Mxbai-large-v1 EmbedPress
Collection
Large datasets of mxbai-large-v1 embeddings with their truncated texts. Useful for distillation • 13 items • Updated
• 2
text stringlengths 4 527 | embedding list |
|---|---|
Who is Vernon L. Grose and what organizations is he a member of? | [
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Who frequently co-sponsors the writing workshop? | [
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What was the average household size in this area? | [
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What type of course did she attend in 1986? | [
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What was the median age in the town? | [
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What is the Fermi–Pasta–Ulam–Tsingou problem? | [
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What is the male-to-female ratio for individuals aged 18 and over in the county? | [
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What is the population of Ritchie County according to the 2010 census? | [
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What is the genre of the film Badmashiyaan? | [
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When and where was Cynthia Cozette Lee born? | [-0.3531525135040283,-0.08842575550079346,0.5724379420280457,-0.753325879573822,-1.1999201774597168,(...TRUNCATED) |
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in Data Studio
Embedpress: mixedbread large on the MLDR queries dataset
This is the query portion of the MLDR dataset, embedded with Mixedbread AI's mixedbread-ai/mxbai-embed-large-v1. For each document, we take the first 510 tokens (the model's max length -2 special tokens), and embed it, not using any instructions. Because the model was trained using Matryoshka Representation Learning, these embeddings can safely be truncated.
These are mainly useful for large-scale knowledge distillation.
The dataset consists of 10000 rows, each row has three keys:
embedding: The 1024-dimensional embeddingtext: The original text, truncated to the slice that was actually seen by the model
Because we truncate the original text, this can be directly used for training in, e.g., sentence-transformers, without having to worry about manually truncating text, matching etc.
Acknowledgments
Thanks Mixedbread AI for a GPU grant for research into small retrieval models.
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Size of downloaded dataset files:
42 MB
Size of the auto-converted Parquet files:
42 MB
Number of rows:
10,000