|
--- |
|
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 |
|
|