Sentence Similarity
sentence-transformers
PyTorch
Transformers
roberta
feature-extraction
text-embeddings-inference
Instructions to use hunkim/model1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hunkim/model1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hunkim/model1") 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 hunkim/model1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hunkim/model1") model = AutoModel.from_pretrained("hunkim/model1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hunkim/model1: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/hunkim/model1/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://hunkim/model1@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hunkim/model1/resolve/refs%2Fpr%2F1/pytorch_model.bin
443 MB
- Xet hash:
- dd3398aabd9891f3f7c80bd7bf238789111aab141954152c5497fdad96654d18
- Size of remote file:
- 443 MB
- SHA256:
- e118e3b26d3996e56bf93f177c15783e7b01c9147e1bf9c9209f817c9e1495d4
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