Package: hanlp
HanLP: Han Language Processing中文|日本語|Docs|ForumThe multilingual NLP library for researchers and companies, built on PyTorch and TensorFlow 2.x, for advancing
state-of-the-art deep learning techniques in both academia and industry. HanLP was designed from day one to be
efficient, user-friendly and extendable.Thanks to open-access corpora like Universal Dependencies and OntoNotes, HanLP 2.1 now offers 10 joint tasks on130
languages: tokenization, lemmatization, part-of-speech tagging, token feature extraction, dependency parsing,
constituency parsing, semantic role labeling, semantic dependency parsing, abstract meaning representation (AMR)
parsing.For end users, HanLP offers light-weighted RESTful APIs and native Python APIs.RESTful APIsTiny packages in several KBs for agile development and mobile applications. Although anonymous users are welcomed, an
auth key is suggested
anda free one can be applied hereunder
theCC BY-NC-SA 4.0license.Click to expand tutorials for RESTful APIsPythonpipinstallhanlp_restfulCreate a client with our API endpoint and your auth.fromhanlp_restfulimportHanLPClientHanLP=HanLPClient('https://hanlp.hankcs.com/api',auth=None,language='mul')# mul: multilingual, zh: ChineseJavaInsert the following dependency into yourpom.xml.<dependency><groupId>com.hankcs.hanlp.restful</groupId><artifactId>hanlp-restful</artifactId><version>0.0.15</version></dependency>Create a client with our API endpoint and your auth.HanLPClientHanLP=newHanLPClient("https://hanlp.hankcs.com/api",null,"mul");// mul: multilingual, zh: ChineseQuick StartNo matter which language you use, the same interface can be used to parse a document.HanLP.parse("In 2021, HanLPv2.1 delivers state-of-the-art multilingual NLP techniques to production environments. 2021年、HanLPv2.1は次世代の最先端多言語NLP技術を本番環境に導入します。2021年 HanLPv2.1为生产环境带来次世代最先进的多语种NLP技术。")Seedocsfor visualization, annotation guidelines and more details.Native APIspipinstallhanlpHanLP requires Python 3.6 or later. GPU/TPU is suggested but not mandatory.Quick StartimporthanlpHanLP=hanlp.load(hanlp.pretrained.mtl.UD_ONTONOTES_TOK_POS_LEM_FEA_NER_SRL_DEP_SDP_CON_XLMR_BASE)print(HanLP(['In 2021, HanLPv2.1 delivers state-of-the-art multilingual NLP techniques to production environments.','2021年、HanLPv2.1は次世代の最先端多言語NLP技術を本番環境に導入します。','2021年 HanLPv2.1为生产环境带来次世代最先进的多语种NLP技术。']))In particular, the PythonHanLPClientcan also be used as a callable function following the same semantics.
Seedocsfor visualization, annotation guidelines and more details.To process Chinese or Japanese, HanLP provides mono-lingual models in each language which significantly outperform the
multi-lingual model. Seedocsfor the list of models.Train Your Own ModelsTo write DL models is not hard, the real hard thing is to write a model able to reproduce the scores in papers. The
snippet below shows how to surpass the state-of-the-art tokenizer in 6 minutes.tokenizer=TransformerTaggingTokenizer()save_dir='data/model/cws/sighan2005_pku_bert_base_96.7'tokenizer.fit(SIGHAN2005_PKU_TRAIN_ALL,SIGHAN2005_PKU_TEST,# Conventionally, no devset is used. See Tian et al. (2020).save_dir,'bert-base-chinese',max_seq_len=300,char_level=True,hard_constraint=True,sampler_builder=SortingSamplerBuilder(batch_size=32),epochs=3,adam_epsilon=1e-6,warmup_steps=0.1,weight_decay=0.01,word_dropout=0.1,seed=1660853059,)tokenizer.evaluate(SIGHAN2005_PKU_TEST,save_dir)The result is guaranteed to be96.73as the random seed is fixed. Different from some overclaiming papers and
projects, HanLP promises every single digit in our scores is reproducible. Any issues on reproducibility will be treated
and solved as a top-priority fatal bug.PerformanceThe performance of multi-task learning models is shown in the following table.langcorporamodeltokposnerdepconsrlsdplemfeaamrfinecoarsectbpku863udpkumsraontonotesSemEval16DMPASPSDmulUD2.7OntoNotes5small98.62----93.23--74.4279.1076.8570.63-91.1993.6785.3487.7184.51-base98.97----90.32--80.3278.7471.2373.63-92.6096.0481.1985.0882.13-zhopensmall97.25-96.66-----95.0084.5787.6273.4084.57------base97.50-97.07-----96.0487.1189.8477.7887.11------closesmall96.7095.9396.8797.5695.05-96.2295.7476.7984.4488.1375.8174.28------base97.5296.4496.9997.5995.29-96.4895.7277.7785.2988.5776.5273.76------ernie96.9597.2996.7697.6495.22-97.3196.4777.9585.6789.1778.5174.10------Multi-task learning models often under-perform their single-task learning counterparts according to our latest
research. Similarly, mono-lingual models often outperform multi-lingual models. Therefore, we strongly recommend the
use ofa single-task mono-lingual modelif you are
targeting at high accuracy instead of faster speed.A state-of-the-artAMR modelhas been released.CitingIf you use HanLP in your research, please citeour EMNLP paper:@inproceedings{he-choi-2021-stem,title="The Stem Cell Hypothesis: Dilemma behind Multi-Task Learning with Transformer Encoders",author="He, Han and Choi, Jinho D.",booktitle="Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",month=nov,year="2021",address="Online and Punta Cana, Dominican Republic",publisher="Association for Computational Linguistics",url="https://aclanthology.org/2021.emnlp-main.451",pages="5555--5577",abstract="Multi-task learning with transformer encoders (MTL) has emerged as a powerful technique to improve performance on closely-related tasks for both accuracy and efficiency while a question still remains whether or not it would perform as well on tasks that are distinct in nature. We first present MTL results on five NLP tasks, POS, NER, DEP, CON, and SRL, and depict its deficiency over single-task learning. We then conduct an extensive pruning analysis to show that a certain set of attention heads get claimed by most tasks during MTL, who interfere with one another to fine-tune those heads for their own objectives. Based on this finding, we propose the Stem Cell Hypothesis to reveal the existence of attention heads naturally talented for many tasks that cannot be jointly trained to create adequate embeddings for all of those tasks. Finally, we design novel parameter-free probes to justify our hypothesis and demonstrate how attention heads are transformed across the five tasks during MTL through label analysis.",}LicenseCodesHanLP is licensed underApache License 2.0. You can use HanLP in your commercial products for free. We would
appreciate it if you add a link to HanLP on your website.ModelsUnless otherwise specified, all models in HanLP are licensed
underCC BY-NC-SA 4.0.Referenceshttps://hanlp.hankcs.com/docs/references.html