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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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- ncbi_disease |
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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: BioBERT-mnli-snli-scinli-scitail-mednli-stsb-ncbi |
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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: ncbi_disease |
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type: ncbi_disease |
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config: ncbi_disease |
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split: test |
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args: ncbi_disease |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.8604187437686939 |
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- name: Recall |
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type: recall |
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value: 0.8989583333333333 |
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- name: F1 |
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type: f1 |
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value: 0.879266428935303 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9870188186308527 |
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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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# BioBERT-mnli-snli-scinli-scitail-mednli-stsb-ncbi |
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This model is a fine-tuned version of [pritamdeka/BioBERT-mnli-snli-scinli-scitail-mednli-stsb](https://huggingface.co/pritamdeka/BioBERT-mnli-snli-scinli-scitail-mednli-stsb) on the ncbi_disease dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0814 |
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- Precision: 0.8604 |
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- Recall: 0.8990 |
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- F1: 0.8793 |
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- Accuracy: 0.9870 |
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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: 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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| No log | 1.0 | 340 | 0.0481 | 0.8308 | 0.8438 | 0.8372 | 0.9840 | |
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| 0.0715 | 2.0 | 680 | 0.0497 | 0.8337 | 0.8771 | 0.8548 | 0.9857 | |
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| 0.0152 | 3.0 | 1020 | 0.0588 | 0.8596 | 0.8802 | 0.8698 | 0.9858 | |
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| 0.0152 | 4.0 | 1360 | 0.0589 | 0.8589 | 0.8875 | 0.8730 | 0.9873 | |
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| 0.0059 | 5.0 | 1700 | 0.0693 | 0.8412 | 0.8938 | 0.8667 | 0.9852 | |
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| 0.003 | 6.0 | 2040 | 0.0770 | 0.8701 | 0.9 | 0.8848 | 0.9863 | |
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| 0.003 | 7.0 | 2380 | 0.0787 | 0.861 | 0.8969 | 0.8786 | 0.9863 | |
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| 0.0014 | 8.0 | 2720 | 0.0760 | 0.8655 | 0.8979 | 0.8814 | 0.9872 | |
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| 0.0007 | 9.0 | 3060 | 0.0817 | 0.8589 | 0.8938 | 0.8760 | 0.9865 | |
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| 0.0007 | 10.0 | 3400 | 0.0814 | 0.8604 | 0.8990 | 0.8793 | 0.9870 | |
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### Framework versions |
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- Transformers 4.29.1 |
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- Pytorch 2.0.1+cpu |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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