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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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- accuracy |
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model-index: |
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- name: roberta-finetuned-CPV_Spanish |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# roberta-finetuned-CPV_Spanish |
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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). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0460 |
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- F1: 0.7937 |
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- Roc Auc: 0.8857 |
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- Accuracy: 0.7398 |
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- Coverage Error: 10.3171 |
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- Label Ranking Average Precision Score: 0.7977 |
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## Intended uses & limitations |
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This model only predicts the first two digits of the CPV codes. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | Coverage Error | Label Ranking Average Precision Score | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|:--------:|:--------------:|:-------------------------------------:| |
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| 0.0359 | 1.0 | 9054 | 0.0368 | 0.7527 | 0.8361 | 0.6920 | 14.2585 | 0.7318 | |
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| 0.0314 | 2.0 | 18108 | 0.0332 | 0.7753 | 0.8518 | 0.7198 | 12.9053 | 0.7612 | |
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| 0.0235 | 3.0 | 27162 | 0.0332 | 0.7824 | 0.8656 | 0.7284 | 11.8961 | 0.7767 | |
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| 0.0166 | 4.0 | 36216 | 0.0348 | 0.7824 | 0.8725 | 0.7289 | 11.3928 | 0.7821 | |
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| 0.0114 | 5.0 | 45270 | 0.0371 | 0.7825 | 0.8799 | 0.7271 | 10.8051 | 0.7871 | |
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| 0.0079 | 6.0 | 54324 | 0.0398 | 0.7829 | 0.8765 | 0.7260 | 11.0922 | 0.7831 | |
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| 0.0042 | 7.0 | 63378 | 0.0414 | 0.7889 | 0.8798 | 0.7317 | 10.7793 | 0.7891 | |
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| 0.0025 | 8.0 | 72432 | 0.0434 | 0.7895 | 0.8847 | 0.7317 | 10.3856 | 0.7924 | |
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| 0.0014 | 9.0 | 81486 | 0.0451 | 0.7928 | 0.8860 | 0.7356 | 10.3086 | 0.7960 | |
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| 0.001 | 10.0 | 90540 | 0.0460 | 0.7937 | 0.8857 | 0.7398 | 10.3171 | 0.7977 | |
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### Framework versions |
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- Transformers 4.16.2 |
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- Pytorch 1.9.1 |
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- Datasets 1.18.4 |
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- Tokenizers 0.11.6 |
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