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--- |
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license: mit |
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base_model: neuralmind/bert-large-portuguese-cased |
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tags: |
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- generated_from_trainer |
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datasets: |
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- harem |
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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_harem_bert-large-portuguese-cased |
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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: harem |
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type: harem |
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config: default |
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split: test |
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args: default |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.7077353867693384 |
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- name: Recall |
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type: recall |
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value: 0.7553231228987672 |
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- name: F1 |
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type: f1 |
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value: 0.7307553306830503 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9551379448220711 |
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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_harem_bert-large-portuguese-cased |
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This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on the harem dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2487 |
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- Precision: 0.7077 |
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- Recall: 0.7553 |
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- F1: 0.7308 |
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- Accuracy: 0.9551 |
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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: 3e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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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: 300 |
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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 | 16 | 0.6334 | 0.0163 | 0.0078 | 0.0106 | 0.8468 | |
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| No log | 2.0 | 32 | 0.4537 | 0.2614 | 0.3112 | 0.2841 | 0.8826 | |
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| No log | 3.0 | 48 | 0.3117 | 0.5262 | 0.5671 | 0.5458 | 0.9231 | |
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| No log | 4.0 | 64 | 0.2421 | 0.5852 | 0.6631 | 0.6217 | 0.9385 | |
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| No log | 5.0 | 80 | 0.2099 | 0.5950 | 0.6855 | 0.6370 | 0.9479 | |
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| No log | 6.0 | 96 | 0.2153 | 0.6810 | 0.7464 | 0.7122 | 0.9551 | |
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| No log | 7.0 | 112 | 0.2270 | 0.6894 | 0.7198 | 0.7043 | 0.9546 | |
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| No log | 8.0 | 128 | 0.2213 | 0.6918 | 0.7437 | 0.7168 | 0.9554 | |
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| No log | 9.0 | 144 | 0.2299 | 0.7021 | 0.7564 | 0.7283 | 0.9545 | |
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| No log | 10.0 | 160 | 0.2256 | 0.7002 | 0.7591 | 0.7284 | 0.9562 | |
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| No log | 11.0 | 176 | 0.2169 | 0.7100 | 0.7736 | 0.7404 | 0.9568 | |
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| No log | 12.0 | 192 | 0.2266 | 0.6981 | 0.7740 | 0.7341 | 0.9571 | |
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| No log | 13.0 | 208 | 0.2322 | 0.7093 | 0.7620 | 0.7347 | 0.9570 | |
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| No log | 14.0 | 224 | 0.2487 | 0.7077 | 0.7553 | 0.7308 | 0.9551 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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