Text Classification
Transformers
Keras
Safetensors
English
Bengali
bert
indian-sign-language
multilingual
Instructions to use rahul2025/isl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rahul2025/isl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rahul2025/isl")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rahul2025/isl") model = AutoModelForSequenceClassification.from_pretrained("rahul2025/isl", device_map="auto") - Notebooks
- Google Colab
- Kaggle
ISL Intent Classifier (mBERT)
Multilingual BERT fine-tuned for Indian Sign Language greeting intent classification.
Labels
| ID | Label |
|---|---|
| 0 | HELLO |
| 1 | GOOD_MORNING |
| 2 | GOOD_AFTERNOON |
| 3 | GOOD_EVENING |
| 4 | GOOD_NIGHT |
| 5 | HOW_ARE_YOU |
| 6 | ALRIGHT |
| 7 | PLEASED |
| 8 | THANK_YOU |
Usage via Inference API
import requests
API_URL = "https://api-inference.huggingface.co/models/rahul2025/isl"
headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}
response = requests.post(API_URL, headers=headers, json={"inputs": "good morning"})
print(response.json())
# [{"label": "GOOD_MORNING", "score": 0.98}]
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