
<!-- 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. -->

# narrativa-finetuned-wmt22-en-pt-br

This model is a fine-tuned version of [Narrativa/mbart-large-50-finetuned-opus-en-pt-translation](https://huggingface.co/Narrativa/mbart-large-50-finetuned-opus-en-pt-translation) on an unknown dataset.

## 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: 5e-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: 3.0

### Training results

- epoch                   =        3.0
- eval_bleu               =    66.8543
- eval_gen_len            =    13.5105
- eval_loss               =     0.4934
- eval_runtime            = 0:00:20.01
- eval_samples            =        190
- eval_samples_per_second =      9.492
- eval_steps_per_second   =      2.398

### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1

{"tags": ["generated_from_trainer"], "model-index": [{"name": "narrativa-finetuned-wmt22-en-pt-br", "results": []}]}
transformers pytorch mbart text2text-generation generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #mbart #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 




# narrativa-finetuned-wmt22-en-pt-br

This model is a fine-tuned version of Narrativa/mbart-large-50-finetuned-opus-en-pt-translation on an unknown dataset.

## 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: 5e-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: 3.0

### Training results

- epoch                   =        3.0
- eval_bleu               =    66.8543
- eval_gen_len            =    13.5105
- eval_loss               =     0.4934
- eval_runtime            = 0:00:20.01
- eval_samples            =        190
- eval_samples_per_second =      9.492
- eval_steps_per_second   =      2.398

### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1

# narrativa-finetuned-wmt22-en-pt-br

This model is a fine-tuned version of Narrativa/mbart-large-50-finetuned-opus-en-pt-translation on an unknown dataset. ## 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: 5e-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: 3.0 ### Training results

- epoch                   =        3.0
- eval_bleu               =    66.8543
- eval_gen_len            =    13.5105
- eval_loss               =     0.4934
- eval_runtime            = 0:00:20.01
- eval_samples            =        190
- eval_samples_per_second =      9.492
- eval_steps_per_second   =      2.398 ### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #mbart #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 # narrativa-finetuned-wmt22-en-pt-br

This model is a fine-tuned version of Narrativa/mbart-large-50-finetuned-opus-en-pt-translation on an unknown dataset. ## 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: 5e-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: 3.0 ### Training results

- epoch                   =        3.0
- eval_bleu               =    66.8543
- eval_gen_len            =    13.5105
- eval_loss               =     0.4934
- eval_runtime            = 0:00:20.01
- eval_samples            =        190
- eval_samples_per_second =      9.492
- eval_steps_per_second   =      2.398 ### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #mbart #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
# narrativa-finetuned-wmt22-en-pt-br

This model is a fine-tuned version of Narrativa/mbart-large-50-finetuned-opus-en-pt-translation on an unknown dataset.## 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: 5e-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: 3.0### Training results

- epoch                   =        3.0
- eval_bleu               =    66.8543
- eval_gen_len            =    13.5105
- eval_loss               =     0.4934
- eval_runtime            = 0:00:20.01
- eval_samples            =        190
- eval_samples_per_second =      9.492
- eval_steps_per_second   =      2.398### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1