772,3.2160,3.1773,0.2233,0.2227,0.2427,0.0031,2.8600,2.4098,1.0000,0.6932,0.7257 33 | 31,237,694,0,0.49,768,2.9738,3.2121,3.1799,0.2236,0.2477,0.1845,0.4056,1.6309,1.5399,0.9747,0.7068,0.7136 34 | 32,273,837,0,0.48,768,2.9691,3.2264,3.1965,0.2271,0.2477,0.1845,0.4382,1.5730,1.4531,0.9634,0.7341,0.7257 35 | 33,314,487,2,0.4,768,2.9932,3.2352,3.2092,0.2293,0.2477,0.1845,0.0129,1.8887,1.6521,1.0000,0.7614,0.7670 36 | 34,362,418,0,0.11,768,3.0319,3.2361,3.2057,0.2155,0.2477,0.1845,0.1698,1.4774,1.3105,0.9972,0.7568,0.7670 37 | 35,417,407,0,0.05,768,3.0302,3.2475,3.2061,0.2086,0.2477,0.1845,0.4383,1.4737,1.3491,0.9664,0.7727,0.7670 38 | 36,480,657,0,0.0,768,3.0222,3.2486,3.2161,0.2229,0.2477,0.1845,0.4833,1.4482,1.3598,0.9521,0.7727,0.7597 39 | 37,552,442,1,0.22,768,3.0221,3.2661,3.2291,0.2192,0.2477,0.1845,0.0341,1.6526,1.5460,0.9982,0.7795,0.7816 40 | 38,636,398,1,0.37,768,3.0647,3.2704,3.2278,0.2091,0.2477,0.1845,0.0694,1.4929,1.4124,0.9953,0.7614,0.7670 41 | 39,732,369,2,0.04,768,3.0756,3.2638,3.2329,0.2172,0.2477,0.1845,0.0129,2.1731,2.0603,1.0000,0.7773,0.7694 42 | 40,843,75,2,0.43,768,3.0729,3.2547,3.2399,0.2289,0.2477,0.1845,0.3652,1.3215,1.2349,0.9217,0.8045,0.7985 43 | 41,971,179,1,0.18,768,3.0462,3.2546,3.2412,0.2317,0.2477,0.1845,0.1425,1.2724,1.2880,0.9794,0.8114,0.7718 44 | 42,1118,442,0,0.09,768,3.0578,3.2625,3.2406,0.2272,0.2477,0.1845,0.3408,1.1830,1.2257,0.9562,0.8045,0.7888 45 | 43,1287,111,0,0.14,768,3.0335,3.2698,3.2511,0.2300,0.2477,0.1845,0.3600,1.1561,1.1965,0.9510,0.8159,0.7816 46 | 44,1481,242,0,0.37,768,3.0191,3.2807,3.2624,0.2350,0.2477,0.1845,0.4154,1.1600,1.1983,0.9332,0.8227,0.7840 47 | 45,1706,107,0,0.35000000000000003,768,3.0191,3.2819,3.2689,0.2386,0.2477,0.1845,0.4236,1.1371,1.1747,0.9279,0.8250,0.7864 48 | 46,1964,150,0,0.11,768,3.0296,3.2919,3.2768,0.2378,0.2477,0.1845,0.4064,1.1333,1.1580,0.9328,0.8182,0.7864 49 | 47,2261,38,2,0.41000000000000003,768,3.0503,3.2770,3.2586,0.2375,0.2477,0.1845,0.5888,1.2962,1.1931,0.8815,0.7864,0.8131 50 | 48,2603,248,2,0.23,768,3.1370,3.2087,3.1817,0.2240,0.2477,0.1845,0.3282,0.9520,0.8890,0.9220,0.8386,0.8180 51 | 49,2997,670,1,0.29,768,3.1705,3.2029,3.1709,0.2149,0.2477,0.1845,0.2490,0.7994,0.7521,0.9523,0.8705,0.8325 52 | -------------------------------------------------------------------------------- /checkpoints/pos_tag/marathi/mlp/fast/all_results.tsv: -------------------------------------------------------------------------------- 1 | run,ndata,hidden_size,nlayers,dropout,embedding_size,train_loss,dev_loss,test_loss,train_acc,dev_acc,test_acc,base_train_loss,base_dev_loss,base_test_loss,base_train_acc,base_dev_acc,base_test_acc 2 | 0,1,776,0,0.09,300,3.3692,3.9369,3.9485,1.0000,0.0295,0.0097,0.6320,4.0449,4.0851,1.0000,0.0295,0.0121 3 | 1,2,433,2,0.27,300,3.4150,3.9388,3.9574,0.5000,0.0750,0.0631,0.0003,10.2424,10.8002,1.0000,0.0909,0.0704 4 | 2,3,310,1,0.38,300,3.4594,3.8383,3.8939,0.3333,0.0750,0.0631,0.0332,4.7153,5.0441,1.0000,0.1568,0.1262 5 | 3,4,300,1,0.42,300,3.5025,3.7511,3.7950,0.2500,0.0750,0.0631,0.0630,4.0685,4.3995,1.0000,0.3523,0.3495 6 | 4,5,1009,2,0.39,300,3.5443,3.7610,3.7729,0.2000,0.0750,0.0631,0.0007,9.8643,10.1322,1.0000,0.3273,0.3155 7 | 5,6,657,2,0.41000000000000003,300,3.4466,3.7894,3.8095,0.3333,0.0295,0.0097,0.0029,8.1640,8.2910,1.0000,0.3318,0.3204 8 | 6,7,455,2,0.23,300,3.3873,3.7261,3.7725,0.2857,0.0295,0.0097,0.0054,7.9620,7.8918,1.0000,0.3591,0.3592 9 | 7,8,103,2,0.09,300,3.3517,3.7335,3.7848,0.2500,0.0750,0.0631,0.2481,4.4093,4.4823,1.0000,0.3864,0.3908 10 | 8,9,203,2,0.14,300,3.3315,3.6785,3.7215,0.2222,0.0750,0.0631,0.0667,5.9905,6.1315,1.0000,0.3705,0.3641 11 | 9,11,150,1,0.31,300,3.3452,3.7212,3.7707,0.2727,0.0295,0.0097,0.3952,3.8112,3.8704,0.9091,0.4205,0.4199 12 | 10,12,72,0,0.15,300,3.3005,3.6762,3.7460,0.2500,0.0295,0.0097,1.2209,3.3629,3.3864,1.0000,0.4182,0.4248 13 | 11,14,467,2,0.27,300,3.3123,3.6689,3.7604,0.2857,0.2477,0.1845,0.0591,8.5295,8.5860,1.0000,0.3477,0.3544 14 | 12,16,377,2,0.5,300,3.2284,3.6062,3.7053,0.3125,0.2477,0.1845,0.3090,6.9973,7.3089,0.9375,0.3864,0.3883 15 | 13,19,436,0,0.44,300,3.2628,3.6070,3.6858,0.3158,0.2477,0.1845,1.5699,3.4508,3.4725,0.9474,0.4705,0.4490 16 | 14,22,382,0,0.1,300,3.2534,3.5070,3.5812,0.3182,0.2477,0.1845,1.4004,3.3891,3.3566,0.9545,0.4159,0.4175 17 | 15,25,326,2,0.5,300,3.2925,3.4958,3.5842,0.3200,0.2477,0.1845,0.6208,2.9378,3.3609,0.8800,0.5159,0.5049 18 | 16,29,326,2,0.49,300,3.2668,3.4356,3.5035,0.3103,0.2477,0.1845,0.6252,2.6262,2.9348,0.8621,0.4977,0.4636 19 | 17,33,247,2,0.44,300,3.1988,3.3715,3.4346,0.3030,0.2477,0.1845,0.6911,2.3678,2.6842,0.8182,0.5182,0.5024 20 | 18,38,179,2,0.05,300,3.1643,3.3323,3.3669,0.2632,0.2477,0.1845,0.3731,2.5019,2.8258,0.9211,0.5182,0.4927 21 | 19,44,137,2,0.02,300,3.1723,3.3167,3.3429,0.2500,0.2477,0.1845,0.4389,2.3551,2.6778,0.9318,0.5614,0.5631 22 | 20,50,786,2,0.23,300,3.0956,3.2986,3.3360,0.2600,0.2477,0.1845,0.0558,3.3206,3.8085,0.9800,0.5318,0.4951 23 | 21,58,410,1,0.31,300,3.1162,3.3154,3.3330,0.2414,0.2477,0.1845,0.3947,2.1947,2.4581,0.9310,0.5705,0.6092 24 | 22,67,388,1,0.37,300,3.1033,3.3082,3.3010,0.2537,0.2227,0.2427,0.4702,2.1443,2.2505,0.9254,0.5955,0.6189 25 | 23,77,38,2,0.25,300,3.0437,3.2968,3.2921,0.2727,0.2227,0.2427,0.9054,2.3154,2.2300,0.7403,0.5159,0.5121 26 | 24,88,467,1,0.41000000000000003,300,3.0919,3.2944,3.2658,0.2500,0.2227,0.2427,0.5797,2.1038,2.0154,0.8977,0.5773,0.6311 27 | 25,102,176,0,0.09,300,3.0914,3.2847,3.2428,0.2549,0.2227,0.2427,0.4969,1.9774,1.8155,0.9314,0.6205,0.6578 28 | 26,117,57,2,0.02,300,3.0558,3.2519,3.2185,0.2479,0.2227,0.2427,0.4167,2.0484,1.9015,0.9231,0.5773,0.6141 29 | 27,135,968,2,0.15,300,3.0885,3.2537,3.2054,0.2296,0.2227,0.2427,0.1918,2.5926,2.5765,0.9407,0.6341,0.6796 30 | 28,155,268,0,0.01,300,3.0558,3.2311,3.1870,0.2194,0.2227,0.2427,0.4841,1.8874,1.7029,0.9161,0.6023,0.6723 31 | 29,179,270,0,0.3,300,3.0118,3.2186,3.1788,0.2235,0.2227,0.2427,0.4486,1.8480,1.6652,0.9162,0.6318,0.6723 32 | 30,206,596,2,0.24,300,2.9772,3.2160,3.1773,0.2233,0.2227,0.2427,0.3680,1.8620,1.8774,0.9029,0.6659,0.6942 33 | 31,237,694,0,0.49,300,2.9738,3.2121,3.1799,0.2236,0.2477,0.1845,0.6094,1.7732,1.6944,0.8903,0.6227,0.6869 34 | 32,273,837,0,0.48,300,2.9691,3.2264,3.1965,0.2271,0.2477,0.1845,0.4969,1.7347,1.6141,0.9084,0.6545,0.6966 35 | 33,314,487,2,0.4,300,2.9932,3.2352,3.2092,0.2293,0.2477,0.1845,0.6036,1.7259,1.6603,0.8567,0.6636,0.7063 36 | 34,362,418,0,0.11,300,3.0319,3.2361,3.2057,0.2155,0.2477,0.1845,0.6181,1.6255,1.5026,0.8950,0.6818,0.7160 37 | 35,417,407,0,0.05,300,3.0302,3.2475,3.2061,0.2086,0.2477,0.1845,0.6215,1.6198,1.4867,0.8945,0.6864,0.7257 38 | 36,480,657,0,0.0,300,3.0222,3.2486,3.2161,0.2229,0.2477,0.1845,0.6330,1.5937,1.4526,0.8875,0.6932,0.7306 39 | 37,552,442,1,0.22,300,3.0221,3.2661,3.2291,0.2192,0.2477,0.1845,0.5160,1.5811,1.4795,0.8804,0.6841,0.7306 40 | 38,636,398,1,0.37,300,3.0647,3.2704,3.2278,0.2091,0.2477,0.1845,0.6235,1.5608,1.4112,0.8601,0.6841,0.7451 41 | 39,732,369,2,0.04,300,3.0756,3.2638,3.2329,0.2172,0.2477,0.1845,0.6908,1.5926,1.5041,0.8402,0.7091,0.7476 42 | 40,843,75,2,0.43,300,3.0729,3.2547,3.2399,0.2289,0.2477,0.1845,0.7163,1.4145,1.3668,0.8304,0.7341,0.7549 43 | 41,971,179,1,0.18,300,3.0462,3.2546,3.2412,0.2317,0.2477,0.1845,0.5856,1.3852,1.2847,0.8661,0.7273,0.7646 44 | 42,1118,442,0,0.09,300,3.0578,3.2625,3.2406,0.2272,0.2477,0.1845,0.7608,1.4283,1.3133,0.8497,0.7295,0.7597 45 | 43,1287,111,0,0.14,300,3.0335,3.2698,3.2511,0.2300,0.2477,0.1845,0.7009,1.4156,1.3047,0.8547,0.7409,0.7767 46 | 44,1481,242,0,0.37,300,3.0191,3.2807,3.2624,0.2350,0.2477,0.1845,0.8274,1.4502,1.3339,0.8346,0.7386,0.7646 47 | 45,1706,107,0,0.35000000000000003,300,3.0191,3.2819,3.2689,0.2386,0.2477,0.1845,0.8191,1.4194,1.3087,0.8283,0.7364,0.7694 48 | 46,1964,150,0,0.11,300,3.0296,3.2919,3.2768,0.2378,0.2477,0.1845,0.7647,1.3732,1.2568,0.8340,0.7409,0.7767 49 | 47,2261,38,2,0.41000000000000003,300,3.0503,3.2770,3.2586,0.2375,0.2477,0.1845,0.7715,1.4617,1.3321,0.8107,0.7386,0.7646 50 | 48,2603,248,2,0.23,300,3.1370,3.2087,3.1817,0.2240,0.2477,0.1845,0.6034,1.1784,1.0261,0.8513,0.7864,0.7816 51 | 49,2997,670,1,0.29,300,3.1705,3.2029,3.1709,0.2149,0.2477,0.1845,0.5443,0.9431,0.9116,0.8579,0.7864,0.8034 52 | -------------------------------------------------------------------------------- /checkpoints/dep_label/turkish/mlp/random/all_results.tsv: -------------------------------------------------------------------------------- 1 | run,ndata,hidden_size,nlayers,dropout,embedding_size,train_loss,dev_loss,test_loss,train_acc,dev_acc,test_acc,base_train_loss,base_dev_loss,base_test_loss,base_train_acc,base_dev_acc,base_test_acc 2 | 0,1,776,0,0.09,272,4.2977,4.8398,4.8400,1.0000,0.0733,0.0728,0.0002,4.9430,4.9384,1.0000,0.0639,0.0656 3 | 1,2,433,2,0.27,145,4.3219,4.8407,4.8380,0.5000,0.0399,0.0449,0.0000,6.4339,6.4490,1.0000,0.0562,0.0580 4 | 2,3,310,1,0.38,182,4.3458,4.7498,4.7474,0.3333,0.0399,0.0449,0.0001,4.5475,4.5357,1.0000,0.1148,0.1223 5 | 3,4,300,1,0.42,205,4.3692,4.7299,4.7282,0.2500,0.0399,0.0449,0.0002,4.5296,4.5304,1.0000,0.1204,0.1219 6 | 4,5,1009,2,0.39,134,4.3923,4.7306,4.7305,0.2000,0.0382,0.0356,0.0000,7.6569,7.6039,1.0000,0.1223,0.1318 7 | 5,6,657,2,0.41000000000000003,245,4.4150,4.7064,4.7039,0.1667,0.0382,0.0356,0.0000,5.5638,5.5230,1.0000,0.1743,0.1833 8 | 6,7,455,2,0.23,133,4.3188,4.6955,4.6913,0.2857,0.0803,0.0842,0.0000,5.7529,5.6999,1.0000,0.1403,0.1481 9 | 7,9,103,2,0.09,108,4.3889,4.6664,4.6593,0.2222,0.0803,0.0842,0.0013,4.4524,4.4152,1.0000,0.1334,0.1409 10 | 8,11,203,2,0.14,93,4.4481,4.6379,4.6317,0.1818,0.0803,0.0842,0.0005,4.3731,4.3259,1.0000,0.1549,0.1606 11 | 9,13,150,1,0.31,212,4.3732,4.6234,4.6170,0.1538,0.0603,0.0618,0.0017,4.2715,4.2592,1.0000,0.1374,0.1412 12 | 10,16,72,0,0.15,103,4.3132,4.5547,4.5466,0.1250,0.0603,0.0618,0.0607,4.7009,4.6814,1.0000,0.1536,0.1590 13 | 11,20,467,2,0.27,113,4.2239,4.5626,4.5523,0.2000,0.0603,0.0618,0.0002,5.4361,5.3324,1.0000,0.1339,0.1382 14 | 12,24,377,2,0.5,99,4.2258,4.5079,4.5010,0.1667,0.0603,0.0618,0.0023,4.5461,4.4861,1.0000,0.2432,0.2518 15 | 13,29,436,0,0.44,148,4.1728,4.4881,4.4778,0.1379,0.0603,0.0618,0.0669,4.2770,4.2659,1.0000,0.2386,0.2397 16 | 14,36,382,0,0.1,165,4.1035,4.4654,4.4572,0.1389,0.0399,0.0449,0.0515,4.3499,4.3454,1.0000,0.2372,0.2377 17 | 15,43,326,2,0.5,60,4.1082,4.4258,4.4167,0.1395,0.0803,0.0842,0.0396,4.6579,4.5785,1.0000,0.2449,0.2565 18 | 16,53,326,2,0.49,84,4.0532,4.3878,4.3823,0.1321,0.0603,0.0618,0.0142,4.3475,4.3065,1.0000,0.2570,0.2600 19 | 17,64,247,2,0.44,238,4.1258,4.3104,4.3059,0.1406,0.1962,0.1957,0.0016,3.9034,3.8475,1.0000,0.2667,0.2777 20 | 18,78,179,2,0.05,122,4.0619,4.2947,4.2860,0.1410,0.0603,0.0618,0.0031,4.1111,4.0508,1.0000,0.2727,0.2832 21 | 19,95,137,2,0.02,144,4.0132,4.2510,4.2395,0.1474,0.1962,0.1957,0.0053,3.9648,3.9345,1.0000,0.2773,0.2827 22 | 20,115,786,2,0.23,79,4.0429,4.1871,4.1754,0.1478,0.1962,0.1957,0.0003,5.6321,5.5333,1.0000,0.2730,0.2807 23 | 21,140,410,1,0.31,159,3.9892,4.1622,4.1476,0.1500,0.1962,0.1957,0.0069,3.9183,3.8672,1.0000,0.2857,0.2924 24 | 22,171,388,1,0.37,154,3.9798,4.1331,4.1180,0.1462,0.1962,0.1957,0.0111,3.9067,3.8512,1.0000,0.2950,0.3050 25 | 23,208,38,2,0.25,148,3.9598,4.1132,4.0989,0.1490,0.1962,0.1957,1.5645,3.6588,3.6137,0.8365,0.2538,0.2650 26 | 24,253,467,1,0.41000000000000003,115,3.9428,4.0887,4.0744,0.1542,0.1962,0.1957,0.0304,4.0839,4.0253,1.0000,0.3000,0.3076 27 | 25,308,176,0,0.09,124,3.9156,4.0696,4.0547,0.1623,0.1962,0.1957,0.8726,4.3279,4.2814,0.9610,0.2683,0.2756 28 | 26,374,57,2,0.02,240,3.9032,4.0647,4.0515,0.1684,0.1962,0.1957,0.1312,3.9211,3.8914,0.9920,0.2991,0.2976 29 | 27,456,968,2,0.15,50,3.9031,4.0526,4.0377,0.1689,0.1962,0.1957,0.0013,6.5508,6.5592,1.0000,0.2900,0.2874 30 | 28,554,268,0,0.01,235,3.8853,4.0536,4.0378,0.1715,0.1962,0.1957,0.4176,4.4027,4.3389,1.0000,0.2941,0.3069 31 | 29,675,270,0,0.3,144,3.9006,4.0450,4.0301,0.1793,0.1962,0.1957,1.4598,4.0678,4.0219,0.8548,0.2833,0.2920 32 | 30,821,596,2,0.24,188,3.8662,4.0354,4.0204,0.1937,0.1962,0.1957,0.0013,5.7105,5.6726,1.0000,0.3398,0.3371 33 | 31,999,694,0,0.49,59,3.8821,4.0313,4.0164,0.1922,0.1962,0.1957,1.9434,3.6172,3.5857,0.6446,0.3057,0.3068 34 | 32,1215,837,0,0.48,176,3.8716,4.0164,4.0014,0.1942,0.1962,0.1957,1.9083,3.8483,3.8099,0.7745,0.3139,0.3175 35 | 33,1478,487,2,0.4,75,3.8831,4.0064,3.9924,0.1949,0.1962,0.1957,1.3499,3.2694,3.2305,0.8471,0.3389,0.3414 36 | 34,1799,418,0,0.11,210,3.9257,3.9975,3.9853,0.1907,0.1962,0.1957,1.6316,3.9416,3.9493,0.7671,0.3326,0.3330 37 | 35,2189,407,0,0.05,85,3.9750,3.9886,3.9762,0.1900,0.1962,0.1957,2.9088,3.7826,3.7503,0.4833,0.3114,0.3136 38 | 36,2663,657,0,0.0,102,3.9763,3.9861,3.9747,0.1930,0.1962,0.1957,2.4960,3.7970,3.7830,0.5456,0.3318,0.3373 39 | 37,3240,442,1,0.22,225,3.9721,3.9833,3.9710,0.1948,0.1962,0.1957,0.3449,3.2577,3.1800,0.9895,0.4025,0.4096 40 | 38,3943,398,1,0.37,187,3.9641,3.9806,3.9679,0.1963,0.1962,0.1957,1.2296,2.9263,2.8612,0.8324,0.4040,0.4069 41 | 39,4797,369,2,0.04,195,3.9608,3.9783,3.9648,0.1928,0.1962,0.1957,0.2863,3.6750,3.6309,0.9756,0.4074,0.4113 42 | 40,5837,75,2,0.43,230,3.9723,3.9758,3.9617,0.1902,0.1962,0.1957,1.6846,2.6365,2.6142,0.7739,0.4392,0.4280 43 | 41,7102,179,1,0.18,187,3.9767,3.9714,3.9565,0.1899,0.1962,0.1957,1.9557,2.8467,2.8149,0.6321,0.4120,0.4035 44 | 42,8642,442,0,0.09,112,3.9689,3.9713,3.9560,0.1892,0.1962,0.1957,2.3340,3.0409,2.9924,0.4968,0.3927,0.3925 45 | 43,10515,111,0,0.14,188,3.9640,3.9695,3.9539,0.1924,0.1962,0.1957,2.0584,2.9271,2.8675,0.5520,0.4184,0.4191 46 | 44,12794,242,0,0.37,94,3.9629,3.9690,3.9530,0.1920,0.1962,0.1957,2.6593,2.9082,2.8727,0.4286,0.3860,0.3850 47 | 45,15567,107,0,0.35000000000000003,182,3.9536,3.9680,3.9524,0.1940,0.1962,0.1957,2.3091,2.7252,2.6698,0.5042,0.4239,0.4258 48 | 46,18941,150,0,0.11,215,3.9534,3.9685,3.9525,0.1949,0.1962,0.1957,2.1681,2.7525,2.6921,0.5256,0.4319,0.4321 49 | 47,23047,38,2,0.41000000000000003,292,3.9520,3.9679,3.9520,0.1953,0.1962,0.1957,2.4676,2.6018,2.5732,0.4475,0.4221,0.4253 50 | 48,28042,248,2,0.23,98,3.9531,3.9672,3.9519,0.1943,0.1962,0.1957,1.5025,2.2489,2.2376,0.7182,0.5173,0.5144 51 | 49,34120,670,1,0.29,67,3.9495,3.9674,3.9518,0.1951,0.1962,0.1957,1.5126,2.2611,2.2535,0.7293,0.5167,0.5166 52 | -------------------------------------------------------------------------------- /checkpoints/pos_tag/basque/mlp/random/all_results.tsv: -------------------------------------------------------------------------------- 1 | run,ndata,hidden_size,nlayers,dropout,embedding_size,train_loss,dev_loss,test_loss,train_acc,dev_acc,test_acc,base_train_loss,base_dev_loss,base_test_loss,base_train_acc,base_dev_acc,base_test_acc 2 | 0,1,776,0,0.09,272,3.3692,3.9303,3.9278,1.0000,0.0409,0.0451,0.0031,4.0866,4.0759,1.0000,0.0422,0.0457 3 | 1,2,433,2,0.27,145,3.4150,3.8795,3.8798,0.5000,0.0409,0.0451,0.0000,6.1069,6.0916,1.0000,0.1109,0.1097 4 | 2,3,310,1,0.38,182,3.4594,3.8335,3.8316,0.3333,0.0409,0.0451,0.0008,3.8604,3.8623,1.0000,0.1282,0.1313 5 | 3,4,300,1,0.42,205,3.5025,3.7332,3.7291,0.2500,0.0409,0.0451,0.0009,3.4957,3.4847,1.0000,0.1916,0.1940 6 | 4,5,1009,2,0.39,134,3.3783,3.6733,3.6676,0.4000,0.2451,0.2489,0.0000,6.8722,6.7774,1.0000,0.2346,0.2396 7 | 5,6,657,2,0.41000000000000003,245,3.4466,3.6918,3.6863,0.3333,0.2451,0.2489,0.0000,5.1108,5.0527,1.0000,0.2121,0.2131 8 | 6,8,455,2,0.23,133,3.3517,3.6372,3.6321,0.2500,0.2451,0.2489,0.0001,4.9080,4.8726,1.0000,0.2681,0.2625 9 | 7,9,103,2,0.09,108,3.4237,3.6453,3.6397,0.2222,0.2451,0.2489,0.0031,3.5839,3.5718,1.0000,0.2310,0.2325 10 | 8,12,203,2,0.14,93,3.2206,3.5767,3.5726,0.3333,0.1651,0.1604,0.0013,4.0767,4.0600,1.0000,0.2675,0.2681 11 | 9,14,150,1,0.31,212,3.2437,3.5497,3.5454,0.2857,0.2451,0.2489,0.0060,3.2489,3.2485,1.0000,0.2777,0.2785 12 | 10,18,72,0,0.15,103,3.2638,3.5673,3.5625,0.2222,0.2451,0.2489,0.3415,3.4503,3.4726,1.0000,0.2587,0.2531 13 | 11,22,467,2,0.27,113,3.1917,3.5581,3.5515,0.2273,0.2451,0.2489,0.0003,5.3293,5.2746,1.0000,0.3146,0.3151 14 | 12,27,377,2,0.5,99,3.2319,3.5421,3.5360,0.2222,0.2451,0.2489,0.0066,4.0073,3.9959,1.0000,0.3256,0.3254 15 | 13,34,436,0,0.44,148,3.0976,3.4859,3.4779,0.2647,0.2451,0.2489,0.4283,3.4020,3.4147,1.0000,0.2636,0.2602 16 | 14,42,382,0,0.1,165,3.2607,3.3948,3.3933,0.2143,0.2451,0.2489,0.3016,3.5534,3.5679,1.0000,0.2747,0.2756 17 | 15,52,326,2,0.5,60,3.2362,3.3868,3.3851,0.2308,0.2451,0.2489,0.2233,3.1561,3.1486,1.0000,0.3397,0.3385 18 | 16,64,326,2,0.49,84,3.2741,3.3554,3.3601,0.1875,0.2451,0.2489,0.1157,3.2203,3.2301,1.0000,0.3474,0.3480 19 | 17,79,247,2,0.44,238,3.2591,3.3376,3.3460,0.1899,0.2451,0.2489,0.0132,3.2515,3.2481,1.0000,0.3535,0.3509 20 | 18,98,179,2,0.05,122,3.2468,3.2947,3.3043,0.1939,0.2451,0.2489,0.0290,3.4386,3.4600,0.9898,0.3891,0.3884 21 | 19,121,137,2,0.02,144,3.2462,3.2941,3.3042,0.1983,0.2451,0.2489,0.0266,3.2896,3.2999,0.9917,0.3888,0.3878 22 | 20,150,786,2,0.23,79,3.1871,3.2677,3.2773,0.2133,0.2451,0.2489,0.0166,4.4744,4.4684,0.9933,0.4076,0.4063 23 | 21,186,410,1,0.31,159,3.1069,3.2439,3.2515,0.2312,0.2451,0.2489,0.0505,2.9768,2.9830,0.9892,0.4343,0.4362 24 | 22,230,388,1,0.37,154,3.1328,3.2405,3.2480,0.2261,0.2451,0.2489,0.1034,2.8874,2.8822,0.9870,0.4310,0.4338 25 | 23,285,38,2,0.25,148,3.0951,3.2300,3.2395,0.2351,0.2451,0.2489,1.5099,2.5914,2.6050,0.7018,0.4429,0.4409 26 | 24,352,467,1,0.41000000000000003,115,3.1220,3.2206,3.2290,0.2216,0.2451,0.2489,0.3444,2.5527,2.5725,0.9830,0.4709,0.4676 27 | 25,436,176,0,0.09,124,3.1137,3.2085,3.2147,0.2362,0.2451,0.2489,1.9266,3.1253,3.1430,0.6927,0.3569,0.3551 28 | 26,540,57,2,0.02,240,3.1072,3.2159,3.2225,0.2389,0.2451,0.2489,0.4441,2.6306,2.6427,0.9389,0.4780,0.4792 