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--- |
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base_model: dccuchile/bert-base-spanish-wwm-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: Prototipo_5_EMI |
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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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# Prototipo_5_EMI |
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This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co./dccuchile/bert-base-spanish-wwm-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.4215 |
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- Accuracy: 0.538 |
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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: 20 |
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- eval_batch_size: 20 |
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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 | |
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|:-------------:|:------:|:----:|:---------------:|:--------:| |
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| 1.2459 | 0.1481 | 200 | 1.2168 | 0.4493 | |
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| 1.1445 | 0.2963 | 400 | 1.0823 | 0.512 | |
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| 1.1117 | 0.4444 | 600 | 1.0979 | 0.5053 | |
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| 1.0618 | 0.5926 | 800 | 1.0457 | 0.5273 | |
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| 1.0343 | 0.7407 | 1000 | 1.0219 | 0.537 | |
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| 1.1239 | 0.8889 | 1200 | 1.0353 | 0.5257 | |
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| 0.9012 | 1.0370 | 1400 | 1.0637 | 0.5383 | |
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| 0.86 | 1.1852 | 1600 | 1.0682 | 0.5333 | |
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| 0.898 | 1.3333 | 1800 | 1.0341 | 0.5483 | |
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| 0.929 | 1.4815 | 2000 | 1.0437 | 0.5363 | |
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| 0.9921 | 1.6296 | 2200 | 0.9968 | 0.5473 | |
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| 0.9776 | 1.7778 | 2400 | 1.0418 | 0.5553 | |
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| 0.9166 | 1.9259 | 2600 | 0.9874 | 0.5573 | |
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| 0.703 | 2.0741 | 2800 | 1.0564 | 0.556 | |
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| 0.8123 | 2.2222 | 3000 | 1.0582 | 0.561 | |
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| 0.6727 | 2.3704 | 3200 | 1.0942 | 0.5483 | |
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| 0.6843 | 2.5185 | 3400 | 1.1128 | 0.558 | |
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| 0.7528 | 2.6667 | 3600 | 1.0823 | 0.5547 | |
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| 0.7747 | 2.8148 | 3800 | 1.0744 | 0.5497 | |
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| 0.7471 | 2.9630 | 4000 | 1.0749 | 0.5527 | |
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| 0.5774 | 3.1111 | 4200 | 1.1422 | 0.552 | |
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| 0.6105 | 3.2593 | 4400 | 1.2226 | 0.543 | |
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| 0.573 | 3.4074 | 4600 | 1.2427 | 0.5417 | |
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| 0.6047 | 3.5556 | 4800 | 1.2403 | 0.537 | |
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| 0.5334 | 3.7037 | 5000 | 1.2470 | 0.5413 | |
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| 0.5688 | 3.8519 | 5200 | 1.2585 | 0.5507 | |
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| 0.4928 | 4.0 | 5400 | 1.2653 | 0.5437 | |
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| 0.4314 | 4.1481 | 5600 | 1.3419 | 0.541 | |
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| 0.4556 | 4.2963 | 5800 | 1.3677 | 0.5413 | |
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| 0.4815 | 4.4444 | 6000 | 1.3912 | 0.5407 | |
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| 0.4431 | 4.5926 | 6200 | 1.4004 | 0.5347 | |
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| 0.4312 | 4.7407 | 6400 | 1.4161 | 0.5397 | |
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| 0.459 | 4.8889 | 6600 | 1.4215 | 0.538 | |
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### Framework versions |
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- Transformers 4.40.1 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
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