├── .gitignore ├── README.md ├── dist ├── mutate-nlp-0.1.0.tar.gz ├── mutate-nlp-0.1.1.tar.gz ├── mutate-nlp-0.1.2.tar.gz ├── mutate_nlp-0.1.0-py3-none-any.whl ├── mutate_nlp-0.1.1-py3-none-any.whl └── mutate_nlp-0.1.2-py3-none-any.whl ├── mutate ├── __init__.py ├── infer.py ├── parsers │ ├── __init__.py │ ├── parser.py │ └── text_classification.py ├── pipelines │ ├── __init__.py │ └── text_classification.py └── prompt_datasets │ ├── __init__.py │ ├── templates.py │ ├── text_classification.py │ └── utils.py ├── poetry.lock ├── pyproject.toml ├── requirements.txt └── setup.py /.gitignore: -------------------------------------------------------------------------------- 1 | .vscode/ 2 | **/__pycache__/* 3 | scripts/ 4 | debug/ -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 🦠 Mutate
2 | 3 | A library to synthesize text datasets using Large Language Models (LLM). Mutate reads through the examples in the dataset and 4 | generates similar examples using auto generated few shot prompts. 5 | 6 | ## 1. Installation 7 | 8 | ``` 9 | pip install mutate-nlp 10 | ``` 11 | 12 | or 13 | 14 | ``` 15 | pip install git+https://github.com/infinitylogesh/mutate 16 | ``` 17 | 18 | 19 | ## 2. Usage 20 | 21 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1dPDVl3EvmsnJc7lxWYdAnTtlJgJjR2O2?usp=sharing) 22 | 23 | 24 | ### 2.1 Synthesize text data from local csv files 25 | 26 | ```python 27 | from mutate import pipeline 28 | 29 | pipe = pipeline("text-classification-synthesis", 30 | model="EleutherAI/gpt-neo-2.7B", 31 | device=1) 32 | 33 | task_desc = "Each item in the following contains movie reviews and corresponding sentiments. Possible sentimets are neg and pos" 34 | 35 | 36 | # returns a python generator 37 | text_synth_gen = pipe("csv", 38 | data_files=["local/path/sentiment_classfication.csv"], 39 | task_desc=task_desc, 40 | text_column="text", 41 | label_column="label", 42 | text_column_alias="Comment", 43 | label_column_alias="sentiment", 44 | shot_count=5, 45 | class_names=["pos","neg"]) 46 | 47 | #Loop through the generator to synthesize examples by class 48 | for synthesized_examples in text_synth_gen: 49 | print(synthesized_examples) 50 | ``` 51 | 52 |
53 | Show Output 54 | 55 | ```python 56 | { 57 | "text": ["The story was very dull and was a waste of my time. This was not a film I would ever watch. The acting was bad. I was bored. There were no surprises. They showed one dinosaur,", 58 | "I did not like this film. It was a slow and boring film, it didn't seem to have any plot, there was nothing to it. The only good part was the ending, I just felt that the film should have ended more abruptly."] 59 | "label":["neg","neg"] 60 | } 61 | 62 | { 63 | "text":["The Bell witch is one of the most interesting, yet disturbing films of recent years. It’s an odd and unique look at a very real, but very dark issue. With its mixture of horror, fantasy and fantasy adventure, this film is as much a horror film as a fantasy film. And it‘s worth your time. While the movie has its flaws, it is worth watching and if you are a fan of a good fantasy or horror story, you will not be disappointed."], 64 | "label":["pos"] 65 | } 66 | 67 | # and so on ..... 68 | 69 | ``` 70 |
71 | 72 | 73 | ### 2.2 Synthesize text data from 🤗 datasets 74 | 75 | Under the hood Mutate uses the wonderful 🤗 datasets library for dataset processing, So it supports 🤗 datasets out of the box. 76 | 77 | ```python 78 | 79 | from mutate import pipeline 80 | 81 | pipe = pipeline("text-classification-synthesis", 82 | model="EleutherAI/gpt-neo-2.7B", 83 | device=1) 84 | 85 | task_desc = "Each item in the following contains customer service queries expressing the mentioned intent" 86 | 87 | synthesizerGen = pipe("banking77", 88 | task_desc=task_desc, 89 | text_column="text", 90 | label_column="label", 91 | # if the `text_column` doesn't have a meaningful value 92 | text_column_alias="Queries", 93 | label_column_alias="Intent", # if the `label_column` doesn't have a meaningful value 94 | shot_count=5, 95 | dataset_args=["en"]) 96 | 97 | 98 | for exp in synthesizerGen: 99 | print(exp) 100 | 101 | ``` 102 | 103 |
104 | Show Output 105 | 106 | ```python 107 | {"text":["How can i know if my account has been activated? (This is the one that I am confused about)", 108 | "Thanks! My card activated"], 109 | "label":["activate_my_card", 110 | "activate_my_card"] 111 | } 112 | 113 | { 114 | "text": ["How do i activate this new one? Is it possible?", 115 | "what is the activation process for this card?"], 116 | "label":["activate_my_card", 117 | "activate_my_card"] 118 | } 119 | 120 | # and so on ..... 121 | 122 | ``` 123 |
124 | 125 | 126 | ### 2.3 I am feeling lucky : Infinetly loop through the dataset to generate examples indefinetly 127 | 128 | **Caution**: Infinetly looping through the dataset has a higher chance of duplicate examples to be generated. 129 | 130 | ```python 131 | 132 | from mutate import pipeline 133 | 134 | pipe = pipeline("text-classification-synthesis", 135 | model="EleutherAI/gpt-neo-2.7B", 136 | device=1) 137 | 138 | task_desc = "Each item in the following contains movie reviews and corresponding sentiments. Possible sentimets are neg and pos" 139 | 140 | 141 | # returns a python generator 142 | text_synth_gen = pipe("csv", 143 | data_files=["local/path/sentiment_classfication.csv"], 144 | task_desc=task_desc, 145 | text_column="text", 146 | label_column="label", 147 | text_column_alias="Comment", 148 | label_column_alias="sentiment", 149 | class_names=["pos","neg"], 150 | # Flag to generate indefinite examples 151 | infinite_loop=True) 152 | 153 | #Infinite loop 154 | for exp in synthesizerGen: 155 | print(exp) 156 | ``` 157 | 158 | 159 | ## 3. Support 160 | ### 3.1 Currently supports 161 | - **Text classification dataset synthesis** : Few Shot text data synsthesize for text classification datasets using Causal LLMs ( GPT like ) 162 | 163 | ### 3.2 Roadmap: 164 | - **Other types of text Dataset synthesis** - NER , sentence pairs etc 165 | - Finetuning support for better quality generation 166 | - Pseudo labelling 167 | 168 | 169 | ## 4. Credit 170 | - [EleutherAI](https://eluether.ai) for democratizing Large LMs. 171 | - This library uses 🤗 [Datasets](https://huggingface.co/docs/datasets) and 🤗 [Transformers](https://huggingface.co/docs/transformers) for processing datasets and models. 172 | 173 | 174 | ## 5. References 175 | 176 | The Idea of generating examples from Large Language Model is inspired by the works below, 177 | - [A Few More Examples May Be Worth Billions of Parameters](https://arxiv.org/abs/2110.04374) by Yuval Kirstain, Patrick Lewis, Sebastian Riedel, Omer Levy 178 | - [GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation](https://arxiv.org/abs/2104.08826) by Kang Min Yoo, Dongju Park, Jaewook Kang, Sang-Woo Lee, Woomyeong Park 179 | - [Data Augmentation using Pre-trained Transformer Models](https://arxiv.org/abs/2003.02245) by Varun Kumar, Ashutosh Choudhary, Eunah Cho 180 | 181 | -------------------------------------------------------------------------------- /dist/mutate-nlp-0.1.0.tar.