---
base_model:
- google/mt5-small
datasets:
- syubraj/roman2nepali-transliteration
language:
- ne
- en
library_name: transformers
license: apache-2.0
pipeline_tag: translation
tags:
- nepali
- roman english
- translation
- transliteration
new_version: syubraj/romaneng2nep
---

# Model Card for Model ID

Model Trained for 8500 steps on <110k dataset.





### Model Description

<!-- Provide a longer summary of what this model is. -->

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:** (google/mt5-small)
- **Language(s) (NLP, Nepali, English):** 
- **License:** [Apache license 2.0]
- **Finetuned from model [google/mt5-small]:** 

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]


## How to Get Started with the Model

Use the code below to get started with the model.

```Python

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 = "timilai kasto cha?"  # Example Romanized Nepali text
translated_text = translate(source_text)
print(f"Translated Text: {translated_text}")
```



### Training Data

[syubraj/roman2nepali-transliteration](https://huggingface.co/datasets/syubraj/roman2nepali-transliteration)


#### Training Hyperparameters

- **Training regime:**
```Python
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 |