29 | 27,668,968,2,0.15,50,3.1027,3.2096,3.2163,0.2440,0.2451,0.2489,0.0643,3.3888,3.4224,0.9805,0.5019,0.5029 30 | 28,827,268,0,0.01,235,3.0901,3.2093,3.2148,0.2443,0.2451,0.2489,1.6406,3.0024,3.0106,0.7364,0.4160,0.4101 31 | 29,1024,270,0,0.3,144,3.0957,3.2097,3.2136,0.2461,0.2451,0.2489,1.1155,2.5297,2.5531,0.7725,0.4870,0.4847 32 | 30,1267,596,2,0.24,188,3.0778,3.2124,3.2168,0.2478,0.2451,0.2489,0.0696,2.7905,2.8016,0.9818,0.5472,0.5463 33 | 31,1568,694,0,0.49,59,3.0801,3.2165,3.2222,0.2487,0.2451,0.2489,2.0053,2.3720,2.3751,0.5300,0.4766,0.4772 34 | 32,1941,837,0,0.48,176,3.0839,3.2188,3.2254,0.2442,0.2451,0.2489,1.4823,2.1365,2.1440,0.6430,0.5200,0.5198 35 | 33,2402,487,2,0.4,75,3.0813,3.2206,3.2281,0.2452,0.2451,0.2489,0.6237,1.8406,1.8828,0.9363,0.6139,0.6088 36 | 34,2973,418,0,0.11,210,3.0869,3.2254,3.2324,0.2395,0.2451,0.2489,1.3157,2.2905,2.3021,0.6892,0.5229,0.5215 37 | 35,3680,407,0,0.05,85,3.1026,3.2298,3.2368,0.2408,0.2451,0.2489,1.8025,2.2879,2.2897,0.5766,0.4870,0.4902 38 | 36,4555,657,0,0.0,102,3.1025,3.2354,3.2424,0.2397,0.2451,0.2489,1.7780,2.2285,2.2294,0.5748,0.5052,0.5007 39 | 37,5638,442,1,0.22,225,3.0921,3.2388,3.2463,0.2458,0.2451,0.2489,0.3852,1.6342,1.6583,0.9615,0.6740,0.6731 40 | 38,6978,398,1,0.37,187,3.0913,3.2435,3.2515,0.2475,0.2451,0.2489,0.3758,1.5181,1.5478,0.9660,0.6974,0.6926 41 | 39,8637,369,2,0.04,195,3.0890,3.2484,3.2569,0.2473,0.2451,0.2489,0.9227,1.7137,1.7247,0.8009,0.6568,0.6576 42 | 40,10690,75,2,0.43,230,3.0893,3.2537,3.2630,0.2445,0.2451,0.2489,1.2624,1.6766,1.7000,0.7049,0.6543,0.6544 43 | 41,13231,179,1,0.18,187,3.0944,3.2572,3.2671,0.2431,0.2451,0.2489,0.6435,1.4803,1.5002,0.8757,0.7076,0.7027 44 | 42,16376,442,0,0.09,112,3.0925,3.2616,3.2720,0.2432,0.2451,0.2489,1.8495,2.0659,2.0786,0.5674,0.5334,0.5315 45 | 43,20269,111,0,0.14,188,3.0921,3.2662,3.2770,0.2421,0.2451,0.2489,1.6828,1.9210,1.9378,0.6102,0.5724,0.5672 46 | 44,25087,242,0,0.37,94,3.0955,3.2700,3.2813,0.2411,0.2451,0.2489,1.9603,2.1159,2.1215,0.5471,0.5206,0.5185 47 | 45,31050,107,0,0.35000000000000003,182,3.0977,3.2744,3.2864,0.2409,0.2451,0.2489,1.7753,1.9574,1.9608,0.5986,0.5726,0.5673 48 | 46,38431,150,0,0.11,215,3.1135,3.2046,3.2097,0.2414,0.2451,0.2489,1.6719,1.8534,1.8589,0.6128,0.5803,0.5813 49 | 47,47567,38,2,0.41000000000000003,292,3.1426,3.1782,3.1808,0.2416,0.2451,0.2489,1.5460,1.6285,1.6422,0.6425,0.6298,0.6292 50 | 48,58874,248,2,0.23,98,3.1617,3.1725,3.1745,0.2422,0.2451,0.2489,0.7802,0.9946,1.0043,0.8163,0.7717,0.7748 51 | 49,72869,670,1,0.29,67,3.1687,3.1713,3.1730,0.2446,0.2451,0.2489,0.9370,1.0876,1.0944,0.7899,0.7600,0.7599 52 | -------------------------------------------------------------------------------- /checkpoints/pos_tag/turkish/mlp/random/all_results.tsv: -------------------------------------------------------------------------------- 1 | run,ndata,hidden_size,nlayers,dropout,embedding_size,train_loss,dev_loss,test_loss,train_acc,dev_acc,test_acc,base_train_loss,base_dev_loss,base_test_loss,base_train_acc,base_dev_acc,base_test_acc 2 | 0,1,776,0,0.09,272,3.1699,3.6996,3.7007,1.0000,0.0945,0.0926,0.0035,3.8477,3.8383,1.0000,0.1103,0.1146 3 | 1,2,433,2,0.27,145,3.2224,3.7304,3.7296,0.5000,0.0945,0.0926,0.0000,6.9526,6.9464,1.0000,0.0679,0.0633 4 | 2,3,310,1,0.38,182,3.2730,3.6749,3.6745,0.3333,0.0945,0.0926,0.0007,3.8098,3.7997,1.0000,0.1998,0.2044 5 | 3,4,300,1,0.42,205,3.1144,3.7085,3.7067,0.5000,0.0370,0.0404,0.0008,3.9577,3.9328,1.0000,0.1078,0.1110 6 | 4,5,1009,2,0.39,134,3.2032,3.7350,3.7346,0.4000,0.0370,0.0404,0.0000,10.3373,10.3479,1.0000,0.1122,0.1151 7 | 5,6,657,2,0.41000000000000003,245,3.2767,3.6287,3.6205,0.3333,0.0370,0.0404,0.0000,5.2847,5.1829,1.0000,0.1436,0.1489 8 | 6,7,455,2,0.23,133,3.2223,3.5652,3.5514,0.2857,0.2600,0.2734,0.0001,5.2298,5.1466,1.0000,0.1851,0.1927 9 | 7,9,103,2,0.09,108,3.2676,3.4979,3.4832,0.2222,0.0945,0.0926,0.0028,3.4336,3.3704,1.0000,0.1820,0.1912 10 | 8,11,203,2,0.14,93,3.3981,3.5296,3.5208,0.1818,0.0945,0.0926,0.0015,3.6773,3.6298,1.0000,0.1759,0.1747 11 | 9,13,150,1,0.31,212,3.2964,3.4914,3.4790,0.2308,0.0945,0.0926,0.0060,3.3385,3.3118,1.0000,0.1870,0.1894 12 | 10,16,72,0,0.15,103,3.2635,3.3745,3.3577,0.2500,0.2600,0.2734,0.2791,3.8796,3.8436,1.0000,0.1556,0.1624 13 | 11,20,467,2,0.27,113,3.2029,3.3799,3.3625,0.2500,0.0945,0.0926,0.0003,4.5137,4.4124,1.0000,0.2380,0.2524 14 | 12,24,377,2,0.5,99,3.2200,3.3893,3.3734,0.2500,0.0945,0.0926,0.0068,3.7996,3.7329,1.0000,0.2274,0.2383 15 | 13,29,436,0,0.44,148,3.2491,3.3090,3.2904,0.2069,0.0945,0.0926,0.3473,3.2787,3.2202,1.0000,0.2648,0.2828 16 | 14,36,382,0,0.1,165,3.1946,3.2496,3.2275,0.2500,0.2600,0.2734,0.2117,3.3551,3.3410,1.0000,0.2874,0.2898 17 | 15,44,326,2,0.5,60,3.1583,3.2265,3.1999,0.2727,0.2600,0.2734,0.0837,3.1999,3.1277,1.0000,0.3689,0.3817 18 | 16,53,326,2,0.49,84,3.1319,3.2123,3.1834,0.2642,0.2600,0.2734,0.0476,3.3917,3.2822,1.0000,0.3580,0.3726 19 | 17,65,247,2,0.44,238,3.0629,3.2077,3.1744,0.2923,0.2600,0.2734,0.0056,3.2999,3.1785,1.0000,0.3748,0.3949 20 | 18,79,179,2,0.05,122,3.0625,3.1735,3.1387,0.2785,0.2600,0.2734,0.0048,3.5253,3.4179,1.0000,0.3711,0.3809 21 | 19,97,137,2,0.02,144,2.9722,3.1678,3.1276,0.3093,0.2600,0.2734,0.0076,3.3789,3.2406,1.0000,0.3839,0.4062 22 | 20,118,786,2,0.23,79,2.9615,3.1447,3.1022,0.3051,0.2600,0.2734,0.0006,4.7696,4.4776,1.0000,0.3973,0.4237 23 | 21,144,410,1,0.31,159,2.9193,3.1159,3.0724,0.3264,0.2600,0.2734,0.0195,3.1050,2.9724,1.0000,0.4074,0.4273 24 | 22,175,388,1,0.37,154,2.9286,3.1021,3.0599,0.3086,0.2600,0.2734,0.0441,2.9804,2.8439,1.0000,0.4110,0.4313 25 | 23,214,38,2,0.25,148,2.9238,3.1093,3.0662,0.2991,0.2600,0.2734,1.4429,2.6958,2.6059,0.7150,0.3708,0.3868 26 | 24,261,467,1,0.41000000000000003,115,2.9639,3.0937,3.0527,0.3065,0.2600,0.2734,0.2166,2.6782,2.5446,1.0000,0.4503,0.4643 27 | 25,319,176,0,0.09,124,2.9209,3.0868,3.0418,0.3135,0.2600,0.2734,1.8857,3.1787,3.1404,0.6708,0.3169,0.3258 28 | 26,389,57,2,0.02,240,2.9200,3.0821,3.0370,0.2982,0.2600,0.2734,0.3323,2.9060,2.8189,0.9743,0.4422,0.4560 29 | 27,474,968,2,0.15,50,2.9156,3.0837,3.0375,0.3017,0.2600,0.2734,0.0123,3.8373,3.5954,0.9979,0.4721,0.4931 30 | 28,579,268,0,0.01,235,2.8954,3.0876,3.0380,0.3057,0.2600,0.2734,1.4995,3.1557,3.1145,0.7737,0.3607,0.3708 31 | 29,706,270,0,0.3,144,2.9369,3.0800,3.0346,0.2918,0.2600,0.2734,1.0191,2.7021,2.5811,0.7946,0.4519,0.4624 32 | 30,862,596,2,0.24,188,2.9659,3.0717,3.0290,0.2749,0.2600,0.2734,0.0200,3.3204,3.1988,0.9942,0.4930,0.5017 33 | 31,1051,694,0,0.49,59,2.9693,3.0693,3.0263,0.2712,0.2600,0.2734,1.9234,2.4119,2.3310,0.5423,0.4255,0.4359 34 | 32,1283,837,0,0.48,176,2.9607,3.0718,3.0283,0.2783,0.2600,0.2734,1.4216,2.2811,2.2086,0.6711,0.4841,0.4916 35 | 33,1565,487,2,0.4,75,2.9658,3.0676,3.0244,0.2754,0.2600,0.2734,0.6424,1.8754,1.8129,0.9553,0.5808,0.5895 36 | 34,1909,418,0,0.11,210,2.9911,3.0617,3.0194,0.2782,0.2600,0.2734,1.1649,2.3245,2.3193,0.7260,0.5124,0.5134 37 | 35,2330,407,0,0.05,85,3.0312,3.0575,3.0163,0.2712,0.2600,0.2734,1.7428,2.1452,2.1164,0.5794,0.4947,0.4991 38 | 36,2843,657,0,0.0,102,3.0535,3.0577,3.0172,0.2613,0.2600,0.2734,1.6284,2.1299,2.0878,0.6060,0.5046,0.5026 39 | 37,3469,442,1,0.22,225,3.0524,3.0570,3.0161,0.2603,0.2600,0.2734,0.7408,1.6114,1.5983,0.8876,0.6196,0.6229 40 | 38,4232,398,1,0.37,187,3.0470,3.0564,3.0138,0.2647,0.2600,0.2734,0.5596,1.4812,1.4613,0.9390,0.6462,0.6489 41 | 39,5164,369,2,0.04,195,3.0342,3.0560,3.0120,0.2705,0.2600,0.2734,0.4173,1.5645,1.5494,0.9400,0.6531,0.6540 42 | 40,6301,75,2,0.43,230,3.0334,3.0566,3.0125,0.2719,0.2600,0.2734,1.1078,1.4345,1.4198,0.7083,0.6380,0.6433 43 | 41,7688,179,1,0.18,187,3.0389,3.0558,3.0123,0.2713,0.2600,0.2734,0.4848,1.3335,1.2974,0.9365,0.6871,0.6999 44 | 42,9381,442,0,0.09,112,3.0230,3.0562,3.0124,0.2739,0.2600,0.2734,1.7681,1.9129,1.8623,0.5629,0.5429,0.5499 45 | 43,11446,111,0,0.14,188,3.0319,3.0554,3.0112,0.2717,0.2600,0.2734,1.5825,1.7535,1.7211,0.6071,0.5683,0.5847 46 | 44,13966,242,0,0.37,94,3.0245,3.0554,3.0112,0.2727,0.2600,0.2734,1.9149,1.9863,1.9425,0.5406,0.5249,0.5332 47 | 45,17040,107,0,0.35000000000000003,182,3.0259,3.0545,3.0106,0.2712,0.2600,0.2734,1.6310,1.7312,1.6946,0.5994,0.5748,0.5846 48 | 46,20792,150,0,0.11,215,3.0234,3.0545,3.0107,0.2710,0.2600,0.2734,1.5733,1.6847,1.6501,0.6058,0.5834,0.5872 49 | 47,25369,38,2,0.41000000000000003,292,3.0271,3.0544,3.0102,0.2706,0.2600,0.2734,1.4739,1.5251,1.5097,0.6098,0.5985,0.6038 50 | 48,30954,248,2,0.23,98,3.0310,3.0543,3.0100,0.2698,0.2600,0.2734,0.6410,0.9492,0.9506,0.8554,0.7649,0.7588 51 | 49,37769,670,1,0.29,67,3.0263,3.0542,3.0096,0.2703,0.2600,0.2734,0.8363,1.0519,1.0546,0.8037,0.7387,0.7371 52 | -------------------------------------------------------------------------------- /checkpoints/dep_label/basque/mlp/random/all_results.tsv: -------------------------------------------------------------------------------- 1 | run,ndata,hidden_size,nlayers,dropout,embedding_size,train_loss,dev_loss,test_loss,train_acc,dev_acc,test_acc,base_train_loss,base_dev_loss,base_test_loss,base_train_acc,base_dev_acc,base_test_acc 2 | 0,1,776,0,0.09,272,4.2977,4.8486,4.8464,1.0000,0.0583,0.0620,0.0002,5.1186,5.1205,1.0000,0.0348,0.0350 3 | 1,2,433,2,0.27,145,4.3219,4.7686,4.7695,0.5000,0.0583,0.0620,0.0000,5.9695,5.9882,1.0000,0.1610,0.1595 4 | 2,3,310,1,0.38,182,4.3458,4.7663,4.7639,0.3333,0.0446,0.0503,0.0002,4.5305,4.5342,1.0000,0.1792,0.1776 5 | 3,4,300,1,0.42,205,4.3692,4.7506,4.7481,0.2500,0.0446,0.0503,0.0004,4.5327,4.5367,1.0000,0.1237,0.1233 6 | 4,5,1009,2,0.39,134,4.3923,4.7222,4.7209,0.2000,0.0446,0.0503,0.0000,8.1844,8.2050,1.0000,0.1454,0.1460 7 | 5,6,657,2,0.41000000000000003,245,4.4150,4.7348,4.7336,0.1667,0.0446,0.0503,0.0000,5.7137,5.7304,1.0000,0.1295,0.1234 8 | 6,8,455,2,0.23,133,4.2519,4.6811,4.6806,0.2500,0.0583,0.0620,0.0000,5.8089,5.8280,1.0000,0.1879,0.1860 9 | 7,9,103,2,0.09,108,4.1432,4.6454,4.6466,0.3333,0.1781,0.1729,0.0024,4.3196,4.3282,1.0000,0.1851,0.1833 10 | 8,12,203,2,0.14,93,4.1418,4.6337,4.6370,0.3333,0.1781,0.1729,0.0007,5.0609,5.0925,1.0000,0.1935,0.1889 11 | 9,14,150,1,0.31,212,4.1214,4.6303,4.6342,0.2857,0.1781,0.1729,0.0019,4.1720,4.1879,1.0000,0.2099,0.2068 12 | 10,18,72,0,0.15,103,4.1796,4.6243,4.6241,0.2222,0.1781,0.1729,0.0326,4.3904,4.4160,1.0000,0.2276,0.2221 13 | 11,22,467,2,0.27,113,4.1782,4.6000,4.5996,0.1818,0.1781,0.1729,0.0002,5.5952,5.5950,1.0000,0.2333,0.2300 14 | 12,27,377,2,0.5,99,4.2565,4.6206,4.6202,0.1481,0.1781,0.1729,0.0047,5.0093,5.0178,1.0000,0.2386,0.2321 15 | 13,33,436,0,0.44,148,4.1645,4.5883,4.5889,0.1515,0.1781,0.1729,0.0291,4.3819,4.3919,1.0000,0.2281,0.2258 16 | 14,41,382,0,0.1,165,4.2582,4.4588,4.4636,0.1463,0.1781,0.1729,0.0586,4.3108,4.3169,1.0000,0.2425,0.2394 17 | 15,51,326,2,0.5,60,4.1912,4.4159,4.4210,0.1569,0.1781,0.1729,0.0842,4.1962,4.2197,1.0000,0.2680,0.2635 18 | 16,63,326,2,0.49,84,4.0539,4.3584,4.3662,0.2063,0.1781,0.1729,0.0335,4.1316,4.1667,1.0000,0.2684,0.2657 19 | 17,77,247,2,0.44,238,4.0918,4.2848,4.2927,0.1818,0.1781,0.1729,0.0015,3.9230,3.9462,1.0000,0.2854,0.2801 20 | 18,96,179,2,0.05,122,4.0469,4.2124,4.2215,0.1771,0.1781,0.1729,0.0033,4.2176,4.2269,1.0000,0.2893,0.2887 21 | 19,118,137,2,0.02,144,4.0593,4.2202,4.2295,0.1780,0.1781,0.1729,0.0063,4.0016,4.0292,1.0000,0.3048,0.2986 22 | 20,146,786,2,0.23,79,3.9710,4.1624,4.1727,0.1918,0.1781,0.1729,0.0004,5.5076,5.5346,1.0000,0.3061,0.3045 23 | 21,180,410,1,0.31,159,3.9271,4.1401,4.1505,0.2000,0.1781,0.1729,0.0077,3.8976,3.9110,1.0000,0.3243,0.3200 24 | 22,223,388,1,0.37,154,3.9568,4.1036,4.1137,0.1883,0.1781,0.1729,0.0137,3.7840,3.7896,1.0000,0.3277,0.3259 25 | 23,276,38,2,0.25,148,3.9372,4.0926,4.1043,0.1957,0.1781,0.1729,1.4006,3.4737,3.4829,0.8297,0.3135,0.3064 26 | 24,341,467,1,0.41000000000000003,115,3.9166,4.0885,4.0990,0.1906,0.1781,0.1729,0.0520,3.8855,3.9395,1.0000,0.3288,0.3266 27 | 25,421,176,0,0.09,124,3.9630,4.0733,4.0849,0.1829,0.1781,0.1729,1.0157,4.0118,4.0302,0.9287,0.3174,0.3130 28 | 26,520,57,2,0.02,240,3.9760,4.0688,4.0818,0.1769,0.1781,0.1729,0.2553,3.7148,3.7736,0.9865,0.3465,0.3399 29 | 27,642,968,2,0.15,50,3.9504,4.0634,4.0783,0.1869,0.1781,0.1729,0.0030,5.5282,5.5852,1.0000,0.3505,0.3446 30 | 28,794,268,0,0.01,235,3.9443,4.0604,4.0750,0.1864,0.1781,0.1729,0.5922,3.9473,3.9957,0.9824,0.3532,0.3476 31 | 29,981,270,0,0.3,144,3.9348,4.0653,4.0794,0.1865,0.1781,0.1729,1.7466,3.7589,3.7751,0.7768,0.3324,0.3283 32 | 30,1212,596,2,0.24,188,3.9375,4.0690,4.0847,0.1873,0.1781,0.1729,0.0026,4.7314,4.8955,1.0000,0.4079,0.3998 33 | 31,1498,694,0,0.49,59,3.9606,4.0735,4.0909,0.1883,0.1781,0.1729,2.2245,3.3760,3.4002,0.5681,0.3351,0.3320 34 | 32,1851,837,0,0.48,176,3.9618,4.0754,4.0932,0.1880,0.1781,0.1729,0.9848,3.3757,3.4252,0.8439,0.3988,0.3926 35 | 33,2287,487,2,0.4,75,3.9453,4.0810,4.0992,0.1928,0.1781,0.1729,1.5460,3.0282,3.0788,0.7613,0.4056,0.3965 36 | 34,2826,418,0,0.11,210,3.9428,4.0884,4.1073,0.1921,0.1781,0.1729,2.1040,3.4681,3.5152,0.6437,0.3777,0.3694 37 | 35,3492,407,0,0.05,85,3.9432,4.0932,4.1125,0.1913,0.1781,0.1729,2.6307,3.5711,3.6040,0.5123,0.3529,0.3499 38 | 36,4315,657,0,0.0,102,3.9368,4.1014,4.1211,0.1910,0.1781,0.1729,2.6847,3.4880,3.5141,0.5029,0.3652,0.3613 39 | 37,5332,442,1,0.22,225,3.9415,4.1022,4.1225,0.1885,0.1781,0.1729,0.8422,2.8600,2.9202,0.9089,0.4588,0.4521 40 | 38,6589,398,1,0.37,187,3.9428,4.1075,4.1284,0.1867,0.1781,0.1729,1.1778,2.7142,2.7484,0.8334,0.4669,0.4637 41 | 39,8142,369,2,0.04,195,3.9394,4.1132,4.1345,0.1868,0.1781,0.1729,0.7252,3.0114,3.0735,0.8978,0.4763,0.4703 42 | 40,10061,75,2,0.43,230,3.9273,4.1195,4.1417,0.1912,0.1781,0.1729,1.8204,2.5797,2.6331,0.6357,0.4972,0.4904 43 | 41,12432,179,1,0.18,187,3.9322,4.1239,4.1468,0.1911,0.1781,0.1729,1.7256,2.6441,2.6939,0.6585,0.4741,0.4650 44 | 42,15362,442,0,0.09,112,3.9288,4.1296,4.1531,0.1911,0.1781,0.1729,2.2966,2.9107,2.9436,0.5097,0.4234,0.4188 45 | 43,18983,111,0,0.14,188,3.9284,4.1353,4.1596,0.1918,0.1781,0.1729,2.1027,2.7752,2.7977,0.5531,0.4488,0.4489 46 | 44,23457,242,0,0.37,94,3.9277,4.1417,4.1665,0.1914,0.1781,0.1729,2.5643,2.8859,2.9103,0.4556,0.4088,0.4048 47 | 45,28985,107,0,0.35000000000000003,182,3.9274,4.1479,4.1734,0.1907,0.1781,0.1729,2.2938,2.6967,2.7325,0.5202,0.4537,0.4470 48 | 46,35816,150,0,0.11,215,3.9489,4.0577,4.0741,0.1892,0.1781,0.1729,2.1584,2.6305,2.6403,0.5431,0.4734,0.4692 49 | 47,44258,38,2,0.41000000000000003,292,3.9870,4.0258,4.0387,0.1841,0.1781,0.1729,2.2886,2.4248,2.4502,0.5186,0.5029,0.4943 50 | 48,54689,248,2,0.23,98,4.0154,4.0178,4.0295,0.1796,0.1781,0.1729,1.2714,1.7914,1.8145,0.7414,0.6226,0.6189 51 | 49,67578,670,1,0.29,67,4.0297,4.0163,4.0275,0.1766,0.1781,0.1729,1.3354,1.8363,1.8592,0.7429,0.6215,0.6152 52 | -------------------------------------------------------------------------------- /checkpoints/pos_tag/turkish/mlp/fast/all_results.tsv: -------------------------------------------------------------------------------- 1 | run,ndata,hidden_size,nlayers,dropout,embedding_size,train_loss,dev_loss,test_loss,train_acc,dev_acc,test_acc,base_train_loss,base_dev_loss,base_test_loss,base_train_acc,base_dev_acc,base_test_acc 2 | 0,1,776,0,0.09,300,3.1699,3.6996,3.7007,1.0000,0.0945,0.0926,0.7726,3.6897,3.6875,1.0000,0.1067,0.1058 3 | 1,2,433,2,0.27,300,3.2224,3.7304,3.7296,0.5000,0.0945,0.0926,0.0002,6.0700,6.0134,1.0000,0.0577,0.0619 4 | 2,3,310,1,0.38,300,3.2730,3.6749,3.6745,0.3333,0.0945,0.0926,0.0214,3.5648,3.5451,1.0000,0.2413,0.2434 5 | 3,4,300,1,0.42,300,3.1144,3.7085,3.7067,0.5000,0.0370,0.0404,0.0209,3.6868,3.6631,1.0000,0.2298,0.2334 6 | 4,5,1009,2,0.39,300,3.2032,3.7350,3.7346,0.4000,0.0370,0.0404,0.0001,8.5133,8.4223,1.0000,0.2391,0.2433 7 | 5,6,657,2,0.41000000000000003,300,3.2767,3.6287,3.6205,0.3333,0.0370,0.0404,0.0018,4.9036,4.7279,1.0000,0.3760,0.3989 8 | 6,7,455,2,0.23,300,3.2223,3.5652,3.5514,0.2857,0.2600,0.2734,0.0011,4.8363,4.6619,1.0000,0.4107,0.4255 9 | 7,9,103,2,0.09,300,3.2676,3.4979,3.4832,0.2222,0.0945,0.0926,0.1302,3.4805,3.3478,1.0000,0.3689,0.3758 10 | 8,11,203,2,0.14,300,3.3981,3.5296,3.5208,0.1818,0.0945,0.0926,0.0232,3.5216,3.4155,1.0000,0.3700,0.3733 11 | 9,13,150,1,0.31,300,3.2964,3.4914,3.4790,0.2308,0.0945,0.0926,0.4375,2.9268,2.8663,1.0000,0.3757,0.3772 12 | 10,16,72,0,0.15,300,3.2635,3.3745,3.3577,0.2500,0.2600,0.2734,0.2385,2.6951,2.6327,1.0000,0.4544,0.4671 13 | 11,20,467,2,0.27,300,3.2029,3.3799,3.3625,0.2500,0.0945,0.0926,0.0037,3.7903,3.6483,1.0000,0.4351,0.4394 14 | 12,24,377,2,0.5,300,3.2200,3.3893,3.3734,0.2500,0.0945,0.0926,0.1034,2.9697,2.8834,1.0000,0.4553,0.4572 15 | 13,29,436,0,0.44,300,3.2491,3.3090,3.2904,0.2069,0.0945,0.0926,0.3063,2.2654,2.2099,1.0000,0.5424,0.5525 16 | 14,36,382,0,0.1,300,3.1946,3.2496,3.2275,0.2500,0.2600,0.2734,0.2822,2.1983,2.1511,1.0000,0.5627,0.5736 17 | 15,44,326,2,0.5,300,3.1583,3.2265,3.1999,0.2727,0.2600,0.2734,0.3176,2.5430,2.4771,0.9773,0.5681,0.5841 18 | 16,53,326,2,0.49,300,3.1319,3.2123,3.1834,0.2642,0.2600,0.2734,0.2555,2.3668,2.3201,1.0000,0.5926,0.6049 19 | 17,65,247,2,0.44,300,3.0629,3.2077,3.1744,0.2923,0.2600,0.2734,0.4184,2.0948,2.0244,0.9385,0.5608,0.5705 20 | 18,79,179,2,0.05,300,3.0625,3.1735,3.1387,0.2785,0.2600,0.2734,0.2240,2.0145,1.9238,0.9873,0.6118,0.6251 21 | 19,97,137,2,0.02,300,2.9722,3.1678,3.1276,0.3093,0.2600,0.2734,0.4186,2.0315,1.9313,0.9485,0.5926,0.6011 22 | 20,118,786,2,0.23,300,2.9615,3.1447,3.1022,0.3051,0.2600,0.2734,0.0067,2.2085,2.0659,1.0000,0.6522,0.6634 23 | 21,144,410,1,0.31,300,2.9193,3.1159,3.0724,0.3264,0.2600,0.2734,0.0780,1.6540,1.5659,1.0000,0.6906,0.7019 24 | 22,175,388,1,0.37,300,2.9286,3.1021,3.0599,0.3086,0.2600,0.2734,0.1150,1.6040,1.5235,0.9943,0.7088,0.7173 25 | 