---
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- roc_auc
- f1
model-index:
- name: results_RoBERTa
  results: []
datasets:
- alecmontero/dataset_tweetsmx_areasCPC
language:
- es
library_name: transformers
---

<!-- 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. -->

# results_RoBERTa

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1365
- Roc Auc: 0.8669
- Hamming Loss: 0.0454
- F1 Score: 0.7761
- Accuracy: 0.4712
- Precision: 0.7977
- Recall: 0.7665

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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 | Roc Auc | Hamming Loss | F1 Score | Accuracy | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------------:|:--------:|:--------:|:---------:|:------:|
| No log        | 1.0   | 374  | 0.1904          | 0.6981  | 0.0674       | 0.4749   | 0.3440   | 0.7840    | 0.4297 |
| 0.2476        | 2.0   | 748  | 0.1674          | 0.7439  | 0.0612       | 0.5672   | 0.3802   | 0.8482    | 0.5228 |
| 0.1597        | 3.0   | 1122 | 0.1512          | 0.7955  | 0.0545       | 0.6516   | 0.4163   | 0.8172    | 0.6218 |
| 0.1597        | 4.0   | 1496 | 0.1414          | 0.8087  | 0.0511       | 0.6736   | 0.4324   | 0.8251    | 0.6535 |
| 0.1222        | 5.0   | 1870 | 0.1395          | 0.8344  | 0.0490       | 0.7153   | 0.4378   | 0.8190    | 0.7038 |
| 0.09          | 6.0   | 2244 | 0.1385          | 0.8485  | 0.0477       | 0.7552   | 0.4645   | 0.8182    | 0.7315 |
| 0.0663        | 7.0   | 2618 | 0.1391          | 0.8544  | 0.0466       | 0.7617   | 0.4712   | 0.7936    | 0.7401 |
| 0.0663        | 8.0   | 2992 | 0.1365          | 0.8669  | 0.0454       | 0.7761   | 0.4712   | 0.7977    | 0.7665 |
| 0.0461        | 9.0   | 3366 | 0.1375          | 0.8617  | 0.0460       | 0.7711   | 0.4699   | 0.7956    | 0.7569 |
| 0.0293        | 10.0  | 3740 | 0.1388          | 0.8636  | 0.0448       | 0.7736   | 0.4926   | 0.7953    | 0.7592 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1