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README.md
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- sentence-similarity
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---
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#
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 2048 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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<!--- Describe your model here -->
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## Usage (Sentence-Transformers)
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Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
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```
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pip install -U sentence-transformers
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```
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Then you can use the model like this:
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```python
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from sentence_transformers import SentenceTransformer
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sentences = ["This is an example sentence", "Each sentence is converted"]
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model = SentenceTransformer('{MODEL_NAME}')
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embeddings = model.encode(sentences)
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print(embeddings)
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```
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## Evaluation Results
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For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
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## Training
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The model was trained with the parameters:
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## Citing & Authors
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- sentence-similarity
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---
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# SGPT-1.3B-weightedmean-nli-bitfit
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## Usage
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For usage instructions, refer to our codebase: https://github.com/Muennighoff/sgpt
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## Evaluation Results
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For eval results, refer to our paper: https://arxiv.org/abs/2202.08904
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## Training
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The model was trained with the parameters:
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## Citing & Authors
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```bibtex
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@article{muennighoff2022sgpt,
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title={SGPT: GPT Sentence Embeddings for Semantic Search},
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author={Muennighoff, Niklas},
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journal={arXiv preprint arXiv:2202.08904},
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year={2022}
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}
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```
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