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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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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: bert-base-spanish-wwm-uncased-finetuned-political_elsalvadore |
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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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# bert-base-spanish-wwm-uncased-finetuned-political_elsalvadore |
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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: 1.3034 |
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- Accuracy: 0.78 |
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- F1: 0.7796 |
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- Precision: 0.7794 |
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- Recall: 0.78 |
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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: 32 |
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- eval_batch_size: 32 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| No log | 1.0 | 66 | 0.6370 | 0.7156 | 0.7164 | 0.7237 | 0.7156 | |
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| 0.62 | 2.0 | 132 | 0.6332 | 0.7356 | 0.7342 | 0.7333 | 0.7356 | |
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| 0.62 | 3.0 | 198 | 0.7050 | 0.7356 | 0.7383 | 0.7488 | 0.7356 | |
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| 0.2306 | 4.0 | 264 | 0.8853 | 0.7356 | 0.7339 | 0.7330 | 0.7356 | |
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| 0.2306 | 5.0 | 330 | 1.0291 | 0.7244 | 0.7260 | 0.7300 | 0.7244 | |
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| 0.0704 | 6.0 | 396 | 1.1888 | 0.7333 | 0.7360 | 0.7416 | 0.7333 | |
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| 0.0704 | 7.0 | 462 | 1.3355 | 0.72 | 0.7245 | 0.7376 | 0.72 | |
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| 0.0225 | 8.0 | 528 | 1.3803 | 0.7444 | 0.7465 | 0.7500 | 0.7444 | |
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| 0.0225 | 9.0 | 594 | 1.4822 | 0.7267 | 0.7303 | 0.7415 | 0.7267 | |
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| 0.0108 | 10.0 | 660 | 1.4441 | 0.7333 | 0.7353 | 0.7386 | 0.7333 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.1.2 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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