Text Classification
PEFT
TensorBoard
Safetensors
English
Chinese
multilingual
intent-classification
mmbert
lora
vllm-semantic-router
Instructions to use llm-semantic-router/mmbert-intent-classifier-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use llm-semantic-router/mmbert-intent-classifier-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("jhu-clsp/mmBERT-base") model = PeftModel.from_pretrained(base_model, "llm-semantic-router/mmbert-intent-classifier-lora") - Notebooks
- Google Colab
- Kaggle
Upload mmBERT LoRA adapter: mmBERT Intent Classifier with LoRA - 14-class MMLU-Pro classification (77.9% accuracy)
b6e5d70 verified | { | |
| "category_to_idx": { | |
| "biology": 0, | |
| "business": 1, | |
| "chemistry": 2, | |
| "computer science": 3, | |
| "economics": 4, | |
| "engineering": 5, | |
| "health": 6, | |
| "history": 7, | |
| "law": 8, | |
| "math": 9, | |
| "other": 10, | |
| "philosophy": 11, | |
| "physics": 12, | |
| "psychology": 13 | |
| }, | |
| "idx_to_category": { | |
| "0": "biology", | |
| "1": "business", | |
| "2": "chemistry", | |
| "3": "computer science", | |
| "4": "economics", | |
| "5": "engineering", | |
| "6": "health", | |
| "7": "history", | |
| "8": "law", | |
| "9": "math", | |
| "10": "other", | |
| "11": "philosophy", | |
| "12": "physics", | |
| "13": "psychology" | |
| } | |
| } |