llm-semantic-router / mmbert32k-feedback-detector-lora

huggingface.co
Total runs: 25
24-hour runs: 0
7-day runs: 1
30-day runs: -6
Model's Last Updated: February 02 2026
text-classification

Introduction of mmbert32k-feedback-detector-lora

Model Details of mmbert32k-feedback-detector-lora

mmBERT-32K Feedback Detector (LoRA Adapter)

LoRA adapter for 4-class user feedback/satisfaction classification based on mmBERT-32K-YaRN .

Model Description

This is the LoRA adapter version. For the merged model, see mmbert32k-feedback-detector-merged .

Classes
  • SAT : User is satisfied
  • NEED_CLARIFICATION : User needs more explanation
  • WRONG_ANSWER : System provided incorrect information
  • WANT_DIFFERENT : User wants alternative options
Base Model
LoRA Configuration
  • Rank : 8
  • Alpha : 16
  • Dropout : 0.1
  • Target Modules : attn.Wqkv, attn.Wo, mlp.Wi, mlp.Wo
  • Trainable Parameters : 1.69M (0.55% of base model)
Performance
Metric Score
Accuracy 98.46%
F1 Macro 97.69%
Usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from peft import PeftModel

# Load base model and adapter
base_model = "llm-semantic-router/mmbert-32k-yarn"
adapter = "llm-semantic-router/mmbert32k-feedback-detector-lora"

tokenizer = AutoTokenizer.from_pretrained(adapter)
model = AutoModelForSequenceClassification.from_pretrained(base_model, num_labels=4)
model = PeftModel.from_pretrained(model, adapter)

# Inference
text = "Thanks, that's exactly what I needed!"
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
License

Apache 2.0

Runs of llm-semantic-router mmbert32k-feedback-detector-lora on huggingface.co

25
Total runs
0
24-hour runs
0
3-day runs
1
7-day runs
-6
30-day runs

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