├── training_data.zip ├── Human evaluation.pdf ├── cleansing_operations.pdf ├── test_and_validation_data.zip ├── Run.py ├── inference.py ├── .gitignore ├── train.py ├── README.md ├── LICENCE └── utils.py /training_data.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mattia-decao/hiero-transformer/HEAD/training_data.zip -------------------------------------------------------------------------------- /Human evaluation.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mattia-decao/hiero-transformer/HEAD/Human evaluation.pdf -------------------------------------------------------------------------------- /cleansing_operations.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mattia-decao/hiero-transformer/HEAD/cleansing_operations.pdf -------------------------------------------------------------------------------- /test_and_validation_data.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mattia-decao/hiero-transformer/HEAD/test_and_validation_data.zip -------------------------------------------------------------------------------- /Run.py: -------------------------------------------------------------------------------- 1 | # Load environment and model 2 | from transformers import AutoTokenizer, AutoModelForSeq2SeqLM 3 | import torch 4 | 5 | tokenizer = AutoTokenizer.from_pretrained("mattiadc/hiero-transformer") 6 | model = AutoModelForSeq2SeqLM.from_pretrained("mattiadc/hiero-transformer").to('cuda:0').eval() 7 | 8 | # Traduction 9 | #@title Traduction 10 | 11 | language_input = 'tnt' #@param ["ea", "tnt"] 12 | language_output = 'de' #@param ["de", "en", "tnt", "lKey", "wordClass"] 13 | sentence_input = '*ra m p,t' #@param {type:"string"} 14 | # resulted_input_tnt = '' #@param {type:"string"} 15 | all_outputs = True #@param {type:"boolean"} 16 | 17 | # If you desire to add capital letters (e.g. in proper names) you need to add the asterisk * before the letter you want to capitalize in the transliteration 18 | 19 | if language_input == 'tnt': 20 | sentence_input = (sentence_input 21 | 22 | .replace('*X', 'H̱') 23 | .replace('*S', 'Š') 24 | .replace('*T', 'Ṯ') 25 | .replace('*D', 'Ḏ') 26 | .replace('*A', 'Ꜣ') 27 | .replace('*H', 'Ḥ') 28 | 29 | .replace('X', 'ẖ') 30 | .replace('S', 'š') 31 | .replace('T', 'ṯ') 32 | .replace('D', 'ḏ') 33 | .replace('A', 'ꜣ') 34 | .replace('H', 'ḥ') 35 | 36 | .replace('*j', 'J') 37 | .replace('*i', 'I') 38 | .replace('*y', 'Y') 39 | .replace('*a', 'Ꜥ') 40 | .replace('*w', 'W') 41 | .replace('*b', 'B') 42 | .replace('*p', 'P') 43 | .replace('*f', 'F') 44 | .replace('*m', 'M') 45 | .replace('*n', 'N') 46 | .replace('*r', 'R') 47 | .replace('*h', 'H') 48 | .replace('*x', 'Ḫ') 49 | .replace('*s', 'S') 50 | .replace('*z', 'Z') 51 | .replace('*q', 'Q') 52 | .replace('*k', 'K') 53 | .replace('*g', 'G') 54 | .replace('*t', 'T') 55 | .replace('*d', 'D') 56 | .replace('a', 'ꜥ') 57 | .replace('x', 'ḫ') 58 | .replace ('i', 'i̯') 59 | 60 | ) 61 | print(sentence_input) 62 | 63 | lang_to_m2m_lang_id = { 64 | 'ea': 'ar', 65 | 'tnt': 'ar', 66 | 'en': 'en', 67 | 'de': 'de', 68 | 'lKey': 'my', 69 | 'tnt': 'lo', 70 | 'wordClass': 'th', 71 | } 72 | 73 | langs = [ 74 | ('ea', 'de'), 75 | ('ea', 'en'), 76 | ('ea', 'tnt'), 77 | ('ea', 'lKey'), 78 | ('ea', 'wordClass'), 79 | ('tnt', 'de'), 80 | ('tnt', 'en'), 81 | ('tnt', 'lKey'), 82 | ('tnt', 'wordClass'), 83 | ] 84 | 85 | def get_translation(language_input, language_output, sentence_input): 86 | with torch.no_grad(): 87 | with torch.cuda.amp.autocast(): 88 | tokenizer.src_lang = lang_to_m2m_lang_id[language_input] 89 | tokenizer.tgt_lang = lang_to_m2m_lang_id[language_output] 90 | 91 | model_inputs = tokenizer([sentence_input], return_tensors="pt").to(model.device) 92 | generated_tokens = model.generate( 93 | **model_inputs, 94 | num_beams=10, 95 | max_length=32, 96 | forced_bos_token_id=tokenizer.get_lang_id(lang_to_m2m_lang_id[language_output])) 97 | return tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0] 98 | 99 | if not all_outputs: 100 | assert (language_input, language_output) in langs, 'Coppia lingue non valida' 101 | result = get_translation(language_input, language_output, sentence_input) 102 | else: 103 | result = { 104 | language_output: get_translation(language_input, language_output, sentence_input) 105 | for language_input_tmp, language_output in langs if language_input == language_input_tmp 106 | } 107 | result 108 | -------------------------------------------------------------------------------- /inference.py: -------------------------------------------------------------------------------- 1 | import string 2 | 3 | import datasets 4 | import pandas as pd 5 | import torch 6 | from tqdm.auto import tqdm 7 | from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer 8 | 9 | from utils import lang_to_m2m_lang_id, load_data_from_folder, processed_data 10 | 11 | # Load data 12 | test_data = load_data_from_folder("test_data") 13 | 14 | # Filter and extract data 15 | # Dict[str, Dict[str, List[Dict[str, str]]]] 16 | # {src_lang: {tgt_lang: [{'source': ..., 'target': ...}]}} 17 | test_data = processed_data(test_data) 18 | 19 | 20 | # Load model to generate predictions 21 | model = M2M100ForConditionalGeneration.from_pretrained("ea9all").to("cuda:0").eval() 22 | tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M") 23 | 24 | # Produce predictions 25 | for src_lang, values in test_data.items(): 26 | for tgt_lang, data in values.items(): 27 | for element in tqdm(data): 28 | with torch.no_grad(): 29 | with torch.cuda.amp.autocast(): 30 | tokenizer.src_lang = lang_to_m2m_lang_id[src_lang] 31 | tokenizer.tgt_lang = lang_to_m2m_lang_id[tgt_lang] 32 | 33 | model_inputs = tokenizer( 34 | [element["source"]], return_tensors="pt" 35 | ).to(model.device) 36 | generated_tokens = model.generate( 37 | **model_inputs, 38 | num_beams=10, 39 | forced_bos_token_id=tokenizer.get_lang_id( 40 | lang_to_m2m_lang_id[tgt_lang] 41 | ) 42 | ) 43 | element["prediction"] = tokenizer.batch_decode( 44 | generated_tokens, skip_special_tokens=True 45 | )[0] 46 | 47 | # Calculate metrics 48 | metrics = { 49 | src_lang: { 50 | tgt_lang: {m: datasets.load_metric(m) for m in ("sacrebleu", "rouge")} 51 | for tgt_lang, _ in values.items() 52 | } 53 | for src_lang, values in test_data.items() 54 | } 55 | for src_lang, values in test_data.items(): 56 | for tgt_lang, data in values.items(): 57 | for element in data: 58 | for metric in metrics[src_lang][tgt_lang].values(): 59 | metric.add_batch( 60 | predictions=[ 61 | element["prediction"].strip(string.punctuation).lower().split() 62 | ], 63 | references=[ 64 | [element["target"].strip(string.punctuation).lower().split()] 65 | ], 66 | ) 67 | 68 | metrics = { 69 | src_lang: { 70 | tgt_lang: {name: metric.compute() for name, metric in metrics.items()} 71 | for tgt_lang, metrics in values.items() 72 | } 73 | for src_lang, values in metrics.items() 74 | } 75 | 76 | # Compute tables 77 | tables = { 78 | "sacrebleu": { 79 | src_lang: { 80 | tgt_lang: metric["sacrebleu"]["score"] 81 | for tgt_lang, metric in values.items() 82 | } 83 | for src_lang, values in metrics.items() 84 | }, 85 | "rougeL": { 86 | src_lang: { 87 | tgt_lang: 100 * metric["rouge"]["rougeL"].mid.fmeasure 88 | for tgt_lang, metric in values.items() 89 | } 90 | for src_lang, values in metrics.items() 91 | }, 92 | } 93 | 94 | print("sacrebleu") 95 | print(pd.DataFrame(tables["sacrebleu"]).T) 96 | print("rougeL") 97 | print(pd.DataFrame(tables["rougeL"]).T) 98 | -------------------------------------------------------------------------------- /.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 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 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 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; otherwise, check them in: 88 | # .python-version 89 | 90 | # pipenv 91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 94 | # install all needed dependencies. 95 | #Pipfile.lock 96 | 97 | # poetry 98 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 99 | # This is especially recommended for binary packages to ensure reproducibility, and is more 100 | # commonly ignored for libraries. 101 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 102 | #poetry.lock 103 | 104 | # pdm 105 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. 106 | #pdm.lock 107 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it 108 | # in version control. 109 | # https://pdm.fming.dev/#use-with-ide 110 | .pdm.toml 111 | 112 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm 113 | __pypackages__/ 114 | 115 | # Celery stuff 116 | celerybeat-schedule 117 | celerybeat.pid 118 | 119 | # SageMath parsed files 120 | *.sage.py 121 | 122 | # Environments 123 | .env 124 | .venv 125 | env/ 126 | venv/ 127 | ENV/ 128 | env.bak/ 129 | venv.bak/ 130 | 131 | # Spyder project settings 132 | .spyderproject 133 | .spyproject 134 | 135 | # Rope project settings 136 | .ropeproject 137 | 138 | # mkdocs documentation 139 | /site 140 | 141 | # mypy 142 | .mypy_cache/ 143 | .dmypy.json 144 | dmypy.json 145 | 146 | # Pyre type checker 147 | .pyre/ 148 | 149 | # pytype static type analyzer 150 | .pytype/ 151 | 152 | # Cython debug symbols 153 | cython_debug/ 154 | 155 | # PyCharm 156 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can 157 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 158 | # and can be added to the global gitignore or merged into this file. For a more nuclear 159 | # option (not recommended) you can uncomment the following to ignore the entire idea folder. 160 | #.idea/ 161 | -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | import copy 2 | import json 3 | import shutil 4 | 5 | import numpy as np 6 | import torch 7 | from tqdm.auto import tqdm 8 | from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer 9 | 10 | from utils import ( 11 | batch_it, 12 | clean_data, 13 | lang_to_m2m_lang_id, 14 | load_data_from_folder, 15 | processed_data, 16 | training_step, 17 | validation_step, 18 | ) 19 | 20 | # Epochs, batch, periods variables 21 | epochs = 20 22 | batch_size = 16 23 | eval_period = 1000 24 | total_steps = 0 25 | best_eval_loss = float("inf") 26 | max_models = 1 27 | topk_models = [] 28 | 29 | # Choose the pairs of languages to train and validate 30 | langs = [ 31 | ("ea", "de"), 32 | ("ea", "en"), 33 | # ('ea', 'tnt'), 34 | # ('ea', 'lKey'), 35 | # ('ea', 'wordClass'), 36 | # ('tnt', 'de'), 37 | # ('tnt', 'en'), 38 | # ('tnt', 'lKey'), 39 | # ('tnt', 'wordClass'), 40 | ] 41 | 42 | 43 | # Load data 44 | training_data = load_data_from_folder("training_data") 45 | validation_data = load_data_from_folder("validation_data") 46 | 47 | # Clean data 48 | training_data = clean_data(training_data) 49 | 50 | # Filter and extract data 51 | # Dict[str, Dict[str, List[Dict[str, str]]]] 52 | # {src_lang: {tgt_lang: [{'source': ..., 'target': ...}]}} 53 | training_data = processed_data(training_data) 54 | validation_data = processed_data(validation_data) 55 | 56 | 57 | # Adding traduction of corpus and vocabulary 58 | 59 | with open("translations_de2en.json", encoding="utf-8") as f: 60 | translations = json.load(f) 61 | 62 | for lang in ("ea", "tnt"): 63 | ids_sentence = { 64 | element["metadata"]["id_sentence"] 65 | for element in training_data[lang]["en"] 66 | if "id_sentence" in element["metadata"] 67 | } 68 | 69 | for element in training_data[lang]["de"]: 70 | if ( 71 | "id_sentence" in element["metadata"] 72 | and element["metadata"]["id_sentence"] not in ids_sentence 73 | ): 74 | new_element = copy.deepcopy(element) 75 | new_element["target"] = translations[element["target"]] 76 | new_element["metadata"]["target_lang"] = "en" 77 | training_data[lang]["en"].append(new_element) 78 | 79 | print( 80 | f'{lang} -> en: Dopo la traduzione abbiamo {len(training_data[lang]["en"])} datapoints.' 81 | ) 82 | 83 | # loading model 84 | model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M").to( 85 | "cuda:0" 86 | ) 87 | tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M") 88 | optimizer = torch.optim.Adam(model.parameters(), lr=3e-5) 89 | 90 | 91 | # Training 92 | validation_losses = {} 93 | validation_data_batched = [ 94 | (src_lang, trg_lang, batch) 95 | for src_lang, values in validation_data.items() 96 | for trg_lang, data in values.items() 97 | for batch in batch_it(data, batch_size) 98 | if (src_lang, trg_lang) in langs 99 | ] 100 | 101 | for epoch in range(epochs): 102 | print(f"Starting epoch {epoch + 1}") 103 | 104 | for src_lang, values in training_data.items(): 105 | for data in values.values(): 106 | np.random.shuffle(data) 107 | 108 | training_data_batched = [ 109 | (src_lang, trg_lang, batch) 110 | for src_lang, values in training_data.items() 111 | for trg_lang, data in values.items() 112 | for batch in batch_it(data, batch_size) 113 | if (src_lang, trg_lang) in langs 114 | ] 115 | 116 | np.random.shuffle(training_data_batched) 117 | 118 | iterator = tqdm(training_data_batched) 119 | for src_lang, tgt_lang, batch in iterator: 