Sentence Similarity
sentence-transformers
PyTorch
Transformers
roberta
feature-extraction
text-embeddings-inference
Instructions to use addy88/eli5-all-mpnet-base-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use addy88/eli5-all-mpnet-base-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("addy88/eli5-all-mpnet-base-v2") 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 addy88/eli5-all-mpnet-base-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("addy88/eli5-all-mpnet-base-v2") model = AutoModel.from_pretrained("addy88/eli5-all-mpnet-base-v2") - Notebooks
- Google Colab
- Kaggle
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## Citing & Authors
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## Citing & Authors
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This model was trained by [sentence-transformers](https://www.sbert.net/).
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If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
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```bibtex
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@inproceedings{reimers-2019-sentence-bert,
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
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author = "Reimers, Nils and Gurevych, Iryna",
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
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month = "11",
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year = "2019",
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publisher = "Association for Computational Linguistics",
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url = "http://arxiv.org/abs/1908.10084",
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}
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```
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