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.json from michaeleliot/claim-model: direct link, hf CLI and curl.
- Browser
- Download file 33.4 MB
-
https://huggingface.co/michaeleliot/claim-model/resolve/main/tokenizer.json
- Command line
-
hf download hf://michaeleliot/claim-model/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/michaeleliot/claim-model/resolve/main/tokenizer.json
33.4 MB
- Xet hash:
- 0eba2a2f980d6750372572f73863ef45039729ed13f414124805c57e744c5711
- Size of remote file:
- 33.4 MB
- SHA256:
- 216e2a79606fe879c9f17c529c71cd241338407fd5646b595ffd3c4b9ea1d503
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