  We present fastHan, an open-source toolkit for four basic tasks in Chinese
natural language processing: Chinese word segmentation (CWS), Part-of-Speech
(POS) tagging, named entity recognition (NER), and dependency parsing. The
backbone of fastHan is a multi-task model based on a pruned BERT, which uses
the first 8 layers in BERT. We also provide a 4-layer base model compressed
from the 8-layer model. The joint-model is trained and evaluated on 13 corpora
of four tasks, yielding near state-of-the-art (SOTA) performance in dependency
parsing and NER, achieving SOTA performance in CWS and POS. Besides, fastHan's
transferability is also strong, performing much better than popular
segmentation tools on a non-training corpus. To better meet the need of
practical application, we allow users to use their own labeled data to further
fine-tune fastHan. In addition to its small size and excellent performance,
fastHan is user-friendly. Implemented as a python package, fastHan isolates
users from the internal technical details and is convenient to use. The project
is released on Github.

Computation and Language