GTE modernbert Embedpress
Collection
A bunch of datasets embedded with GTE modernbert. Sponsored by Mixedbread AI • 14 items • Updated
text stringlengths 18 1.2k | embedding list |
|---|---|
Who was President when the first Peanuts cartoon was published? | [
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Which American-born Sinclair won the Nobel Prize for Literature in 1930? | [
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Where in England was Dame Judi Dench born? | [
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William Christensen of Madison, New Jersey, has claimed to have the world's biggest collection of what? | [
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In which decade did Billboard magazine first publish and American hit chart? | [
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Where was horse racing's Breeders' Cup held in 1988? | [
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From which country did Angola achieve independence in 1975? | [
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Which city does David Soul come from? | [
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Who won Super Bowl XX? | [
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Which was the first European country to abolish capital punishment? | [
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What first appeared in Ohio in 1914 to affect transport? | [
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In what field did the brother of the author of The Turn of the Screw find fame? | [
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In which country did he widespread use of ISDN begin in 1988? | [
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End of preview. Expand in Data Studio
Embedpress: Alibaba-NLP/gte-modernbert-base on the mandarjoshi/trivia_qa dataset
This is the mandarjoshi/trivia_qa dataset, embedded with Alibaba-NLP/gte-modernbert-base.
For each example, we embed the text directly (no additional instruction prompt). Embeddings have dimensionality 768.
These embeddings are intended for tasks like large-scale distillation, retrieval, and similarity search. Because the raw text may exceed the model’s limit, we recommend truncating to the model’s maximum token length at build time.
Schema
text(string) — the query text used for embeddingembedding(float32[768]) — the vector representation fromAlibaba-NLP/gte-modernbert-base
Split
train— 87622 examples
Notes
- Produced with
Alibaba-NLP/gte-modernbert-basefrom Hugging Face Hub. - If you need a smaller embedding size (e.g., matryoshka/truncated vectors), you can safely slice the embeddings without re-embedding.
Acknowledgments
Thanks Mixedbread AI for a GPU grant for research into small retrieval models.
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