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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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model-index: |
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- name: PubMedBERT-MNLI-MedNLI |
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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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# PubMedBERT-MNLI-MedNLI |
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This model is a fine-tuned version of [PubMedBERT](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the [MNLI](https://huggingface.co/datasets/multi_nli) dataset first and then on the [MedNLI](https://physionet.org/content/mednli/1.0.0/) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9501 |
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- Accuracy: 0.8667 |
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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 NLI tasks related to biomedical data and even be adapted to fact-checking tasks. It can be used from the Huggingface pipeline method as follows: |
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```python |
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from transformers import TextClassificationPipeline, AutoModel, AutoTokenizer, AutoModelForSequenceClassification |
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model = AutoModelForSequenceClassification.from_pretrained("pritamdeka/PubMedBERT-MNLI-MedNLI", num_labels=3, id2label = {1: 'entailment', 0: 'contradiction',2:'neutral'}) |
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tokenizer = AutoTokenizer.from_pretrained("pritamdeka/PubMedBERT-MNLI-MedNLI") |
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pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True, device=0, batch_size=128) |
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pipe(['ALDH1 expression is associated with better breast cancer outcomes', |
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'In a series of 577 breast carcinomas, expression of ALDH1 detected by immunostaining correlated with poor prognosis.']) |
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``` |
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The output for the above will be: |
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```python |
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[[{'label': 'contradiction', 'score': 0.10193759202957153}, |
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{'label': 'entailment', 'score': 0.2933262586593628}, |
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{'label': 'neutral', 'score': 0.6047361493110657}], |
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[{'label': 'contradiction', 'score': 0.21726925671100616}, |
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{'label': 'entailment', 'score': 0.24485822021961212}, |
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{'label': 'neutral', 'score': 0.5378724932670593}]] |
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``` |
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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: 32 |
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- eval_batch_size: 32 |
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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: 20.0 |
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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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| 0.5673 | 1.42 | 500 | 0.4358 | 0.8437 | |
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| 0.2898 | 2.85 | 1000 | 0.4845 | 0.8523 | |
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| 0.1669 | 4.27 | 1500 | 0.6233 | 0.8573 | |
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| 0.1087 | 5.7 | 2000 | 0.7263 | 0.8573 | |
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| 0.0728 | 7.12 | 2500 | 0.8841 | 0.8638 | |
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| 0.0512 | 8.55 | 3000 | 0.9501 | 0.8667 | |
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| 0.0372 | 9.97 | 3500 | 1.0440 | 0.8566 | |
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| 0.0262 | 11.4 | 4000 | 1.0770 | 0.8609 | |
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| 0.0243 | 12.82 | 4500 | 1.0931 | 0.8616 | |
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| 0.023 | 14.25 | 5000 | 1.1088 | 0.8631 | |
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| 0.0163 | 15.67 | 5500 | 1.1264 | 0.8581 | |
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| 0.0111 | 17.09 | 6000 | 1.1541 | 0.8616 | |
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| 0.0098 | 18.52 | 6500 | 1.1542 | 0.8631 | |
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| 0.0074 | 19.94 | 7000 | 1.1653 | 0.8638 | |
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### Framework versions |
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- Transformers 4.22.0.dev0 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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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 (Paper accepted at BioNLP2023@ACL2023, citation will be updated) |
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``` |
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Multiple Evidence Combination for Fact-Checking of Health-Related Information |
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} |
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``` |
