Sentence Similarity
sentence-transformers
PyTorch
Transformers
bert
feature-extraction
text-embeddings-inference
Instructions to use prodm93/singularity_retriever_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use prodm93/singularity_retriever_v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("prodm93/singularity_retriever_v1") 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 prodm93/singularity_retriever_v1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("prodm93/singularity_retriever_v1") model = AutoModel.from_pretrained("prodm93/singularity_retriever_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from prodm93/singularity_retriever_v1: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/prodm93/singularity_retriever_v1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://prodm93/singularity_retriever_v1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/prodm93/singularity_retriever_v1/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 91210467335ed1baaa3dbca8a45dba0cd34bb3ec52c48281349a651c01441ccd
- Size of remote file:
- 438 MB
- SHA256:
- 569e20de6b2ee799a1177ffb7cf6cf691056c33d4e611634ccf4035008db0002
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.