---
base_model: facebook/mbart-large-50-many-to-many-mmt
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: mbart-large-50-many-to-many-mmt-finetuned-hi-to-en
  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. -->

# mbart-large-50-many-to-many-mmt-finetuned-hi-to-en

This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5182
- Bleu: 0.0
- Gen Len: 5.5567

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:----:|:-------:|
| 2.4071        | 0.07  | 500   | 2.4856          | 0.0  | 5.49    |
| 2.6727        | 0.13  | 1000  | 2.3521          | 0.0  | 5.4733  |
| 2.6438        | 0.2   | 1500  | 2.2782          | 0.0  | 5.38    |
| 2.5672        | 0.27  | 2000  | 2.2432          | 0.0  | 5.6367  |
| 2.5561        | 0.34  | 2500  | 2.0945          | 0.0  | 5.4733  |
| 2.5502        | 0.4   | 3000  | 2.0506          | 0.0  | 5.4667  |
| 2.5326        | 0.47  | 3500  | 2.0163          | 0.0  | 5.4367  |
| 2.4979        | 0.54  | 4000  | 1.9795          | 0.0  | 5.4267  |
| 2.4771        | 0.61  | 4500  | 1.9002          | 0.0  | 5.28    |
| 2.5121        | 0.67  | 5000  | 1.8497          | 0.0  | 5.5433  |
| 2.4568        | 0.74  | 5500  | 1.7447          | 0.0  | 5.66    |
| 2.4211        | 0.81  | 6000  | 1.6948          | 0.0  | 5.5633  |
| 2.4702        | 0.88  | 6500  | 1.5632          | 0.0  | 5.4267  |
| 2.4161        | 0.94  | 7000  | 1.5122          | 0.0  | 5.5633  |
| 2.2322        | 1.01  | 7500  | 1.3694          | 0.0  | 5.45    |
| 1.5244        | 1.08  | 8000  | 1.3296          | 0.0  | 5.55    |
| 1.5234        | 1.14  | 8500  | 1.2637          | 0.0  | 5.5267  |
| 1.5902        | 1.21  | 9000  | 1.2621          | 0.0  | 5.6133  |
| 1.4645        | 1.28  | 9500  | 1.1856          | 0.0  | 5.55    |
| 1.5426        | 1.35  | 10000 | 1.1528          | 0.0  | 5.6267  |
| 1.5414        | 1.41  | 10500 | 1.1533          | 0.0  | 5.6067  |
| 1.4708        | 1.48  | 11000 | 1.1136          | 0.0  | 5.5533  |
| 1.4625        | 1.55  | 11500 | 1.0736          | 0.0  | 5.54    |
| 1.5805        | 1.62  | 12000 | 1.0700          | 0.0  | 5.5733  |
| 1.5295        | 1.68  | 12500 | 1.0171          | 0.0  | 5.4867  |
| 1.5585        | 1.75  | 13000 | 0.9770          | 0.0  | 5.6133  |
| 1.5602        | 1.82  | 13500 | 0.9106          | 0.0  | 5.5867  |
| 1.5361        | 1.89  | 14000 | 0.8823          | 0.0  | 5.5333  |
| 1.5217        | 1.95  | 14500 | 0.8353          | 0.0  | 5.5167  |
| 1.3229        | 2.02  | 15000 | 0.7296          | 0.0  | 5.4967  |
| 0.916         | 2.09  | 15500 | 0.6979          | 0.0  | 5.56    |
| 0.9075        | 2.15  | 16000 | 0.6797          | 0.0  | 5.5333  |
| 0.8575        | 2.22  | 16500 | 0.6372          | 0.0  | 5.52    |
| 0.8702        | 2.29  | 17000 | 0.6288          | 0.0  | 5.4967  |
| 0.8741        | 2.36  | 17500 | 0.6297          | 0.0  | 5.5033  |
| 0.8764        | 2.42  | 18000 | 0.6038          | 0.0  | 5.5233  |
| 0.9152        | 2.49  | 18500 | 0.5853          | 0.0  | 5.4933  |
| 0.8598        | 2.56  | 19000 | 0.5731          | 0.0  | 5.54    |
| 0.8586        | 2.63  | 19500 | 0.5558          | 0.0  | 5.5533  |
| 0.9005        | 2.69  | 20000 | 0.5388          | 0.0  | 5.5233  |
| 0.889         | 2.76  | 20500 | 0.5269          | 0.0  | 5.5067  |
| 0.8772        | 2.83  | 21000 | 0.5301          | 0.0  | 5.5333  |
| 0.8713        | 2.9   | 21500 | 0.5208          | 0.0  | 5.5567  |
| 0.8962        | 2.96  | 22000 | 0.5182          | 0.0  | 5.5567  |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0