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--- |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: nominal-groups-recognition-bert-base-spanish-wwm-cased |
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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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# nominal-groups-recognition-bert-base-spanish-wwm-cased |
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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 the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3568 |
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- Ng Precision: 0.7280 |
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- Ng Recall: 0.7767 |
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- Ng F1: 0.7516 |
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- Ng Number: 3198 |
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- Overall Precision: 0.7280 |
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- Overall Recall: 0.7767 |
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- Overall F1: 0.7516 |
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- Overall Accuracy: 0.8992 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 13 |
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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: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Ng Precision | Ng Recall | Ng F1 | Ng Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:------------:|:---------:|:------:|:---------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 0.3955 | 1.0 | 228 | 0.2778 | 0.7129 | 0.7492 | 0.7306 | 3198 | 0.7129 | 0.7492 | 0.7306 | 0.8924 | |
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| 0.2186 | 2.0 | 456 | 0.2763 | 0.7318 | 0.7711 | 0.7509 | 3198 | 0.7318 | 0.7711 | 0.7509 | 0.8990 | |
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| 0.1586 | 3.0 | 684 | 0.2960 | 0.7274 | 0.7733 | 0.7496 | 3198 | 0.7274 | 0.7733 | 0.7496 | 0.8992 | |
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| 0.119 | 4.0 | 912 | 0.3330 | 0.7283 | 0.7727 | 0.7498 | 3198 | 0.7283 | 0.7727 | 0.7498 | 0.8982 | |
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| 0.0943 | 5.0 | 1140 | 0.3568 | 0.7280 | 0.7767 | 0.7516 | 3198 | 0.7280 | 0.7767 | 0.7516 | 0.8992 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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