Seq vs Seq: the Ettin Suite of Paired Encoders and Decoders
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lightonai/Reason-ModernColBERT
Sentence Similarity • 0.1B • Updated • 1.76k • 201 -
lightonai/GTE-ModernColBERT-v1
Sentence Similarity • 0.1B • Updated • 15.5k • 134 -
lightonai/answerai-colbert-small-v1
Sentence Similarity • 0.0B • Updated • 1.06k • 3 -
lightonai/colbertv2.0
Sentence Similarity • 0.1B • Updated • 1.73k • 4
French language models. These model were trained in early 2021 following the then scaling laws and using the exact same training data as the CamemBERT
Bringing BERT into modernity via both architecture changes and scaling
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answerdotai/ModernBERT-base
Fill-Mask • 0.1B • Updated • 1.26M • 907 -
lightonai/GTE-ModernColBERT-v1
Sentence Similarity • 0.1B • Updated • 15.5k • 134 -
lightonai/Reason-ModernColBERT
Sentence Similarity • 0.1B • Updated • 1.76k • 201 -
lightonai/modernbert-embed-large
Sentence Similarity • 0.4B • Updated • 88.6k • • 25
A suite of autoregressive generative models for protein sequences, with up to 1.2Bparameters, trained on over 280 million protein sequences.
900M models trained on 25BT to compare different data processing choices (filtering, sentence dedup, minhash, etc)
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lightonai/Reason-ModernColBERT
Sentence Similarity • 0.1B • Updated • 1.76k • 201 -
lightonai/GTE-ModernColBERT-v1
Sentence Similarity • 0.1B • Updated • 15.5k • 134 -
lightonai/answerai-colbert-small-v1
Sentence Similarity • 0.0B • Updated • 1.06k • 3 -
lightonai/colbertv2.0
Sentence Similarity • 0.1B • Updated • 1.73k • 4
Bringing BERT into modernity via both architecture changes and scaling
-
answerdotai/ModernBERT-base
Fill-Mask • 0.1B • Updated • 1.26M • 907 -
lightonai/GTE-ModernColBERT-v1
Sentence Similarity • 0.1B • Updated • 15.5k • 134 -
lightonai/Reason-ModernColBERT
Sentence Similarity • 0.1B • Updated • 1.76k • 201 -
lightonai/modernbert-embed-large
Sentence Similarity • 0.4B • Updated • 88.6k • • 25
French language models. These model were trained in early 2021 following the then scaling laws and using the exact same training data as the CamemBERT
A suite of autoregressive generative models for protein sequences, with up to 1.2Bparameters, trained on over 280 million protein sequences.
900M models trained on 25BT to compare different data processing choices (filtering, sentence dedup, minhash, etc)