Sentence Similarity
sentence-transformers
PyTorch
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use Collab-uniba/github-issues-preprocessed-mpnet-st-e10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Collab-uniba/github-issues-preprocessed-mpnet-st-e10 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Collab-uniba/github-issues-preprocessed-mpnet-st-e10") 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 Collab-uniba/github-issues-preprocessed-mpnet-st-e10 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Collab-uniba/github-issues-preprocessed-mpnet-st-e10") model = AutoModel.from_pretrained("Collab-uniba/github-issues-preprocessed-mpnet-st-e10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0f9a807cf30dd9c35cf34ff1ca0da1bbf5808a52464005267e3eef0925f546cc
- Size of remote file:
- 438 MB
- SHA256:
- 49a15635d0f02eb6ba191af9ba0f3a735f0403a79ab1095f523a6a2d1b0045a5
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