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Found 6 papers, 1 papers with code
QuaLA-MiniLM: a Quantized Length Adaptive MiniLM
Our quantized length-adaptive MiniLM model (QuaLA-MiniLM) is trained only once, dynamically fits any inference scenario, and achieves an accuracy-efficiency trade-off superior to any other efficient approaches per any computational budget on the SQuAD1. 1 dataset (up to x8. 8 speedup with <1% accuracy loss).
Dynamic-TinyBERT: Boost TinyBERT's Inference Efficiency by Dynamic Sequence Length
Additionally, a separate model must be trained for each inference scenario with its distinct computational budget.
Training Compact Models for Low Resource Entity Tagging using Pre-trained Language Models
In this work-in-progress we combined the effectiveness of transfer learning provided by pre-trained masked language models with a semi-supervised approach to train a fast and compact model using labeled and unlabeled examples.
Term Set Expansion based NLP Architect by Intel AI Lab
We present SetExpander, a corpus-based system for expanding a seed set of terms into amore complete set of terms that belong to the same semantic class.
SetExpander: End-to-end Term Set Expansion Based on Multi-Context Term Embeddings
We present SetExpander, a corpus-based system for expanding a seed set of terms into a more complete set of terms that belong to the same semantic class.
Term Set Expansion based on Multi-Context Term Embeddings: an End-to-end Workflow
We present SetExpander, a corpus-based system for expanding a seed set of terms into a more complete set of terms that belong to the same semantic class.
