Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use thtang/ALL_679283 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use thtang/ALL_679283 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("thtang/ALL_679283") 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 thtang/ALL_679283 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("thtang/ALL_679283") model = AutoModel.from_pretrained("thtang/ALL_679283") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d8fa8f923e17d2fb89891ed61fb52faca729d22bb5f5f52e09eb049417bc184e
- Size of remote file:
- 471 MB
- SHA256:
- a25103d52ee08a89907bc83ef1cd3000005732131befb7b6bd39b48410fd370e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.