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/infinitylogesh/mutate/216534466ed19e6e449ed93f1c6cdd39b9c679c9/dist/mutate-nlp-0.1.0.tar.gz -------------------------------------------------------------------------------- /dist/mutate-nlp-0.1.1.tar.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/infinitylogesh/mutate/216534466ed19e6e449ed93f1c6cdd39b9c679c9/dist/mutate-nlp-0.1.1.tar.gz -------------------------------------------------------------------------------- /dist/mutate-nlp-0.1.2.tar.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/infinitylogesh/mutate/216534466ed19e6e449ed93f1c6cdd39b9c679c9/dist/mutate-nlp-0.1.2.tar.gz -------------------------------------------------------------------------------- /dist/mutate_nlp-0.1.0-py3-none-any.whl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/infinitylogesh/mutate/216534466ed19e6e449ed93f1c6cdd39b9c679c9/dist/mutate_nlp-0.1.0-py3-none-any.whl -------------------------------------------------------------------------------- /dist/mutate_nlp-0.1.1-py3-none-any.whl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/infinitylogesh/mutate/216534466ed19e6e449ed93f1c6cdd39b9c679c9/dist/mutate_nlp-0.1.1-py3-none-any.whl -------------------------------------------------------------------------------- /dist/mutate_nlp-0.1.2-py3-none-any.whl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/infinitylogesh/mutate/216534466ed19e6e449ed93f1c6cdd39b9c679c9/dist/mutate_nlp-0.1.2-py3-none-any.whl -------------------------------------------------------------------------------- /mutate/__init__.py: -------------------------------------------------------------------------------- 1 | from typing import Dict, List, Optional, Union 2 | 3 | from transformers import PreTrainedModel 4 | 5 | from mutate.pipelines.text_classification import TextClassificationSynthesize 6 | 7 | SUPPORTED_TASKS = {"text-classification-synthesis": TextClassificationSynthesize} 8 | 9 | 10 | def pipeline( 11 | task: str, 12 | model: Union[str, PreTrainedModel], 13 | device: Optional[int] = -1, 14 | generation_kwargs: Optional[Dict] = {}, 15 | **kwargs, 16 | ): 17 | """ 18 | 19 | A factory method to return the pipeline corresponding to a given task. 20 | 21 | Pipelines are made of: 22 | - A `Prompt Dataset` - that converts a given dataset to suitable prompts 23 | - Model inference - to generate text from the prompt 24 | - A `Parser` to parse the generated texts for examples 25 | 26 | Args: 27 | task (str): Name of the task to synthesize or psuedo label examples. Currently supported tasks are: 28 | 29 | - `text-classification-synthesize` - will return `TextClassificationSynthesize` pipeline for synthesizing examples for text classification. 30 | 31 | model (Union[str, PreTrainedModel]): Path or 🤗 model hub identifier. Currently supports Causal LM models only. 32 | device (Optional[int]): GPU Device to run the inference. Defaults to -1, runs on CPU. 33 | generate_kwargs : Keyword arguments to override default generation params. This will be passed as generation params to model.generate - 34 | https://github.com/huggingface/transformers/blob/fcb4f11c9232cee2adce8140a3a7689578ea97de/src/transformers/generation_utils.py#L803 35 | 36 | Examples: 37 | -------- 38 | 39 | Using the default generation params: 40 | 41 | >> pipe = pipeline( 42 | "text-classification-synthesis", 43 | model="EleutherAI/gpt-neo-2.7B", 44 | device=1, 45 | ) 46 | 47 | Overriding the generation params: 48 | 49 | >> pipe = pipeline( 50 | "text-classification-synthesis", 51 | model="EleutherAI/gpt-neo-2.7B", 52 | device=1, 53 | generation_kwargs = { 54 | "max_length":300, 55 | "do_sample":True, 56 | "num_return_sequences":3, 57 | "top_k":40, 58 | "top_p":0.80, 59 | "early_stopping":True, 60 | "no_repeat_ngram_size":2 61 | }) 62 | 63 | Using 🤗 Datasets: 64 | 65 | >> task_desc = "Each item in the following contains customer service queries expressing the mentioned intent" 66 | >> synthesizerGen = pipe( 67 | "banking77", 68 | task_desc=task_desc, 69 | text_column="text", 70 | label_column="label", 71 | text_column_alias="Queries", # as the `text_column` doesn't have a meaningful value 72 | label_column_alias="Intent", # as the `label_column` doesn't have a meaningful value 73 | dataset_args=["en"], 74 | ) 75 | 76 | >> for exp in synthesizerGen: 77 | print(exp) 78 | 79 | 80 | Using local csv files 81 | 82 | >> task_desc = "Each item in the following contains customer comments expressing the mentioned sentiment" 83 | >> pipe( 84 | "csv", 85 | data_files=["local/path/sentiment_classfication.csv"], 86 | task_desc=task_desc, 87 | text_column="text", 88 | label_column="label", 89 | text_column_alias="Comment", 90 | label_column_alias="sentiment", 91 | class_names=["positive","negative","neutral"], 92 | dataset_args=["en"], 93 | ) 94 | 95 | Returns: 96 | the corresponding pipeline class for the task 97 | """ 98 | 99 | if task in SUPPORTED_TASKS: 100 | pipeline_class = SUPPORTED_TASKS[task] 101 | else: 102 | raise ValueError( 103 | f"Task - {task} is not supported. Supported tasks -" 104 | f" {SUPPORTED_TASKS.keys()}" 105 | ) 106 | 107 | return pipeline_class(model, device, **generation_kwargs) 108 | -------------------------------------------------------------------------------- /mutate/infer.py: -------------------------------------------------------------------------------- 1 | from transformers import AutoModelForCausalLM, AutoModelForSeq2SeqLM, AutoTokenizer, AutoConfig 2 | import numpy as np 3 | import torch 4 | from typing import Optional, List, Dict, Union 5 | from torch.utils.data import DataLoader, Dataset 6 | from tokenizers import AddedToken 7 | from tqdm import tqdm 8 | 9 | 10 | class TextGeneration: 11 | def __init__(self, model_name: str, device: Optional[int] = -1): 12 | """ 13 | Class for text generation inference. 14 | Currently supports only CausalLM models 15 | 16 | Args: 17 | model_name (str): model name or path to load model from model hub / local path 18 | device (Optional[int]): GPU Device to run the inference. Positive integer values runs on corresponding device ID. 19 | Defaults to -1 which runs on "cpu". 20 | encoder_decoder (bool): Is the model an encoder-decoder LM model , If not the model will be considered as decoder only. 21 | Defaults to False 22 | """ 23 | self.tokenizer = AutoTokenizer.from_pretrained(model_name) 24 | model_config = AutoConfig.from_pretrained(model_name) 25 | self.is_encoder_decoder = model_config.is_encoder_decoder 26 | 27 | if not self.is_encoder_decoder: 28 | self.model = AutoModelForCausalLM.from_pretrained(model_name,pad_token_id=self.tokenizer.eos_token_id) 29 | else: 30 | self.model = AutoModelForSeq2SeqLM.from_pretrained(model_name,pad_token_id=self.tokenizer.eos_token_id) 31 | # T5 tokenizer does not have next line token in the vocab. This is a work around to make it compatible 32 | # with the prompt templates with new line chars. 33 | # Related issue - https://github.com/google-research/text-to-text-transfer-transformer/issues/390#issuecomment-688417703 34 | self.tokenizer.add_tokens(AddedToken("\n",normalized=False)) 35 | self.model.resize_token_embeddings(len(self.tokenizer)) 36 | 37 | # Done to avoid error from tokenizer, when padding is set True for batch encoding. 