Calibrated Seq2seq Models for Efficient and Generalizable Ultra-fine Entity Typing
Paper • 2311.00835 • Published
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CASENT
CASENT is a lightweight multi-label entity classification model designed for extremely large label space (e.g., UFET and WikiData). It can also be used for entity extraction and tagging when integrated with a span detector. CASENT offers several advantages compared to previous methods: 1) Standard maximum likelihood training; 2) Efficient inference through a single autoregressive decoding pass; 3) Calibrated confidence scores; 4) Strong generalization performance to unseen domains and types.
Paper: Calibrated Seq2Seq Models for Efficient and Generalizable Ultra-fine Entity Typing (EMNLP 2023 Findings)
Repository & Demo: https://github.com/yanlinf/CASENT
Contact: yanlin@megagon.ai
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Base model
google-t5/t5-large
# Load model directly from transformers import AutoTokenizer, T5ForEntityTyping tokenizer = AutoTokenizer.from_pretrained("yanlinf/casent-large") model = T5ForEntityTyping.from_pretrained("yanlinf/casent-large")