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from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from transformers import TrainingArguments, Trainer
import os
import torch
# Load dataset
ds = load_dataset("knkarthick/dialogsum")
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large-cnn")
model = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large-cnn")
# Preprocessing function
def preprocess_function(batch):
source = batch['dialogue']
target = batch['summary']
source_enc = tokenizer(source, padding='max_length', truncation=True, max_length=128)
target_enc = tokenizer(target, padding='max_length', truncation=True, max_length=128)
labels = target_enc['input_ids']
labels = [[(token if token != tokenizer.pad_token_id else -100) for token in label] for label in labels]
return {
'input_ids': source_enc['input_ids'],
'attention_mask': source_enc['attention_mask'],
'labels': labels
}
# Apply preprocessing
df_source = ds.map(preprocess_function, batched=True)
# Training arguments
training_args = TrainingArguments(
output_dir='/content/TextSummarizer_output',
per_device_train_batch_size=8,
num_train_epochs=2,
save_total_limit=1,
save_strategy="epoch",
remove_unused_columns=True,
logging_dir='/content/logs',
logging_steps=50,
)
# Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=df_source['train'],
eval_dataset=df_source['test'],
)
# Train
trainer.train()
# Evaluate
eval_results = trainer.evaluate()
print("Evaluation Results:", eval_results)
# ===> Save to Google Drive path
save_path = "/content/drive/MyDrive/TextSummarizer2/model_directory"
os.makedirs(save_path, exist_ok=True)
# Save model and tokenizer (use safe_serialization for large model.safetensors)
model.save_pretrained(save_path, safe_serialization=True)
tokenizer.save_pretrained(save_path)
print(f"✅ Model and tokenizer saved to: {save_path}")
print("📦 Files saved:", os.listdir(save_path))
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