Contextual Document Embeddings
Paper
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2410.02525
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Published
•
19
Jack Morris
jxm
AI & ML interests
natural language processing, text retrieval, embeddings, inversion
Recent Activity
new activity
about 14 hours ago
jxm/cde-small-v1:Optional: Link to new version
new activity
about 16 hours ago
jxm/cde-small-v2:Amazing model. Does it support multilingual?
posted
an
update
about 17 hours ago
New state-of-the-art BERT-size retrieval model: *cde-small-v2* 🥳🍾
Hi everyone! We at Cornell are releasing a new retrieval model this week. It uses the contextual embeddings framework, is based on ModernBERT backbone, and gets state-of-the-art results on the MTEB benchmark for its model size (140M parameters). cde-small-v2 gets an average score of 65.6 across the 56 datasets and sees improvements from our previous model in *every* task domain (retrieval, classification, etc.).
We made a lot of changes to make this model work. First of all, ModernBERT has a better tokenizer, which probably helped this work out-of-the-box. We also followed the principles from the CDE paper and used harder clusters and better hard-negative filtering, which showed a small performance improvement. And we made a few small changes that have been shown to work on the larger models: we disabled weight decay, masked out the prefix tokens during pooling, and added a residual connection from the first-stage to the second-stage for better gradient flow.
We're still looking for a computer sponsor to help us scale CDE to larger models. Since it's now state-of-the-art at the 100M parameter scale, it seems to be a reasonable bet that we could train a state-of-the-art large model if we had the GPUs. If you're interested in helping with this, please reach out!
Here's a link to the model: https://huggingface.co/jxm/cde-small-v2
And here's a link to the paper: https://huggingface.co/papers/2410.02525
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