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# Clasificacion_sentimientos

This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3399
- Accuracy: 0.9428

## Model description

Se entrena un modelo que es capaz de clasificar si es un comentario postivo o negativo.

## Intended uses & limitations

More information needed

## Training and evaluation data

Se entrenó el modelo usando comentarios de peliculas de la página $https://www.filmaffinity.com/es/main.html$
   - Estos comentarios estan en la base de datos alojada en Kaggle, 
     url : https://www.kaggle.com/ricardomoya/criticas-peliculas-filmaffinity-en-espaniol/code

## Training procedure

La variable review_rate se usó para clasificar los comentarios positivos y negativos así:
Positivos: los rating con 8,9,10.
Negativos: Los rating con 3,2,1.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2566        | 1.0   | 901  | 0.5299          | 0.8935   |
| 0.0963        | 2.0   | 1802 | 0.2885          | 0.9383   |
| 0.0133        | 3.0   | 2703 | 0.3546          | 0.9406   |
| 0.0002        | 4.0   | 3604 | 0.3399          | 0.9428   |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "Clasificacion_sentimientos", "results": []}]}
transformers pytorch tensorboard roberta text-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

Clasificacion\_sentimientos
===========================


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


* Loss: 0.3399
* Accuracy: 0.9428


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


Se entrena un modelo que es capaz de clasificar si es un comentario postivo o negativo.


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


More information needed


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


Se entrenó el modelo usando comentarios de peliculas de la página $URL


* Estos comentarios estan en la base de datos alojada en Kaggle,
url : URL


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


La variable review\_rate se usó para clasificar los comentarios positivos y negativos así:
Positivos: los rating con 8,9,10.
Negativos: Los rating con 3,2,1.


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4


### Training results



### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6



### Training hyperparameters


The following hyperparameters were used during training:


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


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


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


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


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


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6