Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use hlyu/basemodel_1layer_10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hlyu/basemodel_1layer_10 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hlyu/basemodel_1layer_10") 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 hlyu/basemodel_1layer_10 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hlyu/basemodel_1layer_10") model = AutoModel.from_pretrained("hlyu/basemodel_1layer_10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7f365c1dc429e6c41450d0cf0cbdafe31d4bc0c8daa717bb93efb86907e95b4a
- Size of remote file:
- 126 MB
- SHA256:
- 2543271ce1cb6edad7446cdb6d437f7fd6f7eac5f44cb7e459c0e2d39249e61b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.