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# viettel-phobertv2-finetune-viquad

This model is a fine-tuned version of [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2353

## 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: 2e-05
- train_batch_size: 20
- eval_batch_size: 20
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 60
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 0.57  | 200  | 1.9011          |
| No log        | 1.13  | 400  | 1.4840          |
| 2.4264        | 1.7   | 600  | 1.3480          |
| 2.4264        | 2.26  | 800  | 1.2767          |
| 1.1899        | 2.83  | 1000 | 1.2292          |
| 1.1899        | 3.39  | 1200 | 1.2493          |
| 1.1899        | 3.96  | 1400 | 1.2353          |


### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"tags": ["generated_from_trainer"], "model-index": [{"name": "viettel-phobertv2-finetune-viquad", "results": []}]}
transformers pytorch tensorboard roberta question-answering generated_from_trainer endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #endpoints_compatible #region-us 

viettel-phobertv2-finetune-viquad
=================================


This model is a fine-tuned version of vinai/phobert-base-v2 on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.2353


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: 2e-05
* train\_batch\_size: 20
* eval\_batch\_size: 20
* seed: 42
* gradient\_accumulation\_steps: 3
* total\_train\_batch\_size: 60
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 4


### Training results



### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 20
* eval\_batch\_size: 20
* seed: 42
* gradient\_accumulation\_steps: 3
* total\_train\_batch\_size: 60
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 4 ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 20
* eval\_batch\_size: 20
* seed: 42
* gradient\_accumulation\_steps: 3
* total\_train\_batch\_size: 60
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 4 ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3