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--- |
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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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widget: |
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- text: >- |
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SAMPLE 32,441 archived appendix samples fixed in formalin and embedded in |
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paraffin and tested for the presence of abnormal prion protein (PrP). |
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base_model: dmis-lab/biobert-base-cased-v1.1 |
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model-index: |
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- name: BioBert-PubMed200kRCT |
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results: [] |
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license: cc-by-nc-3.0 |
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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-PubMed200kRCT |
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This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.1](https://huggingface.co/dmis-lab/biobert-base-cased-v1.1) on the [PubMed200kRCT](https://github.com/Franck-Dernoncourt/pubmed-rct/tree/master/PubMed_200k_RCT) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2832 |
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- Accuracy: 0.8934 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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The model can be used for text classification tasks of Randomized Controlled Trials that does not have any structure. The text can be classified as one of the following: |
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* BACKGROUND |
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* CONCLUSIONS |
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* METHODS |
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* OBJECTIVE |
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* RESULTS |
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The model can be directly used like this: |
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```python |
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from transformers import TextClassificationPipeline |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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model = AutoModelForSequenceClassification.from_pretrained("pritamdeka/BioBert-PubMed200kRCT") |
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tokenizer = AutoTokenizer.from_pretrained("pritamdeka/BioBert-PubMed200kRCT") |
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pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True) |
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pipe("Treatment of 12 healthy female subjects with CDCA for 2 days resulted in increased BAT activity.") |
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``` |
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Results will be shown as follows: |
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```python |
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[[{'label': 'BACKGROUND', 'score': 0.0027583304326981306}, |
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{'label': 'CONCLUSIONS', 'score': 0.044541116803884506}, |
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{'label': 'METHODS', 'score': 0.19493348896503448}, |
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{'label': 'OBJECTIVE', 'score': 0.003996663726866245}, |
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{'label': 'RESULTS', 'score': 0.7537703514099121}]] |
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``` |
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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: 5e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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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: 2.0 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
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| 0.3587 | 0.14 | 5000 | 0.3137 | 0.8834 | |
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| 0.3318 | 0.29 | 10000 | 0.3100 | 0.8831 | |
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| 0.3286 | 0.43 | 15000 | 0.3033 | 0.8864 | |
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| 0.3236 | 0.58 | 20000 | 0.3037 | 0.8862 | |
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| 0.3182 | 0.72 | 25000 | 0.2939 | 0.8876 | |
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| 0.3129 | 0.87 | 30000 | 0.2910 | 0.8885 | |
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| 0.3078 | 1.01 | 35000 | 0.2914 | 0.8887 | |
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| 0.2791 | 1.16 | 40000 | 0.2975 | 0.8874 | |
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| 0.2723 | 1.3 | 45000 | 0.2913 | 0.8906 | |
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| 0.2724 | 1.45 | 50000 | 0.2879 | 0.8904 | |
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| 0.27 | 1.59 | 55000 | 0.2874 | 0.8911 | |
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| 0.2681 | 1.74 | 60000 | 0.2848 | 0.8928 | |
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| 0.2672 | 1.88 | 65000 | 0.2832 | 0.8934 | |
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### Framework versions |
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- Transformers 4.18.0.dev0 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 1.18.4 |
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- Tokenizers 0.11.6 |
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## Citing & Authors |
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<!--- Describe where people can find more information --> |
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If you use the model kindly cite the following work |
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``` |
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@inproceedings{deka2022evidence, |
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title={Evidence Extraction to Validate Medical Claims in Fake News Detection}, |
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author={Deka, Pritam and Jurek-Loughrey, Anna and others}, |
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booktitle={International Conference on Health Information Science}, |
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pages={3--15}, |
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year={2022}, |
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organization={Springer} |
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} |
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``` |
