---
base_model: daveni/twitter-xlm-roberta-emotion-es
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: xml-roberta-HU-Com
  results: []
---

<!-- 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. -->

# xml-roberta-HU-Com

This model is a fine-tuned version of [daveni/twitter-xlm-roberta-emotion-es](https://huggingface.co./daveni/twitter-xlm-roberta-emotion-es) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3693
- Accuracy: 0.7911
- F1: 0.7440
- Precision: 0.7415
- Recall: 0.7466

## 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: 16
- 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 | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6717        | 1.0   | 90   | 0.5918          | 0.6852   | 0.5272 | 0.6774    | 0.4315 |
| 0.453         | 2.0   | 180  | 0.5358          | 0.7465   | 0.6403 | 0.7570    | 0.5548 |
| 0.2631        | 3.0   | 270  | 0.7088          | 0.7744   | 0.7273 | 0.7152    | 0.7397 |
| 0.1936        | 4.0   | 360  | 0.7078          | 0.7939   | 0.7566 | 0.7278    | 0.7877 |
| 0.1273        | 5.0   | 450  | 1.1057          | 0.7772   | 0.7436 | 0.6988    | 0.7945 |
| 0.066         | 6.0   | 540  | 1.1990          | 0.7799   | 0.7168 | 0.7519    | 0.6849 |
| 0.0286        | 7.0   | 630  | 1.2457          | 0.7994   | 0.7584 | 0.7434    | 0.7740 |
| 0.0261        | 8.0   | 720  | 1.3297          | 0.7799   | 0.7106 | 0.7638    | 0.6644 |
| 0.0097        | 9.0   | 810  | 1.3733          | 0.7855   | 0.7354 | 0.7379    | 0.7329 |
| 0.0071        | 10.0  | 900  | 1.3693          | 0.7911   | 0.7440 | 0.7415    | 0.7466 |


### Framework versions

- Transformers 4.43.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.19.1
- Tokenizers 0.19.1