Delete ai_t5.py

ai_t5.py

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-# -*- coding: utf-8 -*-
-"""AI_t5.ipynb
-
-Automatically generated by Colab.
-
-Original file is located at
-    https://colab.research.google.com/drive/1wUhv0CziUL-fB4pEUCQW8fOnJDIGLgtn
-"""
-
-!pip install transformers[torch] accelerate
-
-# Uninstall conflicting packages
-!pip uninstall -y requests google-colab
-
-# Reinstall google-colab which will bring the compatible requests version
-!pip install google-colab
-
-pip install requests==2.31.0
-
-!pip install rouge_score
-!pip install evaluate
-# !pip install datasets
-
-import numpy as np
-import pandas as pd
-from datasets import Dataset, DatasetDict
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, \
-    Seq2SeqTrainer, Seq2SeqTrainingArguments, DataCollatorForSeq2Seq, get_scheduler
-import evaluate
-import nltk
-from nltk.tokenize import sent_tokenize
-import warnings
-warnings.simplefilter(action='ignore')
-
-data = pd.read_csv('news_summary.csv', encoding='cp437')
-data = data.dropna()
-data.info()
-
-# headlines - column containing headlines which will be used as reference summarizations
-# ctext - column containing full texts of news articles
-# taking a look at the average lengths of both
-
-def length(text):
-    return len(text.split())
-
-print('Mean headline length (words):', data['headlines'].apply(length).mean())
-print('Mean text length (words):', data['ctext'].apply(length).mean())
-
-# splitting the data into train, val, and test, and converting it into Dataset format
-
-train_size = int(0.8 * len(data))
-val_size = int(0.1 * len(data))
-test_size = len(data) - train_size - val_size
-
-train_data = data[:train_size]
-val_data = data[train_size:train_size+val_size]
-test_data = data[train_size+val_size:]
-
-train_dataset = Dataset.from_pandas(train_data)
-val_dataset = Dataset.from_pandas(val_data)
-test_dataset = Dataset.from_pandas(test_data)
-
-dataset = DatasetDict({
-    "train": train_dataset,
-    "validation": val_dataset,
-    "test": test_dataset
-})
-
-dataset
-
-# loading the model tokenizer
-
-model_checkpoint = "google/mt5-small"
-tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
-
-# creating tokenization function with length limits for headlines and texts
-
-max_input_length = 512
-max_target_length = 30
-
-def preprocess_function(examples):
-    model_inputs = tokenizer(
-        examples["ctext"],
-        max_length=max_input_length,
-        truncation=True,
-    )
-    labels = tokenizer(
-        examples["headlines"], max_length=max_target_length, truncation=True
-    )
-    model_inputs["labels"] = labels["input_ids"]
-    return model_inputs
-
-# tokenizing the datasets
-
-tokenized_datasets = dataset.map(preprocess_function, batched=True)
-
-# loading ROUGE metric
-
-rouge_score = evaluate.load("rouge")
-
-import nltk
-nltk.download('punkt')
-
-def three_sentence_summary(text):
-    return "\n".join(sent_tokenize(text)[:3])
-
-
-print(three_sentence_summary(dataset["train"][1]["ctext"]))
-
-def evaluate_baseline(dataset, metric):
-    summaries = [three_sentence_summary(text) for text in dataset["ctext"]]
-    return metric.compute(predictions=summaries, references=dataset["headlines"])
-
-# getting baseline metrics
-
-score = evaluate_baseline(dataset["validation"], rouge_score)
-rouge_names = ["rouge1", "rouge2", "rougeL", "rougeLsum"]
-rouge_dict = dict((rn, round(score[rn] * 100, 2)) for rn in rouge_names)
-rouge_dict
-
-# logging in to Hugging Face Hub
-
-from huggingface_hub import notebook_login
-
-notebook_login()
-
-# loading the pre-trained Seq2Seq model and the data collator
-
-model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
-
-data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)
-
-# setting arguments
-
-batch_size = 8
-num_train_epochs = 8
-# Show the training loss with every epoch
-logging_steps = len(tokenized_datasets["train"]) // batch_size
-output_dir = "mt5-small-finetuned-news-summary-kaggle"
-
-args = Seq2SeqTrainingArguments(
-    output_dir=output_dir,
-    evaluation_strategy="epoch",
-    learning_rate=5.6e-5,
-    per_device_train_batch_size=batch_size,
-    per_device_eval_batch_size=batch_size,
-    weight_decay=0.01,
-    save_total_limit=3,
-    num_train_epochs=num_train_epochs,
-    predict_with_generate=True,       # calculate ROUGE for every epoch
-    logging_steps=logging_steps,
-    push_to_hub=True,
-)
-
-# function for computing ROUGE metrics
-
-def compute_metrics(eval_pred):
-    predictions, labels = eval_pred
-    decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)
-    labels= np.where(labels != -100, labels, tokenizer.pad_token_id)
-    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
-    decoded_preds = ["\n".join(sent_tokenize(pred.strip())) for pred in decoded_preds]
-    decoded_labels = ["\n".join(sent_tokenize(label.strip())) for label in decoded_labels]
-    result = rouge_score.compute(
-       predictions=decoded_preds, references=decoded_labels, use_stemmer=True
-    )
-    result = {key: value * 100 for key, value in result.items()}
-    return {k: round(v, 4) for k, v in result.items()}
-
-# removing columns containing strings
-
-tokenized_datasets = tokenized_datasets.remove_columns(
-    dataset["train"].column_names
-)
-
-# defining Trainer
-
-trainer = Seq2SeqTrainer(
-    model,
-    args,
-    train_dataset=tokenized_datasets["train"],
-    eval_dataset=tokenized_datasets["validation"],
-    data_collator=data_collator,
-    tokenizer=tokenizer,
-    compute_metrics=compute_metrics,
-)
-
-# training the model
-
-trainer.train()
-
-# evaluating the model
-
-trainer.evaluate()
-
-# pushing to Hugging Face Hub
-
-trainer.push_to_hub(commit_message="Training complete", tags="summarization")
-
-from transformers import pipeline
-
-hub_model_id = "shivraj221/mt5-small-finetuned-news-summary-kaggle"
-summarizer = pipeline("summarization", model=hub_model_id)
-
-# function to get a summary of an article with index idx
-
-def print_summary(idx):
-    review = dataset["test"][idx]["ctext"]
-    title = dataset["test"][idx]["headlines"]
-    summary = summarizer(dataset["test"][idx]["ctext"])[0]["summary_text"]
-    print(f"'>>> Article: {review}'")
-    print(f"\n'>>> Headline: {title}'")
-    print(f"\n'>>> Summary: {summary}'")
-
-print_summary(20)