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# Vigec-V5

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: 0.3694
- Bleu: 77.0736
- Gen Len: 10.0475

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 1.195         | 0.01  | 500   | 0.9492          | 43.0845 | 7.2405  |
| 0.978         | 0.01  | 1000  | 0.7804          | 61.0671 | 9.7255  |
| 0.8418        | 0.02  | 1500  | 0.6798          | 64.3811 | 9.9025  |
| 0.8148        | 0.03  | 2000  | 0.6046          | 66.1944 | 10.043  |
| 0.7622        | 0.04  | 2500  | 0.5513          | 68.2851 | 10.1215 |
| 0.7199        | 0.04  | 3000  | 0.5146          | 69.7161 | 10.0795 |
| 0.7898        | 0.05  | 3500  | 0.4869          | 71.1868 | 10.079  |
| 0.6921        | 0.06  | 4000  | 0.4648          | 72.4203 | 10.0345 |
| 0.6827        | 0.07  | 4500  | 0.4490          | 73.2133 | 10.039  |
| 0.6102        | 0.07  | 5000  | 0.4355          | 73.6841 | 10.078  |
| 0.5805        | 0.08  | 5500  | 0.4176          | 74.2559 | 10.059  |
| 0.6806        | 0.09  | 6000  | 0.4081          | 74.7389 | 10.0655 |
| 0.6544        | 0.09  | 6500  | 0.3958          | 75.2603 | 10.025  |
| 0.6244        | 0.1   | 7000  | 0.3904          | 75.9306 | 10.0565 |
| 0.7212        | 0.11  | 7500  | 0.3822          | 76.3268 | 10.0505 |
| 0.5446        | 0.12  | 8000  | 0.3785          | 76.5306 | 10.0505 |
| 0.5574        | 0.12  | 8500  | 0.3741          | 76.7101 | 10.0545 |
| 0.6265        | 0.13  | 9000  | 0.3721          | 76.8858 | 10.043  |
| 0.5379        | 0.14  | 9500  | 0.3695          | 77.001  | 10.051  |
| 0.6164        | 0.14  | 10000 | 0.3694          | 77.0736 | 10.0475 |


### 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-V5", "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-V5
========


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: 0.3694
* Bleu: 77.0736
* Gen Len: 10.0475


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