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 model_head.pkl from ilhkn/mount2_model: direct link, hf CLI and curl.
- Browser
- Download file 37.8 kB
-
https://huggingface.co/ilhkn/mount2_model/resolve/main/model_head.pkl
- Command line
-
hf download hf://ilhkn/mount2_model/model_head.pkl
-
curl -L -o model_head.pkl https://huggingface.co/ilhkn/mount2_model/resolve/main/model_head.pkl
37.8 kB
- Xet hash:
- 9692ca1fd3a77f2df45f3f03f709c8f8f43aba312c2c14cddcea5ccfeeff3a96
- Size of remote file:
- 37.8 kB
- SHA256:
- 4e82bf121ed1d270ecb019382708713613860b722b6b21b58e587d4e04f51fc2
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