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# bert-fa-QA-v1
Persian Question and answer Model Based on Bert Model

This model is a fine-tuned version of [ParsBERT](https://arxiv.org/abs/2005.12515) on PersianQA dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7297

## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.2563        | 1.0   | 1126 | 1.7222          |
| 1.3372        | 2.0   | 2252 | 1.7297          |


### Framework versions

- Transformers 4.9.0
- Pytorch 1.9.0+cu102
- Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model_index": [{"name": "bert-fa-QA-v1", "results": [{"task": {"name": "Question Answering", "type": "question-answering"}}]}]}
transformers pytorch tensorboard bert question-answering generated_from_trainer arxiv:2005.12515 license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #arxiv-2005.12515 #license-apache-2.0 #endpoints_compatible #region-us 

bert-fa-QA-v1
=============


Persian Question and answer Model Based on Bert Model


This model is a fine-tuned version of ParsBERT on PersianQA dataset.
It achieves the following results on the evaluation set:


* Loss: 1.7297


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: 3e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2


### Training results



### Framework versions


* Transformers 4.9.0
* Pytorch 1.9.0+cu102
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2 ### Training results ### Framework versions


* Transformers 4.9.0
* Pytorch 1.9.0+cu102
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #arxiv-2005.12515 #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2 ### Training results ### Framework versions


* Transformers 4.9.0
* Pytorch 1.9.0+cu102
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #arxiv-2005.12515 #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2### Training results### Framework versions


* Transformers 4.9.0
* Pytorch 1.9.0+cu102
* Tokenizers 0.10.3