23,214,38,2,0.25,300,2.9238,3.1093,3.0662,0.2991,0.2600,0.2734,0.4309,1.7080,1.6438,0.9393,0.6570,0.6735 26 | 24,261,467,1,0.41000000000000003,300,2.9639,3.0937,3.0527,0.3065,0.2600,0.2734,0.1284,1.4657,1.4067,0.9923,0.7425,0.7495 27 | 25,319,176,0,0.09,300,2.9209,3.0868,3.0418,0.3135,0.2600,0.2734,0.4779,1.5323,1.5002,0.9561,0.7314,0.7360 28 | 26,389,57,2,0.02,300,2.9200,3.0821,3.0370,0.2982,0.2600,0.2734,0.4277,1.3576,1.2826,0.9434,0.7341,0.7449 29 | 27,474,968,2,0.15,300,2.9156,3.0837,3.0375,0.3017,0.2600,0.2734,0.0222,1.6586,1.5410,0.9979,0.7819,0.7941 30 | 28,579,268,0,0.01,300,2.8954,3.0876,3.0380,0.3057,0.2600,0.2734,0.4211,1.3115,1.2408,0.9620,0.7580,0.7732 31 | 29,706,270,0,0.3,300,2.9369,3.0800,3.0346,0.2918,0.2600,0.2734,0.4253,1.1746,1.1218,0.9448,0.7898,0.7978 32 | 30,862,596,2,0.24,300,2.9659,3.0717,3.0290,0.2749,0.2600,0.2734,0.2017,1.0751,1.0047,0.9710,0.8090,0.8250 33 | 31,1051,694,0,0.49,300,2.9693,3.0693,3.0263,0.2712,0.2600,0.2734,0.4556,0.9677,0.9322,0.9191,0.8209,0.8276 34 | 32,1283,837,0,0.48,300,2.9607,3.0718,3.0283,0.2783,0.2600,0.2734,0.4427,0.9346,0.9018,0.9189,0.8221,0.8290 35 | 33,1565,487,2,0.4,300,2.9658,3.0676,3.0244,0.2754,0.2600,0.2734,0.2132,0.7561,0.7309,0.9585,0.8516,0.8644 36 | 34,1909,418,0,0.11,300,2.9911,3.0617,3.0194,0.2782,0.2600,0.2734,0.4253,0.7978,0.7963,0.9246,0.8486,0.8584 37 | 35,2330,407,0,0.05,300,3.0312,3.0575,3.0163,0.2712,0.2600,0.2734,0.4449,0.7186,0.7023,0.9215,0.8550,0.8622 38 | 36,2843,657,0,0.0,300,3.0535,3.0577,3.0172,0.2613,0.2600,0.2734,0.4225,0.6986,0.6906,0.9230,0.8623,0.8689 39 | 37,3469,442,1,0.22,300,3.0524,3.0570,3.0161,0.2603,0.2600,0.2734,0.1957,0.5563,0.5461,0.9582,0.8868,0.8933 40 | 38,4232,398,1,0.37,300,3.0470,3.0564,3.0138,0.2647,0.2600,0.2734,0.1529,0.4955,0.4824,0.9672,0.9028,0.9039 41 | 39,5164,369,2,0.04,300,3.0342,3.0560,3.0120,0.2705,0.2600,0.2734,0.2965,0.5964,0.5775,0.9357,0.8779,0.8862 42 | 40,6301,75,2,0.43,300,3.0334,3.0566,3.0125,0.2719,0.2600,0.2734,0.2823,0.4803,0.4753,0.9449,0.9021,0.9092 43 | 41,7688,179,1,0.18,300,3.0389,3.0558,3.0123,0.2713,0.2600,0.2734,0.1809,0.4393,0.4146,0.9625,0.9131,0.9186 44 | 42,9381,442,0,0.09,300,3.0230,3.0562,3.0124,0.2739,0.2600,0.2734,0.4350,0.5655,0.5409,0.9138,0.8836,0.8929 45 | 43,11446,111,0,0.14,300,3.0319,3.0554,3.0112,0.2717,0.2600,0.2734,0.4544,0.5642,0.5397,0.9095,0.8866,0.8954 46 | 44,13966,242,0,0.37,300,3.0245,3.0554,3.0112,0.2727,0.2600,0.2734,0.5178,0.5967,0.5745,0.8930,0.8792,0.8845 47 | 45,17040,107,0,0.35000000000000003,300,3.0259,3.0545,3.0106,0.2712,0.2600,0.2734,0.5334,0.6003,0.5764,0.8914,0.8809,0.8852 48 | 46,20792,150,0,0.11,300,3.0234,3.0545,3.0107,0.2710,0.2600,0.2734,0.4735,0.5406,0.5158,0.9031,0.8895,0.9002 49 | 47,25369,38,2,0.41000000000000003,300,3.0271,3.0544,3.0102,0.2706,0.2600,0.2734,0.5588,0.6013,0.5757,0.8873,0.8802,0.8876 50 | 48,30954,248,2,0.23,300,3.0310,3.0543,3.0100,0.2698,0.2600,0.2734,0.1415,0.2673,0.2543,0.9670,0.9427,0.9469 51 | 49,37769,670,1,0.29,300,3.0263,3.0542,3.0096,0.2703,0.2600,0.2734,0.1322,0.2464,0.2352,0.9694,0.9471,0.9517 52 | -------------------------------------------------------------------------------- /checkpoints/dep_label/turkish/mlp/bert/all_results.tsv: -------------------------------------------------------------------------------- 1 | run,ndata,hidden_size,nlayers,dropout,embedding_size,train_loss,dev_loss,test_loss,train_acc,dev_acc,test_acc,base_train_loss,base_dev_loss,base_test_loss,base_train_acc,base_dev_acc,base_test_acc 2 | 0,1,776,0,0.09,768,4.2977,4.8398,4.8400,1.0000,0.0733,0.0728,0.0000,6.2584,6.2648,1.0000,0.0733,0.0728 3 | 1,2,433,2,0.27,768,4.3219,4.8407,4.8380,0.5000,0.0399,0.0449,0.0000,14.0483,14.0215,1.0000,0.0690,0.0730 4 | 2,3,310,1,0.38,768,4.3458,4.7498,4.7474,0.3333,0.0399,0.0449,0.0001,8.4301,8.3923,1.0000,0.1885,0.1881 5 | 3,4,300,1,0.42,768,4.3692,4.7299,4.7282,0.2500,0.0399,0.0449,0.0000,7.9473,7.9324,1.0000,0.2066,0.2075 6 | 4,5,1009,2,0.39,768,4.3923,4.7306,4.7305,0.2000,0.0382,0.0356,0.0000,13.2879,13.3233,1.0000,0.1749,0.1708 7 | 5,6,657,2,0.41000000000000003,768,4.4150,4.7064,4.7039,0.1667,0.0382,0.0356,0.0000,9.5369,9.4878,1.0000,0.2177,0.2176 8 | 6,7,455,2,0.23,768,4.3188,4.6955,4.6913,0.2857,0.0803,0.0842,0.0000,9.5676,9.5218,1.0000,0.1905,0.1941 9 | 7,9,103,2,0.09,768,4.3889,4.6664,4.6593,0.2222,0.0803,0.0842,0.0002,5.0514,5.0313,1.0000,0.2168,0.2201 10 | 8,11,203,2,0.14,768,4.4481,4.6379,4.6317,0.1818,0.0803,0.0842,0.0001,5.8612,5.8235,1.0000,0.1720,0.1759 11 | 9,13,150,1,0.31,768,4.3732,4.6234,4.6170,0.1538,0.0603,0.0618,0.0011,5.3073,5.2750,1.0000,0.1641,0.1611 12 | 10,16,72,0,0.15,768,4.3132,4.5547,4.5466,0.1250,0.0603,0.0618,0.0057,4.9853,4.9282,1.0000,0.1813,0.1916 13 | 11,20,467,2,0.27,768,4.2239,4.5626,4.5523,0.2000,0.0603,0.0618,0.0000,6.6141,6.4687,1.0000,0.1954,0.2008 14 | 12,24,377,2,0.5,768,4.2258,4.5079,4.5010,0.1667,0.0603,0.0618,0.0007,5.2079,5.1666,1.0000,0.2626,0.2661 15 | 13,29,436,0,0.44,768,4.1728,4.4881,4.4778,0.1379,0.0603,0.0618,0.0295,4.8167,4.7648,1.0000,0.2795,0.2816 16 | 14,36,382,0,0.1,768,4.1035,4.4654,4.4572,0.1389,0.0399,0.0449,0.0246,5.0676,5.0102,1.0000,0.2880,0.2950 17 | 15,43,326,2,0.5,768,4.1082,4.4258,4.4167,0.1395,0.0803,0.0842,0.0021,5.0931,5.0539,1.0000,0.3393,0.3410 18 | 16,53,326,2,0.49,768,4.0532,4.3878,4.3823,0.1321,0.0603,0.0618,0.0021,4.9344,4.9365,1.0000,0.3194,0.3209 19 | 17,64,247,2,0.44,768,4.1258,4.3104,4.3059,0.1406,0.1962,0.1957,0.0102,4.2820,4.2970,1.0000,0.3391,0.3413 20 | 18,78,179,2,0.05,768,4.0619,4.2947,4.2860,0.1410,0.0603,0.0618,0.0022,5.0247,5.0017,1.0000,0.3408,0.3445 21 | 19,95,137,2,0.02,768,4.0132,4.2510,4.2395,0.1474,0.1962,0.1957,0.0043,4.6870,4.6132,1.0000,0.3503,0.3619 22 | 20,115,786,2,0.23,768,4.0429,4.1871,4.1754,0.1478,0.1962,0.1957,0.0002,5.7058,5.5640,1.0000,0.3825,0.3996 23 | 21,140,410,1,0.31,768,3.9892,4.1622,4.1476,0.1500,0.1962,0.1957,0.0037,4.0615,4.0021,1.0000,0.4069,0.4215 24 | 22,171,388,1,0.37,768,3.9798,4.1331,4.1180,0.1462,0.1962,0.1957,0.0055,3.8018,3.7313,1.0000,0.4197,0.4370 25 | 23,208,38,2,0.25,768,3.9598,4.1132,4.0989,0.1490,0.1962,0.1957,0.8123,3.2165,3.1700,0.9567,0.3802,0.3888 26 | 24,253,467,1,0.41000000000000003,768,3.9428,4.0887,4.0744,0.1542,0.1962,0.1957,0.0070,3.7850,3.6616,1.0000,0.4389,0.4610 27 | 25,308,176,0,0.09,768,3.9156,4.0696,4.0547,0.1623,0.1962,0.1957,0.1568,3.0994,3.0011,1.0000,0.4360,0.4475 28 | 26,374,57,2,0.02,768,3.9032,4.0647,4.0515,0.1684,0.1962,0.1957,0.0808,3.5263,3.4552,1.0000,0.4472,0.4579 29 | 27,456,968,2,0.15,768,3.9031,4.0526,4.0377,0.1689,0.1962,0.1957,0.0004,5.2622,4.9854,1.0000,0.4877,0.5007 30 | 28,554,268,0,0.01,768,3.8853,4.0536,4.0378,0.1715,0.1962,0.1957,0.2578,2.7875,2.6774,1.0000,0.4856,0.5010 31 | 29,675,270,0,0.3,768,3.9006,4.0450,4.0301,0.1793,0.1962,0.1957,0.3872,2.6437,2.5412,0.9956,0.5031,0.5261 32 | 30,821,596,2,0.24,768,3.8662,4.0354,4.0204,0.1937,0.1962,0.1957,0.0026,3.8571,3.7450,1.0000,0.5423,0.5538 33 | 31,999,694,0,0.49,768,3.8821,4.0313,4.0164,0.1922,0.1962,0.1957,0.5983,2.4502,2.3564,0.9610,0.5406,0.5567 34 | 32,1215,837,0,0.48,768,3.8716,4.0164,4.0014,0.1942,0.1962,0.1957,0.6536,2.2475,2.1455,0.9514,0.5675,0.5820 35 | 33,1478,487,2,0.4,768,3.8831,4.0064,3.9924,0.1949,0.1962,0.1957,0.0437,2.3257,2.1981,0.9993,0.6015,0.6189 36 | 34,1799,418,0,0.11,768,3.9257,3.9975,3.9853,0.1907,0.1962,0.1957,0.6421,2.0421,1.9399,0.9450,0.5963,0.6197 37 | 35,2189,407,0,0.05,768,3.9750,3.9886,3.9762,0.1900,0.1962,0.1957,0.7037,1.8863,1.7741,0.9100,0.6210,0.6405 38 | 36,2663,657,0,0.0,768,3.9763,3.9861,3.9747,0.1930,0.1962,0.1957,0.5205,1.8393,1.7297,0.9471,0.6264,0.6490 39 | 37,3240,442,1,0.22,768,3.9721,3.9833,3.9710,0.1948,0.1962,0.1957,0.2805,1.8056,1.6651,0.9765,0.6444,0.6687 40 | 38,3943,398,1,0.37,768,3.9641,3.9806,3.9679,0.1963,0.1962,0.1957,0.4582,1.6848,1.5471,0.9341,0.6624,0.6793 41 | 39,4797,369,2,0.04,768,3.9608,3.9783,3.9648,0.1928,0.1962,0.1957,0.4631,1.7706,1.6267,0.9250,0.6600,0.6823 42 | 40,5837,75,2,0.43,768,3.9723,3.9758,3.9617,0.1902,0.1962,0.1957,0.7597,1.6587,1.5427,0.8814,0.6740,0.6968 43 | 41,7102,179,1,0.18,768,3.9767,3.9714,3.9565,0.1899,0.1962,0.1957,0.4745,1.5280,1.4065,0.9193,0.6892,0.7084 44 | 42,8642,442,0,0.09,768,3.9689,3.9713,3.9560,0.1892,0.1962,0.1957,0.7974,1.5056,1.3916,0.8550,0.6902,0.7141 45 | 43,10515,111,0,0.14,768,3.9640,3.9695,3.9539,0.1924,0.1962,0.1957,0.8881,1.4779,1.3636,0.8339,0.6984,0.7212 46 | 44,12794,242,0,0.37,768,3.9629,3.9690,3.9530,0.1920,0.1962,0.1957,0.8887,1.4292,1.3176,0.8262,0.7081,0.7267 47 | 45,15567,107,0,0.35000000000000003,768,3.9536,3.9680,3.9524,0.1940,0.1962,0.1957,0.9169,1.3931,1.2910,0.8199,0.7156,0.7335 48 | 46,18941,150,0,0.11,768,3.9534,3.9685,3.9525,0.1949,0.1962,0.1957,0.7641,1.3424,1.2355,0.8440,0.7247,0.7399 49 | 47,23047,38,2,0.41000000000000003,768,3.9520,3.9679,3.9520,0.1953,0.1962,0.1957,1.4084,1.6829,1.5989,0.7399,0.6791,0.6945 50 | 48,28042,248,2,0.23,768,3.9531,3.9672,3.9519,0.1943,0.1962,0.1957,0.6007,1.1869,1.0978,0.8768,0.7558,0.7709 51 | 49,34120,670,1,0.29,768,3.9495,3.9674,3.9518,0.1951,0.1962,0.1957,0.5603,1.1481,1.0482,0.8890,0.7643,0.7834 52 | -------------------------------------------------------------------------------- /checkpoints/dep_label/turkish/mlp/fast/all_results.tsv: -------------------------------------------------------------------------------- 1 | run,ndata,hidden_size,nlayers,dropout,embedding_size,train_loss,dev_loss,test_loss,train_acc,dev_acc,test_acc,base_train_loss,base_dev_loss,base_test_loss,base_train_acc,base_dev_acc,base_test_acc 2 | 0,1,776,0,0.09,300,4.2977,4.8398,4.8400,1.0000,0.0733,0.0728,0.4282,4.8376,4.8409,1.0000,0.0712,0.0719 3 | 1,2,433,2,0.27,300,4.3219,4.8407,4.8380,0.5000,0.0399,0.0449,0.0000,6.1278,6.0915,1.0000,0.0544,0.0520 4 | 2,3,310,1,0.38,300,4.3458,4.7498,4.7474,0.3333,0.0399,0.0449,0.0077,4.2665,4.2541,1.0000,0.2425,0.2405 5 | 3,4,300,1,0.42,300,4.3692,4.7299,4.7282,0.2500,0.0399,0.0449,0.0179,4.2649,4.2552,1.0000,0.2571,0.2585 6 | 4,5,1009,2,0.39,300,4.3923,4.7306,4.7305,0.2000,0.0382,0.0356,0.0001,8.1726,8.0888,1.0000,0.2548,0.2568 7 | 5,6,657,2,0.41000000000000003,300,4.4150,4.7064,4.7039,0.1667,0.0382,0.0356,0.0008,6.9003,6.8228,1.0000,0.2768,0.2823 8 | 6,7,455,2,0.23,300,4.3188,4.6955,4.6913,0.2857,0.0803,0.0842,0.0007,6.4204,6.3631,1.0000,0.2826,0.2868 9 | 7,9,103,2,0.09,300,4.3889,4.6664,4.6593,0.2222,0.0803,0.0842,0.0443,4.7159,4.6440,1.0000,0.2792,0.2781 10 | 8,11,203,2,0.14,300,4.4481,4.6379,4.6317,0.1818,0.0803,0.0842,0.0074,4.7768,4.6733,1.0000,0.2992,0.2967 11 | 9,13,150,1,0.31,300,4.3732,4.6234,4.6170,0.1538,0.0603,0.0618,0.1760,4.1043,4.0527,1.0000,0.2960,0.2963 12 | 10,16,72,0,0.15,300,4.3132,4.5547,4.5466,0.1250,0.0603,0.0618,0.2566,4.0107,3.9815,1.0000,0.2930,0.2962 13 | 11,20,467,2,0.27,300,4.2239,4.5626,4.5523,0.2000,0.0603,0.0618,0.0044,6.2009,6.0054,1.0000,0.2984,0.3028 14 | 12,24,377,2,0.5,300,4.2258,4.5079,4.5010,0.1667,0.0603,0.0618,0.1572,4.8583,4.8145,1.0000,0.3157,0.3178 15 | 13,29,436,0,0.44,300,4.1728,4.4881,4.4778,0.1379,0.0603,0.0618,0.5057,3.6621,3.6395,1.0000,0.3327,0.3327 16 | 14,36,382,0,0.1,300,4.1035,4.4654,4.4572,0.1389,0.0399,0.0449,0.4151,3.6514,3.6340,1.0000,0.3354,0.3379 17 | 15,43,326,2,0.5,300,4.1082,4.4258,4.4167,0.1395,0.0803,0.0842,0.2518,4.4478,4.4078,1.0000,0.3391,0.3450 18 | 16,53,326,2,0.49,300,4.0532,4.3878,4.3823,0.1321,0.0603,0.0618,0.3325,4.1185,4.1459,0.9434,0.3313,0.3326 19 | 17,64,247,2,0.44,300,4.1258,4.3104,4.3059,0.1406,0.1962,0.1957,0.6389,3.6105,3.5935,0.9531,0.3481,0.3497 20 | 18,78,179,2,0.05,300,4.0619,4.2947,4.2860,0.1410,0.0603,0.0618,0.1808,3.9164,3.8633,1.0000,0.3613,0.3680 21 | 19,95,137,2,0.02,300,4.0132,4.2510,4.2395,0.1474,0.1962,0.1957,0.2924,3.5395,3.5132,0.9684,0.3782,0.3778 22 | 20,115,786,2,0.23,300,4.0429,4.1871,4.1754,0.1478,0.1962,0.1957,0.0089,4.6804,4.5435,1.0000,0.4148,0.4298 23 | 21,140,410,1,0.31,300,3.9892,4.1622,4.1476,0.1500,0.1962,0.1957,0.4088,3.0943,3.0314,0.9786,0.4192,0.4319 24 | 22,171,388,1,0.37,300,3.9798,4.1331,4.1180,0.1462,0.1962,0.1957,0.6107,3.0806,3.0118,0.9591,0.4304,0.4449 25 | 23,208,38,2,0.25,300,3.9598,4.1132,4.0989,0.1490,0.1962,0.1957,1.3870,3.2681,3.2439,0.8462,0.3871,0.3885 26 | 24,253,467,1,0.41000000000000003,300,3.9428,4.0887,4.0744,0.1542,0.1962,0.1957,0.6759,2.9582,2.8990,0.9526,0.4418,0.4516 27 | 25,308,176,0,0.09,300,3.9156,4.0696,4.0547,0.1623,0.1962,0.1957,0.9355,2.8996,2.8358,0.9708,0.4476,0.4563 28 | 26,374,57,2,0.02,300,3.9032,4.0647,4.0515,0.1684,0.1962,0.1957,2.1661,3.2258,3.2035,0.5856,0.3557,0.3568 29 | 27,456,968,2,0.15,300,3.9031,4.0526,4.0377,0.1689,0.1962,0.1957,0.0155,4.8617,4.6695,1.0000,0.4869,0.5057 30 | 28,554,268,0,0.01,300,3.8853,4.0536,4.0378,0.1715,0.1962,0.1957,0.9797,2.7175,2.6474,0.9603,0.4865,0.5019 31 | 29,675,270,0,0.3,300,3.9006,4.0450,4.0301,0.1793,0.1962,0.1957,0.9167,2.6158,2.5439,0.9333,0.5115,0.5214 32 | 30,821,596,2,0.24,300,3.8662,4.0354,4.0204,0.1937,0.1962,0.1957,0.2647,3.2162,3.1185,0.9720,0.5378,0.5517 33 | 31,999,694,0,0.49,300,3.8821,4.0313,4.0164,0.1922,0.1962,0.1957,1.0195,2.4492,2.3911,0.8999,0.5392,0.5564 34 | 32,1215,837,0,0.48,300,3.8716,4.0164,4.0014,0.1942,0.1962,0.1957,1.0045,2.3245,2.2732,0.8848,0.5636,0.5755 35 | 33,1478,487,2,0.4,300,3.8831,4.0064,3.9924,0.1949,0.1962,0.1957,1.0788,2.2577,2.1904,0.7936,0.5602,0.5754 36 | 34,1799,418,0,0.11,300,3.9257,3.9975,3.9853,0.1907,0.1962,0.1957,1.0064,2.1855,2.1357,0.8683,0.5799,0.5889 37 | 35,2189,407,0,0.05,300,3.9750,3.9886,3.9762,0.1900,0.1962,0.1957,0.9235,1.9913,1.9436,0.8639,0.6100,0.6254 38 | 36,2663,657,0,0.0,300,3.9763,3.9861,3.9747,0.1930,0.1962,0.1957,1.0242,1.9999,1.9567,0.8457,0.6092,0.6224 39 | 37,3240,442,1,0.22,300,3.9721,3.9833,3.9710,0.1948,0.1962,0.1957,0.8698,1.8913,1.8169,0.8454,0.6292,0.6476 40 | 38,3943,398,1,0.37,300,3.9641,3.9806,3.9679,0.1963,0.1962,0.1957,0.8287,1.8234,1.7464,0.8435,0.6454,0.6610 41 | 39,4797,369,2,0.04,300,3.9608,3.9783,3.9648,0.1928,0.1962,0.1957,1.5070,1.9840,1.9145,0.6984,0.5995,0.6133 42 | 40,5837,75,2,0.43,300,3.9723,3.9758,3.9617,0.1902,0.1962,0.1957,1.0182,1.6943,1.6158,0.8170,0.6614,0.6814 43 | 41,7102,179,1,0.18,300,3.9767,3.9714,3.9565,0.1899,0.1962,0.1957,1.0244,1.6732,1.5911,0.7982,0.6663,0.6818 44 | 42,8642,442,0,0.09,300,3.9689,3.9713,3.9560,0.1892,0.1962,0.1957,1.0659,1.6703,1.6073,0.7899,0.6723,0.6848 45 | 43,10515,111,0,0.14,300,3.9640,3.9695,3.9539,0.1924,0.1962,0.1957,1.1576,1.6402,1.5714,0.7736,0.6756,0.6920 46 | 44,12794,242,0,0.37,300,3.9629,3.9690,3.9530,0.1920,0.1962,0.1957,1.1979,1.6129,1.5423,0.7620,0.6830,0.6973 47 | 45,15567,107,0,0.35000000000000003,300,3.9536,3.9680,3.9524,0.1940,0.1962,0.1957,1.2221,1.5864,1.5198,0.7529,0.6858,0.6978 48 | 46,18941,150,0,0.11,300,3.9534,3.9685,3.9525,0.1949,0.1962,0.1957,1.1930,1.5553,1.4790,0.7575,0.6867,0.7020 49 | 47,23047,38,2,0.41000000000000003,300,3.9520,3.9679,3.9520,0.1953,0.1962,0.1957,1.5810,1.7557,1.6858,0.7005,0.6564,0.6771 50 | 48,28042,248,2,0.23,300,3.9531,3.9672,3.9519,0.1943,0.1962,0.1957,0.8637,1.3278,1.2351,0.8177,0.7283,0.7394 51 | 49,34120,670,1,0.29,300,3.9495,3.9674,3.9518,0.1951,0.1962,0.1957,0.6937,1.2728,1.1820,0.8570,0.7352,0.7507 52 | -------------------------------------------------------------------------------- /checkpoints/pos_tag/basque/mlp/fast/all_results.tsv: -------------------------------------------------------------------------------- 1 | run,ndata,hidden_size,nlayers,dropout,embedding_size,train_loss,dev_loss,test_loss,train_acc,dev_acc,test_acc,base_train_loss,base_dev_loss,base_test_loss,base_train_acc,base_dev_acc,base_test_acc 2 | 0,1,776,0,0.09,300,3.3692,3.9303,3.9278,1.0000,0.0409,0.0451,1.1929,3.9204,3.9173,1.0000,0.0410,0.0451 3 | 1,2,433,2,0.27,300,3.4150,3.8795,3.8798,0.5000,0.0409,0.0451,0.0002,5.2624,5.2639,1.0000,0.2049,0.2048 4 | 2,3,310,1,0.38,300,3.4594,3.8335,3.8316,0.3333,0.0409,0.0451,0.0377,3.7272,3.7216,1.0000,0.3136,0.3130 5 | 3,4,300,1,0.42,300,3.5025,3.7332,3.7291,0.2500,0.0409,0.0451,0.0691,3.3959,3.3834,1.0000,0.3153,0.3148 6 | 4,5,1009,2,0.39,300,3.3783,3.6733,3.6676,0.4000,0.2451,0.2489,0.0003,6.3357,6.2652,1.0000,0.3299,0.3284 7 | 5,6,657,2,0.41000000000000003,300,3.4466,3.6918,3.6863,0.3333,0.2451,0.2489,0.0034,6.2879,6.2465,1.0000,0.2111,0.2078 8 | 6,8,455,2,0.23,300,3.3517,3.6372,3.6321,0.2500,0.2451,0.2489,0.0029,6.4111,6.3672,1.0000,0.3071,0.3098 9 | 7,9,103,2,0.09,300,3.4237,3.6453,3.6397,0.2222,0.2451,0.2489,0.4428,4.2605,4.2266,1.0000,0.3189,0.3175 10 | 8,12,203,2,0.14,300,3.2206,3.5767,3.5726,0.3333,0.1651,0.1604,0.1404,5.0495,4.9963,1.0000,0.3581,0.3601 11 | 9,14,150,1,0.31,300,3.2437,3.5497,3.5454,0.2857,0.2451,0.2489,0.8076,3.1702,3.1563,0.7857,0.4394,0.4405 12 | 10,18,72,0,0.15,300,3.2638,3.5673,3.5625,0.2222,0.2451,0.2489,1.0839,3.0505,3.0463,1.0000,0.4237,0.4244 13 | 11,22,467,2,0.27,300,3.1917,3.5581,3.5515,0.2273,0.2451,0.2489,0.0342,6.3106,6.2452,1.0000,0.4169,0.4183 14 | 12,27,377,2,0.5,300,3.2319,3.5421,3.5360,0.2222,0.2451,0.2489,0.4090,3.9692,3.9254,0.9630,0.4442,0.4486 15 | 13,34,436,0,0.44,300,3.0976,3.4859,3.4779,0.2647,0.2451,0.2489,0.8804,2.8202,2.8135,1.0000,0.5129,0.5116 16 | 14,42,382,0,0.1,300,3.2607,3.3948,3.3933,0.2143,0.2451,0.2489,0.8122,2.6061,2.6012,1.0000,0.5413,0.5439 17 | 15,52,326,2,0.5,300,3.2362,3.3868,3.3851,0.2308,0.2451,0.2489,0.8141,3.2293,3.2138,0.8269,0.5492,0.5505 18 | 16,64,326,2,0.49,300,3.2741,3.3554,3.3601,0.1875,0.2451,0.2489,0.8308,2.6612,2.6718,0.8438,0.5511,0.5520 19 | 17,79,247,2,0.44,300,3.2591,3.3376,3.3460,0.1899,0.2451,0.2489,1.0020,2.5359,2.5499,0.7975,0.5578,0.5562 20 | 18,98,179,2,0.05,300,3.2468,3.2947,3.3043,0.1939,0.2451,0.2489,0.5748,2.0840,2.1035,0.9184,0.6375,0.6351 21 | 