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# FINE-TUNED-VIQUAD-HGF

This model is a fine-tuned version of [bhavikardeshna/xlm-roberta-base-vietnamese](https://huggingface.co/bhavikardeshna/xlm-roberta-base-vietnamese) on the [UIT-ViQuAD](https://github.com/windhashira06/Demo-QA-Extraction-system/blob/main/Dataset/UIT-ViQuAD.json) dataset.

## Model description

The model is described in [Cascading Adaptors to Leverage English Data to Improve Performance of
Question Answering for Low-Resource Languages](https://arxiv.org/pdf/2112.09866v1.pdf) paper

## Training and evaluation data

A new dataset for the low-resource language as Vietnamese to evaluate MRC models. This dataset comprises over 23,000 human-generated question-answer pairs based on 5,109 passages of 174 Vietnamese articles from Wikipedia. However in processing, I eliminated more than 3000 questions with no answers.                                                         

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

- **EM**: 52.38                                                                                             
- **F1-SCORE**: 77.67

### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "model-index": [{"name": "fine-tuned-viquad-hgf", "results": []}]}
transformers pytorch tensorboard xlm-roberta question-answering generated_from_trainer arxiv:2112.09866 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #arxiv-2112.09866 #endpoints_compatible #region-us 




# FINE-TUNED-VIQUAD-HGF

This model is a fine-tuned version of bhavikardeshna/xlm-roberta-base-vietnamese on the UIT-ViQuAD dataset.

## Model description

The model is described in Cascading Adaptors to Leverage English Data to Improve Performance of
Question Answering for Low-Resource Languages paper

## Training and evaluation data

A new dataset for the low-resource language as Vietnamese to evaluate MRC models. This dataset comprises over 23,000 human-generated question-answer pairs based on 5,109 passages of 174 Vietnamese articles from Wikipedia. However in processing, I eliminated more than 3000 questions with no answers.                                                         

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

- EM: 52.38                                                                                             
- F1-SCORE: 77.67

### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

# FINE-TUNED-VIQUAD-HGF

This model is a fine-tuned version of bhavikardeshna/xlm-roberta-base-vietnamese on the UIT-ViQuAD dataset. ## Model description

The model is described in Cascading Adaptors to Leverage English Data to Improve Performance of
Question Answering for Low-Resource Languages paper ## Training and evaluation data

A new dataset for the low-resource language as Vietnamese to evaluate MRC models. This dataset comprises over 23,000 human-generated question-answer pairs based on 5,109 passages of 174 Vietnamese articles from Wikipedia. However in processing, I eliminated more than 3000 questions with no answers. ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP ### Training results

- EM: 52.38                                                                                             
- F1-SCORE: 77.67 ### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #arxiv-2112.09866 #endpoints_compatible #region-us 
 # FINE-TUNED-VIQUAD-HGF

This model is a fine-tuned version of bhavikardeshna/xlm-roberta-base-vietnamese on the UIT-ViQuAD dataset. ## Model description

The model is described in Cascading Adaptors to Leverage English Data to Improve Performance of
Question Answering for Low-Resource Languages paper ## Training and evaluation data

A new dataset for the low-resource language as Vietnamese to evaluate MRC models. This dataset comprises over 23,000 human-generated question-answer pairs based on 5,109 passages of 174 Vietnamese articles from Wikipedia. However in processing, I eliminated more than 3000 questions with no answers. ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP ### Training results

- EM: 52.38                                                                                             
- F1-SCORE: 77.67 ### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #arxiv-2112.09866 #endpoints_compatible #region-us 
# FINE-TUNED-VIQUAD-HGF

This model is a fine-tuned version of bhavikardeshna/xlm-roberta-base-vietnamese on the UIT-ViQuAD dataset.## Model description

The model is described in Cascading Adaptors to Leverage English Data to Improve Performance of
Question Answering for Low-Resource Languages paper## Training and evaluation data

A new dataset for the low-resource language as Vietnamese to evaluate MRC models. This dataset comprises over 23,000 human-generated question-answer pairs based on 5,109 passages of 174 Vietnamese articles from Wikipedia. However in processing, I eliminated more than 3000 questions with no answers.### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP### Training results

- EM: 52.38                                                                                             
- F1-SCORE: 77.67### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2