Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use seongwoon/LSpecter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use seongwoon/LSpecter with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("seongwoon/LSpecter") 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 seongwoon/LSpecter with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("seongwoon/LSpecter") model = AutoModel.from_pretrained("seongwoon/LSpecter") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 783bdcf2c6d61a91675d2494b8fad2103afc948b319a629b806aba1cea386a58
- Size of remote file:
- 440 MB
- SHA256:
- 434b5feb2f21ce100453d40754163df20b657d4f50073afb6ed2369c0634af63
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