19,121,137,2,0.02,300,3.2462,3.2941,3.3042,0.1983,0.2451,0.2489,0.8384,2.2421,2.2688,0.8347,0.6266,0.6249 22 | 20,150,786,2,0.23,300,3.1871,3.2677,3.2773,0.2133,0.2451,0.2489,0.0400,1.9712,1.9948,0.9933,0.6992,0.6959 23 | 21,186,410,1,0.31,300,3.1069,3.2439,3.2515,0.2312,0.2451,0.2489,0.1701,1.5679,1.5747,0.9839,0.6955,0.6942 24 | 22,230,388,1,0.37,300,3.1328,3.2405,3.2480,0.2261,0.2451,0.2489,0.2583,1.4858,1.4958,0.9739,0.7062,0.7039 25 | 23,285,38,2,0.25,300,3.0951,3.2300,3.2395,0.2351,0.2451,0.2489,0.5029,1.6074,1.6198,0.9193,0.6909,0.6892 26 | 24,352,467,1,0.41000000000000003,300,3.1220,3.2206,3.2290,0.2216,0.2451,0.2489,0.3065,1.3596,1.3635,0.9631,0.7325,0.7312 27 | 25,436,176,0,0.09,300,3.1137,3.2085,3.2147,0.2362,0.2451,0.2489,0.3742,1.4104,1.4095,0.9633,0.7181,0.7186 28 | 26,540,57,2,0.02,300,3.1072,3.2159,3.2225,0.2389,0.2451,0.2489,0.3614,1.4150,1.4204,0.9370,0.7327,0.7296 29 | 27,668,968,2,0.15,300,3.1027,3.2096,3.2163,0.2440,0.2451,0.2489,0.1487,1.3990,1.4122,0.9731,0.7711,0.7709 30 | 28,827,268,0,0.01,300,3.0901,3.2093,3.2148,0.2443,0.2451,0.2489,0.4624,1.2488,1.2518,0.9420,0.7447,0.7457 31 | 29,1024,270,0,0.3,300,3.0957,3.2097,3.2136,0.2461,0.2451,0.2489,0.4693,1.1563,1.1613,0.9287,0.7642,0.7657 32 | 30,1267,596,2,0.24,300,3.0778,3.2124,3.2168,0.2478,0.2451,0.2489,0.5210,1.2052,1.2220,0.8974,0.7755,0.7759 33 | 31,1568,694,0,0.49,300,3.0801,3.2165,3.2222,0.2487,0.2451,0.2489,0.5070,1.0875,1.1094,0.9031,0.7849,0.7843 34 | 32,1941,837,0,0.48,300,3.0839,3.2188,3.2254,0.2442,0.2451,0.2489,0.5383,1.0572,1.0766,0.9016,0.7991,0.7985 35 | 33,2402,487,2,0.4,300,3.0813,3.2206,3.2281,0.2452,0.2451,0.2489,0.2628,0.8857,0.9191,0.9475,0.8516,0.8484 36 | 34,2973,418,0,0.11,300,3.0869,3.2254,3.2324,0.2395,0.2451,0.2489,0.4705,0.9657,0.9881,0.9136,0.8236,0.8217 37 | 35,3680,407,0,0.05,300,3.1026,3.2298,3.2368,0.2408,0.2451,0.2489,0.4647,0.9350,0.9595,0.9158,0.8323,0.8291 38 | 36,4555,657,0,0.0,300,3.1025,3.2354,3.2424,0.2397,0.2451,0.2489,0.4774,0.9049,0.9273,0.9133,0.8396,0.8385 39 | 37,5638,442,1,0.22,300,3.0921,3.2388,3.2463,0.2458,0.2451,0.2489,0.2272,0.7870,0.8171,0.9507,0.8689,0.8670 40 | 38,6978,398,1,0.37,300,3.0913,3.2435,3.2515,0.2475,0.2451,0.2489,0.2880,0.7305,0.7572,0.9390,0.8743,0.8730 41 | 39,8637,369,2,0.04,300,3.0890,3.2484,3.2569,0.2473,0.2451,0.2489,0.3169,0.8916,0.9221,0.9290,0.8679,0.8661 42 | 40,10690,75,2,0.43,300,3.0893,3.2537,3.2630,0.2445,0.2451,0.2489,0.4387,0.8307,0.8589,0.9083,0.8678,0.8633 43 | 41,13231,179,1,0.18,300,3.0944,3.2572,3.2671,0.2431,0.2451,0.2489,0.3144,0.7137,0.7391,0.9287,0.8824,0.8789 44 | 42,16376,442,0,0.09,300,3.0925,3.2616,3.2720,0.2432,0.2451,0.2489,0.5236,0.8150,0.8437,0.8973,0.8593,0.8548 45 | 43,20269,111,0,0.14,300,3.0921,3.2662,3.2770,0.2421,0.2451,0.2489,0.5481,0.8208,0.8488,0.8925,0.8581,0.8539 46 | 44,25087,242,0,0.37,300,3.0955,3.2700,3.2813,0.2411,0.2451,0.2489,0.6111,0.8662,0.8934,0.8746,0.8468,0.8430 47 | 45,31050,107,0,0.35000000000000003,300,3.0977,3.2744,3.2864,0.2409,0.2451,0.2489,0.6085,0.8614,0.8894,0.8752,0.8489,0.8443 48 | 46,38431,150,0,0.11,300,3.1135,3.2046,3.2097,0.2414,0.2451,0.2489,0.5098,0.6219,0.6418,0.8955,0.8735,0.8698 49 | 47,47567,38,2,0.41000000000000003,300,3.1426,3.1782,3.1808,0.2416,0.2451,0.2489,0.6423,0.7219,0.7388,0.8590,0.8466,0.8426 50 | 48,58874,248,2,0.23,300,3.1617,3.1725,3.1745,0.2422,0.2451,0.2489,0.2284,0.3145,0.3279,0.9426,0.9267,0.9229 51 | 49,72869,670,1,0.29,300,3.1687,3.1713,3.1730,0.2446,0.2451,0.2489,0.2574,0.3212,0.3296,0.9381,0.9260,0.9248 52 | -------------------------------------------------------------------------------- /checkpoints/pos_tag/turkish/mlp/bert/all_results.tsv: -------------------------------------------------------------------------------- 1 | run,ndata,hidden_size,nlayers,dropout,embedding_size,train_loss,dev_loss,test_loss,train_acc,dev_acc,test_acc,base_train_loss,base_dev_loss,base_test_loss,base_train_acc,base_dev_acc,base_test_acc 2 | 0,1,776,0,0.09,768,3.1699,3.6996,3.7007,1.0000,0.0945,0.0926,0.0014,4.3652,4.3691,1.0000,0.0944,0.0926 3 | 1,2,433,2,0.27,768,3.2224,3.7304,3.7296,0.5000,0.0945,0.0926,0.0000,10.8002,10.7934,1.0000,0.0695,0.0757 4 | 2,3,310,1,0.38,768,3.2730,3.6749,3.6745,0.3333,0.0945,0.0926,0.0001,7.0202,7.0628,1.0000,0.1963,0.1950 5 | 3,4,300,1,0.42,768,3.1144,3.7085,3.7067,0.5000,0.0370,0.0404,0.0002,6.5000,6.5418,1.0000,0.1994,0.1986 6 | 4,5,1009,2,0.39,768,3.2032,3.7350,3.7346,0.4000,0.0370,0.0404,0.0000,12.5817,12.6607,1.0000,0.1994,0.1960 7 | 5,6,657,2,0.41000000000000003,768,3.2767,3.6287,3.6205,0.3333,0.0370,0.0404,0.0000,7.8576,7.7449,1.0000,0.2846,0.2890 8 | 6,7,455,2,0.23,768,3.2223,3.5652,3.5514,0.2857,0.2600,0.2734,0.0000,8.2298,8.0946,1.0000,0.3092,0.3199 9 | 7,9,103,2,0.09,768,3.2676,3.4979,3.4832,0.2222,0.0945,0.0926,0.0010,4.1263,4.0199,1.0000,0.2989,0.3090 10 | 8,11,203,2,0.14,768,3.3981,3.5296,3.5208,0.1818,0.0945,0.0926,0.0002,3.8292,3.7630,1.0000,0.3179,0.3185 11 | 9,13,150,1,0.31,768,3.2964,3.4914,3.4790,0.2308,0.0945,0.0926,0.0023,3.6202,3.5434,1.0000,0.3209,0.3299 12 | 10,16,72,0,0.15,768,3.2635,3.3745,3.3577,0.2500,0.2600,0.2734,0.0401,3.0376,2.9593,1.0000,0.4226,0.4433 13 | 11,20,467,2,0.27,768,3.2029,3.3799,3.3625,0.2500,0.0945,0.0926,0.0001,4.7019,4.4898,1.0000,0.4129,0.4422 14 | 12,24,377,2,0.5,768,3.2200,3.3893,3.3734,0.2500,0.0945,0.0926,0.0015,3.5523,3.3975,1.0000,0.4326,0.4612 15 | 13,29,436,0,0.44,768,3.2491,3.3090,3.2904,0.2069,0.0945,0.0926,0.1138,2.6503,2.5556,1.0000,0.4757,0.4980 16 | 14,36,382,0,0.1,768,3.1946,3.2496,3.2275,0.2500,0.2600,0.2734,0.0848,2.5495,2.4792,1.0000,0.4983,0.5200 17 | 15,44,326,2,0.5,768,3.1583,3.2265,3.1999,0.2727,0.2600,0.2734,0.0019,3.2306,3.0508,1.0000,0.5328,0.5600 18 | 16,53,326,2,0.49,768,3.1319,3.2123,3.1834,0.2642,0.2600,0.2734,0.0022,2.7911,2.6839,1.0000,0.5690,0.5867 19 | 17,65,247,2,0.44,768,3.0629,3.2077,3.1744,0.2923,0.2600,0.2734,0.0039,2.6126,2.4866,1.0000,0.5680,0.5827 20 | 18,79,179,2,0.05,768,3.0625,3.1735,3.1387,0.2785,0.2600,0.2734,0.0018,2.5827,2.4696,1.0000,0.5917,0.6123 21 | 19,97,137,2,0.02,768,2.9722,3.1678,3.1276,0.3093,0.2600,0.2734,0.0033,2.5603,2.4642,1.0000,0.5821,0.6036 22 | 20,118,786,2,0.23,768,2.9615,3.1447,3.1022,0.3051,0.2600,0.2734,0.0001,3.1464,3.0001,1.0000,0.6071,0.6233 23 | 21,144,410,1,0.31,768,2.9193,3.1159,3.0724,0.3264,0.2600,0.2734,0.0056,2.1858,2.0849,1.0000,0.6241,0.6440 24 | 22,175,388,1,0.37,768,2.9286,3.1021,3.0599,0.3086,0.2600,0.2734,0.0089,1.9439,1.8699,1.0000,0.6444,0.6609 25 | 23,214,38,2,0.25,768,2.9238,3.1093,3.0662,0.2991,0.2600,0.2734,0.6253,1.7606,1.6853,0.9159,0.6098,0.6316 26 | 24,261,467,1,0.41000000000000003,768,2.9639,3.0937,3.0527,0.3065,0.2600,0.2734,0.0129,1.8311,1.7697,1.0000,0.6744,0.6844 27 | 25,319,176,0,0.09,768,2.9209,3.0868,3.0418,0.3135,0.2600,0.2734,0.3928,1.6022,1.5293,0.9718,0.6570,0.6744 28 | 26,389,57,2,0.02,768,2.9200,3.0821,3.0370,0.2982,0.2600,0.2734,0.0899,1.6459,1.5406,1.0000,0.6777,0.6858 29 | 27,474,968,2,0.15,768,2.9156,3.0837,3.0375,0.3017,0.2600,0.2734,0.0003,2.3788,2.2624,1.0000,0.7123,0.7256 30 | 28,579,268,0,0.01,768,2.8954,3.0876,3.0380,0.3057,0.2600,0.2734,0.2387,1.3182,1.2562,0.9810,0.7163,0.7288 31 | 29,706,270,0,0.3,768,2.9369,3.0800,3.0346,0.2918,0.2600,0.2734,0.3347,1.2206,1.1718,0.9674,0.7322,0.7481 32 | 30,862,596,2,0.24,768,2.9659,3.0717,3.0290,0.2749,0.2600,0.2734,0.0037,1.4782,1.4517,1.0000,0.7573,0.7635 33 | 31,1051,694,0,0.49,768,2.9693,3.0693,3.0263,0.2712,0.2600,0.2734,0.3299,1.0758,1.0296,0.9524,0.7664,0.7725 34 | 32,1283,837,0,0.48,768,2.9607,3.0718,3.0283,0.2783,0.2600,0.2734,0.3548,1.0409,0.9994,0.9462,0.7761,0.7821 35 | 33,1565,487,2,0.4,768,2.9658,3.0676,3.0244,0.2754,0.2600,0.2734,0.0706,0.9147,0.8865,0.9936,0.8071,0.8151 36 | 34,1909,418,0,0.11,768,2.9911,3.0617,3.0194,0.2782,0.2600,0.2734,0.3573,0.8790,0.8480,0.9492,0.8000,0.8135 37 | 35,2330,407,0,0.05,768,3.0312,3.0575,3.0163,0.2712,0.2600,0.2734,0.3952,0.8058,0.7720,0.9343,0.8168,0.8272 38 | 36,2843,657,0,0.0,768,3.0535,3.0577,3.0172,0.2613,0.2600,0.2734,0.4356,0.7894,0.7531,0.9254,0.8243,0.8353 39 | 37,3469,442,1,0.22,768,3.0524,3.0570,3.0161,0.2603,0.2600,0.2734,0.2737,0.7109,0.6673,0.9461,0.8376,0.8490 40 | 38,4232,398,1,0.37,768,3.0470,3.0564,3.0138,0.2647,0.2600,0.2734,0.2344,0.6689,0.6254,0.9603,0.8504,0.8601 41 | 39,5164,369,2,0.04,768,3.0342,3.0560,3.0120,0.2705,0.2600,0.2734,0.2741,0.7270,0.6665,0.9479,0.8416,0.8555 42 | 40,6301,75,2,0.43,768,3.0334,3.0566,3.0125,0.2719,0.2600,0.2734,0.3784,0.6658,0.6173,0.9197,0.8543,0.8668 43 | 41,7688,179,1,0.18,768,3.0389,3.0558,3.0123,0.2713,0.2600,0.2734,0.3090,0.6263,0.5738,0.9382,0.8550,0.8713 44 | 42,9381,442,0,0.09,768,3.0230,3.0562,3.0124,0.2739,0.2600,0.2734,0.4163,0.6414,0.5950,0.9157,0.8568,0.8721 45 | 43,11446,111,0,0.14,768,3.0319,3.0554,3.0112,0.2717,0.2600,0.2734,0.4163,0.6199,0.5741,0.9118,0.8602,0.8758 46 | 44,13966,242,0,0.37,768,3.0245,3.0554,3.0112,0.2727,0.2600,0.2734,0.4237,0.5982,0.5494,0.9031,0.8637,0.8766 47 | 45,17040,107,0,0.35000000000000003,768,3.0259,3.0545,3.0106,0.2712,0.2600,0.2734,0.4477,0.5923,0.5425,0.8982,0.8633,0.8790 48 | 46,20792,150,0,0.11,768,3.0234,3.0545,3.0107,0.2710,0.2600,0.2734,0.4207,0.5674,0.5209,0.9066,0.8673,0.8841 49 | 47,25369,38,2,0.41000000000000003,768,3.0271,3.0544,3.0102,0.2706,0.2600,0.2734,0.5335,0.6466,0.5998,0.8860,0.8624,0.8749 50 | 48,30954,248,2,0.23,768,3.0310,3.0543,3.0100,0.2698,0.2600,0.2734,0.1550,0.4113,0.3847,0.9683,0.9056,0.9155 51 | 49,37769,670,1,0.29,768,3.0263,3.0542,3.0096,0.2703,0.2600,0.2734,0.1051,0.3617,0.3367,0.9826,0.9170,0.9249 52 | -------------------------------------------------------------------------------- /checkpoints/dep_label/basque/mlp/bert/all_results.tsv: -------------------------------------------------------------------------------- 1 | run,ndata,hidden_size,nlayers,dropout,embedding_size,train_loss,dev_loss,test_loss,train_acc,dev_acc,test_acc,base_train_loss,base_dev_loss,base_test_loss,base_train_acc,base_dev_acc,base_test_acc 2 | 0,1,776,0,0.09,768,4.2977,4.8486,4.8464,1.0000,0.0583,0.0620,0.0000,6.6474,6.6474,1.0000,0.0583,0.0620 3 | 1,2,433,2,0.27,768,4.3219,4.7686,4.7695,0.5000,0.0583,0.0620,0.0000,15.3762,15.4294,1.0000,0.1545,0.1535 4 | 2,3,310,1,0.38,768,4.3458,4.7663,4.7639,0.3333,0.0446,0.0503,0.0002,7.9545,7.9319,1.0000,0.1567,0.1583 5 | 3,4,300,1,0.42,768,4.3692,4.7506,4.7481,0.2500,0.0446,0.0503,0.0007,7.1934,7.1659,1.0000,0.1645,0.1673 6 | 4,5,1009,2,0.39,768,4.3923,4.7222,4.7209,0.2000,0.0446,0.0503,0.0000,7.8137,7.8342,1.0000,0.2253,0.2275 7 | 5,6,657,2,0.41000000000000003,768,4.4150,4.7348,4.7336,0.1667,0.0446,0.0503,0.0001,6.9416,6.9483,1.0000,0.1673,0.1689 8 | 6,8,455,2,0.23,768,4.2519,4.6811,4.6806,0.2500,0.0583,0.0620,0.0003,10.1039,10.0685,1.0000,0.1235,0.1273 9 | 7,9,103,2,0.09,768,4.1432,4.6454,4.6466,0.3333,0.1781,0.1729,0.0041,7.1342,7.1422,1.0000,0.2258,0.2252 10 | 8,12,203,2,0.14,768,4.1418,4.6337,4.6370,0.3333,0.1781,0.1729,0.0005,7.7397,7.7841,1.0000,0.2170,0.2137 11 | 9,14,150,1,0.31,768,4.1214,4.6303,4.6342,0.2857,0.1781,0.1729,0.0010,6.4577,6.4939,1.0000,0.2536,0.2509 12 | 10,18,72,0,0.15,768,4.1796,4.6243,4.6241,0.2222,0.1781,0.1729,0.0094,6.0592,6.0573,1.0000,0.2542,0.2546 13 | 11,22,467,2,0.27,768,4.1782,4.6000,4.5996,0.1818,0.1781,0.1729,0.0002,6.4585,6.4731,1.0000,0.3035,0.3025 14 | 12,27,377,2,0.5,768,4.2565,4.6206,4.6202,0.1481,0.1781,0.1729,0.0001,5.5243,5.5378,1.0000,0.3133,0.3122 15 | 13,33,436,0,0.44,768,4.1645,4.5883,4.5889,0.1515,0.1781,0.1729,0.0168,5.1313,5.1358,1.0000,0.3089,0.3092 16 | 14,41,382,0,0.1,768,4.2582,4.4588,4.4636,0.1463,0.1781,0.1729,0.0047,3.7746,3.8039,1.0000,0.3869,0.3863 17 | 15,51,326,2,0.5,768,4.1912,4.4159,4.4210,0.1569,0.1781,0.1729,0.0128,3.5042,3.5440,1.0000,0.4003,0.3982 18 | 16,63,326,2,0.49,768,4.0539,4.3584,4.3662,0.2063,0.1781,0.1729,0.0083,3.5863,3.6078,1.0000,0.4418,0.4349 19 | 17,77,247,2,0.44,768,4.0918,4.2848,4.2927,0.1818,0.1781,0.1729,0.0099,3.4233,3.4770,1.0000,0.4642,0.4570 20 | 18,96,179,2,0.05,768,4.0469,4.2124,4.2215,0.1771,0.1781,0.1729,0.0026,3.6600,3.7123,1.0000,0.4859,0.4802 21 | 19,118,137,2,0.02,768,4.0593,4.2202,4.2295,0.1780,0.1781,0.1729,0.0051,3.5556,3.5871,1.0000,0.4805,0.4760 22 | 20,146,786,2,0.23,768,3.9710,4.1624,4.1727,0.1918,0.1781,0.1729,0.0003,4.4471,4.5437,1.0000,0.5140,0.5076 23 | 21,180,410,1,0.31,768,3.9271,4.1401,4.1505,0.2000,0.1781,0.1729,0.0051,3.1909,3.2387,1.0000,0.5253,0.5196 24 | 22,223,388,1,0.37,768,3.9568,4.1036,4.1137,0.1883,0.1781,0.1729,0.0071,2.7090,2.7484,1.0000,0.5616,0.5549 25 | 23,276,38,2,0.25,768,3.9372,4.0926,4.1043,0.1957,0.1781,0.1729,0.1936,2.6642,2.6770,0.9855,0.5324,0.5310 26 | 24,341,467,1,0.41000000000000003,768,3.9166,4.0885,4.0990,0.1906,0.1781,0.1729,0.0079,2.5944,2.6207,1.0000,0.5899,0.5854 27 | 25,421,176,0,0.09,768,3.9630,4.0733,4.0849,0.1829,0.1781,0.1729,0.1800,2.3156,2.3444,1.0000,0.5919,0.5872 28 | 26,520,57,2,0.02,768,3.9760,4.0688,4.0818,0.1769,0.1781,0.1729,0.1557,2.3671,2.4001,1.0000,0.5924,0.5903 29 | 27,642,968,2,0.15,768,3.9504,4.0634,4.0783,0.1869,0.1781,0.1729,0.0006,3.5331,3.5945,1.0000,0.6331,0.6330 30 | 28,794,268,0,0.01,768,3.9443,4.0604,4.0750,0.1864,0.1781,0.1729,0.2692,2.0558,2.0933,0.9987,0.6282,0.6249 31 | 29,981,270,0,0.3,768,3.9348,4.0653,4.0794,0.1865,0.1781,0.1729,0.3759,1.9524,1.9834,0.9898,0.6468,0.6466 32 | 30,1212,596,2,0.24,768,3.9375,4.0690,4.0847,0.1873,0.1781,0.1729,0.0070,2.3993,2.4392,1.0000,0.6874,0.6843 33 | 31,1498,694,0,0.49,768,3.9606,4.0735,4.0909,0.1883,0.1781,0.1729,0.2409,1.7533,1.7934,0.9947,0.6907,0.6902 34 | 32,1851,837,0,0.48,768,3.9618,4.0754,4.0932,0.1880,0.1781,0.1729,0.2706,1.6393,1.6759,0.9881,0.7100,0.7088 35 | 33,2287,487,2,0.4,768,3.9453,4.0810,4.0992,0.1928,0.1781,0.1729,0.3922,1.5694,1.6057,0.9314,0.7224,0.7196 36 | 34,2826,418,0,0.11,768,3.9428,4.0884,4.1073,0.1921,0.1781,0.1729,0.3479,1.5421,1.5779,0.9706,0.7274,0.7249 37 | 35,3492,407,0,0.05,768,3.9432,4.0932,4.1125,0.1913,0.1781,0.1729,0.3676,1.4869,1.5213,0.9651,0.7394,0.7383 38 | 36,4315,657,0,0.0,768,3.9368,4.1014,4.1211,0.1910,0.1781,0.1729,0.3846,1.4259,1.4548,0.9571,0.7537,0.7519 39 | 37,5332,442,1,0.22,768,3.9415,4.1022,4.1225,0.1885,0.1781,0.1729,0.3494,1.3579,1.3962,0.9411,0.7671,0.7670 40 | 38,6589,398,1,0.37,768,3.9428,4.1075,4.1284,0.1867,0.1781,0.1729,0.5260,1.2996,1.3338,0.9032,0.7703,0.7689 41 | 39,8142,369,2,0.04,768,3.9394,4.1132,4.1345,0.1868,0.1781,0.1729,0.4209,1.4112,1.4573,0.9183,0.7766,0.7717 42 | 40,10061,75,2,0.43,768,3.9273,4.1195,4.1417,0.1912,0.1781,0.1729,0.6534,1.2820,1.3189,0.8784,0.7754,0.7717 43 | 41,12432,179,1,0.18,768,3.9322,4.1239,4.1468,0.1911,0.1781,0.1729,0.5129,1.2266,1.2595,0.8989,0.7943,0.7929 44 | 42,15362,442,0,0.09,768,3.9288,4.1296,4.1531,0.1911,0.1781,0.1729,0.4185,1.2027,1.2252,0.9265,0.8030,0.8012 45 | 43,18983,111,0,0.14,768,3.9284,4.1353,4.1596,0.1918,0.1781,0.1729,0.5257,1.1929,1.2142,0.9037,0.8037,0.8040 46 | 44,23457,242,0,0.37,768,3.9277,4.1417,4.1665,0.1914,0.1781,0.1729,0.5154,1.1692,1.1941,0.8977,0.8111,0.8113 47 | 45,28985,107,0,0.35000000000000003,768,3.9274,4.1479,4.1734,0.1907,0.1781,0.1729,0.5459,1.1463,1.1736,0.8915,0.8159,0.8144 48 | 46,35816,150,0,0.11,768,3.9489,4.0577,4.0741,0.1892,0.1781,0.1729,0.4856,0.8117,0.8109,0.9035,0.8385,0.8380 49 | 47,44258,38,2,0.41000000000000003,768,3.9870,4.0258,4.0387,0.1841,0.1781,0.1729,1.1747,1.3186,1.3160,0.7691,0.7393,0.7421 50 | 48,54689,248,2,0.23,768,4.0154,4.0178,4.0295,0.1796,0.1781,0.1729,0.3574,0.6234,0.6203,0.9243,0.8669,0.8684 51 | 49,67578,670,1,0.29,768,4.0297,4.0163,4.0275,0.1766,0.1781,0.1729,0.2791,0.5752,0.5755,0.9458,0.8761,0.8781 52 | -------------------------------------------------------------------------------- /checkpoints/dep_label/basque/mlp/fast/all_results.tsv: -------------------------------------------------------------------------------- 1 | run,ndata,hidden_size,nlayers,dropout,embedding_size,train_loss,dev_loss,test_loss,train_acc,dev_acc,test_acc,base_train_loss,base_dev_loss,base_test_loss,base_train_acc,base_dev_acc,base_test_acc 2 | 0,1,776,0,0.09,300,4.2977,4.8486,4.8464,1.0000,0.0583,0.0620,0.7059,4.8577,4.8545,1.0000,0.0582,0.0619 3 | 1,2,433,2,0.27,300,4.3219,4.7686,4.7695,0.5000,0.0583,0.0620,0.0000,5.3553,5.3675,1.0000,0.2233,0.2222 4 | 2,3,310,1,0.38,300,4.3458,4.7663,4.7639,0.3333,0.0446,0.0503,0.0220,4.2433,4.2452,1.0000,0.2276,0.2264 5 | 3,4,300,1,0.42,300,4.3692,4.7506,4.7481,0.2500,0.0446,0.0503,0.0303,4.2156,4.2170,1.0000,0.2376,0.2378 6 | 4,5,1009,2,0.39,300,4.3923,4.7222,4.7209,0.2000,0.0446,0.0503,0.0003,6.6838,6.6707,1.0000,0.2389,0.2374 7 | 5,6,657,2,0.41000000000000003,300,4.4150,4.7348,4.7336,0.1667,0.0446,0.0503,0.0012,6.1042,6.0985,1.0000,0.2308,0.2291 8 | 6,8,455,2,0.23,300,4.2519,4.6811,4.6806,0.2500,0.0583,0.0620,0.0014,7.3808,7.3384,1.0000,0.2278,0.2271 9 | 7,9,103,2,0.09,300,4.1432,4.6454,4.6466,0.3333,0.1781,0.1729,0.0320,5.3340,5.3227,1.0000,0.2255,0.2256 10 | 8,12,203,2,0.14,300,4.1418,4.6337,4.6370,0.3333,0.1781,0.1729,0.0091,6.7232,6.7118,1.0000,0.2323,0.2317 11 | 9,14,150,1,0.31,300,4.1214,4.6303,4.6342,0.2857,0.1781,0.1729,0.2472,4.8342,4.8399,1.0000,0.2683,0.2652 12 | 10,18,72,0,0.15,300,4.1796,4.6243,4.6241,0.2222,0.1781,0.1729,1.3304,4.0857,4.0925,1.0000,0.2961,0.2976 13 | 11,22,467,2,0.27,300,4.1782,4.6000,4.5996,0.1818,0.1781,0.1729,0.0141,7.7711,7.7190,1.0000,0.3151,0.3144 14 | 12,27,377,2,0.5,300,4.2565,4.6206,4.6202,0.1481,0.1781,0.1729,0.3145,6.5683,6.5258,1.0000,0.3124,0.3130 15 | 13,33,436,0,0.44,300,4.1645,4.5883,4.5889,0.1515,0.1781,0.1729,1.4190,3.9999,4.0048,1.0000,0.3114,0.3132 16 | 14,41,382,0,0.1,300,4.2582,4.4588,4.4636,0.1463,0.1781,0.1729,0.8442,3.6549,3.6663,1.0000,0.3643,0.3643 