We have trained a named entity recognition (NER) model that screens Swedish
job ads for different kinds of useful information (e.g. skills required from a
job seeker). It was obtained by fine-tuning KB-BERT. The biggest challenge we
faced was the creation of a labelled dataset, which required manual annotation.
This paper gives an overview of the methods we employed to make the annotation
process more efficient and to ensure high quality data. We also report on the
performance of the resulting model.

我们训练了一个命名实体识别（NER）模型来筛选瑞典招聘广告中的各种有用信息（例如，求职者所需的技能）。该模型是通过对 KB-BERT 进行微调获得的。我们面临的最大挑战是创建一个带标签的数据集，这需要手动注释。本文概述了我们采用的方法，以提高注释过程的效率并确保数据质量。我们还报告了所得模型的性能。

用命名实体识别标注的招聘广告

Annotated Job Ads with Named Entity Recognition

Nowadays, containerized freight transport is one of the most important
transportation systems that is undergoing an automation process due to the Deep
Learning success. However, it suffers from a lack of annotated data in order to
incorporate state-of-the-art neural network models to its systems. In this
paper we present an innovative methodology to generate a realistic, varied,
balanced, and labelled dataset for visual inspection task of containers in a
dock environment. In addition, we validate this methodology with multiple
visual tasks recurrently found in the state of the art. We prove that the
generated synthetic labelled dataset allows to train a deep neural network that
can be used in a real world scenario. On the other side, using this methodology
we provide the first open synthetic labelled dataset called SeaFront available
in: this https URL

本文介绍了一种创新的方法来生成一个逼真、多样、平衡和带标签的数据集，用于集装箱在码头环境下的视觉检查任务，并且我们证明了生成的合成标记数据集可以用于训练可以在实际环境中使用的深度神经网络。此外，我们提供了第一个开放的合成标记数据集 “SeaFront”。

一种生成用于视觉集装箱检查的合成标记数据集的方法

Methodology for generating synthetic labeled datasets for visual  container inspection

Stock price prediction can be made more efficient by considering the price
fluctuations and understanding the sentiments of people. A limited number of
models understand financial jargon or have labelled datasets concerning stock
price change. To overcome this challenge, we introduced FinALBERT, an ALBERT
based model trained to handle financial domain text classification tasks by
labelling Stocktwits text data based on stock price change. We collected
Stocktwits data for over ten years for 25 different companies, including the
major five FAANG (Facebook, Amazon, Apple, Netflix, Google). These datasets
were labelled with three labelling techniques based on stock price changes. Our
proposed model FinALBERT is fine-tuned with these labels to achieve optimal
results. We experimented with the labelled dataset by training it on
traditional machine learning, BERT, and FinBERT models, which helped us
understand how these labels behaved with different model architectures. Our
labelling method competitive advantage is that it can help analyse the
historical data effectively, and the mathematical function can be easily
customised to predict stock movement.

介绍一个基于 ALBERT 算法的 FinALBERT 模型，训练数据为标记为股票价格变化的 25 个公司的 10 年的 Stocktwits 数据集，通过对传统机器学习、BERT 和 FinBERT 模型训练数据集的实验，得出该标记方法有竞争力优势，可以有效分析历史数据，预测股票走势。