---
library_name: transformers
base_model: mrm8488/electricidad-base-discriminator
tags:
- classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: clasificador-tweets
  results: []
---


# clasificador-tweets

This model is a fine-tuned version of [mrm8488/electricidad-base-discriminator](https://huggingface.co/mrm8488/electricidad-base-discriminator) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9373
- Accuracy: 0.7234

## Model description

This model has been trained to classify tweets into 7 labor-related categories:

- **Low salary**
- **Labor rights**
- **Labor exploitation**
- **Workplace harassment**
- **Abuse of authority**
- **Workplace negligence**
- **Job opportunities**

The model was trained using the dataset "somosnlp-hackathon-2022/es_tweets_laboral," which contains Spanish tweets classified into the 7 mentioned categories.
The dataset has the following characteristics:

- **Training set**: 184 tweets.
- **Test set**: 47 tweets.
  
-Columns:

text: The tweet's text.
intent: The tweet's category.
entities: Additional information about the entities identified in the tweets.

The tokenizer from "mrm8488/electricidad-base-discriminator" was used for tokenization.



## Intended uses & limitations

Classification of tweets related to labor topics.
The model's accuracy is approximately ~72%.
It is designed to classify tweets in Spanish.
The dataset is small (184 tweets for training), which may limit the model's generalization.



## Training and evaluation data

The model was trained for **10 epochs** using accuracy as the evaluation metric. The results on the test set were as follows:

**Loss**: 0.937
**Accuracy**: 72.34%

It should be noted that these results may vary across different runs due to the randomness inherent in model training.

## Training procedure

The training was based on the Transformers library by HuggingFace.


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 23   | 1.7598          | 0.3404   |
| No log        | 2.0   | 46   | 1.5505          | 0.5106   |
| No log        | 3.0   | 69   | 1.3208          | 0.6170   |
| No log        | 4.0   | 92   | 1.1691          | 0.6383   |
| No log        | 5.0   | 115  | 1.1357          | 0.6383   |
| No log        | 6.0   | 138  | 0.9936          | 0.7447   |
| No log        | 7.0   | 161  | 1.0371          | 0.6596   |
| No log        | 8.0   | 184  | 0.9330          | 0.7021   |
| No log        | 9.0   | 207  | 0.9195          | 0.7234   |
| No log        | 10.0  | 230  | 0.9373          | 0.7234   |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0