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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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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: nees-bert-base-portuguese-cased-finetuned-ner |
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results: [] |
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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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# nees-bert-base-portuguese-cased-finetuned-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 an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0255 |
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- Precision: 0.6545 |
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- Recall: 0.7802 |
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- F1: 0.7119 |
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- Accuracy: 0.9952 |
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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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| 0.0344 | 1.0 | 519 | 0.0189 | 0.0 | 0.0 | 0.0 | 0.9949 | |
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| 0.0145 | 2.0 | 1038 | 0.0112 | 0.5050 | 0.4737 | 0.4888 | 0.9952 | |
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| 0.0121 | 3.0 | 1557 | 0.0132 | 0.3684 | 0.1300 | 0.1922 | 0.9953 | |
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| 0.0107 | 4.0 | 2076 | 0.0239 | 0.6366 | 0.7647 | 0.6948 | 0.9955 | |
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| 0.0056 | 5.0 | 2595 | 0.0151 | 0.6845 | 0.7121 | 0.6980 | 0.9950 | |
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| 0.0053 | 6.0 | 3114 | 0.0278 | 0.6432 | 0.7368 | 0.6869 | 0.9943 | |
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| 0.0047 | 7.0 | 3633 | 0.0199 | 0.5682 | 0.7740 | 0.6553 | 0.9953 | |
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| 0.0043 | 8.0 | 4152 | 0.0231 | 0.6429 | 0.7802 | 0.7049 | 0.9951 | |
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| 0.0022 | 9.0 | 4671 | 0.0255 | 0.6487 | 0.7833 | 0.7097 | 0.9955 | |
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| 0.0025 | 10.0 | 5190 | 0.0255 | 0.6545 | 0.7802 | 0.7119 | 0.9952 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.17.1 |
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- Tokenizers 0.15.2 |
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