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--- |
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tags: |
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- generated_from_trainer |
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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: datos-ner |
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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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# datos-ner |
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This model is a fine-tuned version of [dccuchile/distilbert-base-spanish-uncased](https://huggingface.co/dccuchile/distilbert-base-spanish-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0751 |
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- Precision: 0.9516 |
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- Recall: 0.9219 |
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- F1: 0.9365 |
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- Accuracy: 0.9805 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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: 20 |
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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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| No log | 1.0 | 38 | 0.6419 | 0.8947 | 0.2656 | 0.4096 | 0.8357 | |
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| No log | 2.0 | 76 | 0.2665 | 0.8511 | 0.625 | 0.7207 | 0.9276 | |
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| No log | 3.0 | 114 | 0.1322 | 0.9508 | 0.9062 | 0.9280 | 0.9749 | |
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| No log | 4.0 | 152 | 0.0907 | 0.9524 | 0.9375 | 0.9449 | 0.9805 | |
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| No log | 5.0 | 190 | 0.0760 | 0.9683 | 0.9531 | 0.9606 | 0.9833 | |
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| No log | 6.0 | 228 | 0.0644 | 0.9531 | 0.9531 | 0.9531 | 0.9861 | |
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| No log | 7.0 | 266 | 0.0728 | 0.9365 | 0.9219 | 0.9291 | 0.9805 | |
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| No log | 8.0 | 304 | 0.0690 | 0.9365 | 0.9219 | 0.9291 | 0.9805 | |
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| No log | 9.0 | 342 | 0.0709 | 0.9365 | 0.9219 | 0.9291 | 0.9805 | |
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| No log | 10.0 | 380 | 0.0781 | 0.9516 | 0.9219 | 0.9365 | 0.9805 | |
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| No log | 11.0 | 418 | 0.0654 | 0.9365 | 0.9219 | 0.9291 | 0.9805 | |
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| No log | 12.0 | 456 | 0.0746 | 0.9516 | 0.9219 | 0.9365 | 0.9805 | |
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| No log | 13.0 | 494 | 0.0721 | 0.9516 | 0.9219 | 0.9365 | 0.9805 | |
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| 0.1874 | 14.0 | 532 | 0.0739 | 0.9516 | 0.9219 | 0.9365 | 0.9805 | |
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| 0.1874 | 15.0 | 570 | 0.0765 | 0.9516 | 0.9219 | 0.9365 | 0.9805 | |
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| 0.1874 | 16.0 | 608 | 0.0777 | 0.9516 | 0.9219 | 0.9365 | 0.9805 | |
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| 0.1874 | 17.0 | 646 | 0.0756 | 0.9516 | 0.9219 | 0.9365 | 0.9805 | |
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| 0.1874 | 18.0 | 684 | 0.0765 | 0.9516 | 0.9219 | 0.9365 | 0.9805 | |
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| 0.1874 | 19.0 | 722 | 0.0758 | 0.9516 | 0.9219 | 0.9365 | 0.9805 | |
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| 0.1874 | 20.0 | 760 | 0.0751 | 0.9516 | 0.9219 | 0.9365 | 0.9805 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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