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Datasets:
pszemraj
/
qmsum-cleaned

Tasks:
Text2Text Generation
Summarization
Modalities:
Tabular
Text
Formats:
parquet
Languages:
English
Size:
1K - 10K
Tags:
scrolls
qmsum
Libraries:
Datasets
pandas
License:
Dataset card Viewer Files Community
2
qmsum-cleaned
File size: 4,673 Bytes 
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# -*- coding: utf-8 -*-
"""scratchpad

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/notebooks/empty.ipynb
"""

pip install -q datasets transformers

from datasets import load_dataset

dataset = load_dataset("tau/scrolls", "qmsum")

dataset

!pip install clean-text[gpl] -q
from cleantext import clean

train_df = dataset["train"].to_pandas().convert_dtypes()
val_df = dataset["validation"].to_pandas().convert_dtypes()
test_df = dataset["test"].to_pandas().convert_dtypes()

from tqdm.auto import tqdm 

tqdm.pandas()

train_df["input"] = train_df["input"].progress_apply(clean, lower=False, no_urls=True, no_emails=True)
val_df["input"] = val_df["input"].progress_apply(clean, lower=False, no_urls=True, no_emails=True)
test_df["input"] = test_df["input"].progress_apply(clean, lower=False, no_urls=True, no_emails=True)

train_df["output"] = train_df["output"].progress_apply(clean, lower=False, no_urls=True, no_emails=True)
val_df["output"] = val_df["output"].progress_apply(clean, lower=False, no_urls=True, no_emails=True)
test_df["output"] = test_df["output"].progress_apply(clean, lower=False, no_urls=True, no_emails=True)

import re
import re
def fix_punct_whitespace(text: str) -> str:
    # Fix spaces around apostrophes
    text = re.sub(r"([a-zA-Z])\s?'\s?([a-zA-Z])", r"\1'\2", text)

    # Remove spaces before punctuation marks (except for parentheses)
    text = re.sub(r"\s+([.,;:!?])", r"\1", text)

    # Add a space after punctuation marks (except for parentheses) if missing
    text = re.sub(r"([.,;:!?])(?=[^\s])", r"\1 ", text)

    # Handle spaces around parentheses
    text = re.sub(r"\s?\(\s?", r" (", text)
    text = re.sub(r"\s?\)\s?", r")", text)

    # Add a space after a closing parenthesis if:
    # followed by a word or opening parenthesis
    text = re.sub(r"\)(?=[^\s.,;:!?])", r") ", text)

    # Handle spaces around quotation marks
    text = re.sub(r'\s?"', r'"', text)
    text = re.sub(r'"\s?', r'" ', text)

    # Handle spaces around single quotes
    text = re.sub(r"\s?'", r"'", text)
    text = re.sub(r"'\s?", r"' ", text)

    # Handle comma in numbers
    text = re.sub(r"(\d),\s+(\d)", r"\1,\2", text)

    return text.replace("' ", "'")

train_df["input"] = train_df["input"].progress_apply(fix_punct_whitespace)
val_df["input"] = val_df["input"].progress_apply(fix_punct_whitespace)
test_df["input"] = test_df["input"].progress_apply(fix_punct_whitespace)

train_df["output"] = train_df["output"].progress_apply(fix_punct_whitespace)
val_df["output"] = val_df["output"].progress_apply(fix_punct_whitespace)
test_df["output"] = test_df["output"].progress_apply(fix_punct_whitespace)

train_df.head(2)

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("pszemraj/long-t5-tglobal-xl-16384-book-summary-8bit")

def get_token_count(text:str):
    if len(text) < 1:
        return 0
    else:
        return len(tokenizer.encode(text, truncation=False, padding=False))

get_token_count("ayyy waddup my g")

train_df["input_token_count"] = train_df["input"].progress_apply(get_token_count)
val_df["input_token_count"] = val_df["input"].progress_apply(get_token_count)
test_df["input_token_count"] = test_df["input"].progress_apply(get_token_count)

train_df["output_token_count"] = train_df["output"].progress_apply(get_token_count)
val_df["output_token_count"] = val_df["output"].progress_apply(get_token_count)
test_df["output_token_count"] = test_df["output"].progress_apply(get_token_count)



train_df.describe()

"""# New Section"""

# Commented out IPython magic to ensure Python compatibility.
# %%bash
# curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | bash
# apt-get install git-lfs -q
# git lfs install

!pip install -U -q transformers accelerate
from huggingface_hub import notebook_login
notebook_login()

# Commented out IPython magic to ensure Python compatibility.
# %%bash
# 
# git lfs install
# git clone https://huggingface.co/datasets/pszemraj/qmsum-cleaned

from pathlib import Path 

target = Path.cwd() / 'qmsum-cleaned'
target.exists()

train_df.to_parquet(target / 'train.parquet')
val_df.to_parquet(target / 'validation.parquet')
test_df.to_parquet(target/ 'test.parquet')
!ls $target

# Commented out IPython magic to ensure Python compatibility.
# %cd $target
!git pull
!git lfs install && git lfs track *.parquet
!git add . && git commit -a -m add_cleaned
!git push
# %cd ..

# Commented out IPython magic to ensure Python compatibility.
# %%bash
# git config --global user.email "you@example.com"
# git config --global user.name "colab"