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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-512
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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-512
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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: 0.3839
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- Accuracy: 0.8269
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- Precision: 0.8228
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- Recall: 0.8237
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- F1: 0.8232
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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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| 0.124 | 1.0 | 397 | 0.4029 | 0.8177 | 0.8146 | 0.8129 | 0.8137 |
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| 0.0594 | 2.0 | 794 | 0.4561 | 0.8246 | 0.8245 | 0.8161 | 0.8192 |
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| 0.1105 | 3.0 | 1191 | 0.5390 | 0.8120 | 0.8119 | 0.8028 | 0.8059 |
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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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