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# selectra-small-finetuned-amazon-review

This model is a fine-tuned version of [Recognai/selectra_small](https://huggingface.co/Recognai/selectra_small) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6279
- Accuracy: 0.737
- F1: 0.7438
- Precision: 0.7525
- Recall: 0.737

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log        | 0.5   | 500  | 0.7041          | 0.7178   | 0.6724 | 0.6715    | 0.7178 |
| 0.7908        | 1.0   | 1000 | 0.6365          | 0.7356   | 0.7272 | 0.7211    | 0.7356 |
| 0.7908        | 1.5   | 1500 | 0.6204          | 0.7376   | 0.7380 | 0.7387    | 0.7376 |
| 0.6358        | 2.0   | 2000 | 0.6162          | 0.7386   | 0.7377 | 0.7380    | 0.7386 |
| 0.6358        | 2.5   | 2500 | 0.6228          | 0.7274   | 0.7390 | 0.7576    | 0.7274 |
| 0.5827        | 3.0   | 3000 | 0.6188          | 0.7378   | 0.7400 | 0.7425    | 0.7378 |
| 0.5827        | 3.5   | 3500 | 0.6246          | 0.7374   | 0.7416 | 0.7467    | 0.7374 |
| 0.5427        | 4.0   | 4000 | 0.6266          | 0.7446   | 0.7452 | 0.7465    | 0.7446 |
| 0.5427        | 4.5   | 4500 | 0.6331          | 0.7392   | 0.7421 | 0.7456    | 0.7392 |
| 0.5184        | 5.0   | 5000 | 0.6279          | 0.737    | 0.7438 | 0.7525    | 0.737  |


### Framework versions

- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "selectra-small-finetuned-amazon-review", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "amazon_reviews_multi", "type": "amazon_reviews_multi", "args": "es"}, "metrics": [{"type": "accuracy", "value": 0.737, "name": "Accuracy"}, {"type": "f1", "value": 0.7437773019932409, "name": "F1"}, {"type": "precision", "value": 0.7524857881639091, "name": "Precision"}, {"type": "recall", "value": 0.737, "name": "Recall"}]}]}]}
transformers pytorch tensorboard electra text-classification generated_from_trainer dataset:amazon_reviews_multi license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

selectra-small-finetuned-amazon-review
======================================


This model is a fine-tuned version of Recognai/selectra\_small on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:


* Loss: 0.6279
* Accuracy: 0.737
* F1: 0.7438
* Precision: 0.7525
* Recall: 0.737


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: 5


### Training results



### Framework versions


* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* 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: 5 ### Training results ### Framework versions


* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-apache-2.0 #model-index #autotrain_compatible #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: 5 ### Training results ### Framework versions


* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-apache-2.0 #model-index #autotrain_compatible #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: 5### Training results### Framework versions


* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3