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--- |
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base_model: dccuchile/tulio-chilean-spanish-bert |
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license: cc-by-4.0 |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: not-ner-v2 |
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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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# not-ner-v2 |
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This model is a fine-tuned version of [dccuchile/tulio-chilean-spanish-bert](https://huggingface.co./dccuchile/tulio-chilean-spanish-bert) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0838 |
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- Accuracy: 0.9727 |
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- Precision: 0.9723 |
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- Recall: 0.9727 |
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- F1: 0.9724 |
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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: 5e-05 |
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- train_batch_size: 32 |
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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: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.1679 | 0.1595 | 250 | 0.1431 | 0.9555 | 0.9546 | 0.9555 | 0.9549 | |
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| 0.1291 | 0.3191 | 500 | 0.1328 | 0.9595 | 0.9583 | 0.9595 | 0.9586 | |
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| 0.1055 | 0.4786 | 750 | 0.0925 | 0.9653 | 0.9648 | 0.9653 | 0.9650 | |
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| 0.1044 | 0.6382 | 1000 | 0.1415 | 0.9630 | 0.9619 | 0.9630 | 0.9615 | |
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| 0.1094 | 0.7977 | 1250 | 0.1030 | 0.9630 | 0.9624 | 0.9630 | 0.9612 | |
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| 0.0927 | 0.9572 | 1500 | 0.0878 | 0.9710 | 0.9706 | 0.9710 | 0.9707 | |
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| 0.0836 | 1.1168 | 1750 | 0.1265 | 0.9663 | 0.9666 | 0.9663 | 0.9665 | |
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| 0.0651 | 1.2763 | 2000 | 0.1025 | 0.9709 | 0.9702 | 0.9709 | 0.9704 | |
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| 0.0637 | 1.4359 | 2250 | 0.0998 | 0.9676 | 0.9668 | 0.9676 | 0.9667 | |
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| 0.0713 | 1.5954 | 2500 | 0.0838 | 0.9727 | 0.9723 | 0.9727 | 0.9724 | |
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| 0.0561 | 1.7549 | 2750 | 0.0905 | 0.9722 | 0.9722 | 0.9722 | 0.9722 | |
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| 0.058 | 1.9145 | 3000 | 0.1030 | 0.9707 | 0.9701 | 0.9707 | 0.9702 | |
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| 0.0531 | 2.0740 | 3250 | 0.1066 | 0.9714 | 0.9710 | 0.9714 | 0.9711 | |
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| 0.0398 | 2.2336 | 3500 | 0.1035 | 0.9722 | 0.9721 | 0.9722 | 0.9721 | |
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| 0.0444 | 2.3931 | 3750 | 0.1009 | 0.9728 | 0.9725 | 0.9728 | 0.9726 | |
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| 0.037 | 2.5526 | 4000 | 0.1068 | 0.9725 | 0.9721 | 0.9725 | 0.9722 | |
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| 0.0261 | 2.7122 | 4250 | 0.1192 | 0.9735 | 0.9731 | 0.9735 | 0.9732 | |
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| 0.0266 | 2.8717 | 4500 | 0.1191 | 0.9732 | 0.9727 | 0.9732 | 0.9729 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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