# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("sonisatish119/PhysioMindAI-intent-classification-bert")
model = AutoModelForSequenceClassification.from_pretrained("sonisatish119/PhysioMindAI-intent-classification-bert")Quick Links
📖 Intent Classification Model
This model is a DistilBERT-based classifier trained to recognize user intents for appointment booking scenarios.
It can classify queries into multiple intent categories such as booking, rescheduling, cancellations, and more.
📌 Model Details
- Model Type:
DistilBERT - Training Data: 4K+ labeled appointment-related queries
- Framework:
Transformers (Hugging Face) - Languages:
English - Dataset Format: JSON
🛠️ How to Use
You can use this model directly with the Transformers library:
from transformers import pipeline
model = "sonisatish119/PhysioMindAI-intent-classification-bert" # Update with your repo name
classifier = pipeline("text-classification", model=model)
query = "Can I book an appointment for next Monday?"
prediction = classifier(query)
print(prediction) # Output: [{'label': 'book_appointment', 'score': 0.98}]
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sonisatish119/PhysioMindAI-intent-classification-bert")