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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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- __main__ |
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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_model |
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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: __main__ |
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type: __main__ |
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config: local |
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split: test |
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args: local |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.5783305117853887 |
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- name: Recall |
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type: recall |
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value: 0.6134825252106645 |
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- name: F1 |
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type: f1 |
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value: 0.5953881217321357 |
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- name: Accuracy |
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type: accuracy |
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value: 0.7670984455958549 |
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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_model |
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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 __main__ dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.5136 |
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- Precision: 0.5783 |
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- Recall: 0.6135 |
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- F1: 0.5954 |
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- Accuracy: 0.7671 |
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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: 4 |
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- eval_batch_size: 4 |
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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 | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.7447 | 1.0 | 5905 | 0.7678 | 0.4966 | 0.5209 | 0.5085 | 0.7409 | |
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| 0.6153 | 2.0 | 11810 | 0.7378 | 0.5628 | 0.5600 | 0.5614 | 0.7624 | |
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| 0.4623 | 3.0 | 17715 | 0.7959 | 0.5449 | 0.5836 | 0.5636 | 0.7573 | |
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| 0.3629 | 4.0 | 23620 | 0.8921 | 0.5679 | 0.6017 | 0.5843 | 0.7631 | |
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| 0.246 | 5.0 | 29525 | 1.0286 | 0.5878 | 0.5955 | 0.5916 | 0.7685 | |
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| 0.1923 | 6.0 | 35430 | 1.2142 | 0.5926 | 0.5957 | 0.5941 | 0.7689 | |
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| 0.1477 | 7.0 | 41335 | 1.3019 | 0.5681 | 0.6091 | 0.5879 | 0.7591 | |
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| 0.1214 | 8.0 | 47240 | 1.4101 | 0.5834 | 0.6110 | 0.5969 | 0.7659 | |
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| 0.0793 | 9.0 | 53145 | 1.4745 | 0.5848 | 0.6136 | 0.5989 | 0.7688 | |
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| 0.0733 | 10.0 | 59050 | 1.5136 | 0.5783 | 0.6135 | 0.5954 | 0.7671 | |
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### Framework versions |
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- Transformers 4.36.0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.14.4 |
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- Tokenizers 0.15.0 |
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