llm-semantic-router / mmbert32k-intent-classifier-lora

huggingface.co
Total runs: 4
24-hour runs: 0
7-day runs: 0
30-day runs: -2
Model's Last Updated: February 01 2026
text-classification

Introduction of mmbert32k-intent-classifier-lora

Model Details of mmbert32k-intent-classifier-lora

mmBERT-32K Intent Classifier (LoRA Adapter)

LoRA adapter for intent classification based on mmBERT-32K-YaRN (32K context, multilingual).

Model Details
  • Base Model : llm-semantic-router/mmbert-32k-yarn
  • Training Method : LoRA (Low-Rank Adaptation)
  • LoRA Rank : 32
  • LoRA Alpha : 64
  • Trainable Parameters : 6.8M (2.2% of base model)
  • Adapter Size : 27 MB
Training Data
Categories (14 classes)

biology, business, chemistry, computer science, economics, engineering, health, history, law, math, other, philosophy, physics, psychology

Performance
Metric Score
Test Accuracy 80.0%
Adapter Size 27 MB
Usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from peft import PeftModel

# Load base model and LoRA adapter
base_model = AutoModelForSequenceClassification.from_pretrained(
    "llm-semantic-router/mmbert-32k-yarn", num_labels=14
)
model = PeftModel.from_pretrained(base_model, "llm-semantic-router/mmbert32k-intent-classifier-lora")
tokenizer = AutoTokenizer.from_pretrained("llm-semantic-router/mmbert32k-intent-classifier-lora")

# Inference
inputs = tokenizer("How do neural networks learn?", return_tensors="pt")
outputs = model(**inputs)
predicted_class = outputs.logits.argmax().item()
Training Configuration
  • Epochs: 5
  • Batch Size: 16
  • Learning Rate: 2e-4
  • Weight Decay: 0.1
  • Optimizer: AdamW with cosine LR scheduler

Runs of llm-semantic-router mmbert32k-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

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