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--- |
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license: mit |
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base_model: microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: NHS-BiomedNLP-BiomedBERT-hypop |
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results: [] |
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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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# NHS-BiomedNLP-BiomedBERT-hypop |
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This model is a fine-tuned version of [microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4277 |
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- Accuracy: 0.8293 |
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- Precision: 0.8301 |
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- Recall: 0.8375 |
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- F1: 0.8285 |
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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: 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: 6 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.0264 | 1.0 | 397 | 0.4689 | 0.7950 | 0.7974 | 0.8017 | 0.7946 | |
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| 0.5258 | 2.0 | 794 | 0.5543 | 0.7779 | 0.7745 | 0.7743 | 0.7744 | |
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| 3.0689 | 3.0 | 1191 | 0.6701 | 0.8050 | 0.8068 | 0.7957 | 0.7990 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.2+cpu |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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