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# roberta-base-bne-finetuned-sqac

This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the sqac dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2111

## 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: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9971        | 1.0   | 1196 | 0.8646          |
| 0.482         | 2.0   | 2392 | 0.9334          |
| 0.1652        | 3.0   | 3588 | 1.2111          |


### Framework versions

- Transformers 4.11.2
- Pytorch 1.9.0+cu111
- Datasets 1.12.1
- Tokenizers 0.10.3

{"language": "es", "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["sqac"], "metrics": ["f1"], "model-index": [{"name": "roberta-base-bne-finetuned-sqac", "results": [{"task": {"type": "Question-Answering", "name": "Question Answering"}, "dataset": {"name": "sqac", "type": "sqac"}, "metrics": [{"type": "f1", "value": 0.7903, "name": "f1"}]}]}]}
transformers pytorch tensorboard roberta question-answering generated_from_trainer es dataset:sqac license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #es #dataset-sqac #license-apache-2.0 #endpoints_compatible #region-us 

roberta-base-bne-finetuned-sqac
===============================


This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the sqac dataset.
It achieves the following results on the evaluation set:


* Loss: 1.2111


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: 16
* eval\_batch\_size: 16
* 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 4.11.2
* Pytorch 1.9.0+cu111
* Datasets 1.12.1
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* 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 4.11.2
* Pytorch 1.9.0+cu111
* Datasets 1.12.1
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #es #dataset-sqac #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* 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 4.11.2
* Pytorch 1.9.0+cu111
* Datasets 1.12.1
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #es #dataset-sqac #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* 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 4.11.2
* Pytorch 1.9.0+cu111
* Datasets 1.12.1
* Tokenizers 0.10.3