---
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: xlm-roberta-base-MLTC-rob
  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. -->

# xlm-roberta-base-MLTC-rob

This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3645
- F1: 0.8629
- F1 Weighted: 0.8632
- Roc Auc: 0.8598
- Accuracy: 0.6067
- Hamming Loss: 0.1401
- Jaccard Score: 0.7588
- Zero One Loss: 0.3933

## 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: 16
- 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 | F1     | F1 Weighted | Roc Auc | Accuracy | Hamming Loss | Jaccard Score | Zero One Loss |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:-------:|:--------:|:------------:|:-------------:|:-------------:|
| 0.5856        | 1.0   | 73   | 0.5884          | 0.7201 | 0.6619      | 0.6835  | 0.3393   | 0.3162       | 0.5627        | 0.6607        |
| 0.5053        | 2.0   | 146  | 0.4688          | 0.7718 | 0.7159      | 0.7712  | 0.4139   | 0.2288       | 0.6284        | 0.5861        |
| 0.3929        | 3.0   | 219  | 0.4002          | 0.8410 | 0.8413      | 0.8334  | 0.5347   | 0.1665       | 0.7256        | 0.4653        |
| 0.3293        | 4.0   | 292  | 0.3816          | 0.8471 | 0.8453      | 0.8399  | 0.5527   | 0.1600       | 0.7348        | 0.4473        |
| 0.3242        | 5.0   | 365  | 0.3607          | 0.8550 | 0.8538      | 0.8515  | 0.5784   | 0.1485       | 0.7467        | 0.4216        |
| 0.3228        | 6.0   | 438  | 0.3776          | 0.8495 | 0.8462      | 0.8437  | 0.5707   | 0.1562       | 0.7384        | 0.4293        |
| 0.2713        | 7.0   | 511  | 0.4086          | 0.8453 | 0.8412      | 0.8373  | 0.5630   | 0.1626       | 0.7320        | 0.4370        |
| 0.2519        | 8.0   | 584  | 0.3711          | 0.8534 | 0.8531      | 0.8489  | 0.5861   | 0.1510       | 0.7443        | 0.4139        |
| 0.2724        | 9.0   | 657  | 0.3645          | 0.8629 | 0.8632      | 0.8598  | 0.6067   | 0.1401       | 0.7588        | 0.3933        |
| 0.2484        | 10.0  | 730  | 0.3669          | 0.8586 | 0.8585      | 0.8553  | 0.5964   | 0.1446       | 0.7522        | 0.4036        |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.19.1