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
- Xet hash:
- 4f5c98b6f03ec22e1487a50d7ef72684af3f54c7d64fc4f23bc8295ae41374eb
- Size of remote file:
- 1.47 kB
- SHA256:
- cd50c13c68c22ebfc68672991dad4157f66fbb4c88c15453fc1ad01331f228c9
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