120 | loss = training_step( 121 | batch, 122 | model, 123 | tokenizer, 124 | optimizer, 125 | lang_to_m2m_lang_id[src_lang], 126 | lang_to_m2m_lang_id[tgt_lang], 127 | ) 128 | total_steps += 1 129 | iterator.set_postfix( 130 | total_steps=total_steps, loss=loss, src_lang=src_lang, tgt_lang=tgt_lang 131 | ) 132 | 133 | if total_steps % eval_period == 0 and total_steps != 0: 134 | total_eval_loss = 0 135 | total_eval_tokens = 0 136 | 137 | for src_lang, tgt_lang, batch in validation_data_batched: 138 | loss, tokens = validation_step( 139 | batch, 140 | model, 141 | tokenizer, 142 | lang_to_m2m_lang_id[src_lang], 143 | lang_to_m2m_lang_id[tgt_lang], 144 | ) 145 | total_eval_loss += loss * tokens 146 | total_eval_tokens += tokens 147 | 148 | validation_losses[total_steps] = total_eval_loss 149 | with open("validation_losses.json", "w") as f: 150 | json.dump(validation_losses, f) 151 | 152 | if total_eval_loss < best_eval_loss: 153 | print( 154 | f"The model improved! Old loss={best_eval_loss}, new loss={total_eval_loss}" 155 | ) 156 | fname = f"checkpoint_total_steps={total_steps}_loss={total_eval_loss / total_eval_tokens:.2f}" 157 | model.save_pretrained(fname) 158 | topk_models.append(fname) 159 | best_eval_loss = total_eval_loss 160 | 161 | if len(topk_models) > max_models: 162 | fname = topk_models.pop(0) 163 | shutil.rmtree(fname) 164 | print(f"Removing {fname}") 165 | 166 | # Last check before the end 167 | total_eval_loss = 0 168 | total_eval_tokens = 0 169 | 170 | for src_lang, tgt_lang, batch in validation_data_batched: 171 | loss, tokens = validation_step( 172 | batch, 173 | model, 174 | tokenizer, 175 | lang_to_m2m_lang_id[src_lang], 176 | lang_to_m2m_lang_id[tgt_lang], 177 | ) 178 | total_eval_loss += loss * tokens 179 | total_eval_tokens += tokens 180 | 181 | validation_losses[total_steps] = total_eval_loss 182 | with open("validation_losses.json", "w") as f: 183 | json.dump(validation_losses, f) 184 | 185 | if total_eval_loss < best_eval_loss: 186 | print( 187 | f"The model improved! Old loss={best_eval_loss}, new loss={total_eval_loss}" 188 | ) 189 | fname = f"checkpoint_total_steps={total_steps}_loss={total_eval_loss / total_eval_tokens:.2f}" 190 | model.save_pretrained(fname) 191 | topk_models.append(fname) 192 | best_eval_loss = total_eval_loss 193 | 194 | if len(topk_models) > max_models: 195 | fname = topk_models.pop(0) 196 | shutil.rmtree(fname) 197 | print(f"Removing {fname}") 198 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # hiero-transformer 2 | This repository collects additional information for our article _Deep Learning Meets Egyptology: a Hieroglyphic Transformer for Translating Ancient Egyptian_ (De Cao et al. 2024). In particular, here you can find: 3 | - the complete list of the cleansing operations used to clean the data before the model training; 4 | - advice on how to enter the input, both from hieroglyphs and transliteration; 5 | - the examples and the analysis of the human evaluation; 6 | - the code we used to clean, train, evaluate and run the data and the model. 7 | 8 | The model is also on Huggingface: https://huggingface.co/mattiadc/hiero-transformer 9 | 10 | ## Explanation of ".py" files 11 | 12 | Four files collect our code: Run.py, inference.py, train.py, utils.py. 13 | 14 | - **Run.py:** Collects the code to load the environment and the model, as well as an input form we created to facilitate the input entry to the model. To use Run.py beware to divide the environment loading from the input form. 15 | - **inference.py:** Collects the code we used to load the test.data, generate the predictions and calculate the metrics. 16 | - **train.py:** Collects the code we used to load the model, the variables, the data and to train the model. 17 | - **utils.py:** Collects various code of the training functions, and the code we used to process, filter and clean the data. 18 | 19 | ## Cleansing operations 20 | 21 | Every cleansing operation was meticulously documented along with a concise description highlighting its purpose, implementation, and the rationale behind its choice. These operations were compiled into tables, incorporating the regular symbol expression ".*?" to depict an undefined sequence of words, numbers, and/or graphic symbols. 22 | 23 | Furthermore, any text found in the _Subject_ section was retained entirely, including spaces. At the same time, all of our annotations were enclosed within brackets not present in the TLA dataset, specifically "(£" "£)". 24 | 25 | The meanings of the cleaning procedures were derived from the _Manuel de Codage_ (Buurman et al. 1988; Hans Van Den Berg) conventions, the _Berlin Text System 3.1 (V 3.0) user manual_ (Kupreyev and Sperveslage 2011), or realized by us. 26 | 27 | The "cleansing_operations.pdf" file contains the management of translations, transliterations, Gardiner code and part-of-speech tags. 28 | 29 | 30 | ## Model functioning tips 31 | Hiero-transformer is a useful tool, but it could generate inaccurate results, especially if the input provided isn't correct. Users need to be aware of this and able to distinguish any potential machine-generated mistakes. To help you get better output using Hiero-transformer, here are some tips. 32 | 33 | ### Hieroglyphic input 34 | You will need to use the Gardiner code to provide hieroglyphs to Hiero-transformer. This code requires some preparation. 35 | - **Cleaning:** Remove any brackets, graphic signs, or letters (which are not part of the hieroglyph) attached to them, like you might see working with Jsesh (Rosmorduc). 36 | - **Separation:** Use spaces to separate individual hieroglyphs and erase any other character. 37 | 38 | Remember that the model is trained on Old and Middle Egyptian hieroglyphs. It might struggle with later stages of the language or grammatical forms developed after the Second Intermediate Period. 39 | For best results, we recommend using a sign list like Gardiner's (Gardiner 1957) or, even better, Allen's (Allen 2014). 40 | 41 | 42 | ### Transliteration input 43 | To provide Hiero-transformer with transliteration, you will need to use the same conventions used by the TLA. 44 | - **Capitalization:** Proper nouns need to be capitalized. 45 | - **Hyphens:** You need to use hyphens (-) to separate individual words within proper nouns (e.g., _sḥtp-jb-rꜥ_) or concepts (e.g., _wꜣḏ-wr_). Otherwise, the model will translate them as separate words. 