EttinX Cross Encoder: Natural Language Inference (NLI) This cross encoder performs sequence classification for contradiction/neutral/entailment labels. This has drop in compatibility with comparable sentence transformers cross encoders. To train this model, I added teacher logits to the all nli dataset dleemiller/all nli distill from the dleemiller/ModernCE large nli model. This significantly improves performance above standard training. This 32m architecture is based on ModernBERT and is an excellent candidate for lightweight CPU inference . Features High performing: Achieves 83.80% and 88.20% (Micro F1) on MNLI mismatched and SNLI test. Efficient architecture: Based on the Ettin 32m encoder design (32M parameters), offering faster inference speeds. Extended context length: Processes sequences up to 8192 tokens, great for LLM output evals. Performance Model MNLI Mismatched SNLI Test Context Length Parameters dleemiller/ModernCE large nli 0.9202 0.9110 8192 395M dleemiller/ModernCE base nli 0.9034 0.9025 8192 149M cross encoder/nli deberta v3 large 0.9049 0.9220 512 435M cross encoder/nli deberta v3 base 0.9004 0.9234 512 184M cross encoder/nli distilroberta base 0.8398 0.8838 512…
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