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--- |
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license: mit |
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base_model: neuralmind/bert-base-portuguese-cased |
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tags: |
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- generated_from_trainer |
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datasets: |
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- harem |
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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: NER_harem_bert-base-portuguese-cased |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: harem |
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type: harem |
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config: default |
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split: test |
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args: default |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.6852879944482998 |
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- name: Recall |
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type: recall |
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value: 0.7377661561449383 |
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- name: F1 |
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type: f1 |
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value: 0.7105594531390537 |
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- name: Accuracy |
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type: accuracy |
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value: 0.952219112355058 |
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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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# NER_harem_bert-base-portuguese-cased |
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This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the harem dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2351 |
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- Precision: 0.6853 |
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- Recall: 0.7378 |
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- F1: 0.7106 |
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- Accuracy: 0.9522 |
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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: 3e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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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: 300 |
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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 | 16 | 0.7692 | 0.0 | 0.0 | 0.0 | 0.8358 | |
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| No log | 2.0 | 32 | 0.4831 | 0.3140 | 0.2731 | 0.2921 | 0.8790 | |
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| No log | 3.0 | 48 | 0.3405 | 0.4692 | 0.4897 | 0.4793 | 0.9119 | |
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| No log | 4.0 | 64 | 0.2747 | 0.5481 | 0.6156 | 0.5799 | 0.9340 | |
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| No log | 5.0 | 80 | 0.2282 | 0.6077 | 0.6758 | 0.6399 | 0.9443 | |
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| No log | 6.0 | 96 | 0.2145 | 0.6267 | 0.6892 | 0.6565 | 0.9479 | |
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| No log | 7.0 | 112 | 0.2223 | 0.6395 | 0.6926 | 0.6650 | 0.9493 | |
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| No log | 8.0 | 128 | 0.2100 | 0.6822 | 0.7378 | 0.7089 | 0.9530 | |
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| No log | 9.0 | 144 | 0.2077 | 0.6810 | 0.7497 | 0.7137 | 0.9537 | |
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| No log | 10.0 | 160 | 0.2173 | 0.6846 | 0.7460 | 0.7140 | 0.9523 | |
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| No log | 11.0 | 176 | 0.2226 | 0.7001 | 0.7594 | 0.7285 | 0.9542 | |
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| No log | 12.0 | 192 | 0.2204 | 0.7015 | 0.7568 | 0.7281 | 0.9538 | |
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| No log | 13.0 | 208 | 0.2278 | 0.6746 | 0.7411 | 0.7063 | 0.9533 | |
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| No log | 14.0 | 224 | 0.2351 | 0.6853 | 0.7378 | 0.7106 | 0.9522 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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