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:
- 2716934620da81652aac886abf167974c6d93b9b7de66c56f068aa71e617c787
- Size of remote file:
- 14.6 kB
- SHA256:
- 40a3307ae6934992ccb693053290474251860e08cf042c544e4bafe4ab3c0d49
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