Generative AI Model for text summarization

import torch

from transformers import BartForConditionalGeneration, BartTokenizer, Trainer, TrainingArguments


# Define your dataset and dataloader (not provided here, as it depends on your data format)


# Load the BART model and tokenizer

model_name = "facebook/bart-large-cnn"  # You can choose a different model

tokenizer = BartTokenizer.from_pretrained(model_name)

model = BartForConditionalGeneration.from_pretrained(model_name)


# Set up training arguments

training_args = TrainingArguments(

    output_dir="./output",

    num_train_epochs=3,  # Adjust as needed

    per_device_train_batch_size=4,  # Adjust as needed

    save_steps=10_000,  # Save the model checkpoint after a certain number of steps

    logging_steps=100,  # Log training progress every N steps

    evaluation_strategy="steps",  # Evaluate every N steps

    eval_steps=1000,  # Evaluate every N steps

    save_total_limit=5,  # Limit the number of saved checkpoints

)


# Initialize Trainer

trainer = Trainer(

    model=model,

    args=training_args,

    data_collator=None,  # Define your data collator

    train_dataset=None,  # Provide your training dataset

    data_collator=None,  # Define your data collator

    tokenizer=tokenizer,

    compute_metrics=None,  # Define your evaluation metrics

)


# Train the model

trainer.train()


# Save the trained model

trainer.save_model()


# Load the trained model

model = BartForConditionalGeneration.from_pretrained("output")


# Testing (Generating summaries)

input_text = "Your input text here..."  # Provide your input text

input_ids = tokenizer.encode(input_text, return_tensors="pt", max_length=1024, truncation=True)


# Generate the summary

output_ids = model.generate(input_ids, max_length=150, num_return_sequences=1)


# Decode and print the generated summary

generated_summary = tokenizer.decode(output_ids[0], skip_special_tokens=True)

print("Generated Summary:", generated_summary)

 

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