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# clasificador-reviews-amazon

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the amazon_polarity dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4642
- Accuracy: 0.926

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

Los conjuntos de train y de test se han reducido respecto al dataset original amazon_polarity para mantener unos tiempos de ejecución relativamente cortos.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-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: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3674        | 1.0   | 625  | 0.2204          | 0.928    |
| 0.1924        | 2.0   | 1250 | 0.3444          | 0.926    |
| 0.0974        | 3.0   | 1875 | 0.4642          | 0.926    |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["classification", "generated_from_trainer"], "datasets": ["amazon_polarity"], "metrics": ["accuracy"], "model-index": [{"name": "clasificador-reviews-amazon", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "amazon_polarity", "type": "amazon_polarity", "config": "amazon_polarity", "split": "test", "args": "amazon_polarity"}, "metrics": [{"type": "accuracy", "value": 0.926, "name": "Accuracy"}]}]}]}
transformers pytorch bert text-classification classification generated_from_trainer dataset:amazon_polarity license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #classification #generated_from_trainer #dataset-amazon_polarity #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

clasificador-reviews-amazon
===========================


This model is a fine-tuned version of bert-base-uncased on the amazon\_polarity dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4642
* Accuracy: 0.926


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


More information needed


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


More information needed


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


Los conjuntos de train y de test se han reducido respecto al dataset original amazon\_polarity para mantener unos tiempos de ejecución relativamente cortos.


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


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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: 3.0


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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: 3.0 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #bert #text-classification #classification #generated_from_trainer #dataset-amazon_polarity #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-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: 3.0 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3