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--- |
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language: |
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- en |
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license: apache-2.0 |
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library_name: transformers |
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tags: |
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- drugs |
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- classification |
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- bert |
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datasets: |
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- lloydmeta/drug_dataset_cleaned |
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widget: |
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- text: 'I have been taking ambien or zolphidem for almost 15 years. ' |
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example_title: Drugs for insomnia |
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--- |
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# Model Card for drug-BERT |
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This is a multiclass classification model, built on top of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased), trained on |
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the [Drug Review Dataset (Drugs.com)](https://archive.ics.uci.edu/dataset/462/drug+review+dataset+drugs+com), and |
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is useful for making a best attempt classification for the _condition_ someone has, based on their review of a drug. |
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## Model Details |
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### Model Description |
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This is a multiclass classification model, built on top of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased), trained on |
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the [Drug Review Dataset (Drugs.com)](https://archive.ics.uci.edu/dataset/462/drug+review+dataset+drugs+com), and |
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is useful for making a best attempt classification for the _condition_ someone has, based on their review of a drug. |
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It was created as a learning exercise covering: |
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* Colab |
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* Transformer architecture |
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* Finetuning/training on top of existing NLP models |
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* Huggingface libraries |
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**Developed by:** [lloydmeta](http://github.com/lloydmeta) of [beachape.com](https://beachape.com) |
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**License:** Apache 2.0 |
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**Finetuned from model:** [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) |
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## Uses |
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Classifying (identifying) the _condition_ someone has, based on their review of a drug. |
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### Out-of-Scope Use |
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Actual, clinical diagnosis. |
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## Bias, Risks, and Limitations |
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* Biases from the base `bert-base-uncased` model apply here |
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* Only drugs and conditions in the drugs review dataset are included |
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## How to Get Started with the Model |
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```python |
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from transformers import pipeline |
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condition_from_drug_review_classifier = pipeline("text-classification", model = "lloydmeta/drug-bert") |
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text_sentiment = "I have been taking ambien or zolphidem for almost 15 years." |
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condition_from_drug_review_classifier(text_sentiment) |
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``` |
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## Training Details |
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### Training Data |
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* Trained on [Drug Review Dataset (Drugs.com)](https://archive.ics.uci.edu/dataset/462/drug+review+dataset+drugs+com), cleaned up by removing html tags from reviews, with |
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samples that lacked `condition` removed. |
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* 60% of the data set was split for training data. |
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* Irrelevant columns like `patient_id`, `drugName`, `rating`, `date`, etc were removed |
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### Training Procedure |
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* `review` data was tokenised with a max of 512 |
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* Learning rate: 2e-5 |
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* Epochs: 3 |
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* Weight decay: 0.01 |
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* Per device train batch size: 4 |
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## Evaluation |
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15% of the data set was split for evaluation. |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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25% of the data set was split for testing. |
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## Model Card Authors |
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[lloydmeta](http://github.com/lloydmeta) |
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## Model Card Contact |
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[lloydmeta](http://github.com/lloydmeta) |
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