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README.md
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print(sentence_embeddings)
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```
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## Full Model Architecture
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```
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SentenceTransformer(
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
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)
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```
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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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## Usage (Text Embeddings Inference (TEI))
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[Text Embeddings Inference (TEI)](https://github.com/huggingface/text-embeddings-inference) is a blazing fast inference solution for text embeddings models.
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}'
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```
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Or check the [Text Embeddings Inference API specification](https://huggingface.github.io/text-embeddings-inference/) instead.
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print(sentence_embeddings)
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```
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## Usage (Text Embeddings Inference (TEI))
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[Text Embeddings Inference (TEI)](https://github.com/huggingface/text-embeddings-inference) is a blazing fast inference solution for text embeddings models.
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}'
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```
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Or check the [Text Embeddings Inference API specification](https://huggingface.github.io/text-embeddings-inference/) instead.
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## Full Model Architecture
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```
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SentenceTransformer(
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
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)
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```
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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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