Instructions to use samrawal/bert-base-uncased_clinical-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use samrawal/bert-base-uncased_clinical-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="samrawal/bert-base-uncased_clinical-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("samrawal/bert-base-uncased_clinical-ner") model = AutoModelForTokenClassification.from_pretrained("samrawal/bert-base-uncased_clinical-ner") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
A Named Entity Recognition model for clinical entities (problem, treatment, test)
The model has been trained on the i2b2 (now n2c2) dataset for the 2010 - Relations task. Please visit the n2c2 site to request access to the dataset.
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