---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: xlmroberta-finetuned-sem_eval-rest14-english
  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. -->

# xlmroberta-finetuned-sem_eval-rest14-english

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0880
- F1: 0.4875
- Roc Auc: 0.8755
- Accuracy: 0.7087
- Hamming Loss: 0.0254

## 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: 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: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy | Hamming Loss |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|:------------:|
| No log        | 1.0   | 381  | 0.1858          | 0.0351 | 0.6366  | 0.2288   | 0.0609       |
| 0.2234        | 2.0   | 762  | 0.1354          | 0.1345 | 0.6905  | 0.3325   | 0.0418       |
| 0.1455        | 3.0   | 1143 | 0.1091          | 0.2639 | 0.7709  | 0.5138   | 0.0343       |
| 0.1064        | 4.0   | 1524 | 0.0971          | 0.3768 | 0.8313  | 0.6212   | 0.0282       |
| 0.1064        | 5.0   | 1905 | 0.0917          | 0.3767 | 0.8316  | 0.6312   | 0.0286       |
| 0.0807        | 6.0   | 2286 | 0.0880          | 0.3966 | 0.8459  | 0.65     | 0.0274       |
| 0.0657        | 7.0   | 2667 | 0.0922          | 0.3977 | 0.8513  | 0.6525   | 0.0283       |
| 0.0527        | 8.0   | 3048 | 0.0903          | 0.4129 | 0.8555  | 0.6575   | 0.028        |
| 0.0527        | 9.0   | 3429 | 0.0921          | 0.4298 | 0.8611  | 0.6713   | 0.0278       |
| 0.0453        | 10.0  | 3810 | 0.0882          | 0.4612 | 0.8697  | 0.6887   | 0.0261       |
| 0.0385        | 11.0  | 4191 | 0.0859          | 0.4567 | 0.8701  | 0.6925   | 0.0253       |
| 0.0339        | 12.0  | 4572 | 0.0881          | 0.4600 | 0.8726  | 0.7025   | 0.0258       |
| 0.0339        | 13.0  | 4953 | 0.0882          | 0.4818 | 0.8775  | 0.7063   | 0.0252       |
| 0.0304        | 14.0  | 5334 | 0.0879          | 0.4793 | 0.8742  | 0.7      | 0.0254       |
| 0.0271        | 15.0  | 5715 | 0.0880          | 0.4875 | 0.8755  | 0.7087   | 0.0254       |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1