46 | - **Suffix pronouns:** When using the _=_ sign to indicate a suffix pronoun, always add a space before the sign directly followed by the suffix pronoun letters (e.g., _zꜣ =f m pr_). 47 | - **Yod:** The consonant _j_ is used for the strong radical yod, while _i̯_ represents the weak radical yod. 48 | - **Dots:** Use a dot to separate the verb root and the suffixes (other than pronouns). For example, in the form _sḏm.n =f_, the dot separates _sḏm_ (root) from _n_ (suffix other than pronoun). Dots may also be used for plural/dual forms. 49 | - **Commas:** Commas are used for the feminine suffix and may also be used for plural/dual forms. 50 | 51 | You can provide characters in transliteration either in Unicode (the standard encoding) or according to the computer transcription of the Manuel de Codage (a hieroglyphs-specific encoding system that does not make use of special characters). Furthermore, we ensured the insertion of other characters. 52 | - **Capital letters**: Add an asterisk (*) directly before the letter you want to capitalize. For example, using the MdC system, to get a capitalized _ḏ_, type _*D_ (instead of _D_); similarly, to get a capitalized _d_, type _*d_. 53 | - **Weak radical yod ( _i̯_ )**: Type _i_ to insert this character. 54 | 55 | 56 | ## Human evaluation examples and analysis 57 | As soon as possible, we will add a PDF file in which we have analyzed all the examples we worked on. 58 | 59 | 60 | ## References 61 | 62 | Mattia De Cao, Nicola De Cao, Angelo Colonna, and Alessandro Lenci. 2024. Deep Learning Meets Egyptology: a Hieroglyphic Transformer for Translating Ancient Egyptian. In _Proceedings of the 1st Workshop on Machine Learning for Ancient Languages (ML4AL 2024)_, pages 71–86, Hybrid in Bangkok, Thailand and online. Association for Computational Linguistics. 63 | 64 | Jan Buurman, Nicolas-Christophe Grimal, Michale Hainsworth, Jochen Hallof, and Dirk Van der Plas. 1988. _Inventaire des signes hieroglyphiques en vue de leur saisie informatique: Manuel de codage des textes ieroglyphiques en vue de leur saisie sur ordinateur_, volume 2 of _Informatique et egyptologie_. Imprimerie Lienharte et Cie.; Difussion Boccard, Paris. 65 | 66 | Hans Van Den Berg, _“Manuel de Codage” A standard system for the computer encoding of Egyptian transliteration and hieroglyphic texts_, (last access: 28 July 2023). 67 | 68 | Maxim Kupreyev and Gunnar Sperveslage. 2011. _Berlin Text System 3.1 User Manual: Editorial Software of the Thesaurus Linguae Aegyptiae Project_. 69 | 70 | Alan H. Gardiner. 1957. _Egyptian Grammar, Being an Introduction to the Study of Hieroglyphs_, third edition. Griffith Institute, Oxford. 71 | 72 | James P. Allen. 2014. _Middle Egyptian: An Introduction to the Language and Culture of Hieroglyphs_, 3 edition. Cambridge University Press. 73 | 74 | Serge Rosmorduc, _JSesh Documentation_, (last access 09 September 2023). 75 | 76 | ## Data source reference 77 | 78 | Database snapshot of project "Strukturen und Transformationen des Wortschatzes der ägyptischen Sprache" (excerpt from January 2018), 2018, 79 | ed. by Tonio Sebastian Richter & Ingelore Hafemann on behalf of the Berlin-Brandenburgische Akademie der Wissenschaften and Hans-Werner Fischer-Elfert & Peter Dils on behalf of the Sächsische Akademie der Wissenschaften zu Leipzig, 80 | urn:nbn:de:kobv:b4-opus4-29190, https://nbn-resolving.org/urn:nbn:de:kobv:b4-opus4-29190 (CC BY-SA 4.0 Int.) 81 | -------------------------------------------------------------------------------- /LICENCE: -------------------------------------------------------------------------------- 1 | Attribution-ShareAlike 4.0 International 2 | 3 | ======================================================================= 4 | 5 | Creative Commons Corporation ("Creative Commons") is not a law firm and 6 | does not provide legal services or legal advice. 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For 424 | the avoidance of doubt, this paragraph does not form part of the 425 | public licenses. 426 | 427 | Creative Commons may be contacted at creativecommons.org. 428 | 429 | -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | import json 2 | import os 3 | import re 4 | 5 | import torch 6 | 7 | lang_to_m2m_lang_id = { 8 | "ea": "ar", 9 | "tnt": "lo", 10 | "en": "en", 11 | "de": "de", 12 | "lKey": "my", 13 | "wordClass": "th", 14 | } 15 | 16 | # Processing and filter functions defining 17 | 18 | 19 | # load all files from folder 20 | def load_data_from_folder(folder): 21 | data = [] 22 | files = os.listdir(folder) 23 | print(f"Ci sono {len(files)} files.") 24 | 25 | for fname in files: 26 | if fname.endswith(".json"): 27 | with open(os.path.join(folder, fname), encoding="utf-8") as f: 28 | data += json.load(f) 29 | 30 | print(f"Caricati {len(data)} datapoints.") 31 | 32 | return data 33 | 34 | 35 | def extract_data_standard(data, src_lang, tgt_lang): 36 | # Filter data without ae -> target 37 | data = filter( 38 | lambda datapoint: ( 39 | datapoint["metadata"]["source_lang"] == src_lang 40 | and datapoint["metadata"]["target_lang"] == tgt_lang 41 | and datapoint["source"] != "" 42 | and datapoint["target"] != "" 43 | ), 44 | data, 45 | ) 46 | 47 | data = map( 48 | lambda datapoint: { 49 | "source": datapoint["source"], 50 | "target": datapoint["target"], 51 | "metadata": datapoint["metadata"], 52 | }, 53 | data, 54 | ) 55 | 56 | data = list(data) 57 | 58 | print(f"{src_lang} -> {tgt_lang}: Dopo i filtri abbiamo {len(data)} datapoints.") 59 | return data 60 | 61 | 62 | # Extract ea as source and transliteration as target 63 | def extract_data_transliteration_target(data, src_lang): 64 | # Filter data without ae -> transliteration 65 | data = filter( 66 | lambda datapoint: ( 67 | datapoint["metadata"]["source_lang"] == src_lang 68 | and datapoint["source"] != "" 69 | and datapoint["transliteration"] != "" 70 | ), 71 | data, 72 | ) 73 | 74 | data = map( 75 | lambda datapoint: { 76 | "source": datapoint["source"], 77 | "target": datapoint["transliteration"], 78 | "metadata": datapoint["metadata"], 79 | }, 80 | data, 81 | ) 82 | 83 | data = list(data) 84 | 85 | print(f"{src_lang} -> tnt: Dopo i filtri abbiamo {len(data)} datapoints.") 