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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- conll2003 |
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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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base_model: bert-base-cased |
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model-index: |
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- name: bert-finetuned-ner |
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results: |
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- task: |
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type: token-classification |
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name: Token Classification |
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dataset: |
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name: conll2003 |
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type: conll2003 |
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config: conll2003 |
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split: train |
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args: conll2003 |
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metrics: |
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- type: precision |
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value: 0.9427525378598769 |
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name: Precision |
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- type: recall |
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value: 0.9533826994278021 |
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name: Recall |
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- type: f1 |
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value: 0.9480378211028366 |
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name: F1 |
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- type: accuracy |
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value: 0.9866957084829575 |
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name: Accuracy |
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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-finetuned-ner |
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1128 |
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- Precision: 0.9428 |
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- Recall: 0.9534 |
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- F1: 0.9480 |
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- Accuracy: 0.9867 |
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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: 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: 15 |
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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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| 0.0937 | 1.0 | 1756 | 0.0660 | 0.9179 | 0.9332 | 0.9255 | 0.9825 | |
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| 0.0378 | 2.0 | 3512 | 0.0766 | 0.9246 | 0.9451 | 0.9348 | 0.9843 | |
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| 0.0245 | 3.0 | 5268 | 0.0667 | 0.9241 | 0.9409 | 0.9325 | 0.9843 | |
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| 0.017 | 4.0 | 7024 | 0.0712 | 0.9343 | 0.9505 | 0.9424 | 0.9863 | |
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| 0.0143 | 5.0 | 8780 | 0.0898 | 0.9366 | 0.9492 | 0.9428 | 0.9855 | |
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| 0.0049 | 6.0 | 10536 | 0.0964 | 0.9294 | 0.9482 | 0.9387 | 0.9853 | |
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| 0.0039 | 7.0 | 12292 | 0.1001 | 0.9353 | 0.9512 | 0.9432 | 0.9860 | |
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| 0.0036 | 8.0 | 14048 | 0.1002 | 0.9388 | 0.9522 | 0.9454 | 0.9862 | |
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| 0.0018 | 9.0 | 15804 | 0.1049 | 0.9363 | 0.9495 | 0.9428 | 0.9861 | |
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| 0.0019 | 10.0 | 17560 | 0.1191 | 0.9375 | 0.9497 | 0.9436 | 0.9849 | |
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| 0.0008 | 11.0 | 19316 | 0.1083 | 0.9396 | 0.9530 | 0.9463 | 0.9864 | |
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| 0.0003 | 12.0 | 21072 | 0.1064 | 0.9419 | 0.9530 | 0.9475 | 0.9864 | |
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| 0.0004 | 13.0 | 22828 | 0.1091 | 0.9448 | 0.9527 | 0.9487 | 0.9865 | |
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| 0.0006 | 14.0 | 24584 | 0.1132 | 0.9464 | 0.9542 | 0.9503 | 0.9867 | |
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| 0.0004 | 15.0 | 26340 | 0.1128 | 0.9428 | 0.9534 | 0.9480 | 0.9867 | |
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### Framework versions |
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- Transformers 4.23.1 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.6.1 |
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- Tokenizers 0.13.1 |
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