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"""scratchpad |
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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/notebooks/empty.ipynb |
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""" |
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pip install -q datasets transformers |
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from datasets import load_dataset |
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dataset = load_dataset("tau/scrolls", "qmsum") |
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dataset |
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!pip install clean-text[gpl] -q |
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from cleantext import clean |
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train_df = dataset["train"].to_pandas().convert_dtypes() |
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val_df = dataset["validation"].to_pandas().convert_dtypes() |
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test_df = dataset["test"].to_pandas().convert_dtypes() |
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from tqdm.auto import tqdm |
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tqdm.pandas() |
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train_df["input"] = train_df["input"].progress_apply(clean, lower=False, no_urls=True, no_emails=True) |
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val_df["input"] = val_df["input"].progress_apply(clean, lower=False, no_urls=True, no_emails=True) |
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test_df["input"] = test_df["input"].progress_apply(clean, lower=False, no_urls=True, no_emails=True) |
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train_df["output"] = train_df["output"].progress_apply(clean, lower=False, no_urls=True, no_emails=True) |
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val_df["output"] = val_df["output"].progress_apply(clean, lower=False, no_urls=True, no_emails=True) |
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test_df["output"] = test_df["output"].progress_apply(clean, lower=False, no_urls=True, no_emails=True) |
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import re |
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import re |
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def fix_punct_whitespace(text: str) -> str: |
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text = re.sub(r"([a-zA-Z])\s?'\s?([a-zA-Z])", r"\1'\2", text) |
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text = re.sub(r"\s+([.,;:!?])", r"\1", text) |
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text = re.sub(r"([.,;:!?])(?=[^\s])", r"\1 ", text) |
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text = re.sub(r"\s?\(\s?", r" (", text) |
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text = re.sub(r"\s?\)\s?", r")", text) |
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text = re.sub(r"\)(?=[^\s.,;:!?])", r") ", text) |
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text = re.sub(r'\s?"', r'"', text) |
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text = re.sub(r'"\s?', r'" ', text) |
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text = re.sub(r"\s?'", r"'", text) |
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text = re.sub(r"'\s?", r"' ", text) |
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text = re.sub(r"(\d),\s+(\d)", r"\1,\2", text) |
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return text.replace("' ", "'") |
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train_df["input"] = train_df["input"].progress_apply(fix_punct_whitespace) |
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val_df["input"] = val_df["input"].progress_apply(fix_punct_whitespace) |
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test_df["input"] = test_df["input"].progress_apply(fix_punct_whitespace) |
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train_df["output"] = train_df["output"].progress_apply(fix_punct_whitespace) |
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val_df["output"] = val_df["output"].progress_apply(fix_punct_whitespace) |
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test_df["output"] = test_df["output"].progress_apply(fix_punct_whitespace) |
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train_df.head(2) |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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tokenizer = AutoTokenizer.from_pretrained("pszemraj/long-t5-tglobal-xl-16384-book-summary-8bit") |
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def get_token_count(text:str): |
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if len(text) < 1: |
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return 0 |
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else: |
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return len(tokenizer.encode(text, truncation=False, padding=False)) |
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get_token_count("ayyy waddup my g") |
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train_df["input_token_count"] = train_df["input"].progress_apply(get_token_count) |
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val_df["input_token_count"] = val_df["input"].progress_apply(get_token_count) |
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test_df["input_token_count"] = test_df["input"].progress_apply(get_token_count) |
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train_df["output_token_count"] = train_df["output"].progress_apply(get_token_count) |
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val_df["output_token_count"] = val_df["output"].progress_apply(get_token_count) |
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test_df["output_token_count"] = test_df["output"].progress_apply(get_token_count) |
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train_df.describe() |
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"""# New Section""" |
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!pip install -U -q transformers accelerate |
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from huggingface_hub import notebook_login |
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notebook_login() |
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from pathlib import Path |
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target = Path.cwd() / 'qmsum-cleaned' |
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target.exists() |
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train_df.to_parquet(target / 'train.parquet') |
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val_df.to_parquet(target / 'validation.parquet') |
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test_df.to_parquet(target/ 'test.parquet') |
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!ls $target |
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!git pull |
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!git lfs install && git lfs track *.parquet |
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!git add . && git commit -a -m add_cleaned |
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!git push |
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