86 | return data 87 | 88 | 89 | # Extract transliteration as source and traduction as target 90 | def extract_data_transliteration_source(data, trg_lang): 91 | # Filter data without traduction -> transliteration 92 | data = filter( 93 | lambda datapoint: ( 94 | datapoint["metadata"]["target_lang"] == trg_lang 95 | and datapoint["target"] != "" 96 | and datapoint["transliteration"] != "" 97 | ), 98 | data, 99 | ) 100 | 101 | data = map( 102 | lambda datapoint: { 103 | "source": datapoint["transliteration"], 104 | "target": datapoint["target"], 105 | "metadata": datapoint["metadata"], 106 | }, 107 | data, 108 | ) 109 | 110 | data = list(data) 111 | 112 | print(f"tnt -> {trg_lang}: Dopo i filtri abbiamo {len(data)} datapoints.") 113 | return data 114 | 115 | 116 | # Extract ea as source and lKey/wordClass as target 117 | def extract_data_ea_lKey_or_wordClass(data, lKey_or_wordClass): 118 | assert lKey_or_wordClass in ("lKey", "wordClass") 119 | 120 | # Filter data without ae -> lKey_or_wordClass 121 | data = filter( 122 | lambda datapoint: ( 123 | datapoint["metadata"]["source_lang"] == "ea" 124 | and datapoint["source"] != "" 125 | and datapoint[lKey_or_wordClass] != "" 126 | and "/" not in datapoint[lKey_or_wordClass] 127 | ), 128 | data, 129 | ) 130 | 131 | data = map( 132 | lambda datapoint: { 133 | "source": datapoint["source"], 134 | "target": datapoint[lKey_or_wordClass], 135 | "metadata": datapoint["metadata"], 136 | }, 137 | data, 138 | ) 139 | 140 | data = list(data) 141 | 142 | print(f"ea -> {lKey_or_wordClass}: Dopo i filtri abbiamo {len(data)} datapoints.") 143 | return data 144 | 145 | 146 | # Extract transliteration as source and lKey/wordClass as target 147 | def extract_data_transliteration_lKey_or_wordClass(data, lKey_or_wordClass): 148 | assert lKey_or_wordClass in ("lKey", "wordClass") 149 | 150 | # Filter data without tnt -> lKey_or_wordClass 151 | data = filter( 152 | lambda datapoint: ( 153 | datapoint["transliteration"] != "" 154 | and datapoint[lKey_or_wordClass] != "" 155 | and "/" not in datapoint[lKey_or_wordClass] 156 | ), 157 | data, 158 | ) 159 | 160 | data = map( 161 | lambda datapoint: { 162 | "source": datapoint["transliteration"], 163 | "target": datapoint[lKey_or_wordClass], 164 | "metadata": datapoint["metadata"], 165 | }, 166 | data, 167 | ) 168 | 169 | data = list(data) 170 | 171 | print(f"tnt -> {lKey_or_wordClass}: Dopo i filtri abbiamo {len(data)} datapoints.") 172 | return data 173 | 174 | 175 | # Processing data 176 | def processed_data(data): 177 | return { 178 | "ea": { 179 | "de": extract_data_standard(data, "ea", "de"), 180 | "en": extract_data_standard(data, "ea", "en"), 181 | "tnt": extract_data_transliteration_target(data, "ea"), 182 | "lKey": extract_data_ea_lKey_or_wordClass(data, "lKey"), 183 | "wordClass": extract_data_ea_lKey_or_wordClass(data, "wordClass"), 184 | }, 185 | "tnt": { 186 | "de": extract_data_transliteration_source(data, "de"), 187 | "en": extract_data_transliteration_source(data, "en"), 188 | "lKey": extract_data_transliteration_lKey_or_wordClass(data, "lKey"), 189 | "wordClass": extract_data_transliteration_lKey_or_wordClass( 190 | data, "wordClass" 191 | ), 192 | }, 193 | } 194 | 195 | 196 | # Cleaning functions defining 197 | 198 | 199 | # Hieroglyphs cleaning 200 | def clean_graphics(text: str) -> str: 201 | # Start from double spaces and sentences to delete 202 | text = " ".join(text.split()) 203 | if "{m1}〈S29〉" in text: 204 | text = "" 205 | if "geschrieben" in text: 206 | text = "" 207 | if "SandhiForm" in text: 208 | text = "" 209 | if "Det.-von" in text: 210 | text = "" 211 | if "erhalten" in text: 212 | text = "" 213 | if text == "//": 214 | text = "" 215 | # Comments 216 | text = text.replace('"sic"', "") 217 | text = text.replace('"var"', "") 218 | text = text.replace('"Var"', "") 219 | text = text.replace('"var."', "") 220 | text = text.replace("-var", "") 221 | text = text.replace("-vae", "") 222 | text = text.replace("-+lvar+s", "") 223 | text = text.replace("-+linverted+s", "") 224 | text = text.replace('"ein Vogel"', "/") 225 | text = text.replace('"unleserliches Zeichen"', "/") 226 | text = text.replace('"lb"', "") 227 | text = text.replace('" lb"', "") 228 | text = text.replace('"lb', "") 229 | text = text.replace('"b"', "") 230 | text = text.replace('"hierat"', "") 231 | text = text.replace('"monogr"', "") 232 | text = text.replace('"monogram"', "") 233 | text = text.replace('"Spuren"', "") 234 | text = text.replace('"large"', "") 235 | text = text.replace('"hiero"', "") 236 | text = text.replace('"mutil"', "") 237 | text = text.replace('"composite"', "") 238 | text = text.replace('"vacat"', "") 239 | text = text.replace('"traces"', "") 240 | text = text.replace('"senkrechte Zeichenspur"', "") 241 | text = text.replace('"senkrechtes Zeichen"', "") 242 | # Jsesh graphic elements 243 | text = text.replace("**", "-") 244 | text = text.replace("*", "-") 245 | text = text.replace("//", "/") 246 | text = text.replace("h/", "/") 247 | text = text.replace("v/", "/") 248 | text = text.replace("#b-/#e", "/") 249 | text = text.replace("-:", "-") 250 | text = text.replace(":", "-") 251 | text = text.replace("[?", "").replace("?]", "") 252 | text = text.replace('"⸮"', "").replace('"?"', "") 253 | text = text.replace("\"'⸮'\"", "").replace("\"'?'\"", "") 254 | text = text.replace("[[", "").replace("]]", "") 255 | text = text.replace("[{*", "").replace("*}]", "") 256 | text = text.replace("[{-", "").replace("-}]", "") 257 | text = text.replace("[[*", "").replace("*]]", "") 258 | text = text.replace("[[-", "").replace("-]]", "") 259 | text = text.replace("[(-", "").replace("-)]", "") 260 | text = text.replace("(", "").replace(")", "") 261 | text = text.replace("$", "") 262 | text = text.replace("<1-0>-", "").replace("-<0-2>", "") 263 | text = text.replace("<1-", "").replace("-2>", "") 264 | text = text.replace("-<1", "") 265 | text = text.replace("<2-", "").replace("-1>", "") 266 | text = text.replace("<0-", "").replace("-0>", "") 267 | text = text.replace("<-", "").replace("->", "") 268 | text = text.replace("<", "").replace(">", "") 269 | text = text.replace('⸮"', "") 270 | text = text.replace("##", "") 271 | text = text.replace("v", "") 272 | # Specific phrase elements 273 | text = text.replace("ss", "S29") 274 | text = text.replace("nn", "M22-M22") 275 | text = text.replace('"lc"', "") 276 | # text = text.replace('"tr"', '') 277 | text = text.replace("prwn", "O1") 278 | text = text.replace("rf", "D21-I9") 279 | text = text.replace("ZeA", "Z2A") 280 | text = text.replace("j", "M17") 281 | text = text.replace("y1", "Y1") 282 | text = text.replace("z2", "Z2") 283 | # text = text.replace('-?9', '') 284 | text = text.replace("b1", "B1") 285 | text = text.replace("pS", "F22") 286 | # text = text.replace('-?', '') 287 | text = text.replace("_", "") 288 | # text = text.replace('{{89,263,62}}', '') 289 | # text = text.replace('{{267,6,97}}', '') 290 | text = text.replace('"⸮h"', "") 291 | text = text.replace("!", "") 292 | # [& parenthesis and cleaning residues 293 | text = text.replace('"', "") 294 | text = text.replace("[&", "").replace("&]", "") 295 | text = text.replace("&", "-") 296 | text = re.sub(r"-+", "-", text) 297 | text = text.replace("- ", " ") 298 | text = text.replace(" -", " ") 299 | text = text.strip("-") 300 | # \\Rx, cartouche, \\, space at end and beginning 301 | text = re.sub(r"\\\\R.