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---
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 explotaition**
- **Workplace harasment**
- **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
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