The outbreak COVID-19 virus caused a significant impact on the health of people all over the world. Therefore, it is essential to have a piece of constant and accurate information about the disease with everyone. This paper describes our prediction system for WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets. The dataset for this task contains size 10,000 tweets in English labeled by humans. The ensemble model from our three transformer and deep learning models is used for the final prediction. The experimental result indicates that we have achieved F1 for the INFORMATIVE label on our systems at 88.81% on the test set.

翻译：爆发的COVID-19病毒对全世界人民的健康产生了重大影响。 因此, 向所有人提供有关这一疾病的固定和准确的信息至关重要。 本文描述了我们WNUT-2020任务2: 识别信息COVID-19英语Tweets的预测系统: 识别信息COVID-19英语Tweets。 这项任务的数据集包含由人类标注的10 000个英文推文。 我们三个变压器和深层学习模型的组合模型用于最终预测。 实验结果显示,我们已经在测试集上以88.81%的88.81%的测试系统标识实现了F1。