---
language:
- ne
- en
license: apache-2.0
tags:
- English to Nepali Translator
- MT5 Fine Tuned
- Nepali Translator Dataset
base_model: google/mt5-base
model-index:
- name: mt5-eng2nep
  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. -->

# mt5-eng2nep

## Model description

This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) trained on "Nepali Translator" dataset. This fine-tuned model is for translating English to Nepali language (with limited capability)
## Training and evaluation data
- "Nepali Translator" dataset: (https://github.com/sharad461/nepali-translator)
  

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.8424        | 0.26  | 500  | 2.9765          |
| 3.2655        | 0.53  | 1000 | 2.5221          |
| 2.9425        | 0.79  | 1500 | 2.3160          |
| 2.7533        | 1.05  | 2000 | 2.2170          |
| 2.7147        | 1.32  | 2500 | 2.1432          |
| 2.6331        | 1.58  | 3000 | 2.0872          |
| 2.5863        | 1.84  | 3500 | 2.0526          |
| 2.492         | 2.11  | 4000 | 2.0274          |
| 2.4757        | 2.37  | 4500 | 1.9948          |
| 2.4423        | 2.63  | 5000 | 1.9855          |
| 2.4339        | 2.9   | 5500 | 1.9645          |
| 2.3955        | 3.16  | 6000 | 1.9530          |
| 2.372         | 3.42  | 6500 | 1.9454          |
| 2.3661        | 3.69  | 7000 | 1.9410          |
| 2.3553        | 3.95  | 7500 | 1.9387          |


### Framework versions

- Transformers 4.13.0
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3