Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
semantic-search
chinese
text-embeddings-inference
Instructions to use TaoH/dj with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use TaoH/dj with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("TaoH/dj") 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 TaoH/dj with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("TaoH/dj") model = AutoModel.from_pretrained("TaoH/dj", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from TaoH/dj: direct link, hf CLI and curl.
- Browser
- Download file 409 MB
-
https://huggingface.co/TaoH/dj/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://TaoH/dj/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/TaoH/dj/resolve/main/pytorch_model.bin
409 MB
- Xet hash:
- 54138667d1b81d532bf241473d704f4599f4fe519c265ad821c55de3e919f3f6
- Size of remote file:
- 409 MB
- SHA256:
- ac3038f0a407cc45b3fa5685e47540b7f5a833e741ddf7e915017f96b8405f7d
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