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---
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
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