Instructions to use SoTrxII/analytical-pattern-embedder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use SoTrxII/analytical-pattern-embedder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("SoTrxII/analytical-pattern-embedder") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from SoTrxII/analytical-pattern-embedder: direct link, hf CLI and curl.
- Browser
- Download file 32.2 MB
-
https://huggingface.co/SoTrxII/analytical-pattern-embedder/resolve/main/tokenizer.json
- Command line
-
hf download hf://SoTrxII/analytical-pattern-embedder/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/SoTrxII/analytical-pattern-embedder/resolve/main/tokenizer.json
32.2 MB
- Xet hash:
- ae4829dff37cae35f1c9806dd2cdfb7bcc59174f6b7824f63a3035fd833be504
- Size of remote file:
- 32.2 MB
- SHA256:
- 5e247050b8c792f6d86f1aa9d2acc74f522551fe68ae1f45ab9d5492778717f7
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