In this work, we present an evaluation of smaller BLOOM model variants (350m/560m and 1b3/1b7) on various natural language processing tasks. This includes GLUE - language understanding, prompt-based zero-shot and few-shot text classification and extraction, question answering, prompt-based text generation, and multi-lingual text classification to understand model strengths/weaknesses and behavior. Empirical results show that BLOOM variants under-perform on all GLUE tasks (except WNLI), question-answering, and text generation. The variants bloom for WNLI, with an accuracy of 56.3%, and for prompt-based few-shot text extraction on MIT Movies and ATIS datasets. The BLOOM variants on average have 7% greater accuracy over GPT-2 and GPT-Neo models on Director and Airline Name extraction from MIT Movies and ATIS datasets, respectively.

Contrails, short for condensation trails, are line-shaped ice clouds produced by aircraft engine exhaust when they fly through cold and humid air. They generate a greenhouse effect by absorbing or directing back to Earth approximately 33% of emitted outgoing longwave radiation. They account for over half of the climate change resulting from aviation activities. Avoiding contrails and adjusting flight routes could be an inexpensive and effective way to reduce their impact. An accurate, automated, and reliable detection algorithm is required to develop and evaluate contrail avoidance strategies. Advancement in contrail detection has been severely limited due to several factors, primarily due to a lack of quality-labeled data. Recently, proposed a large human-labeled Landsat-8 contrails dataset. Each contrail is carefully labeled with various inputs in various scenes of Landsat-8 satellite imagery. In this work, we benchmark several popular segmentation models with combinations of different loss functions and encoder backbones. This work is the first to apply state-of-the-art segmentation techniques to detect contrails in low-orbit satellite imagery. Our work can also be used as an open benchmark for contrail segmentation and is publicly available.

代码切换（CS），普遍存在的现象，由于在多语种社区中提供的易于通信，仍然是语言处理中的被解读的问题。其背后的主要原因是：（1）利用大型预磨削多语言模型的最小努力，（2）缺乏注释数据。 CS中多语种模型性能低性能的区别案例是导致切换点的语言中的句子内混合。我们首先将两个序列标记任务 - 在4个不同的语言对中，带有套件的预磨料模型，以识别问题，然后选择最佳的执行模型，CHAR-BERT，其中（寻址（1））。然后，我们提出了一种自我训练方法，通过利用未解释的数据（寻址（2））来利用开关点偏置来重新利用开关点偏压来重新利用开关点偏置。我们终于证明我们的方法通过降低切换点性能之间的差距来对两个任务进行良好的，同时保留两种不同语言对中的两个不同语言对。我们的代码可在此处提供：https://github.com/pc09/emnlp2021-switch-point-biased.caString。