---
license: mit
tags:
- generated_from_trainer
datasets:
- cartesinus/iva_mt_wslot
metrics:
- bleu
model-index:
- name: iva_mt_wslot-m2m100_418M-en-es
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: iva_mt_wslot
      type: iva_mt_wslot
      config: en-es
      split: validation
      args: en-es
    metrics:
    - name: Bleu
      type: bleu
      value: 69.2836
language:
- en
- es
pipeline_tag: translation
---

<!-- 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. -->

# iva_mt_wslot-m2m100_418M-en-es

This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on the iva_mt_wslot dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0115
- Bleu: 69.2836
- Gen Len: 20.2064

## 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: 7
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Bleu    | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 0.0135        | 1.0   | 2104  | 0.0122          | 66.8284 | 20.2851 |
| 0.009         | 2.0   | 4208  | 0.0112          | 68.1164 | 20.1501 |
| 0.0067        | 3.0   | 6312  | 0.0110          | 68.256  | 20.0603 |
| 0.0051        | 4.0   | 8416  | 0.0110          | 68.7002 | 20.1219 |
| 0.0037        | 5.0   | 10520 | 0.0112          | 68.699  | 20.2733 |
| 0.0027        | 6.0   | 12624 | 0.0113          | 68.9916 | 20.209  |
| 0.0023        | 7.0   | 14728 | 0.0115          | 69.2836 | 20.2064 |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3