---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
- f1
model-index:
- name: electricidad-base-ft-diagTrast
  results: []

widget:
- text: "Esta persona tiene una gran necesidad de atención y aprobación de los demás. Utiliza su aspecto físico para atraer la atención y muestra un comportamiento sexualmente seductor e inapropiado. Además, tiene dificultades para establecer relaciones auténticas y profundas con los demás"
- text: "Se comportaba de manera arrogante y altiva, como si estuviera por encima de todos los demás. Solía hablar en tono condescendiente y hacer sentir a los demás que eran inferiores a ella."
- text: "Siempre preocupada por quedarse sola, incluso en situaciones sociales, esta persona se esfuerza desesperadamente por evitar cualquier situación que la haga sentir desamparada, real o imaginada."
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Electricidad fine-tuned diagTrast

This model is a fine-tuned version of [mrm8488/electricidad-base-discriminator](https://huggingface.co/mrm8488/electricidad-base-discriminator) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2111
- Precision: 0.9653
- Recall: 0.9627
- Accuracy: 0.9627
- F1: 0.9622

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:--------:|:------:|
| No log        | 1.0   | 150  | 0.9281          | 0.7399    | 0.6567 | 0.6567   | 0.5989 |
| No log        | 2.0   | 300  | 0.4736          | 0.8680    | 0.8582 | 0.8582   | 0.8581 |
| No log        | 3.0   | 450  | 0.2584          | 0.9215    | 0.9104 | 0.9104   | 0.9110 |
| 0.6826        | 4.0   | 600  | 0.3336          | 0.9190    | 0.9104 | 0.9104   | 0.9036 |
| 0.6826        | 5.0   | 750  | 0.2194          | 0.9458    | 0.9403 | 0.9403   | 0.9398 |
| 0.6826        | 6.0   | 900  | 0.1984          | 0.9451    | 0.9403 | 0.9403   | 0.9397 |
| 0.0262        | 7.0   | 1050 | 0.2012          | 0.9582    | 0.9552 | 0.9552   | 0.9552 |
| 0.0262        | 8.0   | 1200 | 0.2272          | 0.9366    | 0.9328 | 0.9328   | 0.9319 |
| 0.0262        | 9.0   | 1350 | 0.2111          | 0.9653    | 0.9627 | 0.9627   | 0.9622 |
| 0.0044        | 10.0  | 1500 | 0.2156          | 0.9587    | 0.9552 | 0.9552   | 0.9543 |


### Framework versions

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