38 | # as per this - https://github.com/huggingface/transformers/issues/4122 39 | self.tokenizer.pad_token = self.tokenizer.eos_token 40 | self.device = torch.device(f"cuda:{device}" if device>=0 else "cpu") 41 | self.model.to(self.device) 42 | self.model.eval() 43 | 44 | def run_single_batch( 45 | self, 46 | batch, 47 | is_include_prompt_in_generation: Optional[bool] = False, 48 | ignore_prompt_last_line: Optional[bool] = True, 49 | generate_args: Optional[Dict[str, str]] = None, 50 | ): 51 | batch_size = len(batch["input_ids"]) 52 | generated_sequences = self.model.generate( 53 | batch["input_ids"], attention_mask=batch["attention_mask"], **generate_args 54 | ) 55 | generated_sequences = generated_sequences.cpu() 56 | num_return_sequences = len(generated_sequences) // batch_size 57 | 58 | batch_generated_texts = [] 59 | 60 | for idx, generated_sequence in enumerate(generated_sequences): 61 | text = self.tokenizer.decode( 62 | generated_sequence, 63 | skip_special_tokens=True, 64 | clean_up_tokenization_spaces=True, 65 | ) 66 | 67 | prompt_idx = idx // num_return_sequences 68 | decoded_prompt = self.tokenizer.decode( 69 | batch["input_ids"][prompt_idx], 70 | skip_special_tokens=True, 71 | clean_up_tokenization_spaces=True, 72 | ) 73 | 74 | if self.is_encoder_decoder: 75 | # encoder decoder models doesn't return prompts in their generation 76 | # appended with decoded prompt to normalize the generation similar to decoder only 77 | text = decoded_prompt+text 78 | 79 | prompt_length = len(decoded_prompt) 80 | 81 | if ignore_prompt_last_line and "\n" in decoded_prompt: 82 | prompt_length -= len(decoded_prompt.split("\n")[-1]) 83 | 84 | generated_text = text[prompt_length:] 85 | 86 | if is_include_prompt_in_generation: 87 | all_text = batch["prompts"][idx] + generated_text 88 | else: 89 | all_text = generated_text 90 | 91 | batch_generated_texts.append(all_text) 92 | 93 | return batch_generated_texts 94 | 95 | -------------------------------------------------------------------------------- /mutate/parsers/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/infinitylogesh/mutate/216534466ed19e6e449ed93f1c6cdd39b9c679c9/mutate/parsers/__init__.py -------------------------------------------------------------------------------- /mutate/parsers/parser.py: -------------------------------------------------------------------------------- 1 | from abc import ABC, abstractmethod 2 | from jinja2 import Template 3 | from typing import Dict, List, Optional 4 | 5 | 6 | 7 | class GenerationParser(ABC): 8 | def _get_regex( 9 | self, regex_template: str, template_params: Optional[Dict[str, str]] = {} 10 | ): 11 | 12 | if not template_params: 13 | return regex_template 14 | 15 | template = Template(regex_template) 16 | return template.render(**template_params) 17 | 18 | @abstractmethod 19 | def parse(self, generated_text: str) -> List[str]: 20 | raise NotImplementedError("Method to be implemented") 21 | 22 | 23 | -------------------------------------------------------------------------------- /mutate/parsers/text_classification.py: -------------------------------------------------------------------------------- 1 | import re 2 | from typing import Dict, List, Optional 3 | 4 | from mutate.parsers.parser import GenerationParser 5 | 6 | 7 | class TextClassificationSynthesizeParser(GenerationParser): 8 | regex_template = """.*{{label_title}}.*:.*{{label}}.*,.*{{example_title}}.*:(.*)""" 9 | 10 | def __init__( 11 | self, 12 | label_title: str, 13 | example_title: str, 14 | regex_template: Optional[str] = regex_template, 15 | ): 16 | """ 17 | Parser to parse generated text to extract synthesized examples for a given class 18 | 19 | Args: 20 | label_title (str): Name / title of the label that describes the classes. 21 | Example - For sentiment classfication, the label title can be `Sentiment` 22 | example_title (str): Name / title of the label that describes the classes 23 | Example - For sentiment classfication, the example title can be `Review` 24 | regex_template (Optional[str]): Regex and jinja template to parse example generated. Defaults to regex_template. 25 | """ 26 | 27 | self.template_params = { 28 | "label_title": label_title, 29 | "example_title": example_title, 30 | } 31 | 32 | def parse(self, generated_text: str, label: str) -> List[str]: 33 | """ 34 | Args: 35 | generated_text (str): Generated text from the LLM 36 | label (str): Class label 37 | 38 | Returns: 39 | List[str]: List of parsed examples from the generated texts 40 | """ 41 | 42 | self.template_params["label"] = label 43 | regex_pattern = self._get_regex(self.regex_template, self.template_params) 44 | return re.findall(regex_pattern, generated_text) 45 | 46 | 47 | class TextClassificationPseudolabelParser(GenerationParser): 48 | regex_template = ( 49 | """.*{{label_title}}.*:.*(.*).*,.*{{example_title}}.*:.*{{example}}""" 50 | ) 51 | 52 | def __init__( 53 | self, 54 | label_title: str, 55 | example_title: str, 56 | regex_template: Optional[str] = regex_template, 57 | ): 58 | """ 59 | Parser to parse generated text to extract lables generated for a given example 60 | by the text generation model 61 | 62 | Args: 63 | label_title (str): Name / title of the label that describes the classes. 64 | Example - For sentiment classfication, the label title can be `Sentiment` 65 | example_title (str): Name / title of the label that describes the classes 66 | Example - For sentiment classfication, the example title can be `Review` 67 | regex_template (Optional[str]): Regex and jinja template to parse example generated. Defaults to regex_template. 68 | """ 69 | 70 | self.template_params = { 71 | "label_title": label_title, 72 | "example_title": example_title, 73 | } 74 | 75 | def parse(self, generated_text: str, example: str) -> List[str]: 76 | self.template_params["example"] = example 77 | regex_pattern = self._get_regex(self.regex_template, self.template_params) 78 | return re.findall(regex_pattern, generated_text) 79 | -------------------------------------------------------------------------------- /mutate/pipelines/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/infinitylogesh/mutate/216534466ed19e6e449ed93f1c6cdd39b9c679c9/mutate/pipelines/__init__.py -------------------------------------------------------------------------------- /mutate/pipelines/text_classification.py: -------------------------------------------------------------------------------- 1 | from functools import partial 2 | from itertools import cycle 3 | from typing import Dict, List, Optional, Union 4 | 5 | from torch.utils.data import DataLoader 6 | from transformers import PreTrainedModel, set_seed 7 | 8 | from mutate.infer import TextGeneration 9 | from mutate.parsers.text_classification import TextClassificationSynthesizeParser 10 | from mutate.prompt_datasets.text_classification import TextClassSynthesizePromptDataset 11 | 12 | 13 | class TextClassificationSynthesize: 14 | generate_kwargs = { 15 | "max_length": 300, 16 | "do_sample": True, 17 | "num_return_sequences": 3, 18 | "top_k": 40, 19 | "top_p": 0.80, 20 | "early_stopping": True, 21 | "no_repeat_ngram_size": 2, 22 | } 23 | 24 | def __init__( 25 | self, 26 | model: Union[str, PreTrainedModel], 27 | device: Optional[int] = -1, 28 | **generate_kwargs 29 | ): 30 | """ 31 | Pipeline to synthesize Text classification examples from a given dataset. 