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--- |
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license: apache-2.0 |
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base_model: distilbert/distilbert-base-multilingual-cased |
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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: distilbert-base-multilingual-cased-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: 0.7959714100064977 |
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- name: Recall |
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type: recall |
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value: 0.7847533632286996 |
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- name: F1 |
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type: f1 |
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value: 0.7903225806451614 |
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- name: Accuracy |
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type: accuracy |
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value: 0.959060823521215 |
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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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# distilbert-base-multilingual-cased-finetuned-ner-lenerBr |
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This model is a fine-tuned version of [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) on the lener_br dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1904 |
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- Precision: 0.7960 |
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- Recall: 0.7848 |
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- F1: 0.7903 |
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- Accuracy: 0.9591 |
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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: 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 | 490 | 0.2124 | 0.6794 | 0.6842 | 0.6818 | 0.9363 | |
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| 0.2601 | 2.0 | 980 | 0.1744 | 0.701 | 0.7485 | 0.7239 | 0.9486 | |
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| 0.0688 | 3.0 | 1470 | 0.1653 | 0.7344 | 0.7598 | 0.7469 | 0.9522 | |
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| 0.0375 | 4.0 | 1960 | 0.1868 | 0.7764 | 0.7429 | 0.7593 | 0.9546 | |
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| 0.0229 | 5.0 | 2450 | 0.1844 | 0.7748 | 0.7854 | 0.7801 | 0.9560 | |
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| 0.0162 | 6.0 | 2940 | 0.2072 | 0.6896 | 0.7929 | 0.7377 | 0.9462 | |
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| 0.0123 | 7.0 | 3430 | 0.1941 | 0.7612 | 0.7704 | 0.7658 | 0.9548 | |
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| 0.0078 | 8.0 | 3920 | 0.1900 | 0.7701 | 0.7909 | 0.7804 | 0.9581 | |
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| 0.0068 | 9.0 | 4410 | 0.1884 | 0.8000 | 0.7822 | 0.7910 | 0.9593 | |
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| 0.0045 | 10.0 | 4900 | 0.1904 | 0.7960 | 0.7848 | 0.7903 | 0.9591 | |
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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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