clinicalbert-nivra-finetuned

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
model_name = "your-username/clinicalbert-indian-symptoms"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Prepare input
text = "I have fever, headache and body pain for 2 days"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)

# Get prediction
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    probs = torch.softmax(logits, dim=-1)
    predicted_class = torch.argmax(probs, dim=-1).item()

# Get label
label = model.config.id2label[predicted_class]
confidence = probs[predicted_class].item()

print(f"Condition: {label}")
print(f"Confidence: {confidence:.2%}")

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="your-username/clinicalbert-indian-symptoms",
    top_k=5
)

result = classifier("I have persistent cough and chest congestion")
print(result)