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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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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: nees-bert-base-portuguese-cased-finetuned-ner |
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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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# nees-bert-base-portuguese-cased-finetuned-ner |
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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 an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0360 |
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- Precision: 0.5404 |
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- Recall: 0.7230 |
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- F1: 0.6185 |
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- Accuracy: 0.9949 |
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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: 16 |
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- eval_batch_size: 16 |
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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: 15 |
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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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| 0.0352 | 1.0 | 599 | 0.0212 | 0.0154 | 0.0034 | 0.0055 | 0.9956 | |
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| 0.0125 | 2.0 | 1198 | 0.0184 | 0.4039 | 0.2770 | 0.3287 | 0.9958 | |
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| 0.0099 | 3.0 | 1797 | 0.0141 | 0.4528 | 0.4865 | 0.4691 | 0.9958 | |
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| 0.0082 | 4.0 | 2396 | 0.0279 | 0.4558 | 0.5743 | 0.5082 | 0.9958 | |
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| 0.0074 | 5.0 | 2995 | 0.0138 | 0.5153 | 0.6824 | 0.5872 | 0.9960 | |
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| 0.0034 | 6.0 | 3594 | 0.0193 | 0.4934 | 0.6284 | 0.5527 | 0.9953 | |
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| 0.0046 | 7.0 | 4193 | 0.0190 | 0.5305 | 0.7635 | 0.6260 | 0.9960 | |
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| 0.0024 | 8.0 | 4792 | 0.0286 | 0.5670 | 0.6858 | 0.6208 | 0.9955 | |
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| 0.0024 | 9.0 | 5391 | 0.0270 | 0.5889 | 0.5709 | 0.5798 | 0.9953 | |
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| 0.0034 | 10.0 | 5990 | 0.0339 | 0.5623 | 0.6858 | 0.6180 | 0.9953 | |
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| 0.0015 | 11.0 | 6589 | 0.0373 | 0.6122 | 0.7095 | 0.6573 | 0.9947 | |
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| 0.001 | 12.0 | 7188 | 0.0361 | 0.5519 | 0.6284 | 0.5877 | 0.9950 | |
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| 0.0005 | 13.0 | 7787 | 0.0353 | 0.5658 | 0.6824 | 0.6187 | 0.9950 | |
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| 0.0007 | 14.0 | 8386 | 0.0355 | 0.5556 | 0.7264 | 0.6296 | 0.9948 | |
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| 0.0003 | 15.0 | 8985 | 0.0360 | 0.5404 | 0.7230 | 0.6185 | 0.9949 | |
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### Framework versions |
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- Transformers 4.38.1 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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