Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
mpnet
feature-extraction
text-embeddings-inference
Instructions to use Collab-uniba/github-issues-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-mpnet-st-e10 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Collab-uniba/github-issues-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-mpnet-st-e10 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Collab-uniba/github-issues-mpnet-st-e10") model = AutoModel.from_pretrained("Collab-uniba/github-issues-mpnet-st-e10") - Notebooks
- Google Colab
- Kaggle
insert how to cite
Browse files
README.md
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## Citing & Authors
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```
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## Citing & Authors
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```
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@article{Colavito_2025_Benchmarking,
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title = {Benchmarking large language models for automated labeling: The case of issue report classification},
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author = {Giuseppe Colavito and Filippo Lanubile and Nicole Novielli},
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year = 2025,
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journal = {Information and Software Technology},
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volume = 184,
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pages = 107758,
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doi = {https://doi.org/10.1016/j.infsof.2025.107758},
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issn = {0950-5849},
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url = {https://www.sciencedirect.com/science/article/pii/S0950584925000977},
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keywords = {Issue labeling, Generative AI, Software maintenance and evolution}
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}
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```
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