Sentence Similarity
sentence-transformers
ONNX
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:31992
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use Miya67/aiq-scoring-e5-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Miya67/aiq-scoring-e5-small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Miya67/aiq-scoring-e5-small") sentences = [ "query: 問題: そろばん、はさみ、豆腐、拳銃を数えるときに使う、共通の単位は何でしょう? 回答: ちょう", "query: 問題: そろばん、はさみ、豆腐、拳銃を数えるときに使う、共通の単位は何でしょう? 回答: ちょう", "query: 問題: ウルトラマンの故郷としておなじみの「M78星雲」は、何という星座にあるでしょう? 回答: オリオン座", "query: 問題: そろばん、はさみ、豆腐、拳銃を数えるときに使う、共通の単位は何でしょう? 回答: し" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Miya67/aiq-scoring-e5-small: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/Miya67/aiq-scoring-e5-small/resolve/main/tokenizer.json
- Command line
-
hf download hf://Miya67/aiq-scoring-e5-small/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Miya67/aiq-scoring-e5-small/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 77dc3d4fce0d076c99c0646439de3c9ecd4de828e88bd2425b832cb769a45340
- Size of remote file:
- 17.1 MB
- SHA256:
- ef04f2b385d1514f500e779207ace0f53e30895ce37563179e29f4022d28ca38
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