Mxbai-large-v1 EmbedPress
Collection
Large datasets of mxbai-large-v1 embeddings with their truncated texts. Useful for distillation • 13 items • Updated • 2
id stringlengths 4 10 | text stringlengths 18 1.2k | embedding list |
|---|---|---|
tc_0 | Who was President when the first Peanuts cartoon was published? | [
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tc_1 | Which American-born Sinclair won the Nobel Prize for Literature in 1930? | [
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tc_3 | Where in England was Dame Judi Dench born? | [
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tc_4 | William Christensen of Madison, New Jersey, has claimed to have the world's biggest collection of what? | [
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tc_5 | In which decade did Billboard magazine first publish and American hit chart? | [
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tc_6 | Where was horse racing's Breeders' Cup held in 1988? | [
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tc_8 | From which country did Angola achieve independence in 1975? | [
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tc_9 | Which city does David Soul come from? | [
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tc_10 | Who won Super Bowl XX? | [
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tc_11 | Which was the first European country to abolish capital punishment? | [
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tc_12 | What first appeared in Ohio in 1914 to affect transport? | [
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tc_14 | In what field did the brother of the author of The Turn of the Screw find fame? | [
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tc_15 | In which country did he widespread use of ISDN begin in 1988? | [
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Embedpress: mixedbread large on the TriviaQA queries dataset
This is the query portion of the TriviaQA 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 87k rows, each row has three keys:
id: the original id in the fineweb sampleembedding: 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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