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---
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
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