---
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: 0.0465
- F1: 0.7918
- Roc Auc: 0.8860
- Accuracy: 0.7376
- Coverage Error: 10.2744
- Label Ranking Average Precision Score: 0.7973

## Intended uses & limitations

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

## Training and evaluation data

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|:--------:|:--------------:|:-------------------------------------:|
| 0.0354        | 1.0   | 9054  | 0.0362          | 0.7560 | 0.8375  | 0.6963   | 14.0835        | 0.7357                                |
| 0.0311        | 2.0   | 18108 | 0.0331          | 0.7756 | 0.8535  | 0.7207   | 12.7880        | 0.7633                                |
| 0.0235        | 3.0   | 27162 | 0.0333          | 0.7823 | 0.8705  | 0.7283   | 11.5179        | 0.7811                                |
| 0.0157        | 4.0   | 36216 | 0.0348          | 0.7821 | 0.8699  | 0.7274   | 11.5836        | 0.7798                                |
| 0.011         | 5.0   | 45270 | 0.0377          | 0.7799 | 0.8787  | 0.7239   | 10.9173        | 0.7841                                |
| 0.008         | 6.0   | 54324 | 0.0395          | 0.7854 | 0.8787  | 0.7309   | 10.9042        | 0.7879                                |
| 0.0042        | 7.0   | 63378 | 0.0421          | 0.7872 | 0.8823  | 0.7300   | 10.5687        | 0.7903                                |
| 0.0025        | 8.0   | 72432 | 0.0439          | 0.7884 | 0.8867  | 0.7305   | 10.2220        | 0.7934                                |
| 0.0015        | 9.0   | 81486 | 0.0456          | 0.7889 | 0.8872  | 0.7316   | 10.1781        | 0.7945                                |
| 0.001         | 10.0  | 90540 | 0.0465          | 0.7918 | 0.8860  | 0.7376   | 10.2744        | 0.7973                                |


### Framework versions

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