Tag
Liquid AI fine-tuned their LFM2.5-Encoder models (230M and 350M) to perform multi-label classification in a single forward pass, eliminating the need for decoding loops or parsing. This demonstrates efficient label scoring for NLP tasks.
Liquid AI releases LFM2.5-Encoder-230M and LFM2.5-Encoder-350M, bidirectional encoders optimized for non-generative tasks like classification and retrieval, offering fast CPU inference at long context.
Liquid AI released two new encoder models, LFM2.5-Encoder-230M and LFM2.5-Encoder-350M, that are fast, easy to train, and strongly multilingual, with speed benchmarks showing over 3.7x improvement on CPU compared to ModernBERT-base.
Liquid AI releases LFM2.5-Encoders (230M and 350M), efficient encoder models optimized for long-context inference on CPU, matching or beating larger encoders on benchmarks with 3.7x speedup over ModernBERT-base.
Liquid AI releases LFM2.5-Encoder-350M, a multilingual bidirectional encoder built on the LFM2 architecture, offering strong quality for its size, 8k context, and efficient on-device performance across 15 languages.
CrossBERT decouples representation learning from token reconstruction, enabling higher masking ratios and better sample efficiency, outperforming BERT on MTEB and GLUE benchmarks.
This paper introduces LeanGuard, a lightweight bidirectional encoder-based safety guardrail that matches the accuracy of larger reasoning-based guardrails while being approximately 100x faster, challenging the assumption that chain-of-thought reasoning is necessary for effective moderation.
The article describes libdeflate's new level 13, a deliberately slow DEFLATE compression level that achieves marginally better compression (0.134% on Silesia) at the cost of being 56x slower than level 12, designed for scenarios where data is compressed once and decompressed many times.
This paper introduces m3BERT, a multilingual bidirectional encoder with a novel pretraining strategy that jointly optimizes representations across transformer layers and multiple embedding dimensions, enabling a single model to be adapted to varied resource constraints. It significantly outperforms state-of-the-art models on the Bing-Click industrial retrieval dataset.