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  - sentence-similarity
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  ---
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- # {MODEL_NAME}
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-
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- This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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-
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- <!--- Describe your model here -->
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-
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- ## Usage (Sentence-Transformers)
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-
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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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- ```
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- pip install -U sentence-transformers
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- ```
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-
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- Then you can use the model like this:
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-
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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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-
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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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- <!--- Describe how your model was evaluated -->
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-
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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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-
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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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- <!--- Describe where people can find more information -->
 
 
 
 
 
 
 
 
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  - sentence-similarity
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  ---
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+ # SGPT-125M-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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+ ```