Text Classification
Transformers
TensorFlow
English
distilbert
ditilbert
text classification
clinical notes
wellnation
Instructions to use oyesaurav/dwellbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oyesaurav/dwellbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="oyesaurav/dwellbert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("oyesaurav/dwellbert") model = AutoModelForSequenceClassification.from_pretrained("oyesaurav/dwellbert") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("oyesaurav/dwellbert")
model = AutoModelForSequenceClassification.from_pretrained("oyesaurav/dwellbert")Quick Links
labels map =
{
"0": "Gastroenterology",
"1": "Neurology",
"2": "Orthopedic",
"3": "Radiology",
"4": "Urology"
}
The fine tuned model has been trained on around 2300 medical transcriptions, to classify medical specialty. More classes will be added as data would be available.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="oyesaurav/dwellbert")