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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: consejo-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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# consejo-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: 0.3066 |
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- Precision: 0.7241 |
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- Recall: 0.6774 |
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- F1: 0.7 |
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- Accuracy: 0.9313 |
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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 | 15 | 1.5724 | 0.0 | 0.0 | 0.0 | 0.6985 | |
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| No log | 2.0 | 30 | 1.3540 | 0.0 | 0.0 | 0.0 | 0.6985 | |
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| No log | 3.0 | 45 | 1.0972 | 0.0 | 0.0 | 0.0 | 0.7099 | |
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| No log | 4.0 | 60 | 0.8615 | 0.5833 | 0.2258 | 0.3256 | 0.7672 | |
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| No log | 5.0 | 75 | 0.7381 | 0.5 | 0.3548 | 0.4151 | 0.8244 | |
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| No log | 6.0 | 90 | 0.6111 | 0.5556 | 0.4839 | 0.5172 | 0.8473 | |
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| No log | 7.0 | 105 | 0.5353 | 0.5185 | 0.4516 | 0.4828 | 0.8550 | |
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| No log | 8.0 | 120 | 0.4786 | 0.5769 | 0.4839 | 0.5263 | 0.8626 | |
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| No log | 9.0 | 135 | 0.4493 | 0.5357 | 0.4839 | 0.5085 | 0.8817 | |
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| No log | 10.0 | 150 | 0.4269 | 0.4839 | 0.4839 | 0.4839 | 0.8779 | |
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| No log | 11.0 | 165 | 0.3977 | 0.5938 | 0.6129 | 0.6032 | 0.8931 | |
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| No log | 12.0 | 180 | 0.3669 | 0.5161 | 0.5161 | 0.5161 | 0.8969 | |
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| No log | 13.0 | 195 | 0.3437 | 0.6786 | 0.6129 | 0.6441 | 0.9237 | |
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| No log | 14.0 | 210 | 0.3389 | 0.6786 | 0.6129 | 0.6441 | 0.9198 | |
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| No log | 15.0 | 225 | 0.3249 | 0.6786 | 0.6129 | 0.6441 | 0.9198 | |
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| No log | 16.0 | 240 | 0.3102 | 0.6897 | 0.6452 | 0.6667 | 0.9275 | |
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| No log | 17.0 | 255 | 0.3094 | 0.6667 | 0.6452 | 0.6557 | 0.9275 | |
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| No log | 18.0 | 270 | 0.3159 | 0.7 | 0.6774 | 0.6885 | 0.9198 | |
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| No log | 19.0 | 285 | 0.3094 | 0.7241 | 0.6774 | 0.7 | 0.9313 | |
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| No log | 20.0 | 300 | 0.3066 | 0.7241 | 0.6774 | 0.7 | 0.9313 | |
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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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