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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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- nerd |
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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_nerd_fine |
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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: nerd |
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type: nerd |
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args: nerd |
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metric: |
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name: Accuracy |
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type: accuracy |
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value: 0.9050232835369201 |
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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_nerd_fine |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the nerd dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3373 |
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- Precision: 0.6326 |
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- Recall: 0.6734 |
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- F1: 0.6524 |
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- Accuracy: 0.9050 |
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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: 16 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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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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| 0.6219 | 1.0 | 8235 | 0.3347 | 0.6066 | 0.6581 | 0.6313 | 0.9015 | |
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| 0.3071 | 2.0 | 16470 | 0.3165 | 0.6349 | 0.6637 | 0.6490 | 0.9060 | |
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| 0.2384 | 3.0 | 24705 | 0.3311 | 0.6373 | 0.6769 | 0.6565 | 0.9068 | |
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| 0.1834 | 4.0 | 32940 | 0.3414 | 0.6349 | 0.6780 | 0.6557 | 0.9069 | |
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| 0.1392 | 5.0 | 41175 | 0.3793 | 0.6334 | 0.6775 | 0.6547 | 0.9068 | |
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### Framework versions |
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- Transformers 4.9.1 |
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- Pytorch 1.9.0+cu102 |
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- Datasets 1.11.0 |
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- Tokenizers 0.10.2 |
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