Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use LinYuting/sentence_transformer_128_dim_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LinYuting/sentence_transformer_128_dim_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LinYuting/sentence_transformer_128_dim_model") 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 LinYuting/sentence_transformer_128_dim_model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("LinYuting/sentence_transformer_128_dim_model") model = AutoModel.from_pretrained("LinYuting/sentence_transformer_128_dim_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from LinYuting/sentence_transformer_128_dim_model: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/LinYuting/sentence_transformer_128_dim_model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://LinYuting/sentence_transformer_128_dim_model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/LinYuting/sentence_transformer_128_dim_model/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- 0af3181eba4604cc43511ef141b1ce92802e14b0fcf6f658eddd675a720c1e5a
- Size of remote file:
- 433 MB
- SHA256:
- 1246aea7f9a909cbcc8b38b1e09936b6cdb816b738df99486060f4e6234020e6
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