  Understanding the evolution of job requirements is becoming more important
for workers, companies and public organizations to follow the fast
transformation of the employment market. Fortunately, recent natural language
processing (NLP) approaches allow for the development of methods to
automatically extract information from job ads and recognize skills more
precisely. However, these efficient approaches need a large amount of annotated
data from the studied domain which is difficult to access, mainly due to
intellectual property. This article proposes a new public dataset, FIJO,
containing insurance job offers, including many soft skill annotations. To
understand the potential of this dataset, we detail some characteristics and
some limitations. Then, we present the results of skill detection algorithms
using a named entity recognition approach and show that transformers-based
models have good token-wise performances on this dataset. Lastly, we analyze
some errors made by our best model to emphasize the difficulties that may arise
when applying NLP approaches.
