Sentence Similarity
sentence-transformers
PyTorch
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use ilhkn/mount2_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ilhkn/mount2_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ilhkn/mount2_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 ilhkn/mount2_model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ilhkn/mount2_model") model = AutoModel.from_pretrained("ilhkn/mount2_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ilhkn/mount2_model: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/ilhkn/mount2_model/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://ilhkn/mount2_model@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ilhkn/mount2_model/resolve/refs%2Fpr%2F1/pytorch_model.bin
438 MB
- Xet hash:
- 6e48c9d6484baaa68c5d814cd088e1c4a521e0a31596537674ff2755841621db
- Size of remote file:
- 438 MB
- SHA256:
- 2039cab61a0b8dbc8690908e317daf65dc785691db079a69836c29e6fee1a38b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.