Text Classification
Transformers
ONNX
Safetensors
modernbert
feedback-detection
user-satisfaction
mmbert
32k-context
Eval Results (legacy)
text-embeddings-inference
Instructions to use llm-semantic-router/mmbert32k-feedback-detector-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use llm-semantic-router/mmbert32k-feedback-detector-merged 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-merged")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("llm-semantic-router/mmbert32k-feedback-detector-merged") model = AutoModelForSequenceClassification.from_pretrained("llm-semantic-router/mmbert32k-feedback-detector-merged", 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"}} |