Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing
    • Website
      • Tasks
      • HuggingChat
      • Collections
      • Languages
      • Organizations
    • Community
      • Blog
      • Posts
      • Daily Papers
      • Hardware
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

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

Text Classification
PEFT
Safetensors
Transformers
feedback-detection
user-satisfaction
lora
modernbert
mmbert
32k-context
Eval Results (legacy)
Model card Files Files and versions
xet
Community

Instructions to use llm-semantic-router/mmbert32k-feedback-detector-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • PEFT

    How to use llm-semantic-router/mmbert32k-feedback-detector-lora with PEFT:

    from peft import PeftModel
    from transformers import AutoModelForSequenceClassification
    
    base_model = AutoModelForSequenceClassification.from_pretrained("llm-semantic-router/mmbert-32k-yarn")
    model = PeftModel.from_pretrained(base_model, "llm-semantic-router/mmbert32k-feedback-detector-lora")
  • Transformers

    How to use llm-semantic-router/mmbert32k-feedback-detector-lora with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="llm-semantic-router/mmbert32k-feedback-detector-lora")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("llm-semantic-router/mmbert32k-feedback-detector-lora", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
mmbert32k-feedback-detector-lora
482 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 28 commits
HuaminChen's picture
HuaminChen
Update: 98.83% accuracy with LoRA rank=64, alpha=128 (validated 2026-02-02)
e503e8d verified 7 months ago
  • checkpoint-4500
    Update: 98.83% accuracy with LoRA rank=64, alpha=128 (validated 2026-02-02) 7 months ago
  • checkpoint-6000
    Update: 98.83% accuracy with LoRA rank=64, alpha=128 (validated 2026-02-02) 7 months ago
  • .gitattributes
    1.84 kB
    Update: 98.83% accuracy with LoRA rank=64, alpha=128 (validated 2026-02-02) 7 months ago
  • README.md
    5.05 kB
    Update: 98.83% accuracy with LoRA rank=64, alpha=128 (validated 2026-02-02) 7 months ago
  • adapter_config.json
    1.05 kB
    Update: 98.83% accuracy with LoRA rank=64, alpha=128 (validated 2026-02-02) 7 months ago
  • adapter_model.safetensors
    54.1 MB
    xet
    Update: 98.83% accuracy with LoRA rank=64, alpha=128 (validated 2026-02-02) 7 months ago
  • label_mapping.json
    194 Bytes
    Upload label_mapping.json with huggingface_hub 7 months ago
  • special_tokens_map.json
    1.05 kB
    Upload folder using huggingface_hub 7 months ago
  • tokenizer.json
    34.4 MB
    xet
    Upload folder using huggingface_hub 7 months ago
  • tokenizer_config.json
    585 Bytes
    Update: 98.83% accuracy with LoRA rank=64, alpha=128 (validated 2026-02-02) 7 months ago
  • training_config.json
    1.06 kB
    Update: 98.83% accuracy with LoRA rank=64, alpha=128 (validated 2026-02-02) 7 months ago