Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use hlyu/basemodel_2layer_0_10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hlyu/basemodel_2layer_0_10 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hlyu/basemodel_2layer_0_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_2layer_0_10 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hlyu/basemodel_2layer_0_10") model = AutoModel.from_pretrained("hlyu/basemodel_2layer_0_10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d637b05a42ef92535a49e2a6708b209588937a0d32a48cc6ade9781a06d46d7c
- Size of remote file:
- 154 MB
- SHA256:
- fe34a5b4df633cd57c229b1ee16c7f5916a1f1c2d09591d9a4c1a67e9826cda3
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