---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: roberta-finetuned-CPV_Spanish
  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-CPV_Spanish

This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on a dataset derived from Spanish Public Procurement documents from 2019. The whole fine-tuning process is available in the following [Kaggle notebook](https://www.kaggle.com/code/marianavasloro/fine-tuned-roberta-for-spanish-cpv-codes).
It achieves the following results on the evaluation set:
- Loss: 0.0460
- F1: 0.7937
- Roc Auc: 0.8857
- Accuracy: 0.7398
- Coverage Error: 10.3171
- Label Ranking Average Precision Score: 0.7977

## Intended uses & limitations

This model only predicts the first two digits of the CPV codes.


## 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: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | F1     | Roc Auc | Accuracy | Coverage Error | Label Ranking Average Precision Score |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|:--------:|:--------------:|:-------------------------------------:|
| 0.0359        | 1.0   | 9054  | 0.0368          | 0.7527 | 0.8361  | 0.6920   | 14.2585        | 0.7318                                |
| 0.0314        | 2.0   | 18108 | 0.0332          | 0.7753 | 0.8518  | 0.7198   | 12.9053        | 0.7612                                |
| 0.0235        | 3.0   | 27162 | 0.0332          | 0.7824 | 0.8656  | 0.7284   | 11.8961        | 0.7767                                |
| 0.0166        | 4.0   | 36216 | 0.0348          | 0.7824 | 0.8725  | 0.7289   | 11.3928        | 0.7821                                |
| 0.0114        | 5.0   | 45270 | 0.0371          | 0.7825 | 0.8799  | 0.7271   | 10.8051        | 0.7871                                |
| 0.0079        | 6.0   | 54324 | 0.0398          | 0.7829 | 0.8765  | 0.7260   | 11.0922        | 0.7831                                |
| 0.0042        | 7.0   | 63378 | 0.0414          | 0.7889 | 0.8798  | 0.7317   | 10.7793        | 0.7891                                |
| 0.0025        | 8.0   | 72432 | 0.0434          | 0.7895 | 0.8847  | 0.7317   | 10.3856        | 0.7924                                |
| 0.0014        | 9.0   | 81486 | 0.0451          | 0.7928 | 0.8860  | 0.7356   | 10.3086        | 0.7960                                |
| 0.001         | 10.0  | 90540 | 0.0460          | 0.7937 | 0.8857  | 0.7398   | 10.3171        | 0.7977                                |


### Framework versions

- Transformers 4.16.2
- Pytorch 1.9.1
- Datasets 1.18.4
- Tokenizers 0.11.6