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--- |
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license: mit |
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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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base_model: neuralmind/bert-base-portuguese-cased |
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model-index: |
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- name: bert-base-portuguese-cased_harem-selective-lowC-sm-first-ner |
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results: |
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- task: |
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type: token-classification |
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name: Token Classification |
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dataset: |
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name: harem |
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type: harem |
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args: selective |
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metrics: |
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- type: precision |
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value: 0.8 |
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name: Precision |
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- type: recall |
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value: 0.8764044943820225 |
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name: Recall |
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- type: f1 |
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value: 0.8364611260053619 |
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name: F1 |
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- type: accuracy |
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value: 0.9764089121887287 |
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name: Accuracy |
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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-portuguese-cased_harem-selective-lowC-sm-first-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 the harem dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1160 |
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- Precision: 0.8 |
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- Recall: 0.8764 |
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- F1: 0.8365 |
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- Accuracy: 0.9764 |
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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: 2 |
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- eval_batch_size: 2 |
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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_ratio: 0.1 |
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- num_epochs: 3 |
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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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| 0.055 | 1.0 | 2517 | 0.0934 | 0.81 | 0.9101 | 0.8571 | 0.9699 | |
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| 0.0236 | 2.0 | 5034 | 0.0883 | 0.8307 | 0.8820 | 0.8556 | 0.9751 | |
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| 0.0129 | 3.0 | 7551 | 0.1160 | 0.8 | 0.8764 | 0.8365 | 0.9764 | |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.10.2+cu102 |
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- Datasets 2.2.2 |
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- Tokenizers 0.12.1 |
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