Delete t5_urdu_translation

t5_urdu_translation

@@ -1,53 +0,0 @@
-import torch
-from datasets import load_dataset
-from transformers import T5Tokenizer, T5ForConditionalGeneration, Trainer, TrainingArguments
-
-# Load dataset (replace 'your_dataset' with your actual dataset path or Hugging Face dataset name)
-dataset = load_dataset('csv', data_files={'train': 'train.csv', 'validation': 'validation.csv'})
-
-# Preprocess dataset
-def preprocess_function(examples):
-    inputs = ["translate English to Urdu: " + ex for ex in examples["English"]]
-    targets = examples["Urdu"]
-    model_inputs = tokenizer(inputs, max_length=512, truncation=True)
-    labels = tokenizer(targets, max_length=512, truncation=True).input_ids
-    model_inputs["labels"] = labels
-    return model_inputs
-
-# Load T5 tokenizer and model
-model_name = "t5-small"
-tokenizer = T5Tokenizer.from_pretrained(model_name)
-model = T5ForConditionalGeneration.from_pretrained(model_name)
-
-# Tokenize datasets
-tokenized_datasets = dataset.map(preprocess_function, batched=True)
-
-# Define training arguments
-training_args = TrainingArguments(
-    output_dir="./t5_urdu_translation",
-    evaluation_strategy="epoch",
-    learning_rate=5e-5,
-    per_device_train_batch_size=16,
-    per_device_eval_batch_size=16,
-    num_train_epochs=3,
-    weight_decay=0.01,
-    save_total_limit=2,
-    predict_with_generate=True,
-    logging_dir="./logs",
-)
-
-# Define Trainer
-trainer = Trainer(
-    model=model,
-    args=training_args,
-    train_dataset=tokenized_datasets["train"],
-    eval_dataset=tokenized_datasets["validation"],
-    tokenizer=tokenizer,
-)
-
-# Train model
-trainer.train()
-
-# Save model
-trainer.save_model("./t5_urdu_translation")
-tokenizer.save_pretrained("./t5_urdu_translation")