Create training/train.py

training/train.py

@@ -0,0 +1,71 @@
+import numpy as np
+import evaluate
+from datasets import load_dataset
+from transformers import (
+    MT5ForConditionalGeneration, 
+    MT5Tokenizer, 
+    Seq2SeqTrainingArguments, 
+    Seq2SeqTrainer, 
+    DataCollatorForSeq2Seq
+)
+
+# Load metrics
+cer_metric = evaluate.load("cer")
+wer_metric = evaluate.load("wer")
+
+model_nm = "google/mt5-small"
+tokenizer = MT5Tokenizer.from_pretrained(model_nm)
+model = MT5ForConditionalGeneration.from_pretrained(model_nm)
+
+def compute_metrics(eval_preds):
+    preds, labels = eval_preds
+    if isinstance(preds, tuple):
+        preds = preds[0]
+    
+    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
+    # Replace -100 in labels as we can't decode them
+    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
+    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
+
+    cer = cer_metric.compute(predictions=decoded_preds, references=decoded_labels)
+    wer = wer_metric.compute(predictions=decoded_preds, references=decoded_labels)
+    
+    return {"cer": cer, "wer": wer}
+
+def tokenize_fn(batch):
+    inputs = tokenizer(batch['source'], padding="max_length", truncation=True, max_length=64)
+    labels = tokenizer(batch['target'], padding="max_length", truncation=True, max_length=64)
+    inputs["labels"] = labels["input_ids"]
+    return inputs
+
+# Load and process data
+dataset = load_dataset('csv', data_files={'train': 'train.csv', 'test': 'val.csv'})
+tokenized_dataset = dataset.map(tokenize_fn, batched=True)
+
+args = Seq2SeqTrainingArguments(
+    output_dir="./translit-results",
+    evaluation_strategy="epoch",
+    learning_rate=2e-4,
+    per_device_train_batch_size=16,
+    per_device_eval_batch_size=16,
+    weight_decay=0.01,
+    save_total_limit=2,
+    num_train_epochs=3,
+    predict_with_generate=True,
+    fp16=True, # Set to False if not using GPU
+    logging_steps=100,
+)
+
+trainer = Seq2SeqTrainer(
+    model=model,
+    args=args,
+    train_dataset=tokenized_dataset["train"],
+    eval_dataset=tokenized_dataset["test"],
+    tokenizer=tokenizer,
+    data_collator=DataCollatorForSeq2Seq(tokenizer, model=model),
+    compute_metrics=compute_metrics
+)
+
+trainer.train()
+model.save_pretrained("./final_model")
+tokenizer.save_pretrained("./final_model")