---
license: mit
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: iva_mt_wslot-m2m100_418M-en-pl-nomassive
  results: []
datasets:
- cartesinus/iva_mt_wslot
---

<!-- 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-pl-nomassive

This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3751
- Bleu: 61.2295
- Gen Len: 23.3774

## 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: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Bleu    | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 0.4315        | 1.0   | 2212  | 0.3475          | 57.0703 | 23.338  |
| 0.2976        | 2.0   | 4424  | 0.3295          | 58.3378 | 23.5036 |
| 0.2153        | 3.0   | 6636  | 0.3347          | 59.3015 | 23.5601 |
| 0.1542        | 4.0   | 8848  | 0.3420          | 59.3942 | 23.4442 |
| 0.1177        | 5.0   | 11060 | 0.3454          | 60.4525 | 23.3501 |
| 0.0858        | 6.0   | 13272 | 0.3529          | 60.6123 | 23.3422 |
| 0.06          | 7.0   | 15484 | 0.3586          | 60.1342 | 23.4478 |
| 0.0479        | 8.0   | 17696 | 0.3668          | 60.6348 | 23.4163 |
| 0.0371        | 9.0   | 19908 | 0.3728          | 61.3986 | 23.4436 |
| 0.0299        | 10.0  | 22120 | 0.3751          | 61.2295 | 23.3774 |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3