Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:1446
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use Nada-10/f-jp-multilingual-e5-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Nada-10/f-jp-multilingual-e5-small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Nada-10/f-jp-multilingual-e5-small") sentences = [ "圧縮機の回転部分の総称であって、動翼を介して気体に仕事を付加する機能をもつ。動翼を植えてないも のを圧縮機ロータという場合がある。", "圧縮機ロータ〔コンプレッサロータ〕はどのように定義されていますか?", "溶存水素の定義を教えてください。", "中圧圧縮機〔中圧コンプレッサ〕の定義は何ですか?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Nada-10/f-jp-multilingual-e5-small: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/Nada-10/f-jp-multilingual-e5-small/resolve/main/tokenizer.json
- Command line
-
hf download hf://Nada-10/f-jp-multilingual-e5-small/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Nada-10/f-jp-multilingual-e5-small/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 6803d80dfd27b26211a967d9e4a230d7caffa8564ff4adb8f96610825c9b6c92
- Size of remote file:
- 17.1 MB
- SHA256:
- ef04f2b385d1514f500e779207ace0f53e30895ce37563179e29f4022d28ca38
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.