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:
- ff661521123df63acfb1ff62fceef683086ae62002f18de8e43446a4700a8a99
- Size of remote file:
- 1.47 kB
- SHA256:
- 23143600d5d56614f5763f815995233342248e467af9fb60af59aba717baf2b5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.