*?(-|\s|$)", r"\1", text) 302 | text = re.sub(r"\\\\.*?(-|\s|$)", r"\1", text) 303 | text = re.sub(r"\\.*?(-|\s|$)", r"\1", text) 304 | text = re.sub(r"\((.*?)\)\|", r"\1", text) 305 | text = text.replace("\\", "") 306 | text = text.strip() 307 | # Double spaces again and - 308 | text = text.replace("-", " ") 309 | text = " ".join(text.split()) 310 | return text 311 | 312 | 313 | # Traduction cleaning 314 | def clean_traduction(text): 315 | # Start from double spaces and sentences to delete 316 | text = " ".join(text.split()) 317 | if text.endswith("..."): 318 | text = text[:-3].strip() 319 | if text == "?": 320 | text = text.replace("?", "") 321 | if "-??-" in text: 322 | text = "" 323 | text = re.sub(r"--.*?--", "--zerstört--", text) 324 | if "--zerstört--" in text: 325 | text = "" 326 | if "..." in text: 327 | text = "" 328 | if "…" in text: 329 | text = "" 330 | if ". . ." in text: 331 | text = "" 332 | if "_" in text: 333 | text = "" 334 | if "⸮_?" in text: 335 | text = "" 336 | if "?_?" in text: 337 | text = "" 338 | if "---?---" in text: 339 | text = "" 340 | if "---" in text: 341 | text = "" 342 | if "--" in text: 343 | text = "" 344 | if "keine Übersetzung vorhanden" in text: 345 | text = "" 346 | if "Keine Übersetzung möglich" in text: 347 | text = "" 348 | if "--- LEER GEFUNDEN ---" in text: 349 | text = "" 350 | if "---LEER GEFUNDEN---" in text: 351 | text = "" 352 | text = re.sub(r"\(=.*?\)", "", text) 353 | # if text == 'The': 354 | # text = '' 355 | if "[---]" in text: 356 | text = "" 357 | # lhg acronym, other languages, special parenthesis and chapter numbers 358 | text = re.sub(r"\(\((.*?)\)\)", r"\1", text) 359 | text = re.sub(r"\[\[(.*?)\]\]", r"\1", text) 360 | text = text.replace('"arbustes à épines"', "dornige Sträucher") 361 | text = text.replace("rôdeurs", "plünderer") 362 | text = re.sub('\\"(.*?)"', r"\1", text) 363 | text = re.sub(r"(\/[\w+ÄäÖöẞßÜü]+)", " ", text) 364 | text = text.replace("- LHG -", " Leben, Heil, Gesundheit ") 365 | text = text.replace("- LHG", " Leben, Heil, Gesundheit ") 366 | text = text.replace("-LHG", " Leben, Heil, Gesundheit ") 367 | text = text.replace("- {LHG} LHG -", " Leben, Heil, Gesundheit ") 368 | text = text.replace("LHG", "Leben, Heil, Gesundheit") 369 | text = text.replace("l.h.g.", "Leben, Heil, Gesundheit") 370 | text = text.replace("l.h,.g.", "Leben, Heil, Gesundheit") 371 | text = text.replace("l.h-g", "Leben, Heil, Gesundheit") 372 | text = text.replace("l.h.g .", "Leben, Heil, Gesundheit") 373 | text = text.replace("l.h.g -", "Leben, Heil, Gesundheit") 374 | text = text.replace("l.h.g", "Leben, Heil, Gesundheit") 375 | text = text.replace("l.p.h.", "Life, Prosperity, Health") 376 | text = text.replace("LPH", "Life, Prosperity, Health") 377 | text = text.replace("„", "").replace("“", "").replace("”", "") 378 | text = text.replace("⸢", "").replace("⸣", "") 379 | text = re.sub(r"\$\[.*?\]\$", "", text) 380 | text = text.replace("[", "").replace("]", "") 381 | text = text.replace("<", "").replace(">", "") 382 | text = text.replace("𓉘", "").replace("𓊂", "") 383 | text = text.replace("𓍹", "").replace("𓍺", "") 384 | text = text.replace("‚", "").replace("‘", "") 385 | text = re.sub(r"⸮(.*?)\?", r"\1", text) 386 | # text = re.sub('\((.*?)\)[^\|]', ' ', text) !Attention! Problems with other parenthesis 387 | text = re.sub(r"\((.*?)\)\|", r"\1", text) 388 | text = text.replace("|", "") 389 | text = re.sub(r"\[§[0-9]+\]", "", text) 390 | text = re.sub(r"\[§[0-9]+\w+\]", "", text) 391 | text = re.sub(r"§[0-9]+(\s|\.|$|\,|\:|.*?)", r"\1", text) 392 | text = re.sub(r"§\s[0-9]+(\s|\.|$|\,|\:|.*?)", r"\1", text) 393 | text = re.sub(r"§\s[0-9]+-[0-9]+(\s|\.|$|\,|\:|.*?)", r"\1", text) 394 | text = re.sub(r"\-\s(Variante)(.*?)\-", "", text) 395 | text = re.sub(r"^(Variante)(.*?)$", r"\2", text) 396 | text = re.sub(r"(Variante)(.*?)$", "", text) 397 | # und, von, OA, UA acronyms and comments inside parenthesis 398 | text = text.replace("u.", "und") 399 | text = text.replace("v.", "von") 400 | text = text.replace(". ---", "") 401 | text = text.replace("--NN--", "").replace("|NN|", "").replace("NN", "") 402 | text = re.sub(r"\(wört.*?\)", "", text) 403 | text = re.sub(r"\(wört.*?$", "", text) 404 | text = re.sub(r"\[ältere Fassung.*?\]", "", text) 405 | text = re.sub(r"\(älterer Text.*?\)", "", text) 406 | text = re.sub(r"\(oder.*?\)", "", text) 407 | text = re.sub(r"^\[Beischrift.*?\]:", "", text) 408 | text = re.sub(r"\[Beischrift.*?\]", "", text) 409 | text = re.sub(r"\[.*?Beischrift.*?\]", "", text) 410 | text = re.sub(r"(O.?Äg?\.?)", "Oberägypten", text) 411 | text = re.sub(r"(U.?Äg?\.?)", "Unterägypten", text) 412 | text = text.strip("'").strip('"') 413 | text = text.strip() 414 | text = text.lstrip(".") 415 | # 〈〉 and {} parenthesis, and other elements 416 | text = re.sub(r"\{(.*?)\}\〈(.*?)\〉", r"\1", text) 417 | text = re.sub(r"\〈(.*?)\〉\{(.*?)\}", r"\2", text) 418 | text = text.replace("〈〈", "").replace("〉〉", "") 419 | text = text.replace("{{", "").replace("}}", "") 420 | text = re.sub(r"(\{.*?\}\s+[\wÄäÖöẞßÜü.,=:]+\s+)\〈(.*?)\〉", r"\1", text) 421 | text = re.sub(r"\〈(.*?)\〉(\s+[\wÄäÖöẞßÜü.,=:]+\s+\{.*?\})", r"\2", text) 422 | text = re.sub( 423 | r"(\{.*?\}[\wÄäÖöẞßÜü.,=:]+\s+[\wÄäÖöẞßÜü.,=:]+\s+)\〈(.*?)\〉", r"\1", text 424 | ) 425 | text = re.sub( 426 | r"\〈(.*?)\〉([\wÄäÖöẞßÜü.,=:]+\s+[\wÄäÖöẞßÜü.,=:]+\s+\{.*?\})", r"\2", text 427 | ) 428 | text = re.sub( 429 | r"(\{.*?\}\s+[\wÄäÖöẞßÜü.,=:]+\s+[\wÄäÖöẞßÜü.,=:]+\s+)\〈(.*?)\〉", r"\1", text 430 | ) 431 | text = re.sub( 432 | r"\〈(.*?)\〉(\s+[\wÄäÖöẞßÜü.,=:]+\s+[\wÄäÖöẞßÜü.,=:]+\s+\{.*?\})", r"\2", text 433 | ) 434 | text = re.sub(r"\〈(.*?)\〉(\s+[\wÄäÖöẞßÜü.,=:]+\{.*?\})", r"\2", text) 435 | text = re.sub(r"(\{.*?\}[\wÄäÖöẞßÜü.,=:]+\s+)\〈(.*?)\〉", r"\1", text) 436 | text = re.sub(r"\〈(.*?)\〉([\wÄäÖöẞßÜü.,=:]+\s+\{.*?\})", r"\2", text) 437 | text = re.sub(r"\{(.*?)\}\s\〈(.*?)\〉", r"\1", text) 438 | text = re.sub(r"\〈(.*?)\〉\s\{(.*?)