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
Upload folder using huggingface_hub
Browse files
onnx/model_quantized.onnx
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