---
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_Development
  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_Development

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: 0.3499
- Negref Precision: 0.5449
- Negref Recall: 0.5380
- Negref F1: 0.5414
- Neg Precision: 0.9559
- Neg Recall: 0.9694
- Neg F1: 0.9626
- Nsco Precision: 0.8730
- Nsco Recall: 0.9062
- Nsco F1: 0.8893
- Unc Precision: 0.8315
- Unc Recall: 0.8764
- Unc F1: 0.8534
- Usco Precision: 0.6608
- Usco Recall: 0.7383
- Usco F1: 0.6974
- Precision: 0.8205
- Recall: 0.8453
- F1: 0.8327
- Accuracy: 0.9526

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------:|:-------------:|:----------:|:------:|:--------------:|:-----------:|:-------:|:-------------:|:----------:|:------:|:--------------:|:-----------:|:-------:|:---------:|:------:|:------:|:--------:|
| 0.1898        | 1.0   | 1729  | 0.1783          | 0.4516           | 0.5316        | 0.4884    | 0.9351        | 0.9596     | 0.9472 | 0.8079         | 0.8539      | 0.8303  | 0.8193        | 0.7529     | 0.7847 | 0.5816         | 0.6406      | 0.6097  | 0.7596    | 0.8041 | 0.7813 | 0.9452   |
| 0.1163        | 2.0   | 3458  | 0.1724          | 0.4906           | 0.5527        | 0.5198    | 0.9274        | 0.9760     | 0.9511 | 0.8252         | 0.9026      | 0.8622  | 0.8263        | 0.8263     | 0.8263 | 0.5662         | 0.6680      | 0.6129  | 0.7721    | 0.8376 | 0.8036 | 0.9485   |
| 0.0621        | 3.0   | 5187  | 0.1946          | 0.5139           | 0.5063        | 0.5101    | 0.9524        | 0.9618     | 0.9571 | 0.8542         | 0.8836      | 0.8687  | 0.8071        | 0.8726     | 0.8386 | 0.6034         | 0.6836      | 0.6410  | 0.7999    | 0.8249 | 0.8122 | 0.9480   |
| 0.0378        | 4.0   | 6916  | 0.2279          | 0.4923           | 0.5401        | 0.5151    | 0.9450        | 0.9749     | 0.9597 | 0.8568         | 0.8884      | 0.8723  | 0.8259        | 0.8610     | 0.8431 | 0.6179         | 0.6758      | 0.6455  | 0.7940    | 0.8347 | 0.8138 | 0.9490   |
| 0.0192        | 5.0   | 8645  | 0.2495          | 0.5227           | 0.5338        | 0.5282    | 0.9541        | 0.9760     | 0.9649 | 0.8256         | 0.8884      | 0.8558  | 0.8071        | 0.8726     | 0.8386 | 0.6049         | 0.6758      | 0.6384  | 0.7929    | 0.8351 | 0.8135 | 0.9508   |
| 0.0134        | 6.0   | 10374 | 0.2764          | 0.5199           | 0.5232        | 0.5216    | 0.9568        | 0.9672     | 0.9620 | 0.8687         | 0.8955      | 0.8819  | 0.8277        | 0.8533     | 0.8403 | 0.6389         | 0.7188      | 0.6765  | 0.8114    | 0.8347 | 0.8229 | 0.9514   |
| 0.0068        | 7.0   | 12103 | 0.2876          | 0.4880           | 0.5169        | 0.5020    | 0.9470        | 0.9760     | 0.9613 | 0.8593         | 0.8919      | 0.8753  | 0.8494        | 0.8494     | 0.8494 | 0.6456         | 0.7188      | 0.6802  | 0.8010    | 0.8351 | 0.8177 | 0.9508   |
| 0.0059        | 8.0   | 13832 | 0.2886          | 0.4991           | 0.5591        | 0.5274    | 0.9488        | 0.9705     | 0.9595 | 0.8601         | 0.8907      | 0.8751  | 0.8231        | 0.8803     | 0.8507 | 0.6528         | 0.7344      | 0.6912  | 0.7986    | 0.8446 | 0.8209 | 0.9516   |
| 0.0029        | 9.0   | 15561 | 0.3290          | 0.5408           | 0.4895        | 0.5138    | 0.9529        | 0.9716     | 0.9622 | 0.8653         | 0.9002      | 0.8824  | 0.8218        | 0.8726     | 0.8464 | 0.6090         | 0.7422      | 0.6690  | 0.8125    | 0.8358 | 0.8240 | 0.9505   |
| 0.0009        | 10.0  | 17290 | 0.3582          | 0.5438           | 0.5105        | 0.5267    | 0.9519        | 0.9716     | 0.9616 | 0.8757         | 0.9038      | 0.8895  | 0.8218        | 0.8726     | 0.8464 | 0.6737         | 0.75        | 0.7098  | 0.8227    | 0.8413 | 0.8319 | 0.9506   |
| 0.0012        | 11.0  | 19019 | 0.3516          | 0.5139           | 0.5443        | 0.5287    | 0.9539        | 0.9705     | 0.9621 | 0.8834         | 0.9086      | 0.8958  | 0.8291        | 0.8803     | 0.8539 | 0.6761         | 0.75        | 0.7111  | 0.8157    | 0.8489 | 0.8320 | 0.9526   |
| 0.0005        | 12.0  | 20748 | 0.3499          | 0.5449           | 0.5380        | 0.5414    | 0.9559        | 0.9694     | 0.9626 | 0.8730         | 0.9062      | 0.8893  | 0.8315        | 0.8764     | 0.8534 | 0.6608         | 0.7383      | 0.6974  | 0.8205    | 0.8453 | 0.8327 | 0.9526   |


### Framework versions

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