\}", r"\2", text) 439 | text = re.sub(r'"(.*?)\/(.*?)"', r"\1", text) 440 | text = text.replace("〈", "").replace("〉", "") 441 | text = text.replace("{", "").replace("}", "") 442 | text = text.replace("Ꜥ", "ꜥ") 443 | text = text.replace("`", "'") 444 | text = text.replace("#", "") 445 | text = text.replace("≡", "=") 446 | text = text.replace("&", "und") 447 | text = text.replace("$", "") 448 | text = text.replace("(?)", "") 449 | text = re.sub(r"\.\s(oder[\s\wÄäÖöẞßÜü.,=:]+)", "", text) 450 | text = text.replace("*", "") 451 | text = text.replace('"', "") 452 | text = re.sub(r"\(.*?\)", "", text) 453 | text = re.sub(r"\(d\.h\.\s[\s\wÄäÖöẞßÜü.,=:]+", "", text) 454 | # Double spaces again 455 | text = " ".join(text.split()) 456 | return text 457 | 458 | 459 | # Transliteration cleaning 460 | def clean_wChar(text): 461 | # Start from double spaces and sentences to delete 462 | text = " ".join(text.split()) 463 | if "..." in text: 464 | text = "" 465 | if "_" in text: 466 | text = "" 467 | if "-??-" in text: 468 | text = "" 469 | # (()), [[]], ⸮? parenthesis, and two elements 470 | text = re.sub(r"\(\((.*?)\)\)", r"\1", text) 471 | text = re.sub(r"\[\[(.*?)\]\]", r"\1", text) 472 | text = text.replace("⸮", "").replace("?", "") 473 | text = text.replace("~", "") 474 | text = ( 475 | text.replace(".pl.", "") 476 | .replace(".pl", "") 477 | .replace(".{pl}", "") 478 | .replace("{.pl}", "") 479 | .replace(",pl", "") 480 | .replace(".Pl", "") 481 | .replace("pl", "") 482 | ) 483 | # text = text.replace('{(ꜥnḫ-wḏꜣ-snb)} ꜥnḫ', 'ꜥnḫ') 484 | text = text.replace("[", "").replace("]", "") 485 | text = text.replace("-(Zahl)-", "") 486 | text = text.replace("oder ḫr =s", "") 487 | text = text.replace("ON", "").replace("GN", "") 488 | text = text.replace("a", "") 489 | text = text.replace("Zahl", "") 490 | text = text.replace("(", "").replace(")", "") 491 | text = text.replace("⸢", "").replace("⸣", "") 492 | text = text.replace("..1Q..", "/") 493 | text = text.replace("..2Q..", "/ /") 494 | # Inside 〈〉 and {} parenthesis 495 | text = re.sub( 496 | r"(\〈[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\〉.*?\〈[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\〉)(.*?\{[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\}.*?\{[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\})", 497 | r"\2", 498 | text, 499 | ) 500 | text = re.sub(r"(\{.*?\}\s\{.*?\})\s(\〈.*?\〉\s\〈.*?\〉)", r"\1", text) 501 | text = re.sub(r"(\〈.*?\〉\s\〈.*?\〉)\s(\{.*?\}\s\{.*?\})", r"\2", text) 502 | text = re.sub(r"(\{.*?\}\s\{.*?\})\s\〈(.*?)\〉", r"\1", text) 503 | text = re.sub(r"\〈(.*?)\〉\s(\{.*?\}\s\{.*?\})", r"\2", text) 504 | text = re.sub(r"(\{.*?\})\s(\〈.*?\〉\s\〈.*?\〉)", r"\2", text) 505 | text = re.sub(r"(\〈.*?\〉\s\〈.*?\〉)\s(\{.*?\})", r"\1", text) 506 | text = re.sub(r"(\{.*?\}\s.*?\s\{.*?\})\s(\〈.*?\〉)", r"\1", text) 507 | text = re.sub(r"(\〈.*?\〉\s.*?\s\〈.*?\〉)\s(\{.*?\})", r"\1", text) 508 | text = re.sub(r"\{(.*?)\}[^\s]\〈(.*?)\〉", r"\1", text) 509 | text = re.sub(r"\〈(.*?)\〉[^\s]\{(.*?)\}", r"\2", text) 510 | text = re.sub(r"\{(.*?)\}\s[^\s]\〈(.*?)\〉", r"\1", text) 511 | text = re.sub(r"\〈(.*?)\〉\s[^\s]\{(.*?)\}", r"\2", text) 512 | text = re.sub(r"(\{.*?\}[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+)\〈(.*?)\〉", r"\1", text) 513 | text = re.sub(r"(\〈.*?\〉)([a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\{.*?\})", r"\2", text) 514 | text = re.sub(r"(\{.*?\}\s[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+)\〈(.*?)\〉", r"\1", text) 515 | text = re.sub(r"(\〈.*?\〉)(\s[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\{.*?\})", r"\2", text) 516 | text = re.sub(r"(\{.*?\}\s[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\s)\〈(.*?)\〉", r"\1", text) 517 | text = re.sub(r"(\〈.*?\〉)(\s[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\s\{.*?\})", r"\2", text) 518 | text = re.sub(r"(\{.*?\}[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\s)\〈(.*?)\〉", r"\1", text) 519 | text = re.sub(r"(\〈.*?\〉)([a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\s\{.*?\})", r"\2", text) 520 | text = re.sub( 521 | r"(\{.*?\}\s[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\s[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+)\〈(.*?)\〉", 522 | r"\1", 523 | text, 524 | ) 525 | text = re.sub( 526 | r"(\〈.*?\〉)(\s[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\s[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\{.*?\})", 527 | r"\2", 528 | text, 529 | ) 530 | text = re.sub( 531 | r"(\{.*?\}[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\s[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+)\〈(.*?)\〉", 532 | r"\1", 533 | text, 534 | ) 535 | text = re.sub( 536 | r"(\〈.*?\〉)([a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\s[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\{.*?\})", 537 | r"\2", 538 | text, 539 | ) 540 | text = re.sub( 541 | r"(\{.*?\}[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\s[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\s)\〈(.*?)\〉", 542 | r"\1", 543 | text, 544 | ) 545 | text = re.sub( 546 | r"(\〈.*?\〉)([a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\s[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\s\{.*?\})", 547 | r"\2", 548 | text, 549 | ) 550 | text = re.sub( 551 | r"(\{.*?\}[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\s[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\s[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\s)\〈(.*?)\〉", 552 | r"\1", 553 | text, 554 | ) 555 | text = re.sub( 556 | r"\〈(.*?)\〉(\{.*?\}[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\s[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\s[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\s)", 557 | r"\2", 558 | text, 559 | ) 560 | text = re.sub( 561 | r"(\{.*?\}\s[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\s[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\s)\〈(.*?)\〉", 562 | r"\1", 563 | text, 564 | ) 565 | text = re.sub( 566 | r"(\〈.*?\〉)(\s[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\s[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\s\{.*?\})", 567 | r"\2", 568 | text, 569 | ) 570 | text = re.sub( 571 | r"(\{.*?\}\s[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\s[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\s[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+)\〈(.*?)\〉", 572 | r"\1", 573 | text, 574 | ) 575 | text = re.sub( 576 | r"(\〈.*?