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# xlmRoberta-for-VietnameseQA

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the UIT-Viquad_v2 dataset. 
It achieves the following results on the evaluation set:
- Loss: 0.8315

## Model description

Fine-tuned by Honganh Nguyen (FPTU AI Club).

## Intended uses & limitations

More information needed

## Training and evaluation data

Credits to Viet Nguyen (FPTU AI Club) for the training and evaluation data.

Training data: https://github.com/vietnguyen012/QA_viuit/blob/main/train.json

Evaluation data: https://github.com/vietnguyen012/QA_viuit/blob/main/trial/trial.json

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5701        | 1.0   | 2534 | 1.2220          |
| 1.2942        | 2.0   | 5068 | 0.9698          |
| 1.0693        | 3.0   | 7602 | 0.8315          |


### Framework versions

- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "xlmRoberta-for-VietnameseQA", "results": []}]}
transformers pytorch tensorboard xlm-roberta question-answering generated_from_trainer dataset:squad_v2 license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #dataset-squad_v2 #license-mit #endpoints_compatible #region-us 

xlmRoberta-for-VietnameseQA
===========================


This model is a fine-tuned version of xlm-roberta-base on the UIT-Viquad\_v2 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.8315


Model description
-----------------


Fine-tuned by Honganh Nguyen (FPTU AI Club).


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
----------------------------


Credits to Viet Nguyen (FPTU AI Club) for the training and evaluation data.


Training data: URL


Evaluation data: URL


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 12
* eval\_batch\_size: 12
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3


### Training results



### Framework versions


* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 12
* eval\_batch\_size: 12
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #dataset-squad_v2 #license-mit #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 12
* eval\_batch\_size: 12
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #dataset-squad_v2 #license-mit #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 12
* eval\_batch\_size: 12
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3### Training results### Framework versions


* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3