---
license: apache-2.0
base_model: PlanTL-GOB-ES/roberta-base-bne
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NeRUBioS_RoBERTa_base_bne_Training_Testing
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# NeRUBioS_RoBERTa_base_bne_Training_Testing

This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3389
- Negref Precision: 0.5225
- Negref Recall: 0.5800
- Negref F1: 0.5498
- Neg Precision: 0.9521
- Neg Recall: 0.9642
- Neg F1: 0.9581
- Nsco Precision: 0.8732
- Nsco Recall: 0.9062
- Nsco F1: 0.8894
- Unc Precision: 0.8115
- Unc Recall: 0.8718
- Unc F1: 0.8405
- Usco Precision: 0.6862
- Usco Recall: 0.7532
- Usco F1: 0.7181
- Precision: 0.8150
- Recall: 0.8557
- F1: 0.8348
- Accuracy: 0.9505

## 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: 12

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Negref Precision | Negref Recall | Negref F1 | Neg Precision | Neg Recall | Neg F1 | Nsco Precision | Nsco Recall | Nsco F1 | Unc Precision | Unc Recall | Unc F1 | Usco Precision | Usco Recall | Usco F1 | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------:|:-------------:|:----------:|:------:|:--------------:|:-----------:|:-------:|:-------------:|:----------:|:------:|:--------------:|:-----------:|:-------:|:---------:|:------:|:------:|:--------:|
| 0.1896        | 1.0   | 1729  | 0.1858          | 0.4255           | 0.4655        | 0.4446    | 0.9389        | 0.9501     | 0.9445 | 0.8327         | 0.8775      | 0.8545  | 0.7571        | 0.8231     | 0.7887 | 0.5996         | 0.6967      | 0.6445  | 0.7681    | 0.8136 | 0.7902 | 0.9407   |
| 0.1143        | 2.0   | 3458  | 0.1772          | 0.4907           | 0.5419        | 0.5150    | 0.9402        | 0.9600     | 0.9500 | 0.8400         | 0.8933      | 0.8658  | 0.7818        | 0.8359     | 0.8079 | 0.6035         | 0.7121      | 0.6533  | 0.7843    | 0.8369 | 0.8098 | 0.9441   |
| 0.0654        | 3.0   | 5187  | 0.1992          | 0.5314           | 0.4963        | 0.5133    | 0.9513        | 0.9600     | 0.9556 | 0.8648         | 0.8949      | 0.8796  | 0.7916        | 0.8667     | 0.8274 | 0.6033         | 0.7429      | 0.6659  | 0.8086    | 0.8357 | 0.8219 | 0.9459   |
| 0.0407        | 4.0   | 6916  | 0.2400          | 0.5270           | 0.5448        | 0.5357    | 0.9513        | 0.9607     | 0.9560 | 0.8554         | 0.8858      | 0.8703  | 0.8029        | 0.8462     | 0.8240 | 0.6635         | 0.7147      | 0.6881  | 0.8104    | 0.8364 | 0.8232 | 0.9456   |
| 0.0208        | 5.0   | 8645  | 0.2612          | 0.5132           | 0.5698        | 0.5400    | 0.9586        | 0.9600     | 0.9593 | 0.8726         | 0.8964      | 0.8843  | 0.8079        | 0.8410     | 0.8241 | 0.6571         | 0.7095      | 0.6823  | 0.8117    | 0.8426 | 0.8269 | 0.9472   |
| 0.0158        | 6.0   | 10374 | 0.2784          | 0.5019           | 0.5786        | 0.5375    | 0.9520        | 0.9614     | 0.9567 | 0.8669         | 0.9017      | 0.8839  | 0.8177        | 0.8282     | 0.8229 | 0.6490         | 0.7224      | 0.6837  | 0.8041    | 0.8462 | 0.8246 | 0.9485   |
| 0.0098        | 7.0   | 12103 | 0.3086          | 0.5159           | 0.5727        | 0.5428    | 0.9585        | 0.9572     | 0.9578 | 0.8743         | 0.8888      | 0.8815  | 0.8216        | 0.8385     | 0.8299 | 0.6855         | 0.7172      | 0.7010  | 0.8167    | 0.8402 | 0.8283 | 0.9489   |
| 0.0038        | 8.0   | 13832 | 0.3087          | 0.5189           | 0.5433        | 0.5308    | 0.9560        | 0.9614     | 0.9587 | 0.8810         | 0.8956      | 0.8882  | 0.8066        | 0.8769     | 0.8403 | 0.6808         | 0.7455      | 0.7117  | 0.8193    | 0.8452 | 0.8321 | 0.9482   |
| 0.0035        | 9.0   | 15561 | 0.3158          | 0.5147           | 0.5668        | 0.5395    | 0.9573        | 0.9614     | 0.9594 | 0.8820         | 0.8933      | 0.8876  | 0.8063        | 0.8538     | 0.8294 | 0.6573         | 0.7198      | 0.6871  | 0.8144    | 0.8438 | 0.8288 | 0.9501   |
| 0.0016        | 10.0  | 17290 | 0.3380          | 0.5171           | 0.5536        | 0.5348    | 0.9502        | 0.9656     | 0.9579 | 0.8635         | 0.9047      | 0.8836  | 0.8134        | 0.8718     | 0.8416 | 0.6690         | 0.7481      | 0.7063  | 0.8108    | 0.8509 | 0.8304 | 0.9491   |
| 0.0004        | 11.0  | 19019 | 0.3369          | 0.5164           | 0.5786        | 0.5457    | 0.9555        | 0.9649     | 0.9602 | 0.8759         | 0.9024      | 0.8890  | 0.8106        | 0.8667     | 0.8377 | 0.6822         | 0.7506      | 0.7148  | 0.8147    | 0.8538 | 0.8338 | 0.9502   |
| 0.0009        | 12.0  | 20748 | 0.3389          | 0.5225           | 0.5800        | 0.5498    | 0.9521        | 0.9642     | 0.9581 | 0.8732         | 0.9062      | 0.8894  | 0.8115        | 0.8718     | 0.8405 | 0.6862         | 0.7532      | 0.7181  | 0.8150    | 0.8557 | 0.8348 | 0.9505   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2