\〉)(\s[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\s[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\s[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\{.*?\})", 577 | r"\2", 578 | text, 579 | ) 580 | text = re.sub( 581 | r"(\{.*?\}\s[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\s[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\s[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\s)\〈(.*?)\〉", 582 | r"\1", 583 | text, 584 | ) 585 | text = re.sub( 586 | r"\〈(.*?)\〉(\{.*?\}\s[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\s[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\s[a-zA-Z0-9ḤḥḪḫẖꜣꜥḏṯš.,:=i̯]+\s)", 587 | r"\2", 588 | text, 589 | ) 590 | text = re.sub(r"\{(.*?)\}\〈(.*?)\〉", r"\1", text) 591 | text = re.sub(r"\〈(.*?)\〉\-\{(.*?)\}", r"\2", text) 592 | text = re.sub(r"\{(.*?)\}\\-〈(.*?)\〉", r"\1", text) 593 | text = re.sub(r"\〈(.*?)\〉\s\{(.*?)\}", r"\2", text) 594 | text = re.sub(r"\{(.*?)\}\s\〈(.*?)\〉", r"\1", text) 595 | text = re.sub(r"\〈(.*?)\〉\{(.*?)\}", r"\2", text) 596 | # # Fractions 597 | # text = re.sub('\〈\w+\/\w+\〉\s\〈\w+\/\w+\〉\s.*?(\{\w+\/\w+\}\s\{\w+\/\w+\})', r'\1', text) 598 | # 〈〉 and {} parenthesis and other elements 599 | text = text.replace("〈", "").replace("〉", "") 600 | text = text.replace("{", "").replace("}", "") 601 | text = text.replace(":", "") 602 | text = text.replace(".du", "").replace(",du", "") 603 | text = text.replace("≡", "=") 604 | text = text.replace("-Lücke-", "") 605 | text = text.replace("Lücke", "") 606 | text = text.replace("-", " ") 607 | text = text.replace("+", "") 608 | text = text.replace("!", "") 609 | text = text.replace("ø", "") 610 | text = text.replace("𓍹", "").replace("𓍺", "") 611 | text = text.replace("⁝", "") 612 | text = text.replace("Präp.", "") 613 | text = text.replace("𓊆", "").replace("𓊇", "") 614 | # text = text.replace('ð', '') 615 | # text = text.replace('ṯb;w,t', 'ṯbw,t') 616 | text = text.replace("t'", "tꜥ").replace("jmj-r'", "jmj-rꜥ") 617 | text = text.replace("ʾ", "ꜥ") 618 | text = text.strip() 619 | # Double spaces again 620 | text = " ".join(text.split()) 621 | return text 622 | 623 | 624 | # # wordClass semplification 625 | # def clean_wordClass(text): 626 | # text = text.replace('title', 'title_epithet').replace('epith_god', 'title_epithet').replace('epith_king', 'title_epithet').replace('epitheton_title', 'title_epithet') 627 | # text = text.replace('prepositional_adverb', 'adverb') 628 | # text = text.replace('nisbe_adjective_preposition', 'adjective').replace('nisbe_adjective_substantive', 'adjective') 629 | # text = text.replace('substantive_fem', 'substantive').replace('substantive_masc', 'substantive').replace('animal_name', 'substantive').replace('artifact_name', 'substantive') 630 | # text = text.replace('entity_name', 'substantive').replace('gods_name', 'substantive').replace('kings_name', 'substantive').replace('org_name', 'substantive') 631 | # text = text.replace('person_name', 'substantive').replace('place_name', 'substantive').replace('root', 'substantive') 632 | # text = text.replace('cardinal', 'numeral').replace('ordinal', 'numeral') 633 | # text = text.replace('particle_enclitic', 'particle').replace('particle_nonenclitic', 'particle').replace('interjection', 'particle') 634 | # text = text.replace('personal_pronoun', 'pronoun').replace('demonstrative_pronoun', 'pronoun').replace('relative_pronoun', 'pronoun').replace('interrogative_pronoun', 'pronoun') 635 | # text = text.replace('verb_2-gem', 'verb').replace('verb_2-lit', 'verb').replace('verb_3-gem', 'verb').replace('verb_3-inf', 'verb').replace('verb_3-lit', 'verb') 636 | # text = text.replace('verb_4-inf', 'verb').replace('verb_4-lit', 'verb').replace('verb_5-inf', 'verb').replace('verb_5-lit', 'verb').replace('verb_6-lit', 'verb') 637 | # text = text.replace('verb_caus_2-gem', 'verb').replace('verb_caus_2-lit', 'verb').replace('verb_caus_3-gem', 'verb').replace('verb_caus_3-inf', 'verb') 638 | # text = text.replace('verb_caus_3-lit', 'verb').replace('verb_caus_4-inf', 'verb').replace('verb_caus_4-lit', 'verb').replace('verb_caus_5-lit', 'verb') 639 | # text = text.replace('verb_irr', 'verb') 640 | # return text 641 | 642 | 643 | # Clean all data function 644 | def clean_data(data): 645 | for datapoint in data: 646 | datapoint["source"] = clean_graphics(datapoint["source"]) 647 | datapoint["transliteration"] = clean_wChar(datapoint["transliteration"]) 648 | datapoint["target"] = clean_traduction(datapoint["target"]) 649 | # datapoint['wordClass'] = clean_wordClass(datapoint['wordClass']) 650 | return data 651 | 652 | 653 | # Training functions defining: batch_it, tokenize_batch, training_stes, validations_step 654 | def batch_it(sequence, batch_size=1, return_last=True): 655 | if batch_size <= 0: 656 | raise ValueError( 657 | f"Batch size cannot be nonpositive. Passed `batch_size = {batch_size}`" 658 | ) 659 | 660 | batch = [] 661 | for item in sequence: 662 | if len(batch) == batch_size: 663 | yield batch 664 | batch = [] 665 | batch.append(item) 666 | 667 | if batch and return_last: 668 | yield batch 669 | 670 | 671 | def tokenize_batch(model, batch, tokenizer, src_lang, tgt_lang): 672 | tokenizer.src_lang = src_lang 673 | tokenizer.tgt_lang = tgt_lang 674 | 675 | tokenized_batch = tokenizer( 676 | [element["source"] for element in batch], 677 | text_target=[element["target"] for element in batch], 678 | max_length=64, 679 | padding=True, 680 | truncation=True, 681 | return_tensors="pt", 682 | ).to(model.device) 683 | 684 | tokenized_batch["labels"] = torch.where( 685 | tokenized_batch["labels"] == tokenizer.pad_token_id, 686 | torch.full_like(tokenized_batch["labels"], -100), 687 | tokenized_batch["labels"], 688 | ) 689 | 690 | return tokenized_batch 691 | 692 | 693 | def training_step(batch, model, tokenizer, optimizer, src_lang, tgt_lang): 694 | with torch.cuda.amp.autocast(): 695 | tokenized_batch = tokenize_batch(model, batch, tokenizer, src_lang, tgt_lang) 696 | loss = model(**tokenized_batch).loss 697 | 698 | loss.backward() 699 | optimizer.step() 700 | optimizer.zero_grad() 701 | 702 | return loss.item() 703 | 704 | 705 | def validation_step(batch, model, tokenizer, src_lang, tgt_lang): 706 | with torch.no_grad(): 707 | with torch.cuda.amp.autocast(): 708 | tokenized_batch = tokenize_batch( 709 | model, batch, tokenizer, src_lang, tgt_lang 710 | ) 711 | loss = model(**tokenized_batch).loss 712 | 713 | return loss.item(), (tokenized_batch["labels"] != -100).sum().item() 714 | --------------------------------------------------------------------------------