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--- |
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license: mit |
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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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model-index: |
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- name: bert-base-uncased-finetuned-ner |
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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: conll2003 |
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type: conll2003 |
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config: conll2003 |
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split: validation |
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args: conll2003 |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.9502661343978709 |
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- name: Recall |
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type: recall |
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value: 0.9614607876135981 |
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- name: F1 |
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type: f1 |
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value: 0.9558306842897775 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9925431252677076 |
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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-base-uncased-finetuned-ner |
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This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the conll2003 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0328 |
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- Precision: 0.9503 |
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- Recall: 0.9615 |
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- F1: 0.9558 |
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- Accuracy: 0.9925 |
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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: 3 |
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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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| 0.1407 | 1.0 | 878 | 0.0407 | 0.9355 | 0.9468 | 0.9411 | 0.9902 | |
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| 0.0329 | 2.0 | 1756 | 0.0338 | 0.9471 | 0.9584 | 0.9527 | 0.9920 | |
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| 0.0167 | 3.0 | 2634 | 0.0328 | 0.9503 | 0.9615 | 0.9558 | 0.9925 | |
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### Framework versions |
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- Transformers 4.28.0 |
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- Pytorch 1.13.1 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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