Instructions to use jnferfer/treatment-disease-NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jnferfer/treatment-disease-NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jnferfer/treatment-disease-NER")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("jnferfer/treatment-disease-NER") model = AutoModelForTokenClassification.from_pretrained("jnferfer/treatment-disease-NER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
This is a model to detect treatment and disease mentions in texts from health domains. The dataset used for training consists of PubMed's abstracts and tweets with disease mentions, which are publicly available. It has been semi-automatically labeled with a set of regex rules made ad hoc to find treatment-disease links.
The label t is used for treatment (medication, procedures, etc.) and d for disease mentions.
The current F1 score (Seqeval) is 0.91
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