---
base_model: google/mt5-small
datasets:
- syubraj/roman2nepali-transliteration
language:
- ne
- en
library_name: transformers
license: apache-2.0
metrics:
- bleu
tags:
- generated_from_trainer
model-index:
- name: romaneng2nep_v2
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# romaneng2nep_v2

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co./google/mt5-small) on an [syubraj/roman2nepali-transliteration](https://huggingface.co./datasets/syubraj/roman2nepali-transliteration).
It achieves the following results on the evaluation set:
- Loss: 2.9652
- Gen Len: 5.1538


## MOdel Usage 

```python
!pip install transformers
```

```python
from transformers import AutoTokenizer, MT5ForConditionalGeneration

checkpoint = "syubraj/romaneng2nep_v3"
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 procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Step   | Training Loss | Validation Loss | Gen Len  |
|--------|---------------|-----------------|----------|
| 1000   | 15.0703       | 5.6154          | 2.3840   |
| 2000   | 6.0460        | 4.4449          | 4.6281   |
| 3000   | 5.2580        | 3.9632          | 4.7790   |
| 4000   | 4.8563        | 3.6188          | 5.0053   |
| 5000   | 4.5602        | 3.3491          | 5.3085   |
| 6000   | 4.3146        | 3.1572          | 5.2562   |
| 7000   | 4.1228        | 3.0084          | 5.2197   |
| 8000   | 3.9695        | 2.8727          | 5.2140   |
| 9000   | 3.8342        | 2.7651          | 5.1834   |
| 10000  | 3.7319        | 2.6661          | 5.1977   |
| 11000  | 3.6485        | 2.5864          | 5.1536   |
| 12000  | 3.5541        | 2.5080          | 5.1990   |
| 13000  | 3.4959        | 2.4464          | 5.1775   |
| 14000  | 3.4315        | 2.3931          | 5.1747   |
| 15000  | 3.3663        | 2.3401          | 5.1625   |
| 16000  | 3.3204        | 2.3034          | 5.1481   |
| 17000  | 3.2417        | 2.2593          | 5.1663   |
| 18000  | 3.2186        | 2.2283          | 5.1351   |
| 19000  | 3.1822        | 2.1946          | 5.1573   |
| 20000  | 3.1449        | 2.1690          | 5.1649   |
| 21000  | 3.1067        | 2.1402          | 5.1624   |
| 22000  | 3.0844        | 2.1258          | 5.1479   |
| 23000  | 3.0574        | 2.1066          | 5.1518   |
| 24000  | 3.0357        | 2.0887          | 5.1446   |
| 25000  | 3.0136        | 2.0746          | 5.1559   |
| 26000  | 2.9957        | 2.0609          | 5.1658   |
| 27000  | 2.9865        | 2.0510          | 5.1791   |
| 28000  | 2.9765        | 2.0456          | 5.1574   |
| 29000  | 2.9675        | 2.0386          | 5.1620   |
| 30000  | 2.9678        | 2.0344          | 5.1601   |
| 31000  | 2.9652        | 2.0320          | 5.1538   |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0

### Citation
If you find this model useful, please site the work.

```
@misc {yubraj_sigdel_2024,
	author       = { {Yubraj Sigdel} },
	title        = { romaneng2nep_v3 (Revision dca017e) },
	year         = 2024,
	url          = { https://huggingface.co./syubraj/romaneng2nep_v3 },
	doi          = { 10.57967/hf/3252 },
	publisher    = { Hugging Face }
}
```