17 | 15,51,326,2,0.5,300,4.1912,4.4159,4.4210,0.1569,0.1781,0.1729,0.7702,4.0527,4.0586,0.8824,0.3505,0.3519 18 | 16,63,326,2,0.49,300,4.0539,4.3584,4.3662,0.2063,0.1781,0.1729,0.7605,4.2770,4.2804,0.8889,0.3618,0.3614 19 | 17,77,247,2,0.44,300,4.0918,4.2848,4.2927,0.1818,0.1781,0.1729,0.9501,3.8835,3.8820,0.8961,0.3876,0.3817 20 | 18,96,179,2,0.05,300,4.0469,4.2124,4.2215,0.1771,0.1781,0.1729,0.3821,3.8791,3.8811,0.9583,0.4284,0.4215 21 | 19,118,137,2,0.02,300,4.0593,4.2202,4.2295,0.1780,0.1781,0.1729,0.6613,3.3937,3.4107,0.9237,0.4244,0.4222 22 | 20,146,786,2,0.23,300,3.9710,4.1624,4.1727,0.1918,0.1781,0.1729,0.0239,4.2363,4.2452,1.0000,0.4726,0.4680 23 | 21,180,410,1,0.31,300,3.9271,4.1401,4.1505,0.2000,0.1781,0.1729,0.7060,2.9826,2.9844,0.9333,0.4810,0.4785 24 | 22,223,388,1,0.37,300,3.9568,4.1036,4.1137,0.1883,0.1781,0.1729,0.1331,2.7204,2.7181,1.0000,0.5126,0.5135 25 | 23,276,38,2,0.25,300,3.9372,4.0926,4.1043,0.1957,0.1781,0.1729,1.1351,2.9270,2.9522,0.8551,0.4547,0.4515 26 | 24,341,467,1,0.41000000000000003,300,3.9166,4.0885,4.0990,0.1906,0.1781,0.1729,0.1711,2.6237,2.6344,0.9941,0.5381,0.5353 27 | 25,421,176,0,0.09,300,3.9630,4.0733,4.0849,0.1829,0.1781,0.1729,0.5650,2.4772,2.4942,0.9929,0.5375,0.5352 28 | 26,520,57,2,0.02,300,3.9760,4.0688,4.0818,0.1769,0.1781,0.1729,0.9989,2.5906,2.6127,0.8577,0.5223,0.5225 29 | 27,642,968,2,0.15,300,3.9504,4.0634,4.0783,0.1869,0.1781,0.1729,0.0820,3.0689,3.1174,1.0000,0.5937,0.5893 30 | 28,794,268,0,0.01,300,3.9443,4.0604,4.0750,0.1864,0.1781,0.1729,0.6564,2.2759,2.2983,0.9824,0.5774,0.5735 31 | 29,981,270,0,0.3,300,3.9348,4.0653,4.0794,0.1865,0.1781,0.1729,0.5631,2.1310,2.1584,0.9725,0.6054,0.6014 32 | 30,1212,596,2,0.24,300,3.9375,4.0690,4.0847,0.1873,0.1781,0.1729,0.7449,2.2033,2.2606,0.8721,0.6134,0.6064 33 | 31,1498,694,0,0.49,300,3.9606,4.0735,4.0909,0.1883,0.1781,0.1729,0.6331,1.9983,2.0318,0.9439,0.6355,0.6294 34 | 32,1851,837,0,0.48,300,3.9618,4.0754,4.0932,0.1880,0.1781,0.1729,0.6202,1.9242,1.9523,0.9373,0.6522,0.6490 35 | 33,2287,487,2,0.4,300,3.9453,4.0810,4.0992,0.1928,0.1781,0.1729,0.6242,1.8699,1.9133,0.8806,0.6715,0.6730 36 | 34,2826,418,0,0.11,300,3.9428,4.0884,4.1073,0.1921,0.1781,0.1729,0.6408,1.8159,1.8421,0.9264,0.6714,0.6687 37 | 35,3492,407,0,0.05,300,3.9432,4.0932,4.1125,0.1913,0.1781,0.1729,0.6338,1.7736,1.7982,0.9207,0.6775,0.6769 38 | 36,4315,657,0,0.0,300,3.9368,4.1014,4.1211,0.1910,0.1781,0.1729,0.7622,1.7050,1.7334,0.8848,0.6980,0.6961 39 | 37,5332,442,1,0.22,300,3.9415,4.1022,4.1225,0.1885,0.1781,0.1729,0.6794,1.6126,1.6560,0.8768,0.7127,0.7110 40 | 38,6589,398,1,0.37,300,3.9428,4.1075,4.1284,0.1867,0.1781,0.1729,0.5581,1.5019,1.5486,0.8951,0.7371,0.7316 41 | 39,8142,369,2,0.04,300,3.9394,4.1132,4.1345,0.1868,0.1781,0.1729,0.8187,1.7537,1.8154,0.8277,0.7086,0.7075 42 | 40,10061,75,2,0.43,300,3.9273,4.1195,4.1417,0.1912,0.1781,0.1729,0.8669,1.5382,1.5802,0.8157,0.7232,0.7217 43 | 41,12432,179,1,0.18,300,3.9322,4.1239,4.1468,0.1911,0.1781,0.1729,0.7115,1.4584,1.4962,0.8576,0.7480,0.7475 44 | 42,15362,442,0,0.09,300,3.9288,4.1296,4.1531,0.1911,0.1781,0.1729,0.8813,1.5208,1.5461,0.8341,0.7384,0.7360 45 | 43,18983,111,0,0.14,300,3.9284,4.1353,4.1596,0.1918,0.1781,0.1729,0.9236,1.5034,1.5332,0.8255,0.7419,0.7389 46 | 44,23457,242,0,0.37,300,3.9277,4.1417,4.1665,0.1914,0.1781,0.1729,0.9791,1.5046,1.5389,0.8129,0.7434,0.7426 47 | 45,28985,107,0,0.35000000000000003,300,3.9274,4.1479,4.1734,0.1907,0.1781,0.1729,1.0097,1.5015,1.5370,0.8064,0.7448,0.7433 48 | 46,35816,150,0,0.11,300,3.9489,4.0577,4.0741,0.1892,0.1781,0.1729,0.8521,1.1700,1.1837,0.8264,0.7714,0.7703 49 | 47,44258,38,2,0.41000000000000003,300,3.9870,4.0258,4.0387,0.1841,0.1781,0.1729,1.3664,1.4835,1.5078,0.7151,0.6908,0.6848 50 | 48,54689,248,2,0.23,300,4.0154,4.0178,4.0295,0.1796,0.1781,0.1729,0.3955,0.7872,0.7874,0.9157,0.8352,0.8315 51 | 49,67578,670,1,0.29,300,4.0297,4.0163,4.0275,0.1766,0.1781,0.1729,0.4430,0.7658,0.7691,0.9087,0.8376,0.8369 52 | -------------------------------------------------------------------------------- /checkpoints/pos_tag/basque/mlp/bert/all_results.tsv: -------------------------------------------------------------------------------- 1 | run,ndata,hidden_size,nlayers,dropout,embedding_size,train_loss,dev_loss,test_loss,train_acc,dev_acc,test_acc,base_train_loss,base_dev_loss,base_test_loss,base_train_acc,base_dev_acc,base_test_acc 2 | 0,1,776,0,0.09,768,3.3692,3.9303,3.9278,1.0000,0.0409,0.0451,0.0009,4.9670,4.9517,1.0000,0.0409,0.0451 3 | 1,2,433,2,0.27,768,3.4150,3.8795,3.8798,0.5000,0.0409,0.0451,0.0000,11.4890,11.5368,1.0000,0.1872,0.1862 4 | 2,3,310,1,0.38,768,3.4594,3.8335,3.8316,0.3333,0.0409,0.0451,0.0004,6.0494,6.0415,1.0000,0.3036,0.3045 5 | 3,4,300,1,0.42,768,3.5025,3.7332,3.7291,0.2500,0.0409,0.0451,0.0010,5.1381,5.1069,1.0000,0.3012,0.3047 6 | 4,5,1009,2,0.39,768,3.3783,3.6733,3.6676,0.4000,0.2451,0.2489,0.0000,8.9695,8.8666,1.0000,0.3129,0.3159 7 | 5,6,657,2,0.41000000000000003,768,3.4466,3.6918,3.6863,0.3333,0.2451,0.2489,0.0001,5.8011,5.7443,1.0000,0.2886,0.2873 8 | 6,8,455,2,0.23,768,3.3517,3.6372,3.6321,0.2500,0.2451,0.2489,0.0000,6.4054,6.3117,1.0000,0.3444,0.3485 9 | 7,9,103,2,0.09,768,3.4237,3.6453,3.6397,0.2222,0.2451,0.2489,0.0030,4.5464,4.5022,1.0000,0.3185,0.3202 10 | 8,12,203,2,0.14,768,3.2206,3.5767,3.5726,0.3333,0.1651,0.1604,0.0005,5.0640,4.9911,1.0000,0.3816,0.3899 11 | 9,14,150,1,0.31,768,3.2437,3.5497,3.5454,0.2857,0.2451,0.2489,0.0033,3.8648,3.8393,1.0000,0.3745,0.3760 12 | 10,18,72,0,0.15,768,3.2638,3.5673,3.5625,0.2222,0.2451,0.2489,0.0512,3.5915,3.5677,1.0000,0.3923,0.3908 13 | 11,22,467,2,0.27,768,3.1917,3.5581,3.5515,0.2273,0.2451,0.2489,0.0001,6.1774,6.0936,1.0000,0.4104,0.4099 14 | 12,27,377,2,0.5,768,3.2319,3.5421,3.5360,0.2222,0.2451,0.2489,0.0008,3.7427,3.7201,1.0000,0.4210,0.4220 15 | 13,34,436,0,0.44,768,3.0976,3.4859,3.4779,0.2647,0.2451,0.2489,0.1004,2.9906,2.9643,1.0000,0.4857,0.4909 16 | 14,42,382,0,0.1,768,3.2607,3.3948,3.3933,0.2143,0.2451,0.2489,0.0280,2.3680,2.3692,1.0000,0.5139,0.5131 17 | 15,52,326,2,0.5,768,3.2362,3.3868,3.3851,0.2308,0.2451,0.2489,0.0067,2.4669,2.4591,1.0000,0.5417,0.5403 18 | 16,64,326,2,0.49,768,3.2741,3.3554,3.3601,0.1875,0.2451,0.2489,0.0086,2.1973,2.2166,1.0000,0.5844,0.5830 19 | 17,79,247,2,0.44,768,3.2591,3.3376,3.3460,0.1899,0.2451,0.2489,0.0131,1.9947,2.0046,1.0000,0.6276,0.6265 20 | 18,98,179,2,0.05,768,3.2468,3.2947,3.3043,0.1939,0.2451,0.2489,0.0025,2.0748,2.1043,1.0000,0.6529,0.6495 21 | 19,121,137,2,0.02,768,3.2462,3.2941,3.3042,0.1983,0.2451,0.2489,0.0038,2.1093,2.1125,1.0000,0.6537,0.6543 22 | 20,150,786,2,0.23,768,3.1871,3.2677,3.2773,0.2133,0.2451,0.2489,0.0002,2.6296,2.6625,1.0000,0.6864,0.6847 23 | 21,186,410,1,0.31,768,3.1069,3.2439,3.2515,0.2312,0.2451,0.2489,0.0059,1.7655,1.8054,1.0000,0.6974,0.6948 24 | 22,230,388,1,0.37,768,3.1328,3.2405,3.2480,0.2261,0.2451,0.2489,0.0092,1.5754,1.6119,1.0000,0.7299,0.7242 25 | 23,285,38,2,0.25,768,3.0951,3.2300,3.2395,0.2351,0.2451,0.2489,0.5995,1.5385,1.5500,0.9018,0.6902,0.6875 26 | 24,352,467,1,0.41000000000000003,768,3.1220,3.2206,3.2290,0.2216,0.2451,0.2489,0.0176,1.4552,1.4865,1.0000,0.7565,0.7519 27 | 25,436,176,0,0.09,768,3.1137,3.2085,3.2147,0.2362,0.2451,0.2489,0.1940,1.2745,1.2917,0.9954,0.7517,0.7460 28 | 26,540,57,2,0.02,768,3.1072,3.2159,3.2225,0.2389,0.2451,0.2489,0.2134,1.2945,1.3123,0.9870,0.7521,0.7483 29 | 27,668,968,2,0.15,768,3.1027,3.2096,3.2163,0.2440,0.2451,0.2489,0.0008,1.9589,2.0018,1.0000,0.7782,0.7761 30 | 28,827,268,0,0.01,768,3.0901,3.2093,3.2148,0.2443,0.2451,0.2489,0.2602,1.1430,1.1644,0.9831,0.7804,0.7760 31 | 29,1024,270,0,0.3,768,3.0957,3.2097,3.2136,0.2461,0.2451,0.2489,0.3411,1.0856,1.0978,0.9600,0.7943,0.7940 32 | 30,1267,596,2,0.24,768,3.0778,3.2124,3.2168,0.2478,0.2451,0.2489,0.0114,1.3424,1.3718,1.0000,0.8126,0.8113 33 | 31,1568,694,0,0.49,768,3.0801,3.2165,3.2222,0.2487,0.2451,0.2489,0.3377,0.9835,1.0044,0.9477,0.8171,0.8152 34 | 32,1941,837,0,0.48,768,3.0839,3.2188,3.2254,0.2442,0.2451,0.2489,0.3626,0.9628,0.9803,0.9387,0.8266,0.8251 35 | 33,2402,487,2,0.4,768,3.0813,3.2206,3.2281,0.2452,0.2451,0.2489,0.3034,0.9161,0.9438,0.9342,0.8355,0.8332 36 | 34,2973,418,0,0.11,768,3.0869,3.2254,3.2324,0.2395,0.2451,0.2489,0.3768,0.8976,0.9150,0.9398,0.8354,0.8338 37 | 35,3680,407,0,0.05,768,3.1026,3.2298,3.2368,0.2408,0.2451,0.2489,0.3794,0.8798,0.8959,0.9375,0.8380,0.8387 38 | 36,4555,657,0,0.0,768,3.1025,3.2354,3.2424,0.2397,0.2451,0.2489,0.4391,0.8702,0.8888,0.9212,0.8413,0.8411 39 | 37,5638,442,1,0.22,768,3.0921,3.2388,3.2463,0.2458,0.2451,0.2489,0.3135,0.8421,0.8690,0.9335,0.8540,0.8542 40 | 38,6978,398,1,0.37,768,3.0913,3.2435,3.2515,0.2475,0.2451,0.2489,0.2706,0.7672,0.7953,0.9420,0.8645,0.8633 41 | 39,8637,369,2,0.04,768,3.0890,3.2484,3.2569,0.2473,0.2451,0.2489,0.3789,0.8822,0.9078,0.9162,0.8539,0.8545 42 | 40,10690,75,2,0.43,768,3.0893,3.2537,3.2630,0.2445,0.2451,0.2489,0.3720,0.7417,0.7774,0.9239,0.8661,0.8651 43 | 41,13231,179,1,0.18,768,3.0944,3.2572,3.2671,0.2431,0.2451,0.2489,0.3621,0.7493,0.7719,0.9222,0.8714,0.8724 44 | 42,16376,442,0,0.09,768,3.0925,3.2616,3.2720,0.2432,0.2451,0.2489,0.4077,0.7519,0.7798,0.9153,0.8704,0.8682 45 | 43,20269,111,0,0.14,768,3.0921,3.2662,3.2770,0.2421,0.2451,0.2489,0.4342,0.7405,0.7678,0.9087,0.8703,0.8697 46 | 44,25087,242,0,0.37,768,3.0955,3.2700,3.2813,0.2411,0.2451,0.2489,0.4793,0.7601,0.7857,0.8961,0.8657,0.8671 47 | 45,31050,107,0,0.35000000000000003,768,3.0977,3.2744,3.2864,0.2409,0.2451,0.2489,0.4800,0.7647,0.7902,0.8956,0.8671,0.8685 48 | 46,38431,150,0,0.11,768,3.1135,3.2046,3.2097,0.2414,0.2451,0.2489,0.3967,0.5166,0.5298,0.9119,0.8861,0.8860 49 | 47,47567,38,2,0.41000000000000003,768,3.1426,3.1782,3.1808,0.2416,0.2451,0.2489,0.5675,0.6414,0.6516,0.8801,0.8616,0.8611 50 | 48,58874,248,2,0.23,768,3.1617,3.1725,3.1745,0.2422,0.2451,0.2489,0.1184,0.3233,0.3510,0.9753,0.9279,0.9238 51 | 49,72869,670,1,0.29,768,3.1687,3.1713,3.1730,0.2446,0.2451,0.2489,0.1469,0.3038,0.3218,0.9699,0.9313,0.9283 52 | -------------------------------------------------------------------------------- /checkpoints/pos_tag/english/mlp/random/all_results.tsv: -------------------------------------------------------------------------------- 1 | run,ndata,hidden_size,nlayers,dropout,embedding_size,train_loss,dev_loss,test_loss,train_acc,dev_acc,test_acc,base_train_loss,base_dev_loss,base_test_loss,base_train_acc,base_dev_acc,base_test_acc 2 | 0,1,776,0,0.09,272,3.4594,4.0004,3.9958,1.0000,0.0752,0.0831,0.0037,4.1897,4.1819,1.0000,0.0906,0.0911 3 | 1,2,433,2,0.27,145,3.5025,3.9713,3.9660,0.5000,0.0752,0.0831,0.0000,6.2689,6.2139,1.0000,0.1053,0.1078 4 | 2,3,310,1,0.38,182,3.2676,3.9820,3.9733,0.6667,0.0752,0.0831,0.0005,4.1900,4.1594,1.0000,0.1186,0.1285 5 | 3,4,300,1,0.42,205,3.1699,3.9714,3.9623,0.5000,0.0752,0.0831,0.0010,4.4176,4.3998,1.0000,0.0912,0.0927 6 | 4,5,1009,2,0.39,134,3.2925,3.9692,3.9623,0.4000,0.0752,0.0831,0.0000,12.6959,12.6009,1.0000,0.1101,0.1117 7 | 5,6,657,2,0.41000000000000003,245,3.3863,3.9095,3.9040,0.3333,0.0752,0.0831,0.0000,6.7744,6.7429,1.0000,0.1413,0.1382 8 | 6,8,455,2,0.23,133,3.3568,3.8946,3.8892,0.3750,0.0752,0.0831,0.0001,6.6231,6.6138,1.0000,0.0752,0.0817 9 | 7,10,103,2,0.09,108,3.1132,3.9285,3.9193,0.5000,0.0752,0.0831,0.0033,4.5903,4.5734,1.0000,0.0968,0.1016 10 | 8,13,203,2,0.14,93,2.9461,3.9414,3.9300,0.4615,0.0752,0.0831,0.0012,6.1506,6.1420,1.0000,0.1101,0.1176 11 | 9,16,150,1,0.31,212,3.1675,3.8732,3.8626,0.3750,0.0752,0.0831,0.0057,3.8430,3.8262,1.0000,0.1670,0.1739 12 | 10,21,72,0,0.15,103,3.2129,3.8286,3.8192,0.2857,0.0752,0.0831,0.3776,3.8947,3.8911,1.0000,0.1521,0.1569 13 | 11,26,467,2,0.27,113,3.1497,3.8512,3.8402,0.2692,0.0752,0.0831,0.0003,6.7556,6.7483,1.0000,0.1921,0.1976 14 | 12,33,377,2,0.5,99,3.1121,3.8367,3.8278,0.2121,0.0752,0.0831,0.0087,5.3723,5.3506,1.0000,0.2358,0.2375 15 | 13,42,436,0,0.44,148,3.2689,3.7583,3.7548,0.1905,0.1679,0.1654,0.4935,3.5081,3.5075,1.0000,0.2788,0.2757 16 | 14,53,382,0,0.1,165,3.3205,3.7439,3.7433,0.1887,0.1679,0.1654,0.3284,3.5899,3.5987,1.0000,0.2817,0.2792 17 | 15,68,326,2,0.5,60,3.4352,3.7196,3.7162,0.1618,0.1679,0.1654,0.3306,3.6167,3.6254,1.0000,0.3080,0.3102 18 | 16,85,326,2,0.49,84,3.3949,3.7616,3.7535,0.1529,0.0809,0.0809,0.1283,3.8286,3.8423,0.9882,0.3205,0.3187 19 | 17,108,247,2,0.44,238,3.4337,3.7425,3.7370,0.1667,0.0752,0.0831,0.0519,3.6638,3.6526,0.9815,0.3451,0.3409 20 | 18,137,179,2,0.05,122,3.4350,3.7201,3.7186,0.1460,0.1679,0.1654,0.0369,4.1001,4.0672,0.9854,0.3484,0.3489 21 | 19,173,137,2,0.02,144,3.4630,3.7018,3.7041,0.1618,0.1679,0.1654,0.0636,3.7151,3.6900,0.9827,0.3822,0.3837 22 | 20,219,786,2,0.23,79,3.4974,3.6720,3.6779,0.1553,0.1679,0.1654,0.0485,4.4189,4.3881,0.9863,0.4261,0.4264 23 | 21,278,410,1,0.31,159,3.4853,3.6824,3.6886,0.1655,0.1679,0.1654,0.0712,3.0911,3.1120,0.9892,0.4456,0.4429 24 | 22,351,388,1,0.37,154,3.4800,3.6781,3.6854,0.1681,0.1679,0.1654,0.1187,2.8963,2.8857,0.9886,0.4613,0.4646 25 | 23,445,38,2,0.25,148,3.4471,3.6918,3.6956,0.1685,0.1679,0.1654,0.7853,2.8258,2.8082,0.8674,0.4310,0.4355 26 | 24,563,467,1,0.41000000000000003,115,3.4483,3.6969,3.6994,0.1741,0.1679,0.1654,0.4046,2.7225,2.7117,0.9805,0.4804,0.4834 27 | 25,712,176,0,0.09,124,3.4820,3.6840,3.6888,0.1671,0.1679,0.1654,1.9202,3.1630,3.1433,0.6868,0.3916,0.3914 28 | 26,901,57,2,0.02,240,3.5111,3.6661,3.6733,0.1620,0.1679,0.1654,0.6882,2.4358,2.4718,0.9068,0.5097,0.5032 29 | 27,1141,968,2,0.15,50,3.5529,3.6330,3.6470,0.1639,0.1679,0.1654,0.1178,2.8009,2.8471,0.9711,0.5651,0.5580 30 | 28,1444,268,0,0.01,235,3.5507,3.6144,3.6332,0.1641,0.1679,0.1654,1.5528,2.7696,2.7684,0.7465,0.4799,0.4756 31 | 29,1827,270,0,0.3,144,3.5559,3.6201,3.6392,0.1565,0.1679,0.1654,1.1372,2.2752,2.3286,0.7564,0.5251,0.5173 32 | 30,2313,596,2,0.24,188,3.5344,3.6268,3.6454,0.1634,0.1679,0.1654,0.3797,1.8830,1.9410,0.9395,0.6223,0.6147 33 | 31,2927,694,0,0.49,59,3.5385,3.6268,3.6452,0.1609,0.1679,0.1654,2.1766,2.5230,2.5422,0.5196,0.4420,0.4411 34 | 32,3705,837,0,0.48,176,3.5485,3.6346,3.6531,0.1563,0.1679,0.1654,1.4420,2.0000,2.0567,0.6926,0.5577,0.5528 35 | 33,4689,487,2,0.4,75,3.5555,3.6332,3.6517,0.1536,0.1679,0.1654,0.5450,1.5031,1.5296,0.9147,0.6672,0.6618 36 | 34,5934,418,0,0.11,210,3.5514,3.6444,3.6628,0.1542,0.1679,0.1654,1.2265,2.0091,2.0512,0.7248,0.5790,0.5709 37 | 35,7510,407,0,0.05,85,3.5438,3.6513,3.6695,0.1575,0.1679,0.1654,1.7267,2.2631,2.3161,0.6119,0.5127,0.5052 38 | 36,9505,657,0,0.0,102,3.5602,3.6439,3.6576,0.1557,0.1679,0.1654,1.6861,2.1792,2.2292,0.6208,0.5313,0.5253 39 | 37,12029,442,1,0.22,225,3.5586,3.6414,3.6561,0.1554,0.1679,0.1654,0.3452,1.3557,1.3895,0.9392,0.7087,0.7031 40 | 38,15225,398,1,0.37,187,3.5516,3.6430,3.6572,0.1563,0.1679,0.1654,0.3236,1.2338,1.2453,0.9437,0.7329,0.7295 41 | 39,19268,369,2,0.04,195,3.5555,3.6402,3.6555,0.1567,0.1679,0.1654,0.3145,1.3550,1.4043,0.9328,0.7291,0.7215 42 | 40,24386,75,2,0.43,230,3.5683,3.6238,3.6403,0.1599,0.1679,0.1654,1.1645,1.4158,1.4416,0.7141,0.6637,0.6605 43 | 41,30863,179,1,0.18,187,3.5639,3.6223,3.6412,0.1650,0.1679,0.1654,0.5511,1.1456,1.1651,0.8834,0.7391,0.7384 44 | 42,39061,442,0,0.09,112,3.5763,3.6195,3.6353,0.1654,0.1679,0.1654,1.7114,1.9669,1.9750,0.6050,0.5506,0.5444 45 | 43,49436,111,0,0.14,188,3.5905,3.6100,3.6240,0.1666,0.1679,0.1654,1.5516,1.7067,1.7393,0.6457,0.6083,0.5988 46 | 44,62566,242,0,0.37,94,3.5959,3.6057,3.6198,0.1695,0.1679,0.1654,2.0639,2.1723,2.1999,0.5439,0.5220,0.5163 47 | 45,79184,107,0,0.35000000000000003,182,3.5932,3.6076,3.6199,0.1738,0.1679,0.1654,1.6730,1.7800,1.7863,0.6231,0.5996,0.6003 48 | 46,100217,150,0,0.11,215,3.5876,3.6064,3.6195,0.1765,0.1679,0.1654,1.4887,1.5974,1.6113,0.6556,0.6382,0.6322 49 | 47,126835,38,2,0.41000000000000003,292,3.5935,3.6051,3.6182,0.1758,0.1679,0.1654,1.4407,1.4651,1.4863,0.6464,0.6445,0.6370 50 | 48,160524,248,2,0.23,98,3.6035,3.6000,3.6167,0.1731,0.1679,0.1654,0.7194,0.8558,0.8630,0.8274,0.7922,0.7913 51 | 49,203160,670,1,0.29,67,3.6068,3.5998,3.6198,0.1707,0.1679,0.1654,0.8306,0.9342,0.9396,0.8131,0.7882,0.7858 52 | -------------------------------------------------------------------------------- /checkpoints/dep_label/english/mlp/random/all_results.tsv: -------------------------------------------------------------------------------- 1 | run,ndata,hidden_size,nlayers,dropout,embedding_size,train_loss,dev_loss,test_loss,train_acc,dev_acc,test_acc,base_train_loss,base_dev_loss,base_test_loss,base_train_acc,base_dev_acc,base_test_acc 2 | 0,1,776,0,0.09,272,5.0150,5.5224,5.5219,1.0000,0.1325,0.1333,0.0002,5.9027,5.8812,1.0000,0.0306,0.0333 3 | 1,2,433,2,0.27,145,5.0297,5.5319,5.5304,0.5000,0.0092,0.0109,0.0000,6.4374,6.4595,1.0000,0.0826,0.0848 4 | 2,3,310,1,0.38,182,4.7677,5.4915,5.4897,0.6667,0.1325,0.1333,0.0002,5.6052,5.6007,1.0000,0.0795,0.0820 5 | 3,4,300,1,0.42,205,4.8514,5.4744,5.4744,0.5000,0.1325,0.1333,0.0002,5.4199,5.4089,1.0000,0.1103,0.1112 6 | 4,5,1009,2,0.39,134,4.9072,5.4385,5.4386,0.4000,0.1325,0.1333,0.0000,11.4619,11.4636,1.0000,0.0780,0.0799 7 | 5,6,657,2,0.41000000000000003,245,4.9491,5.4467,5.4476,0.3333,0.1325,0.1333,0.0000,7.2428,7.2623,1.0000,0.1070,0.1051 8 | 6,8,455,2,0.23,133,4.9080,5.4397,5.4411,0.2500,0.0092,0.0109,0.0000,7.5733,7.5662,1.0000,0.0885,0.0899 9 | 7,10,103,2,0.09,108,4.7008,5.4216,5.4221,0.3000,0.0092,0.0109,0.0021,5.5473,5.5552,1.0000,0.0613,0.0610 10 | 8,13,203,2,0.14,93,4.5678,5.3785,5.3780,0.3077,0.0092,0.0109,0.0010,5.9234,5.9292,1.0000,0.1113,0.1118 11 | 9,16,150,1,0.31,212,4.6705,5.3296,5.3292,0.2500,0.0092,0.0109,0.0023,4.9909,4.9931,1.0000,0.1211,0.1239 12 | 10,21,72,0,0.15,103,4.6939,5.2563,5.2569,0.1905,0.0092,0.0109,0.0993,5.3049,5.2928,1.0000,0.1387,0.1404 