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.

fastHan 是一个开源的工具包，提供中文自然语言处理的四个基本任务：中文分词，词性标注，命名实体识别和依存句法分析。使用一个基于裁剪 BERT 的多任务模型。fastHan 在 13 个任务上获得了接近最先进性能的结果，并且在依存句法和命名实体识别方面达到了先进水平，在中文分词和词性标注方面则实现了最先进水平。此外，fastHan 的可移植性也很强，在未经训练的语料库上比流行的分词工具表现更好。用户可以使用自己标记的数据进一步优化 fastHan。fastHan 是一个小型优秀性能的用户友好型 python 包，将用户与内部技术细节隔离开来，方便易用。

fastHan: 一个基于 BERT 的中文 NLP 多任务工具包

fastHan: A BERT-based Multi-Task Toolkit for Chinese NLP

Chinese word segmentation and dependency parsing are two fundamental tasks
for Chinese natural language processing. The dependency parsing is defined on
word-level. Therefore word segmentation is the precondition of dependency
parsing, which makes dependency parsing suffer from error propagation and
unable to directly make use of the character-level pre-trained language model
(such as BERT). In this paper, we propose a graph-based model to integrate
Chinese word segmentation and dependency parsing. Different from previous
transition-based joint models, our proposed model is more concise, which
results in fewer efforts of feature engineering. Our graph-based joint model
achieves better performance than previous joint models and state-of-the-art
results in both Chinese word segmentation and dependency parsing. Besides, when
BERT is combined, our model can substantially reduce the performance gap of
dependency parsing between joint models and gold-segmented word-based models.
Our code is publicly available at this https URL

本文提出了一种基于图形的模型来集成中文分词和依赖分析，相较于之前基于转移的联合模型，我们的模型更简洁，从而在中文分词和依赖分析方面取得了更好的性能，并且当结合 BERT 时，我们的模型可以显著减少联合模型和基于黄金分割词的模型之间的性能差距。