Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Hanlp | 30,911 | 238 | 22 | 6 days ago | 43 | February 25, 2023 | 9 | apache-2.0 | Python | |
Natural Language Processing for the next decade. Tokenization, Part-of-Speech Tagging, Named Entity Recognition, Syntactic & Semantic Dependency Parsing, Document Classification | ||||||||||
Spacy | 27,699 | 1,533 | 1,367 | 3 days ago | 226 | October 16, 2023 | 90 | mit | Python | |
💫 Industrial-strength Natural Language Processing (NLP) in Python | ||||||||||
Nlp Progress | 21,962 | a month ago | 52 | mit | Python | |||||
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks. | ||||||||||
Flair | 13,260 | 24 | 69 | 3 days ago | 31 | October 28, 2023 | 58 | other | Python | |
A very simple framework for state-of-the-art Natural Language Processing (NLP) | ||||||||||
Compromise | 10,947 | 163 | 145 | 9 days ago | 161 | November 16, 2023 | 93 | mit | JavaScript | |
modest natural-language processing | ||||||||||
Corenlp | 9,252 | 6 days ago | 1 | March 03, 2021 | 178 | gpl-3.0 | Java | |||
CoreNLP: A Java suite of core NLP tools for tokenization, sentence segmentation, NER, parsing, coreference, sentiment analysis, etc. | ||||||||||
Stanza | 6,863 | 2 | 91 | 2 hours ago | 22 | December 03, 2023 | 79 | other | Python | |
Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages | ||||||||||
Deeppavlov | 6,402 | 4 | 2 | a day ago | 57 | October 17, 2023 | 42 | apache-2.0 | Python | |
An open source library for deep learning end-to-end dialog systems and chatbots. | ||||||||||
Snips Nlu | 3,796 | 8 | 5 | 6 months ago | 34 | January 15, 2020 | 66 | apache-2.0 | Python | |
Snips Python library to extract meaning from text | ||||||||||
Spark Nlp | 3,514 | 30 | 2 days ago | 133 | October 26, 2023 | 49 | apache-2.0 | Scala | ||
State of the Art Natural Language Processing |
The 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 on 130 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.
Tiny packages in several KBs for agile development and mobile applications. Although anonymous users are welcomed, an auth key is suggested and a free one can be applied here under the CC BY-NC-SA 4.0 license.
pip install hanlp_restful
Create a client with our API endpoint and your auth.
from hanlp_restful import HanLPClient
HanLP = HanLPClient('https://hanlp.hankcs.com/api', auth=None, language='mul') # mul: multilingual, zh: Chinese
Insert the following dependency into your pom.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.
HanLPClient HanLP = new HanLPClient("https://hanlp.hankcs.com/api", null, "mul"); // mul: multilingual, zh: Chinese
No 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。")
See docs for visualization, annotation guidelines and more details.
pip install hanlp
HanLP requires Python 3.6 or later. GPU/TPU is suggested but not mandatory.
import hanlp
HanLP = 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。']))
HanLPClient
can also be used as a callable function following the same semantics.
See docs for visualization, annotation guidelines and more details.To 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 be 96.73
as 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.
The performance of multi-task learning models is shown in the following table.
lang | corpora | model | tok | pos | ner | dep | con | srl | sdp | lem | fea | amr | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
fine | coarse | ctb | pku | 863 | ud | pku | msra | ontonotes | SemEval16 | DM | PAS | PSD | |||||||||
mul | UD2.7 OntoNotes5 |
small | 98.62 | - | - | - | - | 93.23 | - | - | 74.42 | 79.10 | 76.85 | 70.63 | - | 91.19 | 93.67 | 85.34 | 87.71 | 84.51 | - |
base | 98.97 | - | - | - | - | 90.32 | - | - | 80.32 | 78.74 | 71.23 | 73.63 | - | 92.60 | 96.04 | 81.19 | 85.08 | 82.13 | - | ||
zh | open | small | 97.25 | - | 96.66 | - | - | - | - | - | 95.00 | 84.57 | 87.62 | 73.40 | 84.57 | - | - | - | - | - | - |
base | 97.50 | - | 97.07 | - | - | - | - | - | 96.04 | 87.11 | 89.84 | 77.78 | 87.11 | - | - | - | - | - | - | ||
close | small | 96.70 | 95.93 | 96.87 | 97.56 | 95.05 | - | 96.22 | 95.74 | 76.79 | 84.44 | 88.13 | 75.81 | 74.28 | - | - | - | - | - | - | |
base | 97.52 | 96.44 | 96.99 | 97.59 | 95.29 | - | 96.48 | 95.72 | 77.77 | 85.29 | 88.57 | 76.52 | 73.76 | - | - | - | - | - | - | ||
ernie | 96.95 | 97.29 | 96.76 | 97.64 | 95.22 | - | 97.31 | 96.47 | 77.95 | 85.67 | 89.17 | 78.51 | 74.10 | - | - | - | - | - | - |
If you use HanLP in your research, please cite our 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.",
}
HanLP is licensed under Apache 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.
Unless otherwise specified, all models in HanLP are licensed under CC BY-NC-SA 4.0.