Update train_flan_t5.py

train_flan_t5.py

@@ -1,47 +1,3 @@
-from datasets import load_dataset
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, Seq2SeqTrainer, Seq2SeqTrainingArguments
-
-model_checkpoint = "google/flan-t5-large"
-output_dir = "./finetuned-flan-t5"
-
-# Load dataset
-dataset = load_dataset("json", data_files={"train": "train_data.jsonl"})
-
-# Tokenizer
-tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
-
-def preprocess_function(examples):
-    inputs = examples["input"]
-    targets = examples["output"]
-    model_inputs = tokenizer(inputs, max_length=512, truncation=True)
-    labels = tokenizer(targets, max_length=128, truncation=True)
-    model_inputs["labels"] = labels["input_ids"]
-    return model_inputs
-
-tokenized_datasets = dataset.map(preprocess_function, batched=True)
-
-# Model
-model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
-
-# Training arguments
-training_args = Seq2SeqTrainingArguments(
-    output_dir=output_dir,
-    evaluation_strategy="no",
-    learning_rate=5e-5,
-    per_device_train_batch_size=2,
-    num_train_epochs=3,
-    weight_decay=0.01,
-    save_total_limit=2,
-    push_to_hub=False
-)
-
-trainer = Seq2SeqTrainer(
-    model=model,
-    args=training_args,
-    train_dataset=tokenized_datasets["train"]
-)
-
-trainer.train()
-
-model.save_pretrained(output_dir)
-tokenizer.save_pretrained(output_dir)
+tokenizer_config.json
+special_tokens_map.json
+spiece.model (for T5 models)