jhu-clsp
/

ettin-encoder-17m

Model card Files Files and versions Community

orionweller commited on 24 days ago

Commit

9876074

·

verified ·

1 Parent(s): 04c021a

Update README.md

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -14,7 +14,7 @@ pipeline_tag: fill-mask
 > 🎯 **TL;DR**: State-of-the-art paired encoder and decoder models (17M-1B params) trained identically for fair comparison with open data. Encoders beat ModernBERT. Decoders beat Llama 3.2/SmolLM2.
-📄 [Paper (Coming Soon)](https://github.com/jhu-clsp/ettin-encoder-vs-decoder) | 🚀 [GitHub Repository](https://github.com/jhu-clsp/ettin-encoder-vs-decoder)
 This model is part of the Ettin suite - the first collection of paired encoder-only and decoder-only models trained with identical data, architecture, and training recipes. Ettin enables fair comparisons between encoder and decoder architectures across multiple scales, providing state-of-the-art performance for open-data models in their respective size categories.

 > 🎯 **TL;DR**: State-of-the-art paired encoder and decoder models (17M-1B params) trained identically for fair comparison with open data. Encoders beat ModernBERT. Decoders beat Llama 3.2/SmolLM2.
+📄 [Paper](https://arxiv.org/abs/2507.11412) | 🚀 [GitHub Repository](https://github.com/jhu-clsp/ettin-encoder-vs-decoder)
 This model is part of the Ettin suite - the first collection of paired encoder-only and decoder-only models trained with identical data, architecture, and training recipes. Ettin enables fair comparisons between encoder and decoder architectures across multiple scales, providing state-of-the-art performance for open-data models in their respective size categories.