# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("PSSSSA/classifierctmodel")
model = AutoModelForSequenceClassification.from_pretrained("PSSSSA/classifierctmodel", device_map="auto")Quick Links
Medical Text Classifier
This model classifies text as medical or non-medical using DistilBERT.
Usage
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
tokenizer = DistilBertTokenizer.from_pretrained("PSSSSA/classifierctmodel")
model = DistilBertForSequenceClassification.from_pretrained("PSSSSA/classifierctmodel")
Step 3: Wait and Check Again
After updating the README:
- Wait 5-10 minutes for Hugging Face to process
- Refresh your model page
- Look for the inference widget
Step 4: Alternative - Test with curl
Try this curl command to see the raw response:
curl -X POST \
-H "Authorization: Bearer API" \
-H "Content-Type: application/json" \
-d '{"inputs": "I have a headache"}' \
https://api-inference.huggingface.co/models/PSSSSA/classifierctmodel
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="PSSSSA/classifierctmodel")