Intent Classification Model
This is a fine-tuned SentenceTransformer model for intent classification. It was trained on custom intent data including navigation, media controls, library management, and protocol activation commands.
Usage
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('drithh/intent-classifier')
embeddings = model.encode("go to London")
Supported Intents
- Navigation: go to LOCATION, navigate to LOCATION
- Atlas: open atlas, launch atlas
- Map Controls: select LOCATION, show boundaries, hide boundaries
- Library: open library, close library, go to video NUMBER
- Media Controls: play video, pause video, rewind, forward
- News: show news LOCATION, hide news
- Protocols: activate PROTOCOL, deactivate PROTOCOL
Model Details
- Base Model: sentence-transformers/paraphrase-MiniLM-L3-v2
- Fine-tuning: Cosine similarity loss
- Embedding Dimensions: 384
- Training Data: 139,128 training pairs
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from sentence_transformers import SentenceTransformer model = SentenceTransformer("drithh/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]