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tags: |
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- generated_from_trainer |
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base_model: dccuchile/bert-base-spanish-wwm-cased |
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metrics: |
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- accuracy |
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- f1 |
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- recall |
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model-index: |
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- name: ClasificadorV2 |
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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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# ClasificadorV2 |
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This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1411 |
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- Accuracy: 0.5708 |
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- Off By One Accuracy: 0.9434 |
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- F1: 0.5724 |
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- Recall: 0.5708 |
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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: 50 |
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- eval_batch_size: 50 |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 5 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Off By One Accuracy | F1 | Recall | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:-------------------:|:------:|:------:| |
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| 1.2177 | 0.3333 | 500 | 1.0309 | 0.5426 | 0.93 | 0.5414 | 0.5426 | |
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| 1.011 | 0.6667 | 1000 | 0.9836 | 0.565 | 0.9336 | 0.5485 | 0.565 | |
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| 0.9833 | 1.0 | 1500 | 0.9664 | 0.5752 | 0.9448 | 0.5704 | 0.5752 | |
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| 0.9004 | 1.3333 | 2000 | 0.9566 | 0.5728 | 0.9476 | 0.5743 | 0.5728 | |
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| 0.8974 | 1.6667 | 2500 | 0.9583 | 0.5782 | 0.9472 | 0.5784 | 0.5782 | |
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| 0.8912 | 2.0 | 3000 | 0.9480 | 0.5816 | 0.9498 | 0.5768 | 0.5816 | |
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| 0.7935 | 2.3333 | 3500 | 0.9768 | 0.582 | 0.9472 | 0.5800 | 0.582 | |
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| 0.7898 | 2.6667 | 4000 | 0.9831 | 0.5716 | 0.9426 | 0.5715 | 0.5716 | |
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| 0.7801 | 3.0 | 4500 | 0.9969 | 0.5736 | 0.9514 | 0.5759 | 0.5736 | |
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| 0.6714 | 3.3333 | 5000 | 1.0782 | 0.5826 | 0.9392 | 0.5795 | 0.5826 | |
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| 0.6783 | 3.6667 | 5500 | 1.0672 | 0.5724 | 0.9456 | 0.5752 | 0.5724 | |
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| 0.6764 | 4.0 | 6000 | 1.0762 | 0.567 | 0.9458 | 0.5708 | 0.567 | |
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| 0.5986 | 4.3333 | 6500 | 1.1349 | 0.5698 | 0.9412 | 0.5684 | 0.5698 | |
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| 0.5887 | 4.6667 | 7000 | 1.1335 | 0.5706 | 0.9398 | 0.5716 | 0.5706 | |
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| 0.5798 | 5.0 | 7500 | 1.1411 | 0.5708 | 0.9434 | 0.5724 | 0.5708 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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