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# byt5-base-es_guc

This model is a fine-tuned version of [google/byt5-base](https://huggingface.co/google/byt5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7552
- Bleu: 2.594
- Gen Len: 100.8294

## 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: 16
- eval_batch_size: 16
- seed: 65
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Bleu   | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| No log        | 1.0   | 381  | 1.0652          | 0.0167 | 19.0    |
| 1.5288        | 2.0   | 762  | 0.9339          | 0.0402 | 19.0    |
| 1.063         | 3.0   | 1143 | 0.8651          | 0.0311 | 19.0    |
| 0.9558        | 4.0   | 1524 | 0.8271          | 0.1018 | 19.0    |
| 0.9558        | 5.0   | 1905 | 0.8043          | 0.0744 | 19.0    |
| 0.8979        | 6.0   | 2286 | 0.7831          | 0.0786 | 19.0    |
| 0.8598        | 7.0   | 2667 | 0.7699          | 0.086  | 19.0    |
| 0.8346        | 8.0   | 3048 | 0.7630          | 0.0803 | 19.0    |
| 0.8346        | 9.0   | 3429 | 0.7572          | 0.1179 | 19.0    |
| 0.8194        | 10.0  | 3810 | 0.7552          | 0.1133 | 19.0    |


### Framework versions

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

{"language": ["es", "guc"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "byt5-base-es_guc", "results": []}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer es guc license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #es #guc #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

byt5-base-es\_guc
=================


This model is a fine-tuned version of google/byt5-base on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.7552
* Bleu: 2.594
* Gen Len: 100.8294


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: 16
* eval\_batch\_size: 16
* seed: 65
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10.0


### Training results



### Framework versions


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



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 65
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10.0 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #es #guc #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 65
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10.0 ### Training results ### Framework versions


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