Sentence Similarity
sentence-transformers
Safetensors
English
embedding_gemma2
embeddinggemma
transactions
merchant-matching
fintech
Instructions to use suryeah/FinxGemma-Lite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use suryeah/FinxGemma-Lite with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("suryeah/FinxGemma-Lite") 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 suryeah/FinxGemma-Lite: direct link, hf CLI and curl.
- Browser
- Download file 32.2 MB
-
https://huggingface.co/suryeah/FinxGemma-Lite/resolve/main/tokenizer.json
- Command line
-
hf download hf://suryeah/FinxGemma-Lite/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/suryeah/FinxGemma-Lite/resolve/main/tokenizer.json
32.2 MB
- Xet hash:
- 851def8470b69b7c88a22ea7a810a094417137dad946b99bd203e0e653d78107
- Size of remote file:
- 32.2 MB
- SHA256:
- d97c26b42cb5a5c8d28db2cad86832c9c1743157db82514ab6222e473995ca1d
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