---
license: apache-2.0
base_model: PlanTL-GOB-ES/roberta-base-bne
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: RE_NegREF_NSD_Nubes_Training_Test_dataset_RoBERTa_base_bne_fine_tuned
  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. -->

# RE_NegREF_NSD_Nubes_Training_Test_dataset_RoBERTa_base_bne_fine_tuned

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: 0.3480
- Negref Precision: 0.5338
- Negref Recall: 0.5565
- Negref F1: 0.5449
- Neg Precision: 0.9552
- Neg Recall: 0.9593
- Neg F1: 0.9573
- Nsco Precision: 0.8862
- Nsco Recall: 0.9070
- Nsco F1: 0.8964
- Precision: 0.8428
- Recall: 0.8591
- F1: 0.8509
- Accuracy: 0.9569

## 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 | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------:|:-------------:|:----------:|:------:|:--------------:|:-----------:|:-------:|:---------:|:------:|:------:|:--------:|
| 0.0092        | 1.0   | 1729  | 0.3395          | 0.5044           | 0.5051        | 0.5048    | 0.9546        | 0.9600     | 0.9573 | 0.8681         | 0.8911      | 0.8794  | 0.8323    | 0.8430 | 0.8376 | 0.9552   |
| 0.0096        | 2.0   | 3458  | 0.2674          | 0.4694           | 0.5962        | 0.5252    | 0.9483        | 0.9663     | 0.9572 | 0.8717         | 0.8888      | 0.8801  | 0.8070    | 0.8629 | 0.8340 | 0.9545   |
| 0.0069        | 3.0   | 5187  | 0.2922          | 0.5054           | 0.5477        | 0.5257    | 0.9488        | 0.9628     | 0.9557 | 0.8740         | 0.8865      | 0.8802  | 0.8275    | 0.8509 | 0.8390 | 0.9559   |
| 0.0066        | 4.0   | 6916  | 0.2958          | 0.5371           | 0.5639        | 0.5501    | 0.9533        | 0.9614     | 0.9573 | 0.8714         | 0.8971      | 0.8841  | 0.8368    | 0.8576 | 0.8471 | 0.9559   |
| 0.0042        | 5.0   | 8645  | 0.2978          | 0.5475           | 0.5419        | 0.5446    | 0.9584        | 0.9551     | 0.9567 | 0.8850         | 0.8964      | 0.8906  | 0.8491    | 0.8503 | 0.8497 | 0.9558   |
| 0.0072        | 6.0   | 10374 | 0.2875          | 0.5068           | 0.5477        | 0.5265    | 0.9547        | 0.9614     | 0.9580 | 0.8791         | 0.8911      | 0.8850  | 0.8319    | 0.8521 | 0.8419 | 0.9561   |
| 0.0045        | 7.0   | 12103 | 0.3203          | 0.5551           | 0.5624        | 0.5587    | 0.9561        | 0.9628     | 0.9594 | 0.8744         | 0.9002      | 0.8871  | 0.8448    | 0.8591 | 0.8519 | 0.9567   |
| 0.0019        | 8.0   | 13832 | 0.3412          | 0.5339           | 0.5433        | 0.5386    | 0.9565        | 0.9579     | 0.9572 | 0.8921         | 0.9009      | 0.8965  | 0.8468    | 0.8535 | 0.8502 | 0.9560   |
| 0.0013        | 9.0   | 15561 | 0.3270          | 0.5033           | 0.5551        | 0.5279    | 0.9533        | 0.9600     | 0.9566 | 0.8900         | 0.9062      | 0.8981  | 0.8335    | 0.8588 | 0.8459 | 0.9574   |
| 0.0009        | 10.0  | 17290 | 0.3257          | 0.5285           | 0.5580        | 0.5429    | 0.9513        | 0.9593     | 0.9552 | 0.8826         | 0.9039      | 0.8931  | 0.8381    | 0.8582 | 0.8480 | 0.9567   |
| 0.0007        | 11.0  | 19019 | 0.3303          | 0.5299           | 0.5595        | 0.5443    | 0.9586        | 0.9586     | 0.9586 | 0.8859         | 0.9047      | 0.8952  | 0.8423    | 0.8585 | 0.8503 | 0.9574   |
| 0.0           | 12.0  | 20748 | 0.3480          | 0.5338           | 0.5565        | 0.5449    | 0.9552        | 0.9593     | 0.9573 | 0.8862         | 0.9070      | 0.8964  | 0.8428    | 0.8591 | 0.8509 | 0.9569   |


### Framework versions

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