| --- |
| license: mit |
| base_model: neuralmind/bert-base-portuguese-cased |
| tags: |
| - generated_from_trainer |
| datasets: |
| - __main__ |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: ner_model |
| results: |
| - task: |
| name: Token Classification |
| type: token-classification |
| dataset: |
| name: __main__ |
| type: __main__ |
| config: local |
| split: test |
| args: local |
| metrics: |
| - name: Precision |
| type: precision |
| value: 0.5783305117853887 |
| - name: Recall |
| type: recall |
| value: 0.6134825252106645 |
| - name: F1 |
| type: f1 |
| value: 0.5953881217321357 |
| - name: Accuracy |
| type: accuracy |
| value: 0.7670984455958549 |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # ner_model |
| |
| 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. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.5136 |
| - Precision: 0.5783 |
| - Recall: 0.6135 |
| - F1: 0.5954 |
| - Accuracy: 0.7671 |
| |
| ## Model description |
| |
| More information needed |
| |
| ## Intended uses & limitations |
| |
| More information needed |
| |
| ## Training and evaluation data |
| |
| More information needed |
| |
| ## Training procedure |
| |
| ### Training hyperparameters |
| |
| The following hyperparameters were used during training: |
| - learning_rate: 2e-05 |
| - train_batch_size: 4 |
| - eval_batch_size: 4 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 10 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | 0.7447 | 1.0 | 5905 | 0.7678 | 0.4966 | 0.5209 | 0.5085 | 0.7409 | |
| | 0.6153 | 2.0 | 11810 | 0.7378 | 0.5628 | 0.5600 | 0.5614 | 0.7624 | |
| | 0.4623 | 3.0 | 17715 | 0.7959 | 0.5449 | 0.5836 | 0.5636 | 0.7573 | |
| | 0.3629 | 4.0 | 23620 | 0.8921 | 0.5679 | 0.6017 | 0.5843 | 0.7631 | |
| | 0.246 | 5.0 | 29525 | 1.0286 | 0.5878 | 0.5955 | 0.5916 | 0.7685 | |
| | 0.1923 | 6.0 | 35430 | 1.2142 | 0.5926 | 0.5957 | 0.5941 | 0.7689 | |
| | 0.1477 | 7.0 | 41335 | 1.3019 | 0.5681 | 0.6091 | 0.5879 | 0.7591 | |
| | 0.1214 | 8.0 | 47240 | 1.4101 | 0.5834 | 0.6110 | 0.5969 | 0.7659 | |
| | 0.0793 | 9.0 | 53145 | 1.4745 | 0.5848 | 0.6136 | 0.5989 | 0.7688 | |
| | 0.0733 | 10.0 | 59050 | 1.5136 | 0.5783 | 0.6135 | 0.5954 | 0.7671 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.36.0 |
| - Pytorch 2.0.1+cu117 |
| - Datasets 2.14.4 |
| - Tokenizers 0.15.0 |
| |