32 | 33 | Pipelines are made of: 34 | - A `Prompt Dataset` - that converts a given dataset to suitable prompts 35 | - Model inference - to generate text from the prompt 36 | - A `Parser` to parse the generated texts for examples 37 | 38 | Args: 39 | model (Union[str, PreTrainedModel]): Path or 🤗 model hub identifier. Currently supports Causal LM models only. 40 | device (Optional[int], optional): GPU Device to run the inference. Defaults to -1, runs on CPU. 41 | generate_kwargs : Keyword arguments to override default generation params. This will be passed as generation params to model.generate - 42 | https://github.com/huggingface/transformers/blob/fcb4f11c9232cee2adce8140a3a7689578ea97de/src/transformers/generation_utils.py#L803 43 | 44 | Examples: 45 | -------- 46 | 47 | Using the default generation params: 48 | 49 | >> pipe = TextClassificationSynthesize( 50 | model="EleutherAI/gpt-neo-2.7B", 51 | device=1, 52 | ) 53 | 54 | Overriding the generation params: 55 | 56 | >> pipe = TextClassificationSynthesize( 57 | model="EleutherAI/gpt-neo-2.7B", 58 | device=1, 59 | max_length=300, 60 | do_sample=True, 61 | num_return_sequences=3, 62 | top_k=40, 63 | top_p=0.80, 64 | early_stopping=True, 65 | no_repeat_ngram_size=2, 66 | ) 67 | 68 | """ 69 | self.infer = TextGeneration(model_name=model, device=device) 70 | self._collate_fn = partial( 71 | TextClassSynthesizePromptDataset._collate_fn, 72 | self.infer.tokenizer, 73 | self.infer.device, 74 | ) 75 | self.generate_kwargs = ( 76 | self.generate_kwargs if not generate_kwargs else generate_kwargs 77 | ) 78 | 79 | def __call__( 80 | self, 81 | dataset_path: str, 82 | text_column: str, 83 | label_column: str, 84 | task_desc: str, 85 | split: Optional[str] = None, 86 | data_files: Union[Dict, List] = None, 87 | data_dir: Optional[str] = None, 88 | text_column_alias: Optional[str] = None, 89 | label_column_alias: Optional[str] = None, 90 | class_names: Optional[List[str]] = [], 91 | dataset_args: Optional[List[str]] = [], 92 | dataset_kwargs: Optional[Dict[str, str]] = {}, 93 | batch_size: Optional[int] = 2, 94 | shot_count: Optional[int] = 5, 95 | infinite_loop: Optional[bool] = False, 96 | **kwargs 97 | ): 98 | """ 99 | Args: 100 | dataset_path (str): Path or name of the dataset. 101 | Can be Huggingface dataset identifier like - `allenai/c4` or 102 | For local datasets in CSV,JSON - this param can take `csv`,`json` as values 103 | and corresponding path can be sepecified in the `data_files` param. 104 | task_desc (str): Description about the text classification task. A task description will improve 105 | model generation quality. This is a good source about helpful task descriptions - 106 | https://docs.cohere.ai/prompt-engineering-wiki 107 | text_column (str): Name of the column in the dataset which has examples to be used in the prompt and similar 108 | examples are to be generated by the model 109 | label_column (str): Name of the column that contains the label / classes 110 | split (Optional[str]): Which Split of the dataset to load. Usage is same as in 🤗 Datasets. Defaults to `train` split. 111 | data_files (Union[Dict, List], optional): Paths to source data files . Usage is same as in 🤗 Datasets. Defaults to None. 112 | data_dir (Optional[str], optional): Directory of the dataset configuration. Usage is same as in 🤗 Datasets. Defaults to None. 113 | text_column_alias (Optional[str]):alias of `text_column`.If the `text_column` does not have a meaningful title. A meaningful title of the example can 114 | be specified here. Defaults to None. 115 | label_column_alias (Optional[str], optional): alias of `label_column`.If the `label_column` does not have a meaningful title. A meaningful title of the class can 116 | be specified here. Defaults to None. 117 | dataset_args (Optional[List[str]]): additional args to load the 🤗 Dataset. Defaults to []. 118 | dataset_kwargs (Optional[Dict[str,str]]): additional keyword arguments to load the 🤗 Dataset. Defaults to {}. 119 | class_names (Optional[List[str]], optional): Class names to be specified. 120 | If class names are not part of huggingface datasets features.if values in the label columns are label encoded. 121 | class name order is expected to follow the same order as the label encoding.Defaults to None. 122 | batch_size (Optional[Int]): Batch size for inference. Defaults to 2. 123 | shot_count (Optional[int], optional): Number of examples to be used in the few shot prompt. Defaults to 5. 124 | infinite_loop (Optional[bool]): if `True`, loops through the same dataset indefinetly. Can help generate more examples. 125 | defaults to `False` 126 | 127 | Yields: 128 | [Dict[str,str]]: yields the generated examples and corresponding label as dictionary 129 | 130 | Examples: 131 | -------- 132 | 133 | Using 🤗 Datasets: 134 | 135 | >> pipe = TextClassificationSynthesize( 136 | model="EleutherAI/gpt-neo-2.7B", 137 | device=1, 138 | ) 139 | >> task_dec = "Each item in the following contains customer service queries expressing the mentioned intent" 140 | >> synthesizerGen = pipe( 141 | "banking77", 142 | task_desc=task_dec, 143 | text_column="text", 144 | label_column="label", 145 | text_column_alias="Queries", # since the `text_column` doesn't have a meaningful value 146 | label_column_alias="Intent", # since the `label_column` doesn't have a meaningful value 147 | dataset_args=["en"], 148 | ) 149 | 150 | >> for exp in synthesizerGen: 151 | print(exp) 152 | 153 | 154 | Using local csv files 155 | 156 | >> task_dec = "Each item in the following contains customer comments expressing the mentioned sentiment" 157 | >> pipe( 158 | "csv", 159 | data_files=["local/path/sentiment_classfication.csv"], 160 | task_desc=task_dec, 161 | text_column="text", 162 | label_column="label", 163 | text_column_alias="Comment", 164 | label_column_alias="sentiment", 165 | class_names=["positive","negative","neutral"], 166 | dataset_args=["en"], 167 | ) 168 | """ 169 | 170 | dataset = TextClassSynthesizePromptDataset( 171 | path=dataset_path, 172 | data_files=data_files, 173 | data_dir=data_dir, 174 | split=split, 175 | task_desc=task_desc, 176 | text_column=text_column, 177 | text_column_alias=text_column_alias, 178 | label_column=label_column, 179 | label_column_alias=label_column_alias, 180 | class_names=class_names, 181 | shot_count=shot_count, 182 | dataset_args=dataset_args, 183 | dataset_kwargs=dataset_kwargs, 184 | ) 185 | 186 | parser = TextClassificationSynthesizeParser( 187 | label_title=dataset.label_title, example_title=dataset.example_title 188 | ) 189 | 190 | prompt_dataloader = DataLoader( 191 | dataset, batch_size=batch_size, collate_fn=self._collate_fn 192 | ) 193 | 194 | if infinite_loop: 195 | prompt_dataloader = cycle(prompt_dataloader) 196 | 197 | for batch in prompt_dataloader: 198 | batch_parsed_examples = [] 199 | batch_class_names = [] 200 | batch_generated_texts = self.infer.run_single_batch( 201 | batch, generate_args=self.generate_kwargs 202 | ) 203 | 204 | num_return_sequences = len(batch_generated_texts) // len(batch["input_ids"]) 205 | 206 | for idx, generated_text in enumerate(batch_generated_texts): 207 | class_idx = idx // num_return_sequences 208 | class_name = batch["class_names"][class_idx] 209 | 210 | parsed_examples = parser.parse( 211 | generated_text=generated_text, label=class_name 212 | ) 213 | 214 | batch_parsed_examples.extend(parsed_examples) 215 | batch_class_names.extend([class_name] * len(parsed_examples)) 216 | 217 | yield {text_column: batch_parsed_examples, label_column: batch_class_names} 218 | -------------------------------------------------------------------------------- /mutate/prompt_datasets/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/infinitylogesh/mutate/216534466ed19e6e449ed93f1c6cdd39b9c679c9/mutate/prompt_datasets/__init__.py -------------------------------------------------------------------------------- /mutate/prompt_datasets/templates.py: -------------------------------------------------------------------------------- 1 | text_classification_synthesize_template = """ 2 | {{task_prompt}} 3 | {% for label,example in labelled_examples %} 4 | {{label_title}} : {{label}} , {{example_title}} : {{example}} 5 | {%- endfor %} 6 | {{label_title}} : {{labelled_examples[0][0]}} , {{example_title}} :""" 7 | 8 | text_classification_pseudo_label_template = """ 9 | {{task_prompt}} 10 | {% for label,example in labelled_examples %} 11 | {{example_title}} : {{example}} , {{label_tile}} : {{label}} 12 | {%- endfor %} 13 | {{example_title}} : {{unlabelled_example}} , {{label_title}} :""" -------------------------------------------------------------------------------- /mutate/prompt_datasets/text_classification.py: -------------------------------------------------------------------------------- 1 | from abc import ABC, abstractmethod 2 | from typing import Dict, Iterator, List, Optional, Union 3 | 4 | from datasets import load_dataset 5 | from jinja2 import Template 6 | from torch.utils.data import DataLoader, IterableDataset 7 | 8 | from mutate.prompt_datasets.templates import text_classification_synthesize_template 9 | from mutate.prompt_datasets.utils import _create_prompt_for_text_class_synthesize 10 | 11 | 12 | class TextClassSynthesizePromptDataset(IterableDataset): 13 | def __init__( 14 | self, 15 | path: str, 16 | task_desc: str, 17 | text_column: str, 18 | label_column: str, 19 | text_column_alias: Optional[str] = None, 20 | label_column_alias: Optional[str] = None, 21 | split: Optional[str] = None, 22 | data_files: Union[Dict, List] = None, 23 | data_dir: Optional[str] = None, 24 | dataset_args: Optional[List[str]] = [], 25 | dataset_kwargs: Optional[Dict[str, str]] = {}, 26 | class_names: Optional[List[str]] = None, 27 | shot_count: Optional[int] = 5, 28 | prompt_template: Optional[str] = text_classification_synthesize_template, 29 | ): 30 | """ 31 | Iterable dataset to Loop through the dataset by class and generate prompts. 32 | Uses 🤗 Datasets for loading and processing the dataset. 33 | Loading process is similar to 🤗 Datasets - 34 | https://huggingface.co/docs/datasets/loading.html 35 | 36 | Args: 37 | path (str): Path or name of the dataset. 38 | Can be Huggingface dataset identifier like - `allenai/c4` or 39 | For local datasets in CSV,JSON - this param can take `csv`,`json` as values 40 | and corresponding path can be sepecified in the `data_files` param. 41 | task_desc (str): Description about the text classification task. A task description will improve 42 | model generation quality. This is a good source about helpful task descriptions - 43 | https://docs.cohere.ai/prompt-engineering-wiki 44 | text_column (str): Name of the column in the dataset which has examples to be used in the prompt and similar 45 | examples are to be generated by the model 46 | label_column (str): Name of the column that contains the label / classes 47 | split (Optional[str]): Which Split of the dataset to load. Usage is same as in 🤗 Datasets. Defaults to `train` split. 48 | data_files (Union[Dict, List], optional): Paths to source data files . Usage is same as in 🤗 Datasets. Defaults to None. 49 | data_dir (Optional[str], optional): Directory of the dataset configuration. Usage is same as in 🤗 Datasets. Defaults to None. 50 | text_column_alias (Optional[str]):alias of `text_column`.If the `text_column` does not have a meaningful title. A meaningful title of the example can 51 | be specified here. Defaults to None. 52 | label_column_alias (Optional[str], optional): alias of `label_column`.If the `label_column` does not have a meaningful title. A meaningful title of the class can 53 | be specified here. Defaults to None. 54 | dataset_args (Optional[List[str]]): additional args to load the 🤗 Dataset. Defaults to []. 55 | dataset_kwargs (Optional[Dict[str,str]]): additional keyword arguments to load the 🤗 Dataset. Defaults to {}. 56 | class_names (Optional[List[str]], optional): Class names to be specified. 57 | If class names are not part of huggingface datasets features.if values in the label columns are label encoded. 58 | class name order is expected to follow the same order as the label encoding.Defaults to None. 59 | shot_count (Optional[int], optional): Number of examples to be used in the few shot prompt. Defaults to 5. 60 | 61 | Examples: 62 | --------- 63 | 64 | Using 🤗 Datasets: 65 | 66 | >> task_dec = "Each item in the following contains customer service queries expressing the mentioned intent" 67 | >> TextClassSynthesizePromptDataset( 68 | "banking77", 69 | task_desc=task_dec, 70 | text_column="text", 71 | label_column="label", 72 | text_column_alias="Queries", # since the `text_column` doesn't have a meaningful value 73 | label_column_alias="Intent", # since the `label_column` doesn't have a meaningful value 74 | dataset_args=["en"], 75 | ) 76 | 77 | 78 | Using local csv files 79 | 80 | >> task_dec = "Each item in the following contains customer comments expressing the mentioned sentiment" 81 | >> TextClassSynthesizePromptDataset( 82 | "csv", 83 | data_files=["local/path/sentiment_classfication.csv"], 84 | task_desc=task_dec, 85 | text_column="text", 86 | label_column="label", 87 | example_title="Comment", 88 | label_title="sentiment", 89 | class_names=["positive","negative","neutral"], 90 | dataset_args=["en"], 91 | ) 92 | """ 93 | 94 | super().