Text Classification
PEFT
Safetensors
Transformers
feedback-detection
user-satisfaction
lora
modernbert
mmbert
32k-context
Eval Results (legacy)
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
| {"label_to_idx": {"NEED_CLARIFICATION": 0, "SAT": 1, "WANT_DIFFERENT": 2, "WRONG_ANSWER": 3}, "idx_to_label": {"0": "NEED_CLARIFICATION", "1": "SAT", "2": "WANT_DIFFERENT", "3": "WRONG_ANSWER"}} |