Text Classification
sentence-transformers
English
intent-classification
multi-label-classification
voice-notes
asr
routing
mcp
agents
Instructions to use danielrosehill/Voice-Note-Intent-Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use danielrosehill/Voice-Note-Intent-Classifier with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("danielrosehill/Voice-Note-Intent-Classifier") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
File size: 486 Bytes
c4c24a9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | {
"model_version": "0.1.0",
"eval": "5-fold CV out-of-fold on real silver notes",
"real_silver_n": 226,
"synthetic_train_n": 1492,
"micro_p": 0.502,
"micro_r": 0.637,
"micro_f1": 0.562,
"macro_f1": 0.481,
"exact": 0.385,
"none_abstained": "10/26",
"per_label_f1": {
"shopping_list": 0.778,
"todo": 0.411,
"note_to_self": 0.235,
"email_draft": 0.5,
"idea": 0.364,
"brainstorm": 0.447,
"content_draft": 0.353,
"ai_prompt": 0.757
}
} |