A newer version of this model is available: syubraj/romaneng2nep_v3
Model Card for Model ID
Due to compute issues, The model has been trained on multiple iterations:
- Model Trained for 8500 steps on [0 : 5%] of the dataset.
- Model continued from 8500 upto 16500 steps on [5% : 20%] of the dataset
- Model continued from 16500 upto 22000 steps on [20% : 40%] of the dataset
Model Description
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Model type: (Translation)
- Language(s) (NLP): Nepali, English
- License: [Apache license 2.0]
- Finetuned from model : [google/mt5-small]
How to Get Started with the Model
Use the code below to get started with the model.
from transformers import AutoTokenizer, MT5ForConditionalGeneration
checkpoint = "syubraj/RomanEng2Nep-v2"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = MT5ForConditionalGeneration.from_pretrained(checkpoint)
# Set max sequence length
max_seq_len = 20
def translate(text):
# Tokenize the input text with a max length of 20
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=max_seq_len)
# Generate translation
translated = model.generate(**inputs)
# Decode the translated tokens back to text
translated_text = tokenizer.decode(translated[0], skip_special_tokens=True)
return translated_text
# Example usage
source_text = "muskuraudai" # Example Romanized Nepali text
translated_text = translate(source_text)
print(f"Translated Text: {translated_text}")
Training Data
syubraj/roman2nepali-transliteration
Training Hyperparameters
- Training regime:
training_args = Seq2SeqTrainingArguments(
output_dir="/content/drive/MyDrive/romaneng2nep_v2/",
eval_strategy="steps",
learning_rate=2e-5,
per_device_train_batch_size=16,
per_device_eval_batch_size=8,
weight_decay=0.01,
save_total_limit=3,
num_train_epochs=2,
predict_with_generate=True,
)
Training and Validation Metrics
| Step | Training Loss | Validation Loss | Gen Len |
|---|---|---|---|
| 500 | 21.636200 | 9.776628 | 2.001900 |
| 1000 | 10.103400 | 6.105016 | 2.077900 |
| 1500 | 6.830800 | 5.081259 | 3.811600 |
| 2000 | 6.003100 | 4.702793 | 4.237300 |
| 2500 | 5.690200 | 4.469123 | 4.700000 |
| 3000 | 5.443100 | 4.274406 | 4.808300 |
| 3500 | 5.265300 | 4.121417 | 4.749400 |
| 4000 | 5.128500 | 3.989708 | 4.782300 |
| 4500 | 5.007200 | 3.885391 | 4.805100 |
| 5000 | 4.909600 | 3.787640 | 4.874800 |
| 5500 | 4.836000 | 3.715750 | 4.855500 |
| 6000 | 4.733000 | 3.640963 | 4.962000 |
| 6500 | 4.673500 | 3.587330 | 5.011600 |
| 7000 | 4.623800 | 3.531883 | 5.068300 |
| 7500 | 4.567400 | 3.481622 | 5.108500 |
| 8000 | 4.523200 | 3.445404 | 5.092700 |
| 8500 | 4.464000 | 3.413630 | 5.132700 |
| 9000 | 4.423100 | 3.326201 | 5.211700 |
| 9500 | 4.315700 | 3.238422 | 5.200600 |
| 10000 | 4.218200 | 3.143774 | 5.288100 |
| 10500 | 4.133600 | 3.080613 | 5.202300 |
| 11000 | 4.087700 | 3.011713 | 5.271800 |
| 11500 | 4.004300 | 2.957386 | 5.178700 |
| 12000 | 3.956700 | 2.898953 | 5.209600 |
| 12500 | 3.922800 | 2.850440 | 5.210100 |
| 13000 | 3.853400 | 2.796974 | 5.171700 |
| 13500 | 3.807900 | 2.745325 | 5.281200 |
| 14000 | 3.755700 | 2.708517 | 5.223000 |
| 14500 | 3.729300 | 2.678200 | 5.210700 |
| 15000 | 3.673600 | 2.637842 | 5.230200 |
| 15500 | 3.625400 | 2.607649 | 5.264100 |
| 16000 | 3.601100 | 2.592188 | 5.129800 |
| 16500 | 3.608200 | 2.556329 | 5.215800 |
| 17000 | 3.557900 | 2.536781 | 5.162900 |
| 17500 | 3.533500 | 2.504695 | 5.206000 |
| 18000 | 3.500000 | 2.477887 | 5.211600 |
| 18500 | 3.463600 | 2.456758 | 5.201000 |
| 19000 | 3.457100 | 2.433362 | 5.210000 |
| 19500 | 3.435400 | 2.411479 | 5.197600 |
| 20000 | 3.413300 | 2.392534 | 5.221100 |
| 20500 | 3.366100 | 2.378421 | 5.165200 |
| 21000 | 3.363500 | 2.357117 | 5.187300 |
| 21500 | 3.346500 | 2.343485 | 5.193600 |
| 22000 | 3.328300 | 2.331021 | 5.183300 |
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