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# Vigec-V3

This model is a fine-tuned version of [VietAI/vit5-base](https://huggingface.co/VietAI/vit5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2522
- Bleu: 84.4788
- Gen Len: 9.847

## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Bleu    | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 0.8764        | 0.0   | 500   | 0.6120          | 68.0114 | 8.4626  |
| 0.6538        | 0.0   | 1000  | 0.4780          | 76.7403 | 10.015  |
| 0.6234        | 0.01  | 1500  | 0.4207          | 78.1726 | 9.8394  |
| 0.4513        | 0.01  | 2000  | 0.3845          | 79.1939 | 9.8914  |
| 0.4153        | 0.01  | 2500  | 0.3580          | 80.171  | 9.7298  |
| 0.5129        | 0.01  | 3000  | 0.3381          | 80.8668 | 9.8636  |
| 0.5073        | 0.01  | 3500  | 0.3246          | 81.5543 | 9.81    |
| 0.4623        | 0.01  | 4000  | 0.3106          | 82.1255 | 9.8684  |
| 0.4444        | 0.02  | 4500  | 0.2973          | 82.5565 | 9.848   |
| 0.4322        | 0.02  | 5000  | 0.2892          | 82.9623 | 9.872   |
| 0.5029        | 0.02  | 5500  | 0.2803          | 83.3084 | 9.8648  |
| 0.3686        | 0.02  | 6000  | 0.2765          | 83.4828 | 9.8602  |
| 0.4123        | 0.02  | 6500  | 0.2693          | 83.7491 | 9.8432  |
| 0.3593        | 0.03  | 7000  | 0.2674          | 83.8149 | 9.811   |
| 0.3684        | 0.03  | 7500  | 0.2630          | 84.1745 | 9.8668  |
| 0.3683        | 0.03  | 8000  | 0.2590          | 84.2294 | 9.8412  |
| 0.3581        | 0.03  | 8500  | 0.2568          | 84.3428 | 9.8582  |
| 0.3769        | 0.03  | 9000  | 0.2527          | 84.4367 | 9.8598  |
| 0.4479        | 0.03  | 9500  | 0.2522          | 84.4749 | 9.847   |
| 0.2856        | 0.04  | 10000 | 0.2522          | 84.4788 | 9.847   |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "Vigec-V3", "results": []}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

Vigec-V3
========


This model is a fine-tuned version of VietAI/vit5-base on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2522
* Bleu: 84.4788
* Gen Len: 9.847


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: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 10000
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 10000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 10000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 10000
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2