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mmbert-intent-classifier-lora

llm-semantic-router / mmbert-intent-classifier-lora

Run on huggingface.co
huggingface.co
Total runs: 4
24-hour runs: 0
7-day runs: 0
30-day runs: -2
Model's Last Updated: January 12 2026
text-classification

Introduction of mmbert-intent-classifier-lora

Model Details of mmbert-intent-classifier-lora

mmBERT Intent Classifier (LoRA Adapter)

A multilingual intent classification model based on mmBERT (Multilingual ModernBERT) with LoRA adapters for efficient inference.

Model Description

This model classifies text into 14 MMLU-Pro academic categories using a LoRA-enhanced mmBERT backbone. It supports 1800+ languages through mmBERT's multilingual pretraining.

Categories
  • biology, business, chemistry, computer science, economics
  • engineering, health, history, law, math
  • other, philosophy, physics, psychology
Performance
Metric Score
Accuracy 77.9%
F1 (weighted) 78.0%
Training Time 139 seconds (MI300X GPU)
Training Details
  • Base Model : jhu-clsp/mmBERT-base
  • LoRA Rank : 32
  • LoRA Alpha : 64
  • Trainable Parameters : 6.8M / 314M (2.2%)
  • Epochs : 10
  • Batch Size : 64
  • Learning Rate : 2e-5
  • Dataset : TIGER-Lab/MMLU-Pro (9,144 samples)
Usage 
from peft import PeftModel
from transformers import AutoModelForSequenceClassification, AutoTokenizer

# Load base model and LoRA adapter
base_model = AutoModelForSequenceClassification.from_pretrained(
    "jhu-clsp/mmBERT-base",
    num_labels=14
)
model = PeftModel.from_pretrained(base_model, "llm-semantic-router/mmbert-intent-classifier-lora")
tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/mmBERT-base")

# Classify
text = "What are the legal requirements for forming a corporation?"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
outputs = model(**inputs)
predicted_class = outputs.logits.argmax(-1).item()
Multilingual Support

This model supports cross-lingual transfer:

  • Fine-tuned on English MMLU-Pro data
  • Can classify queries in 1800+ languages
  • Best performance on English, good transfer to Chinese, Spanish, French, German, etc.
Part of vLLM Semantic Router

This model is part of the vLLM Semantic Router project - a Mixture-of-Models (MoM) router that understands request intent.

Citation 
@misc{mmbert-intent-classifier,
  author = {vLLM Semantic Router Team},
  title = {mmBERT Intent Classifier with LoRA},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/llm-semantic-router/mmbert-intent-classifier-lora}
}
License

Apache 2.0

Runs of llm-semantic-router mmbert-intent-classifier-lora on huggingface.co

4
Total runs
0
24-hour runs
0
3-day runs
0
7-day runs
-2
30-day runs

More Information About mmbert-intent-classifier-lora huggingface.co Model

More mmbert-intent-classifier-lora license Visit here:

https://choosealicense.com/licenses/apache-2.0

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https://huggingface.co/llm-semantic-router/mmbert-intent-classifier-lora

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mmbert-intent-classifier-lora install

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Url of mmbert-intent-classifier-lora

Provider of mmbert-intent-classifier-lora huggingface.co

llm-semantic-router
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