src/textsummarizer/conponents/model_trainer.py

from transformers import TrainingArguments, Trainer
from transformers import DataCollatorForSeq2Seq
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from datasets import load_dataset, load_from_disk
from src.textsummarizer.entity.config_entity import ModelTrainerConfig
import torch
import  os


class ModelTrainer:
    def __init__(self, config : ModelTrainerConfig):
        self.config = config
        os.environ["WANDB_DISABLED"] = "true"
        
        
    def train(self):
        device = "cuda" if torch.cuda.is_available() else "cpu"
        tokenizer = AutoTokenizer.from_pretrained(self.config.model_ckpt)
        model_pegasus = AutoModelForSeq2SeqLM.from_pretrained(self.config.model_ckpt).to(device)
        seq2seq_data_collator = DataCollatorForSeq2Seq(tokenizer, model=model_pegasus)
        
        #loading data 
        dataset_samsum_pt = load_from_disk(self.config.data_path)
        
        
        trainer_args = TrainingArguments(
            output_dir=self.config.root_dir, num_train_epochs=self.config.num_train_epochs, warmup_steps=self.config.warmup_steps,
            per_device_train_batch_size=self.config.per_device_train_batch_size, per_device_eval_batch_size=self.config.per_device_train_batch_size,
            weight_decay=self.config.weight_decay, logging_steps=self.config.logging_steps,
            evaluation_strategy=self.config.evaluation_strategy, eval_steps=self.config.eval_steps, save_steps=1e6,
            gradient_accumulation_steps=self.config.gradient_accumulation_steps,
            report_to="none"
            
        ) 
        
        
        trainer = Trainer(model=model_pegasus, args=trainer_args,
                  tokenizer=tokenizer, data_collator=seq2seq_data_collator,
                  train_dataset=dataset_samsum_pt["train"], 
                  eval_dataset=dataset_samsum_pt["validation"])
       
        
        trainer.train()

        ## Save model
        model_pegasus.save_pretrained(os.path.join(self.config.root_dir,"pegasus-samsum-model"))
        ## Save tokenizer
        tokenizer.save_pretrained(os.path.join(self.config.root_dir,"tokenizer"))