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
Download eval_results.json from danielrosehill/Voice-Note-Intent-Classifier: direct link, hf CLI and curl.
- Browser
- Download file 486 Bytes
-
https://huggingface.co/danielrosehill/Voice-Note-Intent-Classifier/resolve/main/eval_results.json
- Command line
-
hf download hf://danielrosehill/Voice-Note-Intent-Classifier/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/danielrosehill/Voice-Note-Intent-Classifier/resolve/main/eval_results.json
486 Bytes
| { | |
| "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 | |
| } | |
| } |