# Copyright 2021 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # TEAMS (Training ELECTRA Augmented with Multi-word Selection) **Note:** This project is working in progress and please stay tuned. TEAMS is a text encoder pre-training method that simultaneously learns a generator and a discriminator using multi-task learning. We propose a new pre-training task, multi-word selection, and combine it with previous pre-training tasks for efficient encoder pre-training. We also develop two techniques, attention-based task-specific heads and partial layer sharing, to further improve pre-training effectiveness. Our academic paper [[1]](#1) which describes TEAMS in detail can be found here: https://arxiv.org/abs/2106.00139. ## References [1] Jiaming Shen, Jialu Liu, Tianqi Liu, Cong Yu and Jiawei Han, "Training ELECTRA Augmented with Multi-word Selection", Findings of the Association for Computational Linguistics: ACL 2021.