---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: roberta-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. -->

# roberta-finetuned-sem_eval-rest14-english

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0813
- F1: 0.5700
- Roc Auc: 0.8939
- Accuracy: 0.7312
- Hamming Loss: 0.0225

## 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  | 0.1438          | 0.1120 | 0.6888  | 0.33     | 0.044        |
| 0.2014        | 2.0   | 762  | 0.1059          | 0.3044 | 0.7981  | 0.5587   | 0.0317       |
| 0.1093        | 3.0   | 1143 | 0.0914          | 0.3720 | 0.8325  | 0.6275   | 0.0278       |
| 0.0809        | 4.0   | 1524 | 0.0823          | 0.4290 | 0.8656  | 0.6913   | 0.0244       |
| 0.0809        | 5.0   | 1905 | 0.0862          | 0.4307 | 0.8680  | 0.6963   | 0.0251       |
| 0.06          | 6.0   | 2286 | 0.0811          | 0.4674 | 0.8714  | 0.7013   | 0.0239       |
| 0.0466        | 7.0   | 2667 | 0.0842          | 0.5041 | 0.8714  | 0.7      | 0.0248       |
| 0.0365        | 8.0   | 3048 | 0.0821          | 0.5351 | 0.8846  | 0.7137   | 0.0238       |
| 0.0365        | 9.0   | 3429 | 0.0815          | 0.5375 | 0.8857  | 0.7212   | 0.0234       |
| 0.0299        | 10.0  | 3810 | 0.0812          | 0.5551 | 0.8918  | 0.7312   | 0.0222       |
| 0.0236        | 11.0  | 4191 | 0.0815          | 0.5537 | 0.8940  | 0.7338   | 0.0222       |
| 0.0195        | 12.0  | 4572 | 0.0813          | 0.5700 | 0.8939  | 0.7312   | 0.0225       |
| 0.0195        | 13.0  | 4953 | 0.0829          | 0.5641 | 0.8955  | 0.7362   | 0.022        |
| 0.018         | 14.0  | 5334 | 0.0829          | 0.5662 | 0.8946  | 0.7338   | 0.0221       |
| 0.0157        | 15.0  | 5715 | 0.0824          | 0.5698 | 0.8980  | 0.7362   | 0.0217       |


### Framework versions

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