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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- wikiann |
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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-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: wikiann |
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type: wikiann |
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config: es |
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split: train |
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args: es |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.8655875585178132 |
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- name: Recall |
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type: recall |
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value: 0.889079054604727 |
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- name: F1 |
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type: f1 |
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value: 0.8771760543561292 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9432045651459472 |
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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 was trained from scratch on the wikiann dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2685 |
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- Precision: 0.8656 |
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- Recall: 0.8891 |
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- F1: 0.8772 |
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- Accuracy: 0.9432 |
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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: 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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| 0.245 | 1.0 | 2500 | 0.2470 | 0.8224 | 0.8577 | 0.8397 | 0.9303 | |
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| 0.1472 | 2.0 | 5000 | 0.2469 | 0.8651 | 0.8876 | 0.8762 | 0.9415 | |
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| 0.0965 | 3.0 | 7500 | 0.2685 | 0.8656 | 0.8891 | 0.8772 | 0.9432 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.8.0 |
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- Tokenizers 0.13.2 |
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