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--- |
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license: apache-2.0 |
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base_model: google-bert/bert-base-multilingual-uncased |
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tags: |
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- generated_from_trainer |
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datasets: |
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- lener_br |
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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: bert-base-multilingual-uncased-finetuned-ner-lenerBR |
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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: lener_br |
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type: lener_br |
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config: lener_br |
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split: validation |
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args: lener_br |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.8678256070640177 |
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- name: Recall |
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type: recall |
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value: 0.8758006126427179 |
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- name: F1 |
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type: f1 |
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value: 0.8717948717948718 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9706569722150091 |
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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-multilingual-uncased-finetuned-ner-lenerBR |
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This model is a fine-tuned version of [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased) on the lener_br dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1568 |
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- Precision: 0.8678 |
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- Recall: 0.8758 |
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- F1: 0.8718 |
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- Accuracy: 0.9707 |
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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: 32 |
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- eval_batch_size: 32 |
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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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| No log | 1.0 | 245 | 0.1819 | 0.7691 | 0.8118 | 0.7899 | 0.9585 | |
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| No log | 2.0 | 490 | 0.1487 | 0.7383 | 0.8098 | 0.7724 | 0.9586 | |
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| 0.1325 | 3.0 | 735 | 0.1532 | 0.8662 | 0.8777 | 0.8719 | 0.9683 | |
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| 0.1325 | 4.0 | 980 | 0.1470 | 0.8770 | 0.8800 | 0.8785 | 0.9698 | |
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| 0.0233 | 5.0 | 1225 | 0.1155 | 0.8493 | 0.8839 | 0.8663 | 0.9750 | |
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| 0.0233 | 6.0 | 1470 | 0.1727 | 0.8874 | 0.8822 | 0.8848 | 0.9701 | |
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| 0.0126 | 7.0 | 1715 | 0.1698 | 0.8890 | 0.8853 | 0.8871 | 0.9710 | |
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| 0.0126 | 8.0 | 1960 | 0.1687 | 0.8651 | 0.8783 | 0.8716 | 0.9702 | |
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| 0.0076 | 9.0 | 2205 | 0.1593 | 0.8077 | 0.8797 | 0.8422 | 0.9668 | |
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| 0.0076 | 10.0 | 2450 | 0.1568 | 0.8678 | 0.8758 | 0.8718 | 0.9707 | |
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### Framework versions |
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- Transformers 4.41.1 |
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- Pytorch 2.1.2 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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