Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use hlyu/basemodel_1layer_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hlyu/basemodel_1layer_1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hlyu/basemodel_1layer_1") 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_1 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hlyu/basemodel_1layer_1") model = AutoModel.from_pretrained("hlyu/basemodel_1layer_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eab460c33567de69653aabc170c2792df9fe36a25b15a914ec641ac62d17d052
- Size of remote file:
- 126 MB
- SHA256:
- 6e3fe976b33cdabd57e7b5dcac3b1336d052a72c94bc8e3fb42531a99b25a66c
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