---
license: mit
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: iva_mt_wslot-m2m100_418M-en-pl-noleyzer
  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-noleyzer

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: 0.3387
- Bleu: 56.3512
- Gen Len: 20.7319

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 0.3611        | 1.0   | 4097  | 0.3088          | 53.0678 | 20.6977 |
| 0.2624        | 2.0   | 8194  | 0.2937          | 53.8146 | 20.5946 |
| 0.1904        | 3.0   | 12291 | 0.2955          | 53.7632 | 20.8701 |
| 0.1501        | 4.0   | 16388 | 0.2999          | 55.4482 | 20.6983 |
| 0.1079        | 5.0   | 20485 | 0.3074          | 55.5861 | 20.6366 |
| 0.0852        | 6.0   | 24582 | 0.3165          | 55.8774 | 20.6638 |
| 0.0646        | 7.0   | 28679 | 0.3231          | 55.9306 | 20.555  |
| 0.0464        | 8.0   | 32776 | 0.3294          | 55.9869 | 20.7809 |
| 0.0377        | 9.0   | 36873 | 0.3348          | 56.0674 | 20.7044 |
| 0.0305        | 10.0  | 40970 | 0.3387          | 56.3512 | 20.7319 |


### Framework versions

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