Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
dense
Generated from Trainer
dataset_size:16692
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use michaeleliot/claim-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use michaeleliot/claim-model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("michaeleliot/claim-model") sentences = [ "Al Klug position played tackle", "Alfred Klug position played tackle", "Brad Edwards position played Safety postion", "Michael Jackson's Ghosts position played tackle" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.model from michaeleliot/claim-model: direct link, hf CLI and curl.
- Browser
- Download file 4.69 MB
-
https://huggingface.co/michaeleliot/claim-model/resolve/main/tokenizer.model
- Command line
-
hf download hf://michaeleliot/claim-model/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/michaeleliot/claim-model/resolve/main/tokenizer.model
4.69 MB
- Xet hash:
- f6aa6b6d77b66c0b5905517df9e5261c32195192839dd05e71e37d6b89933074
- Size of remote file:
- 4.69 MB
- SHA256:
- 1299c11d7cf632ef3b4e11937501358ada021bbdf7c47638d13c0ee982f2e79c
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