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@@ -42,7 +42,7 @@ These pairs were obtained from various domains and were carefully selected throu
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  The embedding model was trained using 512 sequence length, but extrapolates to 8k sequence length (or even longer) thanks to ALiBi.
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  This makes our model useful for a range of use cases, especially when processing long documents is needed, including technical question answering and code search.
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- This model has 137 million parameters, which enables fast and memory efficient inference, while delivering impressive performance.
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  Additionally, we provide the following embedding models:
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  - [`jina-embeddings-v2-small-en`](https://huggingface.co/jinaai/jina-embeddings-v2-small-en): 33 million parameters.
 
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  The embedding model was trained using 512 sequence length, but extrapolates to 8k sequence length (or even longer) thanks to ALiBi.
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  This makes our model useful for a range of use cases, especially when processing long documents is needed, including technical question answering and code search.
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+ This model has 161 million parameters, which enables fast and memory efficient inference, while delivering impressive performance.
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  Additionally, we provide the following embedding models:
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  - [`jina-embeddings-v2-small-en`](https://huggingface.co/jinaai/jina-embeddings-v2-small-en): 33 million parameters.