__init__() 95 | 96 | self.dataset = load_dataset( 97 | path, 98 | *dataset_args, 99 | split=split, 100 | data_files=data_files, 101 | data_dir=data_dir, 102 | **dataset_kwargs, 103 | ) 104 | self.text_column = text_column 105 | self.label_column = label_column 106 | self.label_title = label_column_alias if label_column_alias else label_column 107 | self.example_title = text_column_alias if text_column_alias else text_column 108 | self.task_desc = task_desc 109 | self.shot_count = shot_count 110 | self.prompt_template = prompt_template 111 | 112 | if not split: 113 | splits = list(self.dataset.keys()) 114 | split = "train" if "train" in splits else splits[0] 115 | self.dataset = self.dataset[split] 116 | 117 | if text_column not in self.dataset.features: 118 | raise ValueError( 119 | f"""Supplied text_column - `{text_column}` not in the dataset. Columns in the dataset are {list(self.dataset.features)}""" 120 | ) 121 | 122 | if label_column not in self.dataset.features: 123 | raise ValueError( 124 | f"""Supplied label_column - `{label_column}` not in the dataset. 125 | Columns in the dataset are {list(self.dataset.features)}""" 126 | ) 127 | 128 | if class_names: 129 | self.class_names = class_names 130 | elif hasattr(self.dataset.features[label_column], "names"): 131 | self.class_names = self.dataset.features[label_column].names 132 | else: 133 | # TODO: Support Identifying unique class from the label column 134 | raise ValueError( 135 | """Expecting `class_names` to be passed or 136 | the 🤗 datasets to have class names defined in the label column""" 137 | ) 138 | 139 | @staticmethod 140 | def _collate_fn(tokenizer, device, batch): 141 | prompts = [batch_item["prompt"] for batch_item in batch] 142 | class_names = [batch_item["class_name"] for batch_item in batch] 143 | model_inputs = tokenizer.batch_encode_plus( 144 | prompts, padding=True, add_special_tokens=False, return_tensors="pt" 145 | ) 146 | model_inputs = {key: model_inputs[key].to(device) for key in model_inputs} 147 | model_inputs["prompts"] = prompts 148 | model_inputs["class_names"] = class_names 149 | return model_inputs 150 | 151 | @property 152 | def _is_label_encoded(self): 153 | """Naive checking if the values in label column is label encoded""" 154 | if type(self.class_names[0]) == type(self.dataset[self.label_column][0]): 155 | return False 156 | return True 157 | 158 | def __iter__(self) -> Iterator[Dict[str, str]]: 159 | for idx, class_name in enumerate(self.class_names): 160 | filter_value = idx if self._is_label_encoded else class_name 161 | class_dataset = self.dataset.filter( 162 | lambda ex: ex[self.label_column] == filter_value 163 | ) 164 | 165 | for i in range(0, class_dataset.num_rows, self.shot_count): 166 | _to = ( 167 | class_dataset.num_rows 168 | if (i + self.shot_count) > class_dataset.num_rows 169 | else (i + self.shot_count) 170 | ) 171 | texts = class_dataset.select(range(i, _to))[self.text_column] 172 | prompt = _create_prompt_for_text_class_synthesize( 173 | template=self.prompt_template, 174 | task_desc=self.task_desc, 175 | examples=texts, 176 | labels=[class_name] * len(texts), 177 | label_title=self.label_title, 178 | example_title=self.example_title, 179 | ) 180 | 181 | yield {"prompt": prompt, "class_name": class_name} 182 | -------------------------------------------------------------------------------- /mutate/prompt_datasets/utils.py: -------------------------------------------------------------------------------- 1 | from typing import List, Dict, Union, Optional 2 | from jinja2 import Template 3 | 4 | def _create_prompt_for_text_class_synthesize( 5 | template: str, 6 | task_desc: str, 7 | examples: List[str], 8 | labels: List[str], 9 | label_title: str, 10 | example_title: str, 11 | ) -> str: 12 | 13 | template = Template(template) 14 | prompt = template.render( 15 | task_prompt=task_desc, 16 | labelled_examples=list(zip(labels, examples)), 17 | label_title=label_title, 18 | example_title=example_title, 19 | ) 20 | return prompt 21 | 22 | def _create_prompt_for_text_class_pseudo_label( 23 | template: str, 24 | task_desc: str, 25 | labelled_examples: List[str], 26 | unlabelled_example: str, 27 | labels: str, 28 | label_title: str, 29 | example_title: str, 30 | ): 31 | 32 | template = Template(template) 33 | prompt = template.render( 34 | task_prompt=task_desc, 35 | labelled_examples=zip(labels, labelled_examples), 36 | example_title=example_title, 37 | label_title=label_title, 38 | unlabelled_example=unlabelled_example, 39 | ) 40 | return prompt -------------------------------------------------------------------------------- /poetry.lock: -------------------------------------------------------------------------------- 1 | [[package]] 2 | name = "aiohttp" 3 | version = "3.8.3" 4 | description = "Async http client/server framework (asyncio)" 5 | category = "main" 6 | optional = false 7 | python-versions = ">=3.6" 8 | 9 | [package.dependencies] 10 | aiosignal = ">=1.1.2" 11 | async-timeout = ">=4.0.0a3,<5.0" 12 | attrs = ">=17.3.0" 13 | charset-normalizer = ">=2.0,<3.0" 14 | frozenlist = ">=1.1.1" 15 | multidict = ">=4.5,<7.0" 16 | yarl = ">=1.0,<2.0" 17 | 18 | [package.extras] 19 | speedups = ["Brotli", "aiodns", "cchardet"] 20 | 21 | [[package]] 22 | name = "aiosignal" 23 | version = "1.3.1" 24 | description = "aiosignal: a list of registered asynchronous callbacks" 25 | category = "main" 26 | optional = false 27 | python-versions = ">=3.7" 28 | 29 | [package.dependencies] 30 | frozenlist = ">=1.1.0" 31 | 32 | [[package]] 33 | name = "async-timeout" 34 | version = "4.0.2" 35 | description = "Timeout context manager for asyncio programs" 36 | category = "main" 37 | optional = false 38 | python-versions = ">=3.6" 39 | 40 | [[package]] 41 | name = "attrs" 42 | version = "22.2.0" 43 | description = "Classes Without Boilerplate" 44 | category = "main" 45 | optional = false 46 | python-versions = ">=3.6" 47 | 48 | [package.extras] 49 | cov = ["attrs[tests]", "coverage-enable-subprocess", "coverage[toml] (>=5.3)"] 50 | dev = ["attrs[docs,tests]"] 51 | docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope.interface"] 52 | tests = ["attrs[tests-no-zope]", "zope.interface"] 53 | tests-no-zope = ["cloudpickle", "hypothesis", "mypy (>=0.971,<0.990)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"] 54 | tests_no_zope = ["cloudpickle", "hypothesis", "mypy (>=0.971,<0.990)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"] 55 | 56 | [[package]] 57 | name = "certifi" 58 | version = "2022.12.7" 59 | description = "Python package for providing Mozilla's CA Bundle." 60 | category = "main" 61 | optional = false 62 | python-versions = ">=3.6" 63 | 64 | [[package]] 65 | name = "charset-normalizer" 66 | version = "2.1.1" 67 | description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet." 68 | category = "main" 69 | optional = false 70 | python-versions = ">=3.6.0" 71 | 72 | [package.extras] 73 | unicode_backport = ["unicodedata2"] 74 | 75 | [[package]] 76 | name = "colorama" 77 | version = "0.4.6" 78 | description = "Cross-platform colored terminal text." 79 | category = "main" 80 | optional = false 81 | python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7" 82 | 83 | [[package]] 84 | name = "datasets" 85 | version = "2.8.0" 86 | description = "HuggingFace community-driven open-source library of datasets" 87 | category = "main" 88 | optional = false 89 | python-versions = ">=3.7.0" 90 | 91 | [package.dependencies] 92 | aiohttp = "*" 93 | dill = "<0.3.7" 94 | fsspec = {version = ">=2021.11.1", extras = ["http"]} 95 | huggingface-hub = ">=0.2.0,<1.0.0" 96 | multiprocess = "*" 97 | numpy = ">=1.17" 98 | packaging = "*" 99 | pandas = "*" 100 | pyarrow = ">=6.0.0" 101 | pyyaml = ">=5.1" 102 | requests = ">=2.19.0" 103 | responses = "<0.19" 104 | tqdm = ">=4.62.1" 105 | xxhash = "*" 106 | 107 | [package.extras] 108 | apache-beam = ["apache-beam (>=2.26.0)"] 109 | audio = ["librosa"] 110 | benchmarks = ["numpy (==1.18.5)", "tensorflow (==2.3.0)", "torch (==1.7.1)", "transformers (==3.0.2)"] 111 | dev = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0)", "black (>=22.0,<23.