Sentence Similarity
sentence-transformers
Safetensors
Transformers
English
Chinese
qwen3
feature-extraction
text-embeddings-inference
binary code
binary
Instructions to use XingTuLab/BinSeek-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use XingTuLab/BinSeek-Embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("XingTuLab/BinSeek-Embedding") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use XingTuLab/BinSeek-Embedding with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("XingTuLab/BinSeek-Embedding") model = AutoModel.from_pretrained("XingTuLab/BinSeek-Embedding") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d6607e03a01ca1d2d8c9ffd5dba5761484c5ddc1573b827945c5ff16a2919113
- Size of remote file:
- 11.4 MB
- SHA256:
- 1b956d41bb9f0594c469de98b539eb13e4110586f9acbc8b1a914104a7491fc0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.