Trained Models from "General Cross-Architecture Distillation of Pretrained Language Models into Matrix Embeddings"

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