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "flake8 (>=3.8.3)", "isort (>=5.0.0)", "librosa", "lz4", "py7zr", "pytest", "pytest-datadir", "pytest-xdist", "pyyaml (>=5.3.1)", "rarfile (>=4.0)", "s3fs", "s3fs (>=2021.11.1)", "soundfile", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "tensorflow-macos", "torch", "torchaudio (<0.12.0)", "transformers", "zstandard"] 112 | docs = ["s3fs"] 113 | metrics-tests = ["Werkzeug (>=1.0.1)", "bert-score (>=0.3.6)", "jiwer", "langdetect", "mauve-text", "nltk", "requests-file (>=1.5.1)", "rouge-score", "sacrebleu", "sacremoses", "scikit-learn", "scipy", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "spacy (>=3.0.0)", "sqlalchemy", "texttable (>=1.6.3)", "tldextract", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "typer (<0.5.0)"] 114 | quality = ["black (>=22.0,<23.0)", "flake8 (>=3.8.3)", "isort (>=5.0.0)", "pyyaml (>=5.3.1)"] 115 | s3 = ["s3fs"] 116 | tensorflow = ["tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow-macos"] 117 | tensorflow_gpu = ["tensorflow-gpu (>=2.2.0,!=2.6.0,!=2.6.1)"] 118 | tests = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "librosa", "lz4", "py7zr", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "s3fs (>=2021.11.1)", "soundfile", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "tensorflow-macos", "torch", "torchaudio (<0.12.0)", "transformers", "zstandard"] 119 | torch = ["torch"] 120 | vision = ["Pillow (>=6.2.1)"] 121 | 122 | [[package]] 123 | name = "dill" 124 | version = "0.3.6" 125 | description = "serialize all of python" 126 | category = "main" 127 | optional = false 128 | python-versions = ">=3.7" 129 | 130 | [package.extras] 131 | graph = ["objgraph (>=1.7.2)"] 132 | 133 | [[package]] 134 | name = "filelock" 135 | version = "3.9.0" 136 | description = "A platform independent file lock." 137 | category = "main" 138 | optional = false 139 | python-versions = ">=3.7" 140 | 141 | [package.extras] 142 | docs = ["furo (>=2022.12.7)", "sphinx (>=5.3)", "sphinx-autodoc-typehints (>=1.19.5)"] 143 | testing = ["covdefaults (>=2.2.2)", "coverage (>=7.0.1)", "pytest (>=7.2)", "pytest-cov (>=4)", "pytest-timeout (>=2.1)"] 144 | 145 | [[package]] 146 | name = "frozenlist" 147 | version = "1.3.3" 148 | description = "A list-like structure which implements collections.abc.MutableSequence" 149 | category = "main" 150 | optional = false 151 | python-versions = ">=3.7" 152 | 153 | [[package]] 154 | name = "fsspec" 155 | version = "2022.11.0" 156 | description = "File-system specification" 157 | category = "main" 158 | optional = false 159 | python-versions = ">=3.7" 160 | 161 | [package.dependencies] 162 | aiohttp = {version = "<4.0.0a0 || >4.0.0a0,<4.0.0a1 || >4.0.0a1", optional = true, markers = "extra == \"http\""} 163 | requests = {version = "*", optional = true, markers = "extra == \"http\""} 164 | 165 | [package.extras] 166 | abfs = ["adlfs"] 167 | adl = ["adlfs"] 168 | arrow = ["pyarrow (>=1)"] 169 | dask = ["dask", "distributed"] 170 | dropbox = ["dropbox", "dropboxdrivefs", "requests"] 171 | entrypoints = ["importlib-metadata"] 172 | fuse = ["fusepy"] 173 | gcs = ["gcsfs"] 174 | git = ["pygit2"] 175 | github = ["requests"] 176 | gs = ["gcsfs"] 177 | gui = ["panel"] 178 | hdfs = ["pyarrow (>=1)"] 179 | http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "requests"] 180 | libarchive = ["libarchive-c"] 181 | oci = ["ocifs"] 182 | s3 = ["s3fs"] 183 | sftp = ["paramiko"] 184 | smb = ["smbprotocol"] 185 | ssh = ["paramiko"] 186 | tqdm = ["tqdm"] 187 | 188 | [[package]] 189 | name = "huggingface-hub" 190 | version = "0.11.1" 191 | description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" 192 | category = "main" 193 | optional = false 194 | python-versions = ">=3.7.0" 195 | 196 | [package.dependencies] 197 | filelock = "*" 198 | packaging = ">=20.9" 199 | pyyaml = ">=5.1" 200 | requests = "*" 201 | tqdm = "*" 202 | typing-extensions = ">=3.7.4.3" 203 | 204 | [package.extras] 205 | all = ["InquirerPy (==0.3.4)", "Jinja2", "black (==22.3)", "flake8 (>=3.8.3)", "flake8-bugbear", "isort (>=5.5.4)", "jedi", "mypy (==0.982)", "pytest", "pytest-cov", "pytest-env", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3"] 206 | cli = ["InquirerPy (==0.3.4)"] 207 | dev = ["InquirerPy (==0.3.4)", "Jinja2", "black (==22.3)", "flake8 (>=3.8.3)", "flake8-bugbear", "isort (>=5.5.4)", "jedi", "mypy (==0.982)", "pytest", "pytest-cov", "pytest-env", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3"] 208 | fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"] 209 | quality = ["black (==22.3)", "flake8 (>=3.8.3)", "flake8-bugbear", "isort (>=5.5.4)", "mypy (==0.982)"] 210 | tensorflow = ["graphviz", "pydot", "tensorflow"] 211 | testing = ["InquirerPy (==0.3.4)", "Jinja2", "isort (>=5.5.4)", "jedi", "pytest", "pytest-cov", "pytest-env", "soundfile"] 212 | torch = ["torch"] 213 | typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3"] 214 | 215 | [[package]] 216 | name = "idna" 217 | version = "3.4" 218 | description = "Internationalized Domain Names in Applications (IDNA)" 219 | category = "main" 220 | optional = false 221 | python-versions = ">=3.5" 222 | 223 | [[package]] 224 | name = "Jinja2" 225 | version = "3.1.2" 226 | description = "A very fast and expressive template engine." 227 | category = "main" 228 | optional = false 229 | python-versions = ">=3.7" 230 | 231 | [package.dependencies] 232 | MarkupSafe = ">=2.0" 233 | 234 | [package.extras] 235 | i18n = ["Babel (>=2.7)"] 236 | 237 | [[package]] 238 | name = "MarkupSafe" 239 | version = "2.1.1" 240 | description = "Safely add untrusted strings to HTML/XML markup." 