We present fastHan, an open-source toolkit for four basic tasks in Chinese natural language processing: Chinese word segmentation  , Part-of-Speech  tagging, named entity recognition  , and dependency parsing. The kernel of fastHan is a joint many-task model based on a pruned BERT, which uses the first 8 layers in BERT. We also provide a 4-layer base version of model compressed from the 8-layer model. The joint-model is trained and evaluated in 13 corpora of four tasks, yielding near state-of-the-art (SOTA) performance in the dependency parsing task and SOTA performance in the other three tasks . In addition to its small size and excellent performance, fastHan is also very user-friendly. Implemented as a python package, fastHan allows users to easily download and use it. Users can get what they want with one line of code, even if they have little knowledge of deep learning . The project is released on Github.
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.
A
(CWS)
A
(POS)
A
(NER)
R
kernel
backbone
R
joint many-task
multi-task
D
version of
R
in
on
R
the dependency parsing task and
dependency parsing and NER, achieving
R
the other three tasks
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
D
also very
R
allows users to easily download and use it. Users can get what they want with one line of code, even if they have little knowledge of deep learning
isolates users from the internal technical details and is convenient to use