File size: 3,250 Bytes
b653f91 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 |
# src/train_nepali.py
import os
from datasets import load_dataset, DatasetDict, concatenate_datasets
from transformers import (
AutoModelForSeq2SeqLM,
AutoTokenizer,
DataCollatorForSeq2Seq,
Seq2SeqTrainingArguments,
Seq2SeqTrainer,
)
def train_nepali_model():
"""
Fine-tunes a pre-trained NLLB model on the Nepali parallel dataset.
"""
# --- 1. Configuration ---
MODEL_CHECKPOINT = "facebook/nllb-200-distilled-600M"
DATA_DIR = "data/processed"
MODEL_OUTPUT_DIR = "D:\\SIH\\models\\nllb-finetuned-nepali-en"
# --- 2. Load Tokenizer and Model ---
print("Loading tokenizer and model...")
tokenizer = AutoTokenizer.from_pretrained(
MODEL_CHECKPOINT, src_lang="nep_Npan", tgt_lang="eng_Latn"
)
model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_CHECKPOINT)
# --- 3. Load and Preprocess Data ---
print("Loading and preprocessing data...")
nepali_dataset = load_dataset("text", data_files=os.path.join(DATA_DIR, "nepali.ne"))["train"]
english_dataset = load_dataset("text", data_files=os.path.join(DATA_DIR, "nepali.en"))["train"]
# rename the 'text' column to 'ne' and 'en'
nepali_dataset = nepali_dataset.rename_column("text", "ne")
english_dataset = english_dataset.rename_column("text", "en")
# combine the datasets
raw_datasets = concatenate_datasets([nepali_dataset, english_dataset], axis=1)
split_datasets = raw_datasets.train_test_split(train_size=0.95, seed=42)
split_datasets["validation"] = split_datasets.pop("test")
def preprocess_function(examples):
inputs = examples["ne"]
targets = examples["en"]
model_inputs = tokenizer(inputs, text_target=targets, max_length=128, truncation=True)
return model_inputs
tokenized_datasets = split_datasets.map(
preprocess_function,
batched=True,
remove_columns=split_datasets["train"].column_names,
)
# --- 4. Set Up Training Arguments ---
print("Setting up training arguments...")
training_args = Seq2SeqTrainingArguments(
output_dir=MODEL_OUTPUT_DIR,
eval_strategy="epoch",
learning_rate=2e-5,
per_device_train_batch_size=8,
per_device_eval_batch_size=8,
weight_decay=0.01,
save_total_limit=3,
num_train_epochs=3, # Reduced for faster training, can be increased
predict_with_generate=True,
fp16=False, # Set to True if you have a compatible GPU
)
# --- 5. Create the Trainer ---
data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model)
trainer = Seq2SeqTrainer(
model=model,
args=training_args,
train_dataset=tokenized_datasets["train"],
eval_dataset=tokenized_datasets["validation"],
tokenizer=tokenizer,
data_collator=data_collator,
)
# --- 6. Start Training ---
print(f"\n--- Starting model fine-tuning for Nepali-English ---")
trainer.train()
print("--- Training complete ---")
# --- 7. Save the Final Model ---
print(f"Saving final model to {MODEL_OUTPUT_DIR}")
trainer.save_model()
print("Model saved successfully!")
if __name__ == "__main__":
train_nepali_model() |