Instructions to use system-technologies/BiomedNLP-BiomedBERT-abstract-RCT-classifier-public with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use system-technologies/BiomedNLP-BiomedBERT-abstract-RCT-classifier-public with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="system-technologies/BiomedNLP-BiomedBERT-abstract-RCT-classifier-public")# Load model directly from transformers import AutoTokenizer, RCTClassifier tokenizer = AutoTokenizer.from_pretrained("system-technologies/BiomedNLP-BiomedBERT-abstract-RCT-classifier-public") model = RCTClassifier.from_pretrained("system-technologies/BiomedNLP-BiomedBERT-abstract-RCT-classifier-public") - Notebooks
- Google Colab
- Kaggle
System RCT Classifier
Description
This is a BERT-based model intended for identifying Randomized-Controlled Trials based on title and abstract. It is fine-tuned based on https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract, with an additional binary classifier layer.
Model Performance
F1 Score
In out-of-sample testing, the model exhibits an F1 score of 0.93 on a sample of 500 abstracts from PubMed.
Confusion Matrix
Use
This model is made available under the Open RAIL-M license.
For more information on System, visit https://about.system.com
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