Instructions to use jaindeepali010/clinical_ner_finetuned_g2model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jaindeepali010/clinical_ner_finetuned_g2model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jaindeepali010/clinical_ner_finetuned_g2model")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jaindeepali010/clinical_ner_finetuned_g2model") model = AutoModelForMaskedLM.from_pretrained("jaindeepali010/clinical_ner_finetuned_g2model") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
This model is a clinical NER model finetuned using bert-base-uncased model, trained on G2 dataset. Training and validation was done using 80% of the total data (random state=42), while 20% used for testing.
The model was trained for 20 epoch with an early stopping patience of 3 epochs.
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