import torch from datasets import load_dataset from transformers import MarianMTModel, MarianTokenizer, Trainer, TrainingArguments # Step 1: Load the dataset (use a dataset with English-Hindi pairs) dataset = load_dataset("opus_books", "en-hi") # Replace with the appropriate dataset # Step 2: Load the pre-trained MarianMT model for English-Hindi translation model_name = "Helsinki-NLP/opus-mt-en-hi" model = MarianMTModel.from_pretrained(model_name) tokenizer = MarianTokenizer.from_pretrained(model_name) # Step 3: Tokenization function def tokenize_function(examples): return tokenizer(examples['en'], padding="max_length", truncation=True, max_length=128) # Tokenize the dataset tokenized_datasets = dataset.map(tokenize_function, batched=True) # Step 4: Prepare training arguments training_args = TrainingArguments( output_dir="./results", evaluation_strategy="epoch", # Evaluate every epoch learning_rate=2e-5, per_device_train_batch_size=8, per_device_eval_batch_size=8, num_train_epochs=3, weight_decay=0.01, logging_dir="./logs", # Directory for logs logging_steps=10, save_steps=500, save_total_limit=2, ) # Step 5: Define a Trainer trainer = Trainer( model=model, args=training_args, train_dataset=tokenized_datasets["train"], eval_dataset=tokenized_datasets["test"], tokenizer=tokenizer, ) # Step 6: Start Training trainer.train() # Step 7: Save the trained model trainer.save_model("fine_tuned_en_to_hi_model")

README.md

@@ -1,3 +1,14 @@
----
-license: mit
----
+---
+license: mit
+datasets:
+- open-thoughts/OpenThoughts-114k
+metrics:
+- code_eval
+base_model:
+- deepseek-ai/DeepSeek-R1
+new_version: deepseek-ai/DeepSeek-R1
+pipeline_tag: translation
+library_name: flair
+tags:
+- code
+---