241 | category = "main" 242 | optional = false 243 | python-versions = ">=3.7" 244 | 245 | [[package]] 246 | name = "multidict" 247 | version = "6.0.4" 248 | description = "multidict implementation" 249 | category = "main" 250 | optional = false 251 | python-versions = ">=3.7" 252 | 253 | [[package]] 254 | name = "multiprocess" 255 | version = "0.70.14" 256 | description = "better multiprocessing and multithreading in python" 257 | category = "main" 258 | optional = false 259 | python-versions = ">=3.7" 260 | 261 | [package.dependencies] 262 | dill = ">=0.3.6" 263 | 264 | [[package]] 265 | name = "numpy" 266 | version = "1.24.1" 267 | description = "Fundamental package for array computing in Python" 268 | category = "main" 269 | optional = false 270 | python-versions = ">=3.8" 271 | 272 | [[package]] 273 | name = "nvidia-cublas-cu11" 274 | version = "11.10.3.66" 275 | description = "CUBLAS native runtime libraries" 276 | category = "main" 277 | optional = false 278 | python-versions = ">=3" 279 | 280 | [package.dependencies] 281 | setuptools = "*" 282 | wheel = "*" 283 | 284 | [[package]] 285 | name = "nvidia-cuda-nvrtc-cu11" 286 | version = "11.7.99" 287 | description = "NVRTC native runtime libraries" 288 | category = "main" 289 | optional = false 290 | python-versions = ">=3" 291 | 292 | [package.dependencies] 293 | setuptools = "*" 294 | wheel = "*" 295 | 296 | [[package]] 297 | name = "nvidia-cuda-runtime-cu11" 298 | version = "11.7.99" 299 | description = "CUDA Runtime native Libraries" 300 | category = "main" 301 | optional = false 302 | python-versions = ">=3" 303 | 304 | [package.dependencies] 305 | setuptools = "*" 306 | wheel = "*" 307 | 308 | [[package]] 309 | name = "nvidia-cudnn-cu11" 310 | version = "8.5.0.96" 311 | description = "cuDNN runtime libraries" 312 | category = "main" 313 | optional = false 314 | python-versions = ">=3" 315 | 316 | [package.dependencies] 317 | setuptools = "*" 318 | wheel = "*" 319 | 320 | [[package]] 321 | name = "packaging" 322 | version = "23.0" 323 | description = "Core utilities for Python packages" 324 | category = "main" 325 | optional = false 326 | python-versions = ">=3.7" 327 | 328 | [[package]] 329 | name = "pandas" 330 | version = "1.5.2" 331 | description = "Powerful data structures for data analysis, time series, and statistics" 332 | category = "main" 333 | optional = false 334 | python-versions = ">=3.8" 335 | 336 | [package.dependencies] 337 | numpy = [ 338 | {version = ">=1.20.3", markers = "python_version < \"3.10\""}, 339 | {version = ">=1.21.0", markers = "python_version >= \"3.10\""}, 340 | {version = ">=1.23.2", markers = "python_version >= \"3.11\""}, 341 | ] 342 | python-dateutil = ">=2.8.1" 343 | pytz = ">=2020.1" 344 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emitter for Python" 382 | category = "main" 383 | optional = false 384 | python-versions = ">=3.6" 385 | 386 | [[package]] 387 | name = "regex" 388 | version = "2022.10.31" 389 | description = "Alternative regular expression module, to replace re." 390 | category = "main" 391 | optional = false 392 | python-versions = ">=3.6" 393 | 394 | [[package]] 395 | name = "requests" 396 | version = "2.28.1" 397 | description = "Python HTTP for Humans." 398 | category = "main" 399 | optional = false 400 | python-versions = ">=3.7, <4" 401 | 402 | [package.dependencies] 403 | certifi = ">=2017.4.17" 404 | charset-normalizer = ">=2,<3" 405 | idna = ">=2.5,<4" 406 | urllib3 = ">=1.21.1,<1.27" 407 | 408 | [package.extras] 409 | socks = ["PySocks (>=1.5.6,!=1.5.7)"] 410 | use_chardet_on_py3 = ["chardet (>=3.0.2,<6)"] 411 | 412 | [[package]] 413 | name = "responses" 414 | version = "0.18.0" 415 | description = "A utility library for mocking out the `requests` Python library." 416 | category = "main" 417 | optional = false 418 | python-versions = ">=3.7" 419 | 420 | [package.dependencies] 421 | requests = ">=2.0,<3.0" 422 | urllib3 = ">=1.25.10" 423 | 424 | [package.extras] 425 | tests = ["coverage (>=6.0.0)", "flake8", "mypy", "pytest (>=4.6)", "pytest-cov", "pytest-localserver", "types-mock", "types-requests"] 426 | 427 | [[package]] 428 | name = "setuptools" 429 | version = "65.7.0" 430 | description = "Easily download, build, install, upgrade, and uninstall Python packages" 431 | category = "main" 432 | optional = false 433 | python-versions = ">=3.7" 434 | 435 | [package.extras] 436 | docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"] 437 | testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", 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python-versions = "*" 455 | 456 | [package.extras] 457 | dev = ["black (==22.3)", "datasets", "numpy", "pytest", "requests"] 458 | docs = ["setuptools-rust", "sphinx", "sphinx-rtd-theme"] 459 | testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests"] 460 | 461 | [[package]] 462 | name = "torch" 463 | version = "1.13.1" 464 | description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration" 465 | category = "main" 466 | optional = false 467 | python-versions = ">=3.7.0" 468 | 469 | [package.dependencies] 470 | nvidia-cublas-cu11 = {version = "11.10.3.66", markers = "platform_system == \"Linux\""} 471 | nvidia-cuda-nvrtc-cu11 = {version = "11.7.99", markers = "platform_system == \"Linux\""} 472 | nvidia-cuda-runtime-cu11 = {version = "11.7.99", markers = "platform_system == \"Linux\""} 473 | nvidia-cudnn-cu11 = {version = "8.5.0.96", markers = "platform_system == \"Linux\""} 474 | typing-extensions = "*" 475 | 476 | [package.extras] 477 | 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docs_specific = ["hf-doc-builder"] 527 | fairscale = ["fairscale (>0.3)"] 528 | flax = ["flax (>=0.4.1)", "jax (>=0.2.8,!=0.3.2,<=0.3.6)", "jaxlib (>=0.1.65,<=0.3.6)", "optax (>=0.0.8)"] 529 | flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"] 530 | ftfy = ["ftfy"] 531 | integrations = ["optuna", "ray[tune]", "sigopt"] 532 | ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "pyknp (>=0.6.1)", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"] 533 | modelcreation = ["cookiecutter (==1.7.3)"] 534 | natten = ["natten (>=0.14.4)"] 535 | onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"] 536 | onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"] 537 | optuna = ["optuna"] 538 | quality = ["GitPython (<3.1.19)", "black (==22.3)", "datasets (!=2.5.0)", "flake8 (>=3.8.3)", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)"] 539 | ray = ["ray[tune]"] 540 | 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/pyproject.toml: -------------------------------------------------------------------------------- 1 | [tool.poetry] 2 | name = "mutate-nlp" 3 | version = "0.1.2" 4 | description = "Text data synthesize and pseudo labelling using LLMs" 5 | authors = ["Logesh Kumar Umapathi "] 6 | license = "Apache" 7 | readme = "README.md" 8 | packages = [{include = "mutate"}] 9 | 10 | [tool.poetry.dependencies] 11 | python = "^3.8" 12 | pandas = "^1.5.2" 13 | tqdm = "^4.64.1" 14 | numpy = "^1.24.1" 15 | torch = ">=1.0" 16 | Jinja2 = ">=3.0.3" 17 | transformers = ">=4.5.1" 18 | datasets = ">=1.6.2" 19 | 20 | 21 | [build-system] 22 | requires = ["poetry-core"] 23 | build-backend = "poetry.core.masonry.api" 24 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | pandas==1.1.5 2 | tqdm==4.49.0 3 | numpy==1.19.5 4 | torch>=1.0 5 | jinja2==3.0.3 6 | transformers>=4.5.1 7 | datasets==1.6.2 8 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup, find_packages 2 | 3 | setup( 4 | name='mutate', 5 | version='0.1.0', 6 | description='Text data synthesize and pseudo labelling using LLMs', 7 | author='Logesh Kumar Umapathi', 8 | author_email='logeshkumaru@gmail.com', 9 | url='https://github.com/infinitylogesh/mutate', 10 | packages=find_packages(), 11 | install_requires=[ 12 | 'pandas', 13 | 'tqdm', 14 | 'numpy', 15 | 'torch>=1.0', 16 | 'jinja2>=3.0.3', 17 | 'transformers>=4.5.1', 18 | 'datasets>=1.6.2' 19 | ] 20 | ) 21 | --------------------------------------------------------------------------------