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# electricidad (small) fine-tuned diagTrast

This model is a fine-tuned version of [mrm8488/electricidad-small-discriminator](https://huggingface.co/mrm8488/electricidad-small-discriminator) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2070
- Precision: 0.9349
- Recall: 0.9328
- Accuracy: 0.9328
- F1: 0.9330

## 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: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 14

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:--------:|:------:|
| No log        | 1.0   | 150  | 1.4344          | 0.6909    | 0.6045 | 0.6045   | 0.5460 |
| No log        | 2.0   | 300  | 1.1666          | 0.8055    | 0.7537 | 0.7537   | 0.7211 |
| No log        | 3.0   | 450  | 0.9593          | 0.8321    | 0.8134 | 0.8134   | 0.7913 |
| 1.2831        | 4.0   | 600  | 0.7721          | 0.8575    | 0.8433 | 0.8433   | 0.8257 |
| 1.2831        | 5.0   | 750  | 0.6225          | 0.9062    | 0.9030 | 0.9030   | 0.9009 |
| 1.2831        | 6.0   | 900  | 0.4790          | 0.9179    | 0.9179 | 0.9179   | 0.9168 |
| 0.595         | 7.0   | 1050 | 0.3886          | 0.9340    | 0.9328 | 0.9328   | 0.9327 |
| 0.595         | 8.0   | 1200 | 0.3241          | 0.9258    | 0.9254 | 0.9254   | 0.9248 |
| 0.595         | 9.0   | 1350 | 0.2958          | 0.9198    | 0.9179 | 0.9179   | 0.9175 |
| 0.2183        | 10.0  | 1500 | 0.2469          | 0.9191    | 0.9179 | 0.9179   | 0.9175 |
| 0.2183        | 11.0  | 1650 | 0.2352          | 0.9195    | 0.9179 | 0.9179   | 0.9176 |
| 0.2183        | 12.0  | 1800 | 0.2118          | 0.9262    | 0.9254 | 0.9254   | 0.9249 |
| 0.2183        | 13.0  | 1950 | 0.2138          | 0.9195    | 0.9179 | 0.9179   | 0.9176 |
| 0.0984        | 14.0  | 2100 | 0.2070          | 0.9349    | 0.9328 | 0.9328   | 0.9330 |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "accuracy", "f1"], "widget": [{"text": "Esta persona tiene una gran necesidad de atenci\u00f3n y aprobaci\u00f3n de los dem\u00e1s. Utiliza su aspecto f\u00edsico para atraer la atenci\u00f3n y muestra un comportamiento sexualmente seductor e inapropiado. Adem\u00e1s, tiene dificultades para establecer relaciones aut\u00e9nticas y profundas con los dem\u00e1s"}, {"text": "Se comportaba de manera arrogante y altiva, como si estuviera por encima de todos los dem\u00e1s. Sol\u00eda hablar en tono condescendiente y hacer sentir a los dem\u00e1s que eran inferiores a ella."}, {"text": "Siempre preocupada por quedarse sola, incluso en situaciones sociales, esta persona se esfuerza desesperadamente por evitar cualquier situaci\u00f3n que la haga sentir desamparada, real o imaginada."}], "model-index": [{"name": "electricidad-small-ft-diagTrast-1a", "results": []}]}
transformers pytorch tensorboard electra text-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

electricidad (small) fine-tuned diagTrast
=========================================


This model is a fine-tuned version of mrm8488/electricidad-small-discriminator on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2070
* Precision: 0.9349
* Recall: 0.9328
* Accuracy: 0.9328
* F1: 0.9330


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: 8
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 14


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 14 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 14 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 14### Training results### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3