Published May 9, 2022
| Version 1
Dataset
Open
Trained Models from "General Cross-Architecture Distillation of Pretrained Language Models into Matrix Embeddings"
Authors/Creators
- 1. Max Planck Institute for Psycholinguistics
- 2. University of Ulm
Description
Trained models from the paper:
Lukas Galke, Isabell Cuber, Christoph Meyer, Henrik Ferdinand Noelscher, Angelina Sonderecker, and Ansgar Scherp: General Cross-Architecture Distillation of Pretrained Language Models into Matrix Embeddings, in: International Joint Conference on Neural Networks (IJCNN), 2022.
- File seq2mat_hybrid_bidirectional_sbertlike-100p-bsz512 holds the model from pretraining
- File ws2020_transformer_final_models holds the fine-tuned models for each task of the GLUE benchmark