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
- Base: llm-semantic-router/mmbert-32k-yarn
- Architecture: ModernBERT with YaRN RoPE scaling
- Context Length: 32,768 tokens
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