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---
license: mit
tags:
- generated_from_trainer
datasets:
- iva_mt_wslot
metrics:
- bleu
model-index:
- name: iva_mt_wslot-m2m100_418M-en-es-plaintext_10e
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: 51.1501
---
<!-- 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-plaintext_10e
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: 0.0116
- Bleu: 51.1501
- Gen Len: 12.6861
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 0.012 | 1.0 | 2104 | 0.0109 | 47.9124 | 12.7523 |
| 0.0079 | 2.0 | 4208 | 0.0101 | 49.9897 | 12.6763 |
| 0.0059 | 3.0 | 6312 | 0.0101 | 50.5286 | 12.6435 |
| 0.0045 | 4.0 | 8416 | 0.0101 | 49.6821 | 12.5472 |
| 0.0033 | 5.0 | 10520 | 0.0104 | 50.3856 | 12.6638 |
| 0.0024 | 6.0 | 12624 | 0.0107 | 50.359 | 12.7418 |
| 0.0019 | 7.0 | 14728 | 0.0111 | 50.8234 | 12.709 |
| 0.0014 | 8.0 | 16832 | 0.0111 | 50.872 | 12.6671 |
| 0.0011 | 9.0 | 18936 | 0.0114 | 51.3014 | 12.6291 |
| 0.001 | 10.0 | 21040 | 0.0116 | 51.1501 | 12.6861 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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