Text Classification
Transformers
PyTorch
Safetensors
English
bert
bert-base-uncased
disease
medical
text-embeddings-inference
Instructions to use shanover/disease_classifier_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shanover/disease_classifier_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="shanover/disease_classifier_base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("shanover/disease_classifier_base") model = AutoModelForSequenceClassification.from_pretrained("shanover/disease_classifier_base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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A model that classifies a text like "I am
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Basically a symptoms to disease classification. It's a baseline for a chatbot I am working on that will classify user's symptoms into diseases in a natural language environment.
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- medical
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A model that classifies a text like "I am feeling vomiting, breathlessness, and sweating" into it's disease (2 - 'Heart attack').
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Basically a symptoms to disease classification. It's a baseline for a chatbot I am working on that will classify user's symptoms into diseases in a natural language environment.
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