Text Classification
Transformers
PyTorch
Safetensors
English
bert
bert-base-uncased
disease
medical
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("shanover/disease_classifier_base")
model = AutoModelForSequenceClassification.from_pretrained("shanover/disease_classifier_base")Quick Links
The objective is to develop a symptom-to-disease classification model for a natural language chatbot. This model takes input text such as "I am feeling vomiting, breathlessness, and sweating" and accurately identifies the associated disease (2 - 'Heart attack').
In essence, the chatbot's purpose is to analyze users' symptoms and provide relevant disease predictions in real-time conversation.
Labels:
0 - Fungal infection
1 - Diabetes
2 - Heart attack
Will add more diseases in coming days
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="shanover/disease_classifier_base")