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)

Solo Coder