Feature Extraction
sentence-transformers
Safetensors
gemma3_text
retrieval
devdata-search
text-embeddings-inference
Instructions to use ai4data/devdata-search-harrier-270m-cgist with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ai4data/devdata-search-harrier-270m-cgist with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ai4data/devdata-search-harrier-270m-cgist") 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 tokenizer.json from ai4data/devdata-search-harrier-270m-cgist: direct link, hf CLI and curl.
- Browser
- Download file 33.4 MB
-
https://huggingface.co/ai4data/devdata-search-harrier-270m-cgist/resolve/main/tokenizer.json
- Command line
-
hf download hf://ai4data/devdata-search-harrier-270m-cgist/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/ai4data/devdata-search-harrier-270m-cgist/resolve/main/tokenizer.json
33.4 MB
- Xet hash:
- 9abe59178c1065c84a08fda4b8faf2fb03f7abcc6e27ddcb891e461c54ea3a53
- Size of remote file:
- 33.4 MB
- SHA256:
- d6c4c6c7ecda463434a6fafacc071da6de4a1c644d58d2c14de03520ecb8ff32
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