13 | 11,26,467,2,0.27,113,4.5853,5.2066,5.2091,0.1923,0.1325,0.1333,0.0004,6.7129,6.7342,1.0000,0.1571,0.1567 14 | 12,33,377,2,0.5,99,4.4091,5.1400,5.1428,0.2121,0.1325,0.1333,0.0059,5.9322,5.9468,1.0000,0.2242,0.2265 15 | 13,42,436,0,0.44,148,4.5329,5.0996,5.1013,0.1667,0.1325,0.1333,0.0442,4.9975,4.9726,1.0000,0.2316,0.2332 16 | 14,53,382,0,0.1,165,4.5302,5.0520,5.0532,0.1887,0.1325,0.1333,0.0914,5.0158,5.0415,1.0000,0.2382,0.2347 17 | 15,67,326,2,0.5,60,4.5732,5.0467,5.0505,0.1642,0.1325,0.1333,0.1787,5.5966,5.6008,1.0000,0.2537,0.2594 18 | 16,84,326,2,0.49,84,4.4723,4.9956,4.9975,0.1548,0.0871,0.0859,0.1195,5.2404,5.2368,1.0000,0.2693,0.2647 19 | 17,106,247,2,0.44,238,4.5024,4.9224,4.9228,0.1415,0.0871,0.0859,0.0072,4.6387,4.6432,1.0000,0.2908,0.2872 20 | 18,134,179,2,0.05,122,4.4543,4.8790,4.8771,0.1418,0.0871,0.0859,0.0069,4.9062,4.9088,1.0000,0.2900,0.2883 21 | 19,170,137,2,0.02,144,4.5188,4.7848,4.7849,0.1235,0.0871,0.0859,0.0121,4.1941,4.2165,1.0000,0.3387,0.3426 22 | 20,215,786,2,0.23,79,4.4632,4.7362,4.7369,0.1302,0.0788,0.0795,0.0010,5.5568,5.6482,1.0000,0.3342,0.3359 23 | 21,271,410,1,0.31,159,4.4510,4.6909,4.6923,0.1218,0.0871,0.0859,0.0127,3.9414,3.9708,1.0000,0.3622,0.3631 24 | 22,343,388,1,0.37,154,4.4696,4.6608,4.6669,0.1254,0.0871,0.0859,0.0223,3.8135,3.8750,1.0000,0.3836,0.3790 25 | 23,433,38,2,0.25,148,4.4121,4.6623,4.6696,0.1316,0.0871,0.0859,0.7596,3.9237,3.9243,0.9215,0.3515,0.3507 26 | 24,548,467,1,0.41000000000000003,115,4.3892,4.6403,4.6492,0.1296,0.0871,0.0859,0.1110,3.8499,3.8623,1.0000,0.3808,0.3786 27 | 25,692,176,0,0.09,124,4.4028,4.6129,4.6255,0.1272,0.0871,0.0859,1.3083,4.4356,4.4179,0.8757,0.3279,0.3246 28 | 26,875,57,2,0.02,240,4.4205,4.5923,4.6057,0.1223,0.0871,0.0859,0.5285,3.8090,3.8480,0.9474,0.3728,0.3753 29 | 27,1106,968,2,0.15,50,4.4684,4.5463,4.5648,0.1103,0.0871,0.0859,0.0153,5.0193,5.1577,0.9973,0.3956,0.3935 30 | 28,1398,268,0,0.01,235,4.4545,4.5212,4.5432,0.1173,0.1325,0.1333,0.7408,4.0637,4.0939,0.9464,0.3907,0.3888 31 | 29,1767,270,0,0.3,144,4.4411,4.5226,4.5464,0.1160,0.1325,0.1333,1.2545,3.8724,3.8827,0.8376,0.4090,0.4103 32 | 30,2234,596,2,0.24,188,4.4170,4.5303,4.5576,0.1164,0.1325,0.1333,0.4798,3.3720,3.4651,0.9315,0.4698,0.4690 33 | 31,2823,694,0,0.49,59,4.4176,4.5238,4.5528,0.1162,0.1325,0.1333,2.3543,3.4921,3.5443,0.5462,0.3826,0.3774 34 | 32,3568,837,0,0.48,176,4.4223,4.5204,4.5465,0.1174,0.1325,0.1333,1.1973,3.1837,3.2035,0.7932,0.4708,0.4707 35 | 33,4510,487,2,0.4,75,4.4171,4.5164,4.5441,0.1211,0.1325,0.1333,1.1203,2.7029,2.7339,0.8450,0.5104,0.5101 36 | 34,5701,418,0,0.11,210,4.4095,4.5255,4.5558,0.1161,0.1325,0.1333,1.7632,3.4943,3.5387,0.7150,0.4624,0.4547 37 | 35,7206,407,0,0.05,85,4.3972,4.5355,4.5675,0.1153,0.0871,0.0859,2.0946,3.5189,3.5897,0.5994,0.4338,0.4279 38 | 36,9108,657,0,0.0,102,4.4016,4.5345,4.5669,0.1177,0.1325,0.1333,2.0144,3.5546,3.5968,0.6136,0.4488,0.4499 39 | 37,11513,442,1,0.22,225,4.4035,4.5351,4.5687,0.1195,0.1325,0.1333,1.3941,2.6960,2.7405,0.7493,0.5149,0.5094 40 | 38,14552,398,1,0.37,187,4.3894,4.5339,4.5700,0.1192,0.1325,0.1333,0.5762,2.4621,2.5199,0.9342,0.5663,0.5570 41 | 39,18393,369,2,0.04,195,4.3986,4.5308,4.5635,0.1189,0.1325,0.1333,1.5172,2.6237,2.6989,0.7087,0.5338,0.5316 42 | 40,23249,75,2,0.43,230,4.3936,4.5189,4.5551,0.1238,0.1325,0.1333,2.0151,2.4128,2.4427,0.6057,0.5389,0.5394 43 | 41,29386,179,1,0.18,187,4.3826,4.5240,4.5640,0.1228,0.1325,0.1333,1.2127,2.3396,2.3887,0.7695,0.5722,0.5715 44 | 42,37143,442,0,0.09,112,4.4086,4.5086,4.5270,0.1234,0.1325,0.1333,2.3466,2.8219,2.8287,0.5501,0.4983,0.4985 45 | 43,46947,111,0,0.14,188,4.4489,4.4949,4.5047,0.1267,0.1325,0.1333,2.0361,2.5451,2.5539,0.5961,0.5354,0.5341 46 | 44,59340,242,0,0.37,94,4.4787,4.4925,4.4998,0.1266,0.1325,0.1333,2.6371,2.8177,2.8459,0.5040,0.4852,0.4818 47 | 45,75005,107,0,0.35000000000000003,182,4.4953,4.4954,4.4981,0.1297,0.1325,0.1333,2.2248,2.4818,2.4923,0.5662,0.5320,0.5293 48 | 46,94804,150,0,0.11,215,4.4842,4.4942,4.4978,0.1305,0.1325,0.1333,2.0865,2.3994,2.4066,0.5902,0.5565,0.5552 49 | 47,119830,38,2,0.41000000000000003,292,4.4817,4.4899,4.4962,0.1295,0.1325,0.1333,2.3856,2.4094,2.4182,0.5262,0.5286,0.5317 50 | 48,151463,248,2,0.23,98,4.4829,4.4829,4.4924,0.1259,0.1325,0.1333,1.5047,1.7331,1.7183,0.6887,0.6490,0.6510 51 | 49,191445,670,1,0.29,67,4.4810,4.4815,4.4941,0.1229,0.1325,0.1333,1.4980,1.7487,1.7432,0.7010,0.6564,0.6595 52 | -------------------------------------------------------------------------------- /checkpoints/dep_label/english/mlp/fast/all_results.tsv: -------------------------------------------------------------------------------- 1 | run,ndata,hidden_size,nlayers,dropout,embedding_size,train_loss,dev_loss,test_loss,train_acc,dev_acc,test_acc,base_train_loss,base_dev_loss,base_test_loss,base_train_acc,base_dev_acc,base_test_acc 2 | 0,1,776,0,0.09,300,5.0150,5.5224,5.5219,1.0000,0.1325,0.1333,0.0005,5.3857,5.3819,1.0000,0.1399,0.1395 3 | 1,2,433,2,0.27,300,5.0297,5.5319,5.5304,0.5000,0.0092,0.0109,0.0000,5.1874,5.1944,1.0000,0.1389,0.1416 4 | 2,3,310,1,0.38,300,4.7677,5.4915,5.4897,0.6667,0.1325,0.1333,0.0006,5.1905,5.1882,1.0000,0.1393,0.1418 5 | 3,4,300,1,0.42,300,4.8514,5.4744,5.4744,0.5000,0.1325,0.1333,0.0111,5.2638,5.2762,1.0000,0.1348,0.1345 6 | 4,5,1009,2,0.39,300,4.9072,5.4385,5.4386,0.4000,0.1325,0.1333,0.0001,7.9053,8.0101,1.0000,0.1938,0.1911 7 | 5,6,657,2,0.41000000000000003,300,4.9491,5.4467,5.4476,0.3333,0.1325,0.1333,0.0012,6.6865,6.7705,1.0000,0.1580,0.1561 8 | 6,8,455,2,0.23,300,4.9080,5.4397,5.4411,0.2500,0.0092,0.0109,0.0019,6.4815,6.6018,1.0000,0.1994,0.1957 9 | 7,10,103,2,0.09,300,4.7008,5.4216,5.4221,0.3000,0.0092,0.0109,0.0318,5.4414,5.5089,1.0000,0.1982,0.1946 10 | 8,13,203,2,0.14,300,4.5678,5.3785,5.3780,0.3077,0.0092,0.0109,0.0121,6.0788,6.1809,1.0000,0.2458,0.2459 11 | 9,16,150,1,0.31,300,4.6705,5.3296,5.3292,0.2500,0.0092,0.0109,0.1595,4.6902,4.7237,1.0000,0.2521,0.2501 12 | 10,21,72,0,0.15,300,4.6939,5.2563,5.2569,0.1905,0.0092,0.0109,0.1582,4.6119,4.6189,1.0000,0.2495,0.2527 13 | 11,26,467,2,0.27,300,4.5853,5.2066,5.2091,0.1923,0.1325,0.1333,0.0109,7.1015,7.2313,1.0000,0.2683,0.2701 14 | 12,33,377,2,0.5,300,4.4091,5.1400,5.1428,0.2121,0.1325,0.1333,0.2023,6.9486,7.0155,0.9697,0.2871,0.2873 15 | 13,42,436,0,0.44,300,4.5329,5.0996,5.1013,0.1667,0.1325,0.1333,0.4485,4.3119,4.3165,1.0000,0.3331,0.3334 16 | 14,53,382,0,0.1,300,4.5302,5.0520,5.0532,0.1887,0.1325,0.1333,0.3782,4.2127,4.2103,1.0000,0.3420,0.3419 17 | 15,67,326,2,0.5,300,4.5732,5.0467,5.0505,0.1642,0.1325,0.1333,0.6141,5.6256,5.6627,0.9104,0.3545,0.3590 18 | 16,84,326,2,0.49,300,4.4723,4.9956,4.9975,0.1548,0.0871,0.0859,0.5981,4.8530,4.8986,0.9048,0.3776,0.3795 19 | 17,106,247,2,0.44,300,4.5024,4.9224,4.9228,0.1415,0.0871,0.0859,0.8989,4.1266,4.1290,0.8396,0.3715,0.3755 20 | 18,134,179,2,0.05,300,4.4543,4.8790,4.8771,0.1418,0.0871,0.0859,0.3526,4.4307,4.3886,0.9478,0.3979,0.3992 21 | 19,170,137,2,0.02,300,4.5188,4.7848,4.7849,0.1235,0.0871,0.0859,0.7769,3.4488,3.4377,0.8765,0.4160,0.4212 22 | 20,215,786,2,0.23,300,4.4632,4.7362,4.7369,0.1302,0.0788,0.0795,0.0733,4.5567,4.5547,1.0000,0.4416,0.4419 23 | 21,271,410,1,0.31,300,4.4510,4.6909,4.6923,0.1218,0.0871,0.0859,0.6500,3.0846,3.0808,0.9373,0.4647,0.4628 24 | 22,343,388,1,0.37,300,4.4696,4.6608,4.6669,0.1254,0.0871,0.0859,0.8533,2.9370,2.9467,0.9067,0.4831,0.4864 25 | 23,433,38,2,0.25,300,4.4121,4.6623,4.6696,0.1316,0.0871,0.0859,1.2031,3.1260,3.1309,0.7806,0.4338,0.4386 26 | 24,548,467,1,0.41000000000000003,300,4.3892,4.6403,4.6492,0.1296,0.0871,0.0859,0.9179,2.8351,2.8438,0.8650,0.4965,0.5033 27 | 25,692,176,0,0.09,300,4.4028,4.6129,4.6255,0.1272,0.0871,0.0859,0.7904,2.8790,2.8588,0.9581,0.5021,0.5041 28 | 26,875,57,2,0.02,300,4.4205,4.5923,4.6057,0.1223,0.0871,0.0859,0.9444,2.8127,2.8038,0.8229,0.5094,0.5145 29 | 27,1106,968,2,0.15,300,4.4684,4.5463,4.5648,0.1103,0.0871,0.0859,0.1766,3.4164,3.5074,0.9828,0.5700,0.5695 30 | 28,1398,268,0,0.01,300,4.4545,4.5212,4.5432,0.1173,0.1325,0.1333,0.7453,2.5353,2.5264,0.9392,0.5575,0.5617 31 | 29,1767,270,0,0.3,300,4.4411,4.5226,4.5464,0.1160,0.1325,0.1333,0.8475,2.3774,2.3830,0.8970,0.5813,0.5840 32 | 30,2234,596,2,0.24,300,4.4170,4.5303,4.5576,0.1164,0.1325,0.1333,0.6792,2.2875,2.3642,0.8626,0.6125,0.6169 33 | 31,2823,694,0,0.49,300,4.4176,4.5238,4.5528,0.1162,0.1325,0.1333,0.9609,2.1661,2.1820,0.8565,0.6140,0.6174 34 | 32,3568,837,0,0.48,300,4.4223,4.5204,4.5465,0.1174,0.1325,0.1333,0.9141,2.0989,2.1165,0.8537,0.6284,0.6321 35 | 33,4510,487,2,0.4,300,4.4171,4.5164,4.5441,0.1211,0.1325,0.1333,0.8136,1.8158,1.8194,0.8322,0.6643,0.6639 36 | 34,5701,418,0,0.11,300,4.4095,4.5255,4.5558,0.1161,0.1325,0.1333,1.0035,2.0462,2.0688,0.8320,0.6399,0.6418 37 | 35,7206,407,0,0.05,300,4.3972,4.5355,4.5675,0.1153,0.0871,0.0859,0.9640,2.0075,2.0357,0.8308,0.6472,0.6486 38 | 36,9108,657,0,0.0,300,4.4016,4.5345,4.5669,0.1177,0.1325,0.1333,1.0580,1.9643,1.9842,0.8088,0.6510,0.6515 39 | 37,11513,442,1,0.22,300,4.4035,4.5351,4.5687,0.1195,0.1325,0.1333,0.8349,1.6962,1.7157,0.8263,0.6885,0.6900 40 | 38,14552,398,1,0.37,300,4.3894,4.5339,4.5700,0.1192,0.1325,0.1333,0.7168,1.5331,1.5616,0.8485,0.7075,0.7049 41 | 39,18393,369,2,0.04,300,4.3986,4.5308,4.5635,0.1189,0.1325,0.1333,0.8769,1.6789,1.6684,0.8093,0.6934,0.6917 42 | 40,23249,75,2,0.43,300,4.3936,4.5189,4.5551,0.1238,0.1325,0.1333,1.1706,1.6112,1.6244,0.7516,0.6929,0.6910 43 | 41,29386,179,1,0.18,300,4.3826,4.5240,4.5640,0.1228,0.1325,0.1333,0.7775,1.4502,1.4660,0.8317,0.7242,0.7220 44 | 42,37143,442,0,0.09,300,4.4086,4.5086,4.5270,0.1234,0.1325,0.1333,1.1032,1.5493,1.5363,0.7794,0.7090,0.7076 45 | 43,46947,111,0,0.14,300,4.4489,4.4949,4.5047,0.1267,0.1325,0.1333,1.0978,1.4840,1.4632,0.7784,0.7183,0.7212 46 | 44,59340,242,0,0.37,300,4.4787,4.4925,4.4998,0.1266,0.1325,0.1333,1.2474,1.5092,1.4809,0.7531,0.7145,0.7194 47 | 45,75005,107,0,0.35000000000000003,300,4.4953,4.4954,4.4981,0.1297,0.1325,0.1333,1.2163,1.4769,1.4507,0.7567,0.7200,0.7241 48 | 46,94804,150,0,0.11,300,4.4842,4.4942,4.4978,0.1305,0.1325,0.1333,1.1472,1.3979,1.3747,0.7665,0.7293,0.7336 49 | 47,119830,38,2,0.41000000000000003,300,4.4817,4.4899,4.4962,0.1295,0.1325,0.1333,1.6785,1.7758,1.7373,0.6472,0.6493,0.6519 50 | 48,151463,248,2,0.23,300,4.4829,4.4829,4.4924,0.1259,0.1325,0.1333,0.8248,1.0578,1.0298,0.8167,0.7794,0.7790 51 | 49,191445,670,1,0.29,300,4.4810,4.4815,4.4941,0.1229,0.1325,0.1333,0.7932,1.0104,0.9963,0.8267,0.7888,0.7860 52 | -------------------------------------------------------------------------------- /checkpoints/pos_tag/english/mlp/bert/all_results.tsv: -------------------------------------------------------------------------------- 1 | run,ndata,hidden_size,nlayers,dropout,embedding_size,train_loss,dev_loss,test_loss,train_acc,dev_acc,test_acc,base_train_loss,base_dev_loss,base_test_loss,base_train_acc,base_dev_acc,base_test_acc 2 | 0,1,776,0,0.09,768,3.4594,4.0004,3.9958,1.0000,0.0752,0.0831,0.0006,4.5856,4.5430,1.0000,0.0744,0.0815 3 | 1,2,433,2,0.27,768,3.5025,3.9713,3.9660,0.5000,0.0752,0.0831,0.0000,8.0386,7.9155,1.0000,0.1146,0.1199 4 | 2,3,310,1,0.38,768,3.2676,3.9820,3.9733,0.6667,0.0752,0.0831,0.0002,5.9826,5.8938,1.0000,0.1261,0.1380 5 | 3,4,300,1,0.42,768,3.1699,3.9714,3.9623,0.5000,0.0752,0.0831,0.0003,6.8937,6.7931,1.0000,0.1456,0.1477 6 | 4,5,1009,2,0.39,768,3.2925,3.9692,3.9623,0.4000,0.0752,0.0831,0.0000,14.3194,14.1634,1.0000,0.1453,0.1484 7 | 5,6,657,2,0.41000000000000003,768,3.3863,3.9095,3.9040,0.3333,0.0752,0.0831,0.0000,8.5739,8.4797,1.0000,0.1767,0.1775 8 | 6,8,455,2,0.23,768,3.3568,3.8946,3.8892,0.3750,0.0752,0.0831,0.0000,7.2735,7.2181,1.0000,0.2021,0.2035 9 | 7,10,103,2,0.09,768,3.1132,3.9285,3.9193,0.5000,0.0752,0.0831,0.0015,4.8168,4.7770,1.0000,0.1959,0.2002 10 | 8,13,203,2,0.14,768,2.9461,3.9414,3.9300,0.4615,0.0752,0.0831,0.0003,6.0432,5.9910,1.0000,0.1737,0.1785 11 | 9,16,150,1,0.31,768,3.1675,3.8732,3.8626,0.3750,0.0752,0.0831,0.0026,3.8212,3.8068,1.0000,0.2929,0.2984 12 | 10,21,72,0,0.15,768,3.2129,3.8286,3.8192,0.2857,0.0752,0.0831,0.0361,3.5727,3.5530,1.0000,0.3516,0.3535 13 | 11,26,467,2,0.27,768,3.1497,3.8512,3.8402,0.2692,0.0752,0.0831,0.0002,5.0144,4.9852,1.0000,0.3950,0.3971 14 | 12,33,377,2,0.5,768,3.1121,3.8367,3.8278,0.2121,0.0752,0.0831,0.0004,5.2475,5.2176,1.0000,0.3676,0.3651 15 | 13,42,436,0,0.44,768,3.2689,3.7583,3.7548,0.1905,0.1679,0.1654,0.0709,3.1348,3.1238,1.0000,0.4381,0.4431 16 | 14,53,382,0,0.1,768,3.3205,3.7439,3.7433,0.1887,0.1679,0.1654,0.0223,2.8985,2.8975,1.0000,0.4863,0.4891 17 | 15,68,326,2,0.5,768,3.4352,3.7196,3.7162,0.1618,0.1679,0.1654,0.0026,2.7074,2.6628,1.0000,0.5745,0.5720 18 | 16,85,326,2,0.49,768,3.3949,3.7616,3.7535,0.1529,0.0809,0.0809,0.0021,2.8590,2.8473,1.0000,0.5641,0.5593 19 | 17,108,247,2,0.44,768,3.4337,3.7425,3.7370,0.1667,0.0752,0.0831,0.0064,2.3230,2.3093,1.0000,0.5805,0.5811 20 | 18,137,179,2,0.05,768,3.4350,3.7201,3.7186,0.1460,0.1679,0.1654,0.0016,2.3177,2.3251,1.0000,0.6429,0.6372 21 | 19,173,137,2,0.02,768,3.4630,3.7018,3.7041,0.1618,0.1679,0.1654,0.0033,1.8107,1.8632,1.0000,0.6743,0.6660 22 | 20,219,786,2,0.23,768,3.4974,3.6720,3.6779,0.1553,0.1679,0.1654,0.0001,1.8276,1.9258,1.0000,0.7134,0.7061 23 | 21,278,410,1,0.31,768,3.4853,3.6824,3.6886,0.1655,0.1679,0.1654,0.0034,1.2793,1.3243,1.0000,0.7539,0.7428 24 | 22,351,388,1,0.37,768,3.4800,3.6781,3.6854,0.1681,0.1679,0.1654,0.0044,1.1926,1.2331,1.0000,0.7692,0.7641 25 | 23,445,38,2,0.25,768,3.4471,3.6918,3.6956,0.1685,0.1679,0.1654,0.0851,1.3209,1.3681,0.9978,0.7425,0.7366 26 | 24,563,467,1,0.41000000000000003,768,3.4483,3.6969,3.6994,0.1741,0.1679,0.1654,0.0054,1.1437,1.1789,1.0000,0.7810,0.7763 27 | 25,712,176,0,0.09,768,3.4820,3.6840,3.6888,0.1671,0.1679,0.1654,0.0544,1.0958,1.1268,1.0000,0.7907,0.7829 28 | 26,901,57,2,0.02,768,3.5111,3.6661,3.6733,0.1620,0.1679,0.1654,0.1447,1.1733,1.2009,0.9922,0.7699,0.7617 29 | 27,1141,968,2,0.15,768,3.5529,3.6330,3.6470,0.1639,0.1679,0.1654,0.0002,1.3531,1.4268,1.0000,0.8184,0.8159 30 | 28,1444,268,0,0.01,768,3.5507,3.6144,3.6332,0.1641,0.1679,0.1654,0.0858,0.8708,0.8980,1.0000,0.8296,0.8253 31 | 29,1827,270,0,0.3,768,3.5559,3.6201,3.6392,0.1565,0.1679,0.1654,0.0551,0.7712,0.7954,0.9995,0.8464,0.8466 32 | 30,2313,596,2,0.24,768,3.5344,3.6268,3.6454,0.1634,0.1679,0.1654,0.0280,0.8302,0.8691,0.9983,0.8500,0.8455 33 | 31,2927,694,0,0.49,768,3.5385,3.6268,3.6452,0.1609,0.1679,0.1654,0.1111,0.6947,0.7245,0.9898,0.8650,0.8624 34 | 32,3705,837,0,0.48,768,3.5485,3.6346,3.6531,0.1563,0.1679,0.1654,0.1218,0.6514,0.6822,0.9860,0.8734,0.8708 35 | 33,4689,487,2,0.4,768,3.5555,3.6332,3.6517,0.1536,0.1679,0.1654,0.1073,0.6238,0.6688,0.9810,0.8753,0.8695 36 | 34,5934,418,0,0.11,768,3.5514,3.6444,3.6628,0.1542,0.1679,0.1654,0.1615,0.6402,0.6686,0.9826,0.8799,0.8755 37 | 35,7510,407,0,0.05,768,3.5438,3.6513,3.6695,0.1575,0.1679,0.1654,0.1019,0.6025,0.6203,0.9901,0.8833,0.8794 38 | 36,9505,657,0,0.0,768,3.5602,3.6439,3.6576,0.1557,0.1679,0.1654,0.1321,0.5631,0.5735,0.9854,0.8886,0.8864 39 | 37,12029,442,1,0.22,768,3.5586,3.6414,3.6561,0.1554,0.1679,0.1654,0.0711,0.4980,0.5167,0.9891,0.8994,0.8956 40 | 38,15225,398,1,0.37,768,3.5516,3.6430,3.6572,0.1563,0.1679,0.1654,0.0860,0.4252,0.4395,0.9853,0.9085,0.9032 41 | 39,19268,369,2,0.04,768,3.5555,3.6402,3.6555,0.1567,0.1679,0.1654,0.1531,0.4846,0.4902,0.9684,0.9000,0.8978 42 | 40,24386,75,2,0.43,768,3.5683,3.6238,3.6403,0.1599,0.1679,0.1654,0.1609,0.4324,0.4484,0.9688,0.9109,0.9054 43 | 41,30863,179,1,0.18,768,3.5639,3.6223,3.6412,0.1650,0.1679,0.1654,0.1060,0.3728,0.3779,0.9782,0.9177,0.9165 44 | 42,39061,442,0,0.09,768,3.5763,3.6195,3.6353,0.1654,0.1679,0.1654,0.1914,0.3837,0.3839,0.9627,0.9191,0.9184 45 | 43,49436,111,0,0.14,768,3.5905,3.6100,3.6240,0.1666,0.1679,0.1654,0.1672,0.3392,0.3400,0.9659,0.9266,0.9262 46 | 44,62566,242,0,0.37,768,3.5959,3.6057,3.6198,0.1695,0.1679,0.1654,0.2165,0.3447,0.3483,0.9524,0.9257,0.9229 47 | 45,79184,107,0,0.35000000000000003,768,3.5932,3.6076,3.6199,0.1738,0.1679,0.1654,0.2220,0.3413,0.3440,0.9516,0.9267,0.9248 48 | 46,100217,150,0,0.11,768,3.5876,3.6064,3.6195,0.1765,0.1679,0.1654,0.1876,0.3111,0.3121,0.9602,0.9335,0.9314 49 | 47,126835,38,2,0.41000000000000003,768,3.5935,3.6051,3.6182,0.1758,0.1679,0.1654,0.3731,0.4652,0.4660,0.9264,0.9096,0.9071 50 | 48,160524,248,2,0.23,768,3.6035,3.6000,3.6167,0.1731,0.1679,0.1654,0.0994,0.2119,0.2173,0.9792,0.9544,0.9523 51 | 49,203160,670,1,0.29,768,3.6068,3.5998,3.6198,0.1707,0.1679,0.1654,0.0593,0.1857,0.1949,0.9890,0.9598,0.9574 52 | -------------------------------------------------------------------------------- /checkpoints/pos_tag/english/mlp/fast/all_results.tsv: -------------------------------------------------------------------------------- 1 | run,ndata,hidden_size,nlayers,dropout,embedding_size,train_loss,dev_loss,test_loss,train_acc,dev_acc,test_acc,base_train_loss,base_dev_loss,base_test_loss,base_train_acc,base_dev_acc,base_test_acc 2 | 0,1,776,0,0.09,300,3.4594,4.0004,3.9958,1.0000,0.0752,0.0831,0.0490,4.0008,3.9946,1.0000,0.0776,0.0844 3 | 1,2,433,2,0.27,300,3.5025,3.9713,3.9660,0.5000,0.0752,0.0831,0.0000,4.2491,4.2641,1.0000,0.1495,0.1535 4 | 2,3,310,1,0.38,300,3.2676,3.9820,3.9733,0.6667,0.0752,0.0831,0.0064,3.8554,3.8413,1.0000,0.1138,0.1201 5 | 3,4,300,1,0.42,300,3.1699,3.9714,3.9623,0.5000,0.0752,0.0831,0.0080,3.8354,3.8285,1.0000,0.1886,0.1961 6 | 4,5,1009,2,0.39,300,3.2925,3.9692,3.9623,0.4000,0.0752,0.0831,0.0002,9.4257,9.4771,1.0000,0.1512,0.1511 7 | 5,6,657,2,0.41000000000000003,300,3.3863,3.9095,3.9040,0.3333,0.0752,0.0831,0.0011,6.8557,6.8995,1.0000,0.2236,0.2255 8 | 