File size: 2,994 Bytes
b7b5136 816efbe b7b5136 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: roberta-finetuned-CPV_Spanish
results: []
---
# 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 the 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: 0.0417
- F1: 0.7757
- Roc Auc: 0.8684
- Accuracy: 0.7223
- Coverage Error: 11.7873
- Label Ranking Average Precision Score: 0.7728
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | Coverage Error | Label Ranking Average Precision Score |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|:--------:|:--------------:|:-------------------------------------:|
| 0.0582 | 1.0 | 2039 | 0.0554 | 0.6291 | 0.7463 | 0.5235 | 21.9642 | 0.5547 |
| 0.0413 | 2.0 | 4078 | 0.0437 | 0.7054 | 0.7959 | 0.6239 | 17.5374 | 0.6589 |
| 0.0295 | 3.0 | 6117 | 0.0403 | 0.7391 | 0.8285 | 0.6788 | 14.7700 | 0.7197 |
| 0.022 | 4.0 | 8156 | 0.0390 | 0.7562 | 0.8414 | 0.6987 | 13.8217 | 0.7425 |
| 0.0168 | 5.0 | 10195 | 0.0393 | 0.7600 | 0.8547 | 0.7007 | 12.8532 | 0.7542 |
| 0.0127 | 6.0 | 12234 | 0.0396 | 0.7645 | 0.8606 | 0.7099 | 12.3890 | 0.7622 |
| 0.0094 | 7.0 | 14273 | 0.0406 | 0.7642 | 0.8675 | 0.7027 | 11.8679 | 0.7628 |
| 0.0066 | 8.0 | 16312 | 0.0404 | 0.7706 | 0.8641 | 0.7173 | 12.0876 | 0.7681 |
| 0.0052 | 9.0 | 18351 | 0.0411 | 0.7748 | 0.8679 | 0.7182 | 11.8149 | 0.7705 |
| 0.0042 | 10.0 | 20390 | 0.0417 | 0.7757 | 0.8684 | 0.7223 | 11.7873 | 0.7728 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.9.1
- Datasets 1.18.4
- Tokenizers 0.11.6
|