We introduce a new loss function based on cross entropy and SoftTriple loss, TripleEntropy, to improve classification performance for fine-tuning general knowledge pre-trained language models.This loss function can improve the robust RoBERTa baseline model fine-tuned with cross-entropy loss by about 0.02-2.29 percentage points.Thorough tests on popular datasets using our loss function indicate a steady gain.The fewer samples in the training dataset, the higher gain-thus, for smallsized dataset, it is about 0.71 percentage points, for mediumsized-0.86 percentage points, for large-0.20 percentage points, and for extra-large 0.04 percentage points.

UNLABELLED: Many time-consuming analyses of next -: generation sequencing data can be addressed with modern cloud computing. The Apache Hadoop-based solutions have become popular in genomics BECAUSE OF: their scalability in a cloud infrastructure. So far, most of these tools have been used for batch data processing rather than interactive data querying. The SparkSeq software has been created to take advantage of a new MapReduce framework, Apache Spark, for next-generation sequencing data. SparkSeq is a general-purpose, flexible and easily extendable library for genomic cloud computing. It can be used to build genomic analysis pipelines in Scala and run them in an interactive way. SparkSeq opens up the possibility of customized ad hoc secondary analyses and iterative machine learning algorithms. This article demonstrates its scalability and overall fast performance by running the analyses of sequencing datasets. Tests of SparkSeq also prove that the use of cache and HDFS block size can be tuned for the optimal performance on multiple worker nodes. AVAILABILITY AND IMPLEMENTATION: Available under open source Apache 2.0 license: https://bitbucket.org/mwiewiorka/sparkseq/.