6,8,455,2,0.23,300,3.3568,3.8946,3.8892,0.3750,0.0752,0.0831,0.0013,6.3576,6.4396,1.0000,0.2761,0.2768 9 | 7,10,103,2,0.09,300,3.1132,3.9285,3.9193,0.5000,0.0752,0.0831,0.1360,4.3240,4.3427,1.0000,0.2630,0.2650 10 | 8,13,203,2,0.14,300,2.9461,3.9414,3.9300,0.4615,0.0752,0.0831,0.0138,5.4961,5.5722,1.0000,0.2732,0.2753 11 | 9,16,150,1,0.31,300,3.1675,3.8732,3.8626,0.3750,0.0752,0.0831,0.4232,3.4815,3.4935,1.0000,0.3814,0.3807 12 | 10,21,72,0,0.15,300,3.2129,3.8286,3.8192,0.2857,0.0752,0.0831,0.6037,3.4118,3.4122,1.0000,0.3102,0.3051 13 | 11,26,467,2,0.27,300,3.1497,3.8512,3.8402,0.2692,0.0752,0.0831,0.0071,6.2989,6.4083,1.0000,0.3938,0.3941 14 | 12,33,377,2,0.5,300,3.1121,3.8367,3.8278,0.2121,0.0752,0.0831,0.1528,6.2963,6.3657,0.9697,0.3698,0.3725 15 | 13,42,436,0,0.44,300,3.2689,3.7583,3.7548,0.1905,0.1679,0.1654,0.4619,3.0100,3.0085,1.0000,0.4031,0.3984 16 | 14,53,382,0,0.1,300,3.3205,3.7439,3.7433,0.1887,0.1679,0.1654,0.7418,2.9252,2.9232,1.0000,0.4577,0.4521 17 | 15,68,326,2,0.5,300,3.4352,3.7196,3.7162,0.1618,0.1679,0.1654,0.4012,2.9301,2.9185,0.9559,0.5179,0.5212 18 | 16,85,326,2,0.49,300,3.3949,3.7616,3.7535,0.1529,0.0809,0.0809,0.4444,2.8527,2.8562,0.8941,0.5243,0.5208 19 | 17,108,247,2,0.44,300,3.4337,3.7425,3.7370,0.1667,0.0752,0.0831,0.6942,2.4125,2.4268,0.8426,0.5379,0.5386 20 | 18,137,179,2,0.05,300,3.4350,3.7201,3.7186,0.1460,0.1679,0.1654,0.3326,2.4197,2.4225,0.9489,0.5588,0.5573 21 | 19,173,137,2,0.02,300,3.4630,3.7018,3.7041,0.1618,0.1679,0.1654,0.7023,2.1617,2.1728,0.8786,0.5624,0.5603 22 | 20,219,786,2,0.23,300,3.4974,3.6720,3.6779,0.1553,0.1679,0.1654,0.0725,2.1938,2.2359,0.9863,0.6660,0.6660 23 | 21,278,410,1,0.31,300,3.4853,3.6824,3.6886,0.1655,0.1679,0.1654,0.1615,1.7538,1.7714,0.9820,0.6908,0.6854 24 | 22,351,388,1,0.37,300,3.4800,3.6781,3.6854,0.1681,0.1679,0.1654,0.2077,1.7543,1.7595,0.9801,0.6831,0.6809 25 | 23,445,38,2,0.25,300,3.4471,3.6918,3.6956,0.1685,0.1679,0.1654,0.4972,1.9086,1.9550,0.8876,0.6355,0.6314 26 | 24,563,467,1,0.41000000000000003,300,3.4483,3.6969,3.6994,0.1741,0.1679,0.1654,0.2169,1.6676,1.6806,0.9680,0.7078,0.7076 27 | 25,712,176,0,0.09,300,3.4820,3.6840,3.6888,0.1671,0.1679,0.1654,0.4419,1.6558,1.6400,0.9522,0.6992,0.6971 28 | 26,901,57,2,0.02,300,3.5111,3.6661,3.6733,0.1620,0.1679,0.1654,0.6364,1.6155,1.6369,0.8812,0.7045,0.7031 29 | 27,1141,968,2,0.15,300,3.5529,3.6330,3.6470,0.1639,0.1679,0.1654,0.1566,1.5112,1.5384,0.9667,0.7843,0.7832 30 | 28,1444,268,0,0.01,300,3.5507,3.6144,3.6332,0.1641,0.1679,0.1654,0.4719,1.3487,1.3488,0.9404,0.7653,0.7606 31 | 29,1827,270,0,0.3,300,3.5559,3.6201,3.6392,0.1565,0.1679,0.1654,0.4835,1.2151,1.2236,0.9250,0.7881,0.7847 32 | 30,2313,596,2,0.24,300,3.5344,3.6268,3.6454,0.1634,0.1679,0.1654,0.2541,1.1360,1.1925,0.9425,0.8201,0.8175 33 | 31,2927,694,0,0.49,300,3.5385,3.6268,3.6452,0.1609,0.1679,0.1654,0.5438,1.1017,1.1249,0.9006,0.8082,0.8035 34 | 32,3705,837,0,0.48,300,3.5485,3.6346,3.6531,0.1563,0.1679,0.1654,0.5672,1.0617,1.0846,0.8934,0.8161,0.8096 35 | 33,4689,487,2,0.4,300,3.5555,3.6332,3.6517,0.1536,0.1679,0.1654,0.3059,0.8470,0.8807,0.9286,0.8470,0.8436 36 | 34,5934,418,0,0.11,300,3.5514,3.6444,3.6628,0.1542,0.1679,0.1654,0.4810,0.9969,1.0184,0.9109,0.8235,0.8201 37 | 35,7510,407,0,0.05,300,3.5438,3.6513,3.6695,0.1575,0.1679,0.1654,0.4845,0.9952,1.0191,0.9079,0.8263,0.8204 38 | 36,9505,657,0,0.0,300,3.5602,3.6439,3.6576,0.1557,0.1679,0.1654,0.5022,0.9679,0.9799,0.9048,0.8271,0.8233 39 | 37,12029,442,1,0.22,300,3.5586,3.6414,3.6561,0.1554,0.1679,0.1654,0.3105,0.7626,0.7855,0.9278,0.8535,0.8487 40 | 38,15225,398,1,0.37,300,3.5516,3.6430,3.6572,0.1563,0.1679,0.1654,0.2746,0.6619,0.6707,0.9320,0.8637,0.8608 41 | 39,19268,369,2,0.04,300,3.5555,3.6402,3.6555,0.1567,0.1679,0.1654,0.3320,0.7182,0.7211,0.9217,0.8588,0.8556 42 | 40,24386,75,2,0.43,300,3.5683,3.6238,3.6403,0.1599,0.1679,0.1654,0.4200,0.6679,0.6872,0.9087,0.8626,0.8596 43 | 41,30863,179,1,0.18,300,3.5639,3.6223,3.6412,0.1650,0.1679,0.1654,0.3349,0.6065,0.6104,0.9204,0.8711,0.8697 44 | 42,39061,442,0,0.09,300,3.5763,3.6195,3.6353,0.1654,0.1679,0.1654,0.5320,0.7365,0.7370,0.8950,0.8643,0.8628 45 | 43,49436,111,0,0.14,300,3.5905,3.6100,3.6240,0.1666,0.1679,0.1654,0.5628,0.7275,0.7153,0.8871,0.8642,0.8642 46 | 44,62566,242,0,0.37,300,3.5959,3.6057,3.6198,0.1695,0.1679,0.1654,0.6485,0.7667,0.7577,0.8677,0.8539,0.8564 47 | 45,79184,107,0,0.35000000000000003,300,3.5932,3.6076,3.6199,0.1738,0.1679,0.1654,0.6480,0.7677,0.7518,0.8681,0.8547,0.8568 48 | 46,100217,150,0,0.11,300,3.5876,3.6064,3.6195,0.1765,0.1679,0.1654,0.5651,0.6887,0.6709,0.8842,0.8681,0.8697 49 | 47,126835,38,2,0.41000000000000003,300,3.5935,3.6051,3.6182,0.1758,0.1679,0.1654,0.6916,0.7787,0.7691,0.8545,0.8442,0.8450 50 | 48,160524,248,2,0.23,300,3.6035,3.6000,3.6167,0.1731,0.1679,0.1654,0.3515,0.4285,0.4144,0.9108,0.8964,0.9006 51 | 49,203160,670,1,0.29,300,3.6068,3.5998,3.6198,0.1707,0.1679,0.1654,0.3679,0.4277,0.4138,0.9087,0.8969,0.9009 52 | -------------------------------------------------------------------------------- /checkpoints/dep_label/english/mlp/bert/all_results.tsv: -------------------------------------------------------------------------------- 1 | run,ndata,hidden_size,nlayers,dropout,embedding_size,train_loss,dev_loss,test_loss,train_acc,dev_acc,test_acc,base_train_loss,base_dev_loss,base_test_loss,base_train_acc,base_dev_acc,base_test_acc 2 | 0,1,776,0,0.09,768,5.0150,5.5224,5.5219,1.0000,0.1325,0.1333,0.0000,5.9056,5.8918,1.0000,0.1325,0.1333 3 | 1,2,433,2,0.27,768,5.0297,5.5319,5.5304,0.5000,0.0092,0.0109,0.0000,8.7514,8.6742,1.0000,0.1172,0.1202 4 | 2,3,310,1,0.38,768,4.7677,5.4915,5.4897,0.6667,0.1325,0.1333,0.0002,7.7030,7.6557,1.0000,0.1339,0.1349 5 | 3,4,300,1,0.42,768,4.8514,5.4744,5.4744,0.5000,0.1325,0.1333,0.0004,7.4185,7.3603,1.0000,0.1491,0.1482 6 | 4,5,1009,2,0.39,768,4.9072,5.4385,5.4386,0.4000,0.1325,0.1333,0.0000,10.9694,10.8970,1.0000,0.1608,0.1628 7 | 5,6,657,2,0.41000000000000003,768,4.9491,5.4467,5.4476,0.3333,0.1325,0.1333,0.0000,7.5064,7.4754,1.0000,0.1629,0.1650 8 | 6,8,455,2,0.23,768,4.9080,5.4397,5.4411,0.2500,0.0092,0.0109,0.0000,8.2837,8.2586,1.0000,0.1476,0.1530 9 | 7,10,103,2,0.09,768,4.7008,5.4216,5.4221,0.3000,0.0092,0.0109,0.0013,5.8997,5.9133,1.0000,0.1559,0.1603 10 | 8,13,203,2,0.14,768,4.5678,5.3785,5.3780,0.3077,0.0092,0.0109,0.0003,6.1320,6.1445,1.0000,0.2195,0.2192 11 | 9,16,150,1,0.31,768,4.6705,5.3296,5.3292,0.2500,0.0092,0.0109,0.0022,5.1197,5.1343,1.0000,0.2414,0.2385 12 | 10,21,72,0,0.15,768,4.6939,5.2563,5.2569,0.1905,0.0092,0.0109,0.0011,5.0519,5.0626,1.0000,0.2639,0.2684 13 | 11,26,467,2,0.27,768,4.5853,5.2066,5.2091,0.1923,0.1325,0.1333,0.0003,5.9701,5.9707,1.0000,0.2797,0.2799 14 | 12,33,377,2,0.5,768,4.4091,5.1400,5.1428,0.2121,0.1325,0.1333,0.0019,6.9808,6.9803,1.0000,0.2874,0.2859 15 | 13,42,436,0,0.44,768,4.5329,5.0996,5.1013,0.1667,0.1325,0.1333,0.0339,5.0843,5.0632,1.0000,0.3105,0.3134 16 | 14,53,382,0,0.1,768,4.5302,5.0520,5.0532,0.1887,0.1325,0.1333,0.0109,5.0665,5.0644,1.0000,0.3440,0.3445 17 | 15,67,326,2,0.5,768,4.5732,5.0467,5.0505,0.1642,0.1325,0.1333,0.0082,5.4198,5.4032,1.0000,0.3820,0.3862 18 | 16,84,326,2,0.49,768,4.4723,4.9956,4.9975,0.1548,0.0871,0.0859,0.0087,5.0262,4.9879,1.0000,0.3779,0.3821 19 | 17,106,247,2,0.44,768,4.5024,4.9224,4.9228,0.1415,0.0871,0.0859,0.0152,4.1867,4.1665,1.0000,0.4130,0.4184 20 | 18,134,179,2,0.05,768,4.4543,4.8790,4.8771,0.1418,0.0871,0.0859,0.0035,4.7233,4.6688,1.0000,0.4402,0.4422 21 | 19,170,137,2,0.02,768,4.5188,4.7848,4.7849,0.1235,0.0871,0.0859,0.0077,3.5558,3.5376,1.0000,0.4767,0.4846 22 | 20,215,786,2,0.23,768,4.4632,4.7362,4.7369,0.1302,0.0788,0.0795,0.0003,3.8044,3.8408,1.0000,0.5095,0.5128 23 | 21,271,410,1,0.31,768,4.4510,4.6909,4.6923,0.1218,0.0871,0.0859,0.0049,2.6692,2.6993,1.0000,0.5454,0.5453 24 | 22,343,388,1,0.37,768,4.4696,4.6608,4.6669,0.1254,0.0871,0.0859,0.0065,2.3701,2.4120,1.0000,0.5821,0.5811 25 | 23,433,38,2,0.25,768,4.4121,4.6623,4.6696,0.1316,0.0871,0.0859,0.2691,2.5605,2.5923,0.9931,0.5331,0.5388 26 | 24,548,467,1,0.41000000000000003,768,4.3892,4.6403,4.6492,0.1296,0.0871,0.0859,0.0093,2.1909,2.2165,1.0000,0.6231,0.6246 27 | 25,692,176,0,0.09,768,4.4028,4.6129,4.6255,0.1272,0.0871,0.0859,0.0537,2.2366,2.2489,1.0000,0.6230,0.6236 28 | 26,875,57,2,0.02,768,4.4205,4.5923,4.6057,0.1223,0.0871,0.0859,0.1899,2.3714,2.3418,0.9954,0.5989,0.6011 29 | 27,1106,968,2,0.15,768,4.4684,4.5463,4.5648,0.1103,0.0871,0.0859,0.0009,2.4469,2.5130,1.0000,0.6910,0.6875 30 | 28,1398,268,0,0.01,768,4.4545,4.5212,4.5432,0.1173,0.1325,0.1333,0.0791,1.7988,1.8219,1.0000,0.6919,0.6911 31 | 29,1767,270,0,0.3,768,4.4411,4.5226,4.5464,0.1160,0.1325,0.1333,0.1056,1.6451,1.6650,1.0000,0.7140,0.7189 32 | 30,2234,596,2,0.24,768,4.4170,4.5303,4.5576,0.1164,0.1325,0.1333,0.1255,1.5621,1.6214,0.9848,0.7316,0.7329 33 | 31,2823,694,0,0.49,768,4.4176,4.5238,4.5528,0.1162,0.1325,0.1333,0.0716,1.3922,1.4365,0.9996,0.7563,0.7579 34 | 32,3568,837,0,0.48,768,4.4223,4.5204,4.5465,0.1174,0.1325,0.1333,0.1584,1.3041,1.3278,0.9950,0.7716,0.7724 35 | 33,4510,487,2,0.4,768,4.4171,4.5164,4.5441,0.1211,0.1325,0.1333,0.0876,1.1121,1.1251,0.9922,0.7964,0.7960 36 | 34,5701,418,0,0.11,768,4.4095,4.5255,4.5558,0.1161,0.1325,0.1333,0.1652,1.1767,1.2059,0.9963,0.7923,0.7892 37 | 35,7206,407,0,0.05,768,4.3972,4.5355,4.5675,0.1153,0.0871,0.0859,0.1985,1.1436,1.1702,0.9910,0.7986,0.7987 38 | 36,9108,657,0,0.0,768,4.4016,4.5345,4.5669,0.1177,0.1325,0.1333,0.2311,1.0816,1.1149,0.9854,0.8148,0.8119 39 | 37,11513,442,1,0.22,768,4.4035,4.5351,4.5687,0.1195,0.1325,0.1333,0.0852,0.9066,0.9339,0.9947,0.8362,0.8311 40 | 38,14552,398,1,0.37,768,4.3894,4.5339,4.5700,0.1192,0.1325,0.1333,0.1592,0.8320,0.8843,0.9773,0.8432,0.8397 41 | 39,18393,369,2,0.04,768,4.3986,4.5308,4.5635,0.1189,0.1325,0.1333,0.1108,0.8830,0.9007,0.9865,0.8425,0.8409 42 | 40,23249,75,2,0.43,768,4.3936,4.5189,4.5551,0.1238,0.1325,0.1333,0.4244,0.9230,0.9581,0.9241,0.8374,0.8351 43 | 41,29386,179,1,0.18,768,4.3826,4.5240,4.5640,0.1228,0.1325,0.1333,0.1648,0.7072,0.7406,0.9733,0.8655,0.8634 44 | 42,37143,442,0,0.09,768,4.4086,4.5086,4.5270,0.1234,0.1325,0.1333,0.2471,0.7100,0.6948,0.9633,0.8681,0.8721 45 | 43,46947,111,0,0.14,768,4.4489,4.4949,4.5047,0.1267,0.1325,0.1333,0.2755,0.6576,0.6357,0.9561,0.8783,0.8798 46 | 44,59340,242,0,0.37,768,4.4787,4.4925,4.4998,0.1266,0.1325,0.1333,0.2692,0.6140,0.5951,0.9502,0.8835,0.8853 47 | 45,75005,107,0,0.35000000000000003,768,4.4953,4.4954,4.4981,0.1297,0.1325,0.1333,0.3065,0.5959,0.5812,0.9427,0.8882,0.8880 48 | 46,94804,150,0,0.11,768,4.4842,4.4942,4.4978,0.1305,0.1325,0.1333,0.2645,0.5505,0.5386,0.9525,0.8948,0.8966 49 | 47,119830,38,2,0.41000000000000003,768,4.4817,4.4899,4.4962,0.1295,0.1325,0.1333,0.9583,1.0802,1.0515,0.8240,0.8110,0.8117 50 | 48,151463,248,2,0.23,768,4.4829,4.4829,4.4924,0.1259,0.1325,0.1333,0.1488,0.4191,0.4167,0.9703,0.9179,0.9169 51 | 49,191445,670,1,0.29,768,4.4810,4.4815,4.4941,0.1229,0.1325,0.1333,0.1671,0.3936,0.3958,0.9685,0.9203,0.9206 52 | -------------------------------------------------------------------------------- /checkpoints/pos_tag/english/mlp/albert/all_results.tsv: -------------------------------------------------------------------------------- 1 | run,ndata,hidden_size,nlayers,dropout,embedding_size,train_loss,dev_loss,test_loss,train_acc,dev_acc,test_acc,base_train_loss,base_dev_loss,base_test_loss,base_train_acc,base_dev_acc,base_test_acc 2 | 0,1,776,0,0.09,768,3.4594,4.0004,3.9958,1.0000,0.0752,0.0831,0.0000,5.0612,5.0164,1.0000,0.0775,0.0854 3 | 1,2,433,2,0.27,768,3.5025,3.9713,3.9660,0.5000,0.0752,0.0831,0.0000,9.5588,9.4878,1.0000,0.1818,0.1886 4 | 2,3,310,1,0.38,768,3.2676,3.9820,3.9733,0.6667,0.0752,0.0831,0.0000,7.8049,7.7319,1.0000,0.1964,0.2041 5 | 3,4,300,1,0.42,768,3.1699,3.9714,3.9623,0.5000,0.0752,0.0831,0.0000,7.7197,7.6541,1.0000,0.1935,0.1991 6 | 4,5,1009,2,0.39,768,3.2925,3.9692,3.9623,0.4000,0.0752,0.0831,0.0000,12.7539,12.7021,1.0000,0.2115,0.2143 7 | 5,6,657,2,0.41000000000000003,768,3.3863,3.9095,3.9040,0.3333,0.0752,0.0831,0.0000,9.8720,9.8366,1.0000,0.2729,0.2750 8 | 6,8,455,2,0.23,768,3.3568,3.8946,3.8892,0.3750,0.0752,0.0831,0.0000,8.7192,8.7511,1.0000,0.3358,0.3372 9 | 7,10,103,2,0.09,768,3.1132,3.9285,3.9193,0.5000,0.0752,0.0831,0.0002,4.6219,4.6153,1.0000,0.2887,0.2943 10 | 8,13,203,2,0.14,768,2.9461,3.9414,3.9300,0.4615,0.0752,0.0831,0.0000,5.4012,5.4333,1.0000,0.3446,0.3431 11 | 9,16,150,1,0.31,768,3.1675,3.8732,3.8626,0.3750,0.0752,0.0831,0.0002,3.3187,3.3412,1.0000,0.4952,0.4956 12 | 10,21,72,0,0.15,768,3.2129,3.8286,3.8192,0.2857,0.0752,0.0831,0.0008,3.7294,3.7317,1.0000,0.4422,0.4426 13 | 11,26,467,2,0.27,768,3.1497,3.8512,3.8402,0.2692,0.0752,0.0831,0.0000,6.3482,6.3774,1.0000,0.5072,0.5045 14 | 12,33,377,2,0.5,768,3.1121,3.8367,3.8278,0.2121,0.0752,0.0831,0.0001,5.1441,5.1605,1.0000,0.4953,0.4960 15 | 13,42,436,0,0.44,768,3.2689,3.7583,3.7548,0.1905,0.1679,0.1654,0.0062,2.7988,2.8009,1.0000,0.5546,0.5541 16 | 14,53,382,0,0.1,768,3.3205,3.7439,3.7433,0.1887,0.1679,0.1654,0.0076,2.4483,2.4475,1.0000,0.5937,0.5966 17 | 15,68,326,2,0.5,768,3.4352,3.7196,3.7162,0.1618,0.1679,0.1654,0.0001,2.5314,2.5253,1.0000,0.6967,0.6940 18 | 16,85,326,2,0.49,768,3.3949,3.7616,3.7535,0.1529,0.0809,0.0809,0.0001,2.6337,2.6384,1.0000,0.6911,0.6886 19 | 17,108,247,2,0.44,768,3.4337,3.7425,3.7370,0.1667,0.0752,0.0831,0.0002,1.9896,2.0156,1.0000,0.7167,0.7131 20 | 18,137,179,2,0.05,768,3.4350,3.7201,3.7186,0.1460,0.1679,0.1654,0.0002,1.9577,2.0086,1.0000,0.7488,0.7430 21 | 19,173,137,2,0.02,768,3.4630,3.7018,3.7041,0.1618,0.1679,0.1654,0.0008,1.2111,1.2848,1.0000,0.8006,0.7937 22 | 20,219,786,2,0.23,768,3.4974,3.6720,3.6779,0.1553,0.1679,0.1654,0.0000,1.5059,1.6200,1.0000,0.8296,0.8239 23 | 21,278,410,1,0.31,768,3.4853,3.6824,3.6886,0.1655,0.1679,0.1654,0.0005,0.9681,1.0189,1.0000,0.8382,0.8315 24 | 22,351,388,1,0.37,768,3.4800,3.6781,3.6854,0.1681,0.1679,0.1654,0.0006,0.9734,1.0229,1.0000,0.8424,0.8370 25 | 23,445,38,2,0.25,768,3.4471,3.6918,3.6956,0.1685,0.1679,0.1654,0.0081,0.9867,1.0406,1.0000,0.8379,0.8350 26 | 24,563,467,1,0.41000000000000003,768,3.4483,3.6969,3.6994,0.1741,0.1679,0.1654,0.0008,0.9275,0.9651,1.0000,0.8511,0.8473 27 | 25,712,176,0,0.09,768,3.4820,3.6840,3.6888,0.1671,0.1679,0.1654,0.0470,0.7488,0.7709,1.0000,0.8570,0.8518 28 | 26,901,57,2,0.02,768,3.5111,3.6661,3.6733,0.1620,0.1679,0.1654,0.0166,0.7756,0.8124,1.0000,0.8627,0.8584 29 | 27,1141,968,2,0.15,768,3.5529,3.6330,3.6470,0.1639,0.1679,0.1654,0.0000,1.1733,1.2531,1.0000,0.8776,0.8716 30 | 28,1444,268,0,0.01,768,3.5507,3.6144,3.6332,0.1641,0.1679,0.1654,0.0792,0.6042,0.6300,0.9965,0.8835,0.8780 31 | 29,1827,270,0,0.3,768,3.5559,3.6201,3.6392,0.1565,0.1679,0.1654,0.0451,0.5349,0.5562,0.9989,0.8989,0.8943 32 | 30,2313,596,2,0.24,768,3.5344,3.6268,3.6454,0.1634,0.1679,0.1654,0.0076,0.6654,0.7262,0.9996,0.8990,0.8934 33 | 31,2927,694,0,0.49,768,3.5385,3.6268,3.6452,0.1609,0.1679,0.1654,0.0991,0.4897,0.5272,0.9846,0.9052,0.8999 34 | 32,3705,837,0,0.48,768,3.5485,3.6346,3.6531,0.1563,0.1679,0.1654,0.1100,0.4692,0.5000,0.9808,0.9116,0.9077 35 | 33,4689,487,2,0.4,768,3.5555,3.6332,3.6517,0.1536,0.1679,0.1654,0.0519,0.4394,0.4579,0.9889,0.9211,0.9176 36 | 34,5934,418,0,0.11,768,3.5514,3.6444,3.6628,0.1542,0.1679,0.1654,0.1148,0.4277,0.4517,0.9816,0.9171,0.9145 37 | 35,7510,407,0,0.05,768,3.5438,3.6513,3.6695,0.1575,0.1679,0.1654,0.1315,0.4101,0.4242,0.9780,0.9211,0.9181 38 | 36,9505,657,0,0.0,768,3.5602,3.6439,3.6576,0.1557,0.1679,0.1654,0.1440,0.3767,0.3749,0.9744,0.9239,0.9228 39 | 37,12029,442,1,0.22,768,3.5586,3.6414,3.6561,0.1554,0.1679,0.1654,0.0779,0.3330,0.3372,0.9848,0.9295,0.9275 40 | 38,15225,398,1,0.37,768,3.5516,3.6430,3.6572,0.1563,0.1679,0.1654,0.1186,0.3103,0.3109,0.9743,0.9323,0.9307 41 | 39,19268,369,2,0.04,768,3.5555,3.6402,3.6555,0.1567,0.1679,0.1654,0.0933,0.3193,0.3209,0.9803,0.9317,0.9309 42 | 40,24386,75,2,0.43,768,3.5683,3.6238,3.6403,0.1599,0.1679,0.1654,0.1533,0.3196,0.3212,0.9668,0.9319,0.9311 43 | 41,30863,179,1,0.18,768,3.5639,3.6223,3.6412,0.1650,0.1679,0.1654,0.1154,0.2751,0.2718,0.9750,0.9380,0.9377 44 | 42,39061,442,0,0.09,768,3.5763,3.6195,3.6353,0.1654,0.1679,0.1654,0.1400,0.2642,0.2566,0.9708,0.9421,0.9433 45 | 43,49436,111,0,0.14,768,3.5905,3.6100,3.6240,0.1666,0.1679,0.1654,0.1419,0.2459,0.2344,0.9709,0.9457,0.9468 46 | 44,62566,242,0,0.37,768,3.5959,3.6057,3.6198,0.1695,0.1679,0.1654,0.1710,0.2494,0.2381,0.9632,0.9446,0.9455 47 | 45,79184,107,0,0.35000000000000003,768,3.5932,3.6076,3.6199,0.1738,0.1679,0.1654,0.1407,0.2282,0.2144,0.9683,0.9490,0.9502 48 | 46,100217,150,0,0.11,768,3.5876,3.6064,3.6195,0.1765,0.1679,0.1654,0.1317,0.2185,0.2054,0.9709,0.9535,0.9533 49 | 47,126835,38,2,0.41000000000000003,768,3.5935,3.6051,3.6182,0.1758,0.1679,0.1654,0.2457,0.3131,0.2981,0.9482,0.9366,0.9393 50 | 48,160524,248,2,0.23,768,3.6035,3.6000,3.6167,0.1731,0.1679,0.1654,0.0661,0.1696,0.1630,0.9856,0.9642,0.9637 51 | 49,203160,670,1,0.29,768,3.6068,3.5998,3.6198,0.1707,0.1679,0.1654,0.1169,0.1762,0.1691,0.9737,0.9611,0.9618 52 | -------------------------------------------------------------------------------- /checkpoints/pos_tag/english/mlp/roberta/all_results.tsv: -------------------------------------------------------------------------------- 1 | run,ndata,hidden_size,nlayers,dropout,embedding_size,train_loss,dev_loss,test_loss,train_acc,dev_acc,test_acc,base_train_loss,base_dev_loss,base_test_loss,base_train_acc,base_dev_acc,base_test_acc 2 | 0,1,776,0,0.09,768,3.4594,4.0004,3.9958,1.0000,0.0752,0.0831,0.0151,4.5150,4.4854,1.0000,0.0752,0.0831 3 | 1,2,433,2,0.27,768,3.5025,3.9713,3.9660,0.5000,0.0752,0.0831,0.0000,8.4954,8.3816,1.0000,0.1305,0.1374 