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# viettel-videberta-finetune-viquad-model7

This model is a fine-tuned version of [Fsoft-AIC/videberta-base](https://huggingface.co/Fsoft-AIC/videberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6730

## 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: 13
- eval_batch_size: 13
- seed: 42
- gradient_accumulation_steps: 5
- total_train_batch_size: 65
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 0.87  | 260  | 3.2187          |
| 3.6521        | 1.74  | 520  | 2.8990          |
| 3.6521        | 2.61  | 780  | 2.7310          |
| 2.5664        | 3.48  | 1040 | 2.6730          |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"tags": ["generated_from_trainer"], "model-index": [{"name": "viettel-videberta-finetune-viquad-model7", "results": []}]}
transformers pytorch tensorboard deberta-v2 question-answering generated_from_trainer endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #deberta-v2 #question-answering #generated_from_trainer #endpoints_compatible #region-us 

viettel-videberta-finetune-viquad-model7
========================================


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


* Loss: 2.6730


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: 13
* eval\_batch\_size: 13
* seed: 42
* gradient\_accumulation\_steps: 5
* total\_train\_batch\_size: 65
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 4


### Training results



### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 13
* eval\_batch\_size: 13
* seed: 42
* gradient\_accumulation\_steps: 5
* total\_train\_batch\_size: 65
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 4 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #deberta-v2 #question-answering #generated_from_trainer #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 13
* eval\_batch\_size: 13
* seed: 42
* gradient\_accumulation\_steps: 5
* total\_train\_batch\_size: 65
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 4 ### Training results ### Framework versions


* Transformers 4.28.0
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3