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--- |
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license: cc-by-4.0 |
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base_model: NazaGara/NER-fine-tuned-BETO |
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tags: |
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- generated_from_trainer |
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datasets: |
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- conll2002 |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: beto-finetuned-ner-cfv |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: conll2002 |
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type: conll2002 |
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config: es |
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split: validation |
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args: es |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.8614471581830633 |
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- name: Recall |
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type: recall |
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value: 0.8671875 |
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- name: F1 |
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type: f1 |
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value: 0.8643077980075576 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9790072369291234 |
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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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# beto-finetuned-ner-cfv |
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This model is a fine-tuned version of [NazaGara/NER-fine-tuned-BETO](https://huggingface.co/NazaGara/NER-fine-tuned-BETO) on the conll2002 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1659 |
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- Precision: 0.8614 |
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- Recall: 0.8672 |
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- F1: 0.8643 |
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- Accuracy: 0.9790 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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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: 4e-06 |
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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: 16 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.0272 | 1.0 | 1041 | 0.1062 | 0.8549 | 0.8637 | 0.8593 | 0.9786 | |
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| 0.0208 | 2.0 | 2082 | 0.1127 | 0.8443 | 0.8596 | 0.8519 | 0.9782 | |
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| 0.0158 | 3.0 | 3123 | 0.1195 | 0.8545 | 0.8598 | 0.8572 | 0.9787 | |
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| 0.0129 | 4.0 | 4164 | 0.1332 | 0.8629 | 0.8589 | 0.8609 | 0.9782 | |
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| 0.0107 | 5.0 | 5205 | 0.1299 | 0.8555 | 0.8635 | 0.8595 | 0.9786 | |
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| 0.0087 | 6.0 | 6246 | 0.1486 | 0.8564 | 0.8564 | 0.8564 | 0.9782 | |
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| 0.0085 | 7.0 | 7287 | 0.1583 | 0.8618 | 0.8596 | 0.8607 | 0.9783 | |
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| 0.0066 | 8.0 | 8328 | 0.1582 | 0.8604 | 0.8580 | 0.8592 | 0.9783 | |
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| 0.0052 | 9.0 | 9369 | 0.1571 | 0.8554 | 0.8566 | 0.8560 | 0.9781 | |
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| 0.0054 | 10.0 | 10410 | 0.1604 | 0.8628 | 0.8640 | 0.8634 | 0.9787 | |
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| 0.004 | 11.0 | 11451 | 0.1584 | 0.8624 | 0.8670 | 0.8647 | 0.9791 | |
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| 0.0036 | 12.0 | 12492 | 0.1633 | 0.8603 | 0.8658 | 0.8630 | 0.9786 | |
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| 0.0036 | 13.0 | 13533 | 0.1620 | 0.8628 | 0.8658 | 0.8643 | 0.9790 | |
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| 0.0032 | 14.0 | 14574 | 0.1645 | 0.8617 | 0.8676 | 0.8647 | 0.9793 | |
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| 0.0028 | 15.0 | 15615 | 0.1645 | 0.8604 | 0.8670 | 0.8637 | 0.9792 | |
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| 0.003 | 16.0 | 16656 | 0.1659 | 0.8614 | 0.8672 | 0.8643 | 0.9790 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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