4 | 2,3,310,1,0.38,768,3.2676,3.9820,3.9733,0.6667,0.0752,0.0831,0.0011,5.8619,5.7905,1.0000,0.1156,0.1233 5 | 3,4,300,1,0.42,768,3.1699,3.9714,3.9623,0.5000,0.0752,0.0831,0.0015,6.3511,6.2777,1.0000,0.1917,0.1983 6 | 4,5,1009,2,0.39,768,3.2925,3.9692,3.9623,0.4000,0.0752,0.0831,0.0000,13.3955,13.3066,1.0000,0.2217,0.2211 7 | 5,6,657,2,0.41000000000000003,768,3.3863,3.9095,3.9040,0.3333,0.0752,0.0831,0.0002,8.7731,8.7386,1.0000,0.2487,0.2455 8 | 6,8,455,2,0.23,768,3.3568,3.8946,3.8892,0.3750,0.0752,0.0831,0.0002,8.4469,8.4386,1.0000,0.2629,0.2664 9 | 7,10,103,2,0.09,768,3.1132,3.9285,3.9193,0.5000,0.0752,0.0831,0.0040,6.0138,5.9844,1.0000,0.2306,0.2381 10 | 8,13,203,2,0.14,768,2.9461,3.9414,3.9300,0.4615,0.0752,0.0831,0.0011,7.6081,7.5744,1.0000,0.2284,0.2348 11 | 9,16,150,1,0.31,768,3.1675,3.8732,3.8626,0.3750,0.0752,0.0831,0.0106,4.0980,4.0770,1.0000,0.4078,0.4083 12 | 10,21,72,0,0.15,768,3.2129,3.8286,3.8192,0.2857,0.0752,0.0831,0.2135,3.6906,3.6660,1.0000,0.3736,0.3784 13 | 11,26,467,2,0.27,768,3.1497,3.8512,3.8402,0.2692,0.0752,0.0831,0.0003,7.1063,7.0740,1.0000,0.4031,0.4026 14 | 12,33,377,2,0.5,768,3.1121,3.8367,3.8278,0.2121,0.0752,0.0831,0.0056,6.1680,6.1131,1.0000,0.4394,0.4425 15 | 13,42,436,0,0.44,768,3.2689,3.7583,3.7548,0.1905,0.1679,0.1654,0.5303,3.2664,3.2539,1.0000,0.4176,0.4212 16 | 14,53,382,0,0.1,768,3.3205,3.7439,3.7433,0.1887,0.1679,0.1654,0.1541,2.9669,2.9606,1.0000,0.4796,0.4800 17 | 15,68,326,2,0.5,768,3.4352,3.7196,3.7162,0.1618,0.1679,0.1654,0.0333,3.1332,3.1304,1.0000,0.5730,0.5642 18 | 16,85,326,2,0.49,768,3.3949,3.7616,3.7535,0.1529,0.0809,0.0809,0.0351,3.2302,3.2330,1.0000,0.5760,0.5716 19 | 17,108,247,2,0.44,768,3.4337,3.7425,3.7370,0.1667,0.0752,0.0831,0.0800,2.4437,2.4587,1.0000,0.5990,0.5961 20 | 18,137,179,2,0.05,768,3.4350,3.7201,3.7186,0.1460,0.1679,0.1654,0.0069,2.2910,2.3315,1.0000,0.6551,0.6520 21 | 19,173,137,2,0.02,768,3.4630,3.7018,3.7041,0.1618,0.1679,0.1654,0.0183,1.6931,1.7297,1.0000,0.6825,0.6818 22 | 20,219,786,2,0.23,768,3.4974,3.6720,3.6779,0.1553,0.1679,0.1654,0.0004,1.7923,1.8408,1.0000,0.7492,0.7433 23 | 21,278,410,1,0.31,768,3.4853,3.6824,3.6886,0.1655,0.1679,0.1654,0.0189,1.2420,1.2736,1.0000,0.7654,0.7604 24 | 22,351,388,1,0.37,768,3.4800,3.6781,3.6854,0.1681,0.1679,0.1654,0.0277,1.2058,1.2310,1.0000,0.7748,0.7717 25 | 23,445,38,2,0.25,768,3.4471,3.6918,3.6956,0.1685,0.1679,0.1654,0.0946,1.4253,1.4661,0.9978,0.7426,0.7439 26 | 24,563,467,1,0.41000000000000003,768,3.4483,3.6969,3.6994,0.1741,0.1679,0.1654,0.0279,1.1180,1.1342,1.0000,0.7896,0.7857 27 | 25,712,176,0,0.09,768,3.4820,3.6840,3.6888,0.1671,0.1679,0.1654,0.0467,1.0380,1.0565,1.0000,0.7992,0.7946 28 | 26,901,57,2,0.02,768,3.5111,3.6661,3.6733,0.1620,0.1679,0.1654,0.0343,1.0665,1.1016,1.0000,0.8052,0.7983 29 | 27,1141,968,2,0.15,768,3.5529,3.6330,3.6470,0.1639,0.1679,0.1654,0.0013,1.1356,1.2167,1.0000,0.8471,0.8403 30 | 28,1444,268,0,0.01,768,3.5507,3.6144,3.6332,0.1641,0.1679,0.1654,0.0836,0.8154,0.8530,1.0000,0.8432,0.8322 31 | 29,1827,270,0,0.3,768,3.5559,3.6201,3.6392,0.1565,0.1679,0.1654,0.0950,0.7208,0.7588,0.9978,0.8635,0.8528 32 | 30,2313,596,2,0.24,768,3.5344,3.6268,3.6454,0.1634,0.1679,0.1654,0.0601,0.7485,0.8126,0.9922,0.8677,0.8590 33 | 31,2927,694,0,0.49,768,3.5385,3.6268,3.6452,0.1609,0.1679,0.1654,0.1162,0.6478,0.6940,0.9908,0.8780,0.8659 34 | 32,3705,837,0,0.48,768,3.5485,3.6346,3.6531,0.1563,0.1679,0.1654,0.1259,0.6063,0.6515,0.9873,0.8901,0.8772 35 | 33,4689,487,2,0.4,768,3.5555,3.6332,3.6517,0.1536,0.1679,0.1654,0.0466,0.5639,0.6278,0.9934,0.9004,0.8870 36 | 34,5934,418,0,0.11,768,3.5514,3.6444,3.6628,0.1542,0.1679,0.1654,0.1276,0.5728,0.6200,0.9875,0.8980,0.8853 37 | 35,7510,407,0,0.05,768,3.5438,3.6513,3.6695,0.1575,0.1679,0.1654,0.1164,0.5502,0.5901,0.9877,0.8997,0.8903 38 | 36,9505,657,0,0.0,768,3.5602,3.6439,3.6576,0.1557,0.1679,0.1654,0.1438,0.5103,0.5405,0.9817,0.9072,0.8966 39 | 37,12029,442,1,0.22,768,3.5586,3.6414,3.6561,0.1554,0.1679,0.1654,0.1086,0.4510,0.4836,0.9793,0.9118,0.9030 40 | 38,15225,398,1,0.37,768,3.5516,3.6430,3.6572,0.1563,0.1679,0.1654,0.0906,0.3900,0.4143,0.9821,0.9203,0.9121 41 | 39,19268,369,2,0.04,768,3.5555,3.6402,3.6555,0.1567,0.1679,0.1654,0.0892,0.4253,0.4568,0.9828,0.9174,0.9101 42 | 40,24386,75,2,0.43,768,3.5683,3.6238,3.6403,0.1599,0.1679,0.1654,0.1316,0.3873,0.4149,0.9733,0.9224,0.9133 43 | 41,30863,179,1,0.18,768,3.5639,3.6223,3.6412,0.1650,0.1679,0.1654,0.1014,0.3404,0.3559,0.9789,0.9277,0.9225 44 | 42,39061,442,0,0.09,768,3.5763,3.6195,3.6353,0.1654,0.1679,0.1654,0.1827,0.3462,0.3554,0.9650,0.9289,0.9256 45 | 43,49436,111,0,0.14,768,3.5905,3.6100,3.6240,0.1666,0.1679,0.1654,0.1973,0.3281,0.3328,0.9610,0.9343,0.9288 46 | 44,62566,242,0,0.37,768,3.5959,3.6057,3.6198,0.1695,0.1679,0.1654,0.2039,0.3142,0.3202,0.9569,0.9350,0.9290 47 | 45,79184,107,0,0.35000000000000003,768,3.5932,3.6076,3.6199,0.1738,0.1679,0.1654,0.2070,0.3109,0.3166,0.9571,0.9357,0.9303 48 | 46,100217,150,0,0.11,768,3.5876,3.6064,3.6195,0.1765,0.1679,0.1654,0.1815,0.2875,0.2938,0.9618,0.9401,0.9368 49 | 47,126835,38,2,0.41000000000000003,768,3.5935,3.6051,3.6182,0.1758,0.1679,0.1654,0.3571,0.4382,0.4486,0.9334,0.9194,0.9131 50 | 48,160524,248,2,0.23,768,3.6035,3.6000,3.6167,0.1731,0.1679,0.1654,0.1498,0.2305,0.2365,0.9662,0.9511,0.9457 51 | 49,203160,670,1,0.29,768,3.6068,3.5998,3.6198,0.1707,0.1679,0.1654,0.1278,0.2095,0.2181,0.9720,0.9554,0.9492 52 | -------------------------------------------------------------------------------- /checkpoints/dep_label/english/mlp/albert/all_results.tsv: -------------------------------------------------------------------------------- 1 | run,ndata,hidden_size,nlayers,dropout,embedding_size,train_loss,dev_loss,test_loss,train_acc,dev_acc,test_acc,base_train_loss,base_dev_loss,base_test_loss,base_train_acc,base_dev_acc,base_test_acc 2 | 0,1,776,0,0.09,768,5.0150,5.5224,5.5219,1.0000,0.1325,0.1333,0.0000,6.6259,6.6600,1.0000,0.1325,0.1333 3 | 1,2,433,2,0.27,768,5.0297,5.5319,5.5304,0.5000,0.0092,0.0109,0.0000,10.1737,10.1946,1.0000,0.1405,0.1425 4 | 2,3,310,1,0.38,768,4.7677,5.4915,5.4897,0.6667,0.1325,0.1333,0.0000,8.2518,8.2687,1.0000,0.1411,0.1431 5 | 3,4,300,1,0.42,768,4.8514,5.4744,5.4744,0.5000,0.1325,0.1333,0.0000,8.2896,8.3357,1.0000,0.1812,0.1808 6 | 4,5,1009,2,0.39,768,4.9072,5.4385,5.4386,0.4000,0.1325,0.1333,0.0000,17.3360,17.3187,1.0000,0.2126,0.2153 7 | 5,6,657,2,0.41000000000000003,768,4.9491,5.4467,5.4476,0.3333,0.1325,0.1333,0.0000,9.9883,10.0026,1.0000,0.2543,0.2561 8 | 6,8,455,2,0.23,768,4.9080,5.4397,5.4411,0.2500,0.0092,0.0109,0.0000,8.9127,8.9959,1.0000,0.2075,0.2093 9 | 7,10,103,2,0.09,768,4.7008,5.4216,5.4221,0.3000,0.0092,0.0109,0.0002,6.0844,6.1515,1.0000,0.2241,0.2232 10 | 8,13,203,2,0.14,768,4.5678,5.3785,5.3780,0.3077,0.0092,0.0109,0.0000,6.4284,6.5049,1.0000,0.2927,0.2908 11 | 9,16,150,1,0.31,768,4.6705,5.3296,5.3292,0.2500,0.0092,0.0109,0.0001,5.8518,5.8849,1.0000,0.3337,0.3346 12 | 10,21,72,0,0.15,768,4.6939,5.2563,5.2569,0.1905,0.0092,0.0109,0.0000,5.6274,5.6712,1.0000,0.3220,0.3204 13 | 11,26,467,2,0.27,768,4.5853,5.2066,5.2091,0.1923,0.1325,0.1333,0.0000,7.5327,7.5612,1.0000,0.3509,0.3488 14 | 12,33,377,2,0.5,768,4.4091,5.1400,5.1428,0.2121,0.1325,0.1333,0.0002,7.2129,7.2253,1.0000,0.3418,0.3420 15 | 13,42,436,0,0.44,768,4.5329,5.0996,5.1013,0.1667,0.1325,0.1333,0.0036,5.7619,5.7700,1.0000,0.4046,0.4047 16 | 14,53,382,0,0.1,768,4.5302,5.0520,5.0532,0.1887,0.1325,0.1333,0.0029,5.8082,5.8258,1.0000,0.4395,0.4374 17 | 15,67,326,2,0.5,768,4.5732,5.0467,5.0505,0.1642,0.1325,0.1333,0.0005,5.5540,5.6072,1.0000,0.4659,0.4670 18 | 16,84,326,2,0.49,768,4.4723,4.9956,4.9975,0.1548,0.0871,0.0859,0.0008,5.1580,5.2060,1.0000,0.4784,0.4834 19 | 17,106,247,2,0.44,768,4.5024,4.9224,4.9228,0.1415,0.0871,0.0859,0.0011,4.3764,4.4310,1.0000,0.5010,0.5050 20 | 18,134,179,2,0.05,768,4.4543,4.8790,4.8771,0.1418,0.0871,0.0859,0.0004,4.4240,4.3685,1.0000,0.5383,0.5429 21 | 19,170,137,2,0.02,768,4.5188,4.7848,4.7849,0.1235,0.0871,0.0859,0.0015,2.9386,3.0166,1.0000,0.5910,0.5933 22 | 20,215,786,2,0.23,768,4.4632,4.7362,4.7369,0.1302,0.0788,0.0795,0.0000,3.5321,3.6828,1.0000,0.6303,0.6284 23 | 21,271,410,1,0.31,768,4.4510,4.6909,4.6923,0.1218,0.0871,0.0859,0.0005,2.3374,2.4371,1.0000,0.6608,0.6593 24 | 22,343,388,1,0.37,768,4.4696,4.6608,4.6669,0.1254,0.0871,0.0859,0.0006,2.0871,2.1926,1.0000,0.6972,0.6929 25 | 23,433,38,2,0.25,768,4.4121,4.6623,4.6696,0.1316,0.0871,0.0859,0.3494,2.1067,2.1820,0.9746,0.6339,0.6350 26 | 24,548,467,1,0.41000000000000003,768,4.3892,4.6403,4.6492,0.1296,0.0871,0.0859,0.0010,2.0340,2.1376,1.0000,0.7149,0.7120 27 | 25,692,176,0,0.09,768,4.4028,4.6129,4.6255,0.1272,0.0871,0.0859,0.0212,1.7376,1.7970,1.0000,0.7160,0.7169 28 | 26,875,57,2,0.02,768,4.4205,4.5923,4.6057,0.1223,0.0871,0.0859,0.0254,1.8861,1.8975,1.0000,0.7204,0.7170 29 | 27,1106,968,2,0.15,768,4.4684,4.5463,4.5648,0.1103,0.0871,0.0859,0.0001,2.3378,2.5408,1.0000,0.7734,0.7683 30 | 28,1398,268,0,0.01,768,4.4545,4.5212,4.5432,0.1173,0.1325,0.1333,0.0384,1.3987,1.4635,1.0000,0.7695,0.7658 31 | 29,1767,270,0,0.3,768,4.4411,4.5226,4.5464,0.1160,0.1325,0.1333,0.0519,1.2782,1.3659,0.9994,0.7858,0.7818 32 | 30,2234,596,2,0.24,768,4.4170,4.5303,4.5576,0.1164,0.1325,0.1333,0.0215,1.3394,1.4740,0.9987,0.8046,0.7989 33 | 31,2823,694,0,0.49,768,4.4176,4.5238,4.5528,0.1162,0.1325,0.1333,0.1229,1.1016,1.1909,0.9936,0.8103,0.8056 34 | 32,3568,837,0,0.48,768,4.4223,4.5204,4.5465,0.1174,0.1325,0.1333,0.1331,1.0318,1.0923,0.9922,0.8220,0.8144 35 | 33,4510,487,2,0.4,768,4.4171,4.5164,4.5441,0.1211,0.1325,0.1333,0.1239,0.9263,0.9624,0.9796,0.8369,0.8344 36 | 34,5701,418,0,0.11,768,4.4095,4.5255,4.5558,0.1161,0.1325,0.1333,0.1640,0.9371,0.9951,0.9882,0.8331,0.8280 37 | 35,7206,407,0,0.05,768,4.3972,4.5355,4.5675,0.1153,0.0871,0.0859,0.1911,0.9111,0.9788,0.9831,0.8385,0.8328 38 | 36,9108,657,0,0.0,768,4.4016,4.5345,4.5669,0.1177,0.1325,0.1333,0.2255,0.8870,0.9458,0.9736,0.8471,0.8401 39 | 37,11513,442,1,0.22,768,4.4035,4.5351,4.5687,0.1195,0.1325,0.1333,0.0940,0.7444,0.7901,0.9898,0.8675,0.8617 40 | 38,14552,398,1,0.37,768,4.3894,4.5339,4.5700,0.1192,0.1325,0.1333,0.0948,0.6749,0.7136,0.9854,0.8750,0.8697 41 | 39,18393,369,2,0.04,768,4.3986,4.5308,4.5635,0.1189,0.1325,0.1333,0.1716,0.6803,0.6940,0.9693,0.8727,0.8691 42 | 40,23249,75,2,0.43,768,4.3936,4.5189,4.5551,0.1238,0.1325,0.1333,0.2748,0.7523,0.7645,0.9483,0.8720,0.8683 43 | 41,29386,179,1,0.18,768,4.3826,4.5240,4.5640,0.1228,0.1325,0.1333,0.1624,0.5838,0.6153,0.9701,0.8880,0.8827 44 | 42,37143,442,0,0.09,768,4.4086,4.5086,4.5270,0.1234,0.1325,0.1333,0.2017,0.5529,0.5507,0.9658,0.8955,0.8931 45 | 43,46947,111,0,0.14,768,4.4489,4.4949,4.5047,0.1267,0.1325,0.1333,0.2254,0.5133,0.5187,0.9602,0.9006,0.8981 46 | 44,59340,242,0,0.37,768,4.4787,4.4925,4.4998,0.1266,0.1325,0.1333,0.2219,0.4753,0.4802,0.9573,0.9075,0.9059 47 | 45,75005,107,0,0.35000000000000003,768,4.4953,4.4954,4.4981,0.1297,0.1325,0.1333,0.2393,0.4677,0.4740,0.9526,0.9073,0.9066 48 | 46,94804,150,0,0.11,768,4.4842,4.4942,4.4978,0.1305,0.1325,0.1333,0.2268,0.4412,0.4489,0.9561,0.9131,0.9112 49 | 47,119830,38,2,0.41000000000000003,768,4.4817,4.4899,4.4962,0.1295,0.1325,0.1333,0.7464,0.8550,0.8317,0.8522,0.8420,0.8393 50 | 48,151463,248,2,0.23,768,4.4829,4.4829,4.4924,0.1259,0.1325,0.1333,0.1461,0.3456,0.3536,0.9694,0.9307,0.9291 51 | 49,191445,670,1,0.29,768,4.4810,4.4815,4.4941,0.1229,0.1325,0.1333,0.1867,0.3392,0.3516,0.9608,0.9304,0.9263 52 | -------------------------------------------------------------------------------- /checkpoints/dep_label/english/mlp/roberta/all_results.tsv: -------------------------------------------------------------------------------- 1 | run,ndata,hidden_size,nlayers,dropout,embedding_size,train_loss,dev_loss,test_loss,train_acc,dev_acc,test_acc,base_train_loss,base_dev_loss,base_test_loss,base_train_acc,base_dev_acc,base_test_acc 2 | 0,1,776,0,0.09,768,5.0150,5.5224,5.5219,1.0000,0.1325,0.1333,0.0022,5.5952,5.5920,1.0000,0.1325,0.1333 3 | 1,2,433,2,0.27,768,5.0297,5.5319,5.5304,0.5000,0.0092,0.0109,0.0001,9.2408,9.2244,1.0000,0.0493,0.0505 4 | 2,3,310,1,0.38,768,4.7677,5.4915,5.4897,0.6667,0.1325,0.1333,0.0022,6.8361,6.8262,1.0000,0.1394,0.1411 5 | 3,4,300,1,0.42,768,4.8514,5.4744,5.4744,0.5000,0.1325,0.1333,0.0028,7.7359,7.7305,1.0000,0.1472,0.1434 6 | 4,5,1009,2,0.39,768,4.9072,5.4385,5.4386,0.4000,0.1325,0.1333,0.0000,14.1118,14.1154,1.0000,0.1578,0.1577 7 | 5,6,657,2,0.41000000000000003,768,4.9491,5.4467,5.4476,0.3333,0.1325,0.1333,0.0002,10.9247,10.9472,1.0000,0.1516,0.1484 8 | 6,8,455,2,0.23,768,4.9080,5.4397,5.4411,0.2500,0.0092,0.0109,0.0002,11.1422,11.1774,1.0000,0.1265,0.1248 9 | 7,10,103,2,0.09,768,4.7008,5.4216,5.4221,0.3000,0.0092,0.0109,0.0050,7.4139,7.4482,1.0000,0.1367,0.1361 10 | 8,13,203,2,0.14,768,4.5678,5.3785,5.3780,0.3077,0.0092,0.0109,0.0014,8.1310,8.1198,1.0000,0.1914,0.1962 11 | 9,16,150,1,0.31,768,4.6705,5.3296,5.3292,0.2500,0.0092,0.0109,0.0104,6.3322,6.3240,1.0000,0.2169,0.2208 12 | 10,21,72,0,0.15,768,4.6939,5.2563,5.2569,0.1905,0.0092,0.0109,0.1093,5.5106,5.5072,1.0000,0.2131,0.2169 13 | 11,26,467,2,0.27,768,4.5853,5.2066,5.2091,0.1923,0.1325,0.1333,0.0009,9.6258,9.5590,1.0000,0.2733,0.2715 14 | 12,33,377,2,0.5,768,4.4091,5.1400,5.1428,0.2121,0.1325,0.1333,0.0101,8.2829,8.2704,1.0000,0.3203,0.3200 15 | 13,42,436,0,0.44,768,4.5329,5.0996,5.1013,0.1667,0.1325,0.1333,0.2467,5.1802,5.1560,1.0000,0.2897,0.2895 16 | 14,53,382,0,0.1,768,4.5302,5.0520,5.0532,0.1887,0.1325,0.1333,0.2035,4.8479,4.8247,1.0000,0.3687,0.3684 17 | 15,67,326,2,0.5,768,4.5732,5.0467,5.0505,0.1642,0.1325,0.1333,0.0690,6.4750,6.4402,1.0000,0.3753,0.3782 18 | 16,84,326,2,0.49,768,4.4723,4.9956,4.9975,0.1548,0.0871,0.0859,0.0808,5.5345,5.5003,1.0000,0.3827,0.3824 19 | 17,106,247,2,0.44,768,4.5024,4.9224,4.9228,0.1415,0.0871,0.0859,0.1416,4.7376,4.7112,0.9906,0.4096,0.4124 20 | 18,134,179,2,0.05,768,4.4543,4.8790,4.8771,0.1418,0.0871,0.0859,0.0192,4.9864,4.9319,1.0000,0.4411,0.4434 21 | 19,170,137,2,0.02,768,4.5188,4.7848,4.7849,0.1235,0.0871,0.0859,0.0341,3.5763,3.5732,1.0000,0.4684,0.4771 22 | 20,215,786,2,0.23,768,4.4632,4.7362,4.7369,0.1302,0.0788,0.0795,0.0015,3.7617,3.7951,1.0000,0.5334,0.5399 23 | 21,271,410,1,0.31,768,4.4510,4.6909,4.6923,0.1218,0.0871,0.0859,0.0230,2.6002,2.6280,1.0000,0.5563,0.5599 24 | 22,343,388,1,0.37,768,4.4696,4.6608,4.6669,0.1254,0.0871,0.0859,0.0358,2.3502,2.3989,1.0000,0.5928,0.5919 25 | 23,433,38,2,0.25,768,4.4121,4.6623,4.6696,0.1316,0.0871,0.0859,0.6306,2.6174,2.6613,0.9376,0.5226,0.5259 26 | 24,548,467,1,0.41000000000000003,768,4.3892,4.6403,4.6492,0.1296,0.0871,0.0859,0.0415,2.1747,2.2289,1.0000,0.6240,0.6215 27 | 25,692,176,0,0.09,768,4.4028,4.6129,4.6255,0.1272,0.0871,0.0859,0.0846,2.1539,2.1883,1.0000,0.6327,0.6289 28 | 26,875,57,2,0.02,768,4.4205,4.5923,4.6057,0.1223,0.0871,0.0859,0.0552,2.2351,2.2880,1.0000,0.6475,0.6458 29 | 27,1106,968,2,0.15,768,4.4684,4.5463,4.5648,0.1103,0.0871,0.0859,0.0029,2.3342,2.4877,1.0000,0.7068,0.7035 30 | 28,1398,268,0,0.01,768,4.4545,4.5212,4.5432,0.1173,0.1325,0.1333,0.0617,1.6810,1.7254,1.0000,0.7123,0.7072 31 | 29,1767,270,0,0.3,768,4.4411,4.5226,4.5464,0.1160,0.1325,0.1333,0.0658,1.5043,1.5623,1.0000,0.7441,0.7429 32 | 30,2234,596,2,0.24,768,4.4170,4.5303,4.5576,0.1164,0.1325,0.1333,0.3307,1.5202,1.6488,0.9490,0.7462,0.7431 33 | 31,2823,694,0,0.49,768,4.4176,4.5238,4.5528,0.1162,0.1325,0.1333,0.1109,1.2624,1.3350,0.9982,0.7864,0.7827 34 | 32,3568,837,0,0.48,768,4.4223,4.5204,4.5465,0.1174,0.1325,0.1333,0.1167,1.1806,1.2397,0.9975,0.8000,0.7954 35 | 33,4510,487,2,0.4,768,4.4171,4.5164,4.5441,0.1211,0.1325,0.1333,0.1660,1.0416,1.0772,0.9752,0.8186,0.8133 36 | 34,5701,418,0,0.11,768,4.4095,4.5255,4.5558,0.1161,0.1325,0.1333,0.1008,1.0574,1.1193,0.9989,0.8207,0.8154 37 | 35,7206,407,0,0.05,768,4.3972,4.5355,4.5675,0.1153,0.0871,0.0859,0.1412,1.0340,1.1004,0.9968,0.8268,0.8212 38 | 36,9108,657,0,0.0,768,4.4016,4.5345,4.5669,0.1177,0.1325,0.1333,0.1683,0.9738,1.0434,0.9940,0.8395,0.8314 39 | 37,11513,442,1,0.22,768,4.4035,4.5351,4.5687,0.1195,0.1325,0.1333,0.1808,0.8252,0.8534,0.9764,0.8550,0.8502 40 | 38,14552,398,1,0.37,768,4.3894,4.5339,4.5700,0.1192,0.1325,0.1333,0.1620,0.7524,0.7883,0.9760,0.8622,0.8571 41 | 39,18393,369,2,0.04,768,4.3986,4.5308,4.5635,0.1189,0.1325,0.1333,0.2043,0.8224,0.8335,0.9646,0.8551,0.8495 42 | 40,23249,75,2,0.43,768,4.3936,4.5189,4.5551,0.1238,0.1325,0.1333,0.3264,0.8399,0.8643,0.9448,0.8573,0.8543 43 | 41,29386,179,1,0.18,768,4.3826,4.5240,4.5640,0.1228,0.1325,0.1333,0.1876,0.6500,0.6828,0.9670,0.8785,0.8743 44 | 42,37143,442,0,0.09,768,4.4086,4.5086,4.5270,0.1234,0.1325,0.1333,0.2573,0.6579,0.6476,0.9621,0.8777,0.8803 45 | 43,46947,111,0,0.14,768,4.4489,4.4949,4.5047,0.1267,0.1325,0.1333,0.2448,0.5987,0.5848,0.9615,0.8880,0.8908 46 | 44,59340,242,0,0.37,768,4.4787,4.4925,4.4998,0.1266,0.1325,0.1333,0.2898,0.5744,0.5645,0.9490,0.8907,0.8944 47 | 45,75005,107,0,0.35000000000000003,768,4.4953,4.4954,4.4981,0.1297,0.1325,0.1333,0.2687,0.5462,0.5391,0.9506,0.8956,0.8983 48 | 46,94804,150,0,0.11,768,4.4842,4.4942,4.4978,0.1305,0.1325,0.1333,0.2862,0.5218,0.5187,0.9493,0.9019,0.9015 49 | 47,119830,38,2,0.41000000000000003,768,4.4817,4.4899,4.4962,0.1295,0.1325,0.1333,0.9256,1.0447,1.0189,0.8272,0.8180,0.8167 50 | 48,151463,248,2,0.23,768,4.4829,4.4829,4.4924,0.1259,0.1325,0.1333,0.2065,0.4125,0.4058,0.9575,0.9193,0.9178 51 | 49,191445,670,1,0.29,768,4.4810,4.4815,4.4941,0.1229,0.1325,0.1333,0.1259,0.3696,0.3833,0.9765,0.9269,0.9235 52 | --------------------------------------------------------------------------------