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@@ -6,11 +6,9 @@ tags:
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  - sentence-similarity
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  ---
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- # {MODEL_NAME}
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- This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 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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  ## Usage (Sentence-Transformers)
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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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-
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-
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- ## Evaluation Results
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-
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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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-
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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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+ # DistilUSE Podcast Natural Questions
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+ This is a [sentence-transformers](https://www.SBERT.net) model built for asymmetric semantic search of Podcast episodes. It replicates the fine-tuning process of Spotify's podcast search model, as [described here](https://www.pinecone.io/learn/spotify-podcast-search/).
 
 
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  ## Usage (Sentence-Transformers)
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  ```python
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  from sentence_transformers import SentenceTransformer
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+ sentences = ["podcast about climate change", "how to make money on the internet"]
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+ model = SentenceTransformer('pinecone/distiluse-podcast-nq')
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  embeddings = model.encode(sentences)
 
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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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+ James Briggs, [How Spotify Uses Semantic Search for Podcasts](https://www.pinecone.io/learn/spotify-podcast-search/), Pinecone