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"""using_dataset_hugginface.ipynb |
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Automatically generated by Colaboratory. |
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Original file is located at |
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https://colab.research.google.com/drive/1soGxkZu4antYbYG23GioJ6zoSt_GhSNT |
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""" |
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"""**Hugginface loggin for push on Hub**""" |
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import os |
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import time |
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import math |
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from huggingface_hub import login |
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from datasets import load_dataset, concatenate_datasets |
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from functools import reduce |
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from pathlib import Path |
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import pandas as pd |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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HF_TOKEN = '' |
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DATASET_TO_LOAD = 'CoWeSe.txt' |
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EXAMPLE_DATASET_TO_LOAD = 'example.txt' |
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DATASET_TO_UPDATE = 'somosnlp/spanish_medica_llm' |
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login(token = HF_TOKEN) |
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royalListOfCode = {} |
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issues_path = 'dataset' |
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tokenizer = AutoTokenizer.from_pretrained("DeepESP/gpt2-spanish-medium") |
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DATASET_SOURCE_ID = '4' |
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path = Path(__file__).parent.absolute() |
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''' |
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Bibliografy: |
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https://www.w3schools.com/python/python_mysql_getstarted.asp |
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https://www.w3schools.com/python/python_mysql_select.as |
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''' |
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cantemistDstDict = { |
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'raw_text': '', |
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'topic': '', |
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'speciallity': '', |
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'raw_text_type': 'open_text', |
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'topic_type': '', |
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'source': DATASET_SOURCE_ID, |
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'country': 'es', |
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'document_id': '' |
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} |
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totalOfTokens = 0 |
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corpusToLoad = [] |
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countCopySeveralDocument = 0 |
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counteOriginalDocument = 0 |
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FILE_TO_PROCESS = DATASET_TO_LOAD |
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if not os.path.exists(str(path) + os.sep + FILE_TO_PROCESS): |
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FILE_TO_PROCESS = EXAMPLE_DATASET_TO_LOAD |
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with open( str(path) + os.sep + FILE_TO_PROCESS,encoding='utf8') as file: |
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paragraph = '' |
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while True: |
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linesInFile = file.readlines(8192) |
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if not linesInFile: |
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break |
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for index, iLine in enumerate(linesInFile): |
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text = linesInFile[index] if len(linesInFile[index]) > 1 else '' |
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paragraph += text + ' ' |
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if text == '': |
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counteOriginalDocument += 1 |
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idFile = str(counteOriginalDocument) |
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newCorpusRow = cantemistDstDict.copy() |
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listOfTokens = tokenizer.tokenize(paragraph) |
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currentSizeOfTokens = len(listOfTokens) |
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totalOfTokens += currentSizeOfTokens |
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newCorpusRow['raw_text'] = paragraph |
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newCorpusRow['document_id'] = idFile |
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corpusToLoad.append(newCorpusRow) |
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paragraph = '' |
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paragraph = '' |
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df = pd.DataFrame.from_records(corpusToLoad) |
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if os.path.exists(f"{str(path)}/{issues_path}/spanish_medical_llms.jsonl"): |
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os.remove(f"{str(path)}/{issues_path}/spanish_medical_llms.jsonl") |
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df.to_json(f"{str(path)}/{issues_path}/spanish_medical_llms.jsonl", orient="records", lines=True) |
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print( |
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f"Downloaded all the issues for {DATASET_TO_LOAD}! Dataset stored at {issues_path}/spanish_medical_llms.jsonl" |
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) |
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print(' On dataset there are as document ', counteOriginalDocument) |
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print(' On dataset there are as copy document ', countCopySeveralDocument) |
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print(' On dataset there are as size of Tokens ', totalOfTokens) |
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file = Path(f"{str(path)}/{issues_path}/spanish_medical_llms.jsonl") |
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size = file.stat().st_size |
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print ('File size on Kilobytes (kB)', size >> 10) |
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print ('File size on Megabytes (MB)', size >> 20 ) |
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print ('File size on Gigabytes (GB)', size >> 30 ) |
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local_spanish_dataset = load_dataset("json", data_files=f"{str(path)}/{issues_path}/spanish_medical_llms.jsonl", split="train") |
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try: |
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spanish_dataset = load_dataset(DATASET_TO_UPDATE, split="train") |
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spanish_dataset = concatenate_datasets([spanish_dataset, local_spanish_dataset]) |
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except Exception: |
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print ('<=== Error ===>') |
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spanish_dataset = local_spanish_dataset |
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spanish_dataset.push_to_hub(DATASET_TO_UPDATE) |
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print(local_spanish_dataset) |
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