Metadata-Version: 2.1
Name: hanlp
Version: 2.1.0a65
Summary: HanLP: Han Language Processing
Home-page: https://github.com/hankcs/HanLP
Author: hankcs
Author-email: hankcshe@gmail.com
License: Apache License 2.0
Description: # HanLP: Han Language Processing
        
        [中文](https://github.com/hankcs/HanLP/tree/doc-zh) | [日本語](https://github.com/hankcs/HanLP/tree/doc-ja) | [docs](https://hanlp.hankcs.com/docs/) | [1.x](https://github.com/hankcs/HanLP/tree/1.x) | [forum](https://bbs.hankcs.com/) | [![Open In Colab](https://file.hankcs.com/img/colab-badge.svg)](https://colab.research.google.com/drive/1KPX6t1y36TOzRIeB4Kt3uJ1twuj6WuFv?usp=sharing)
        
        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 104 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 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](https://bbs.hankcs.com/t/apply-for-free-hanlp-restful-apis/3178) under the [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license.
        
         ### Python
        
        ```bash
        pip install hanlp_restful
        ```
        
        Create a client with our API endpoint and your auth.
        
        ```python
        from hanlp_restful import HanLPClient
        HanLP = HanLPClient('https://hanlp.hankcs.com/api', auth=None, language='mul')
        ```
        
        ### Java
        
        Insert the following dependency into your `pom.xml`.
        
        ```xml
        <dependency>
          <groupId>com.hankcs.hanlp.restful</groupId>
          <artifactId>hanlp-restful</artifactId>
          <version>0.0.7</version>
        </dependency>
        ```
        
        Create a client with our API endpoint and your auth.
        
        ```java
        HanLPClient HanLP = new HanLPClient("https://hanlp.hankcs.com/api", null, "mul");
        ```
        
        ### Quick Start
        
        No matter which language you use, the same interface can be used to parse a document.
        
        ```python
        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](https://hanlp.hankcs.com/docs/tutorial.html) for visualization, annotation guidelines and more details.
        
        ## Native APIs
        
        ```bash
        pip install hanlp
        ```
        
        HanLP requires Python 3.6 or later. GPU/TPU is suggested but not mandatory.
        
        ### Quick Start
        
        ```python
        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。']))
        ```
        
        In particular, the Python `HanLPClient` can also be used as a callable function following the same semantics. See [docs](https://hanlp.hankcs.com/docs/tutorial.html) for visualization, annotation guidelines and more details.
        
        ## Train Your Own Models
        
        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.
        
        ```python
        tokenizer = TransformerTaggingTokenizer()
        save_dir = 'data/model/cws/sighan2005_pku_bert_base_96.70'
        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=1609836303,
        )
        tokenizer.evaluate(SIGHAN2005_PKU_TEST, save_dir)
        ```
        
        The result is guaranteed to be `96.70` as the random feed 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.
        
        ## Performance
        
        <table><thead><tr><th rowspan="2">lang</th><th rowspan="2">corpora</th><th rowspan="2">model</th><th colspan="2">tok</th><th colspan="4">pos</th><th colspan="3">ner</th><th rowspan="2">dep</th><th rowspan="2">con</th><th rowspan="2">srl</th><th colspan="4">sdp</th><th rowspan="2">lem</th><th rowspan="2">fea</th><th rowspan="2">amr</th></tr><tr><th>fine</th><th>coarse</th><th>ctb</th><th>pku</th><th>863</th><th>ud</th><th>pku</th><th>msra</th><th>ontonotes</th><th>SemEval16</th><th>DM</th><th>PAS</th><th>PSD</th></tr></thead><tbody><tr><td rowspan="2">mul</td><td rowspan="2">UD2.7<br>OntoNotes5</td><td>small</td><td>98.62</td><td>-</td><td>-</td><td>-</td><td>-</td><td>93.23</td><td>-</td><td>-</td><td>74.42</td><td>79.10</td><td>76.85</td><td>70.63</td><td>-</td><td>91.19</td><td>93.67</td><td>85.34</td><td>87.71</td><td>84.51</td><td>-</td></tr><tr><td>base</td><td>98.97</td><td>-</td><td>-</td><td>-</td><td>-</td><td>90.32</td><td>-</td><td>-</td><td>80.32</td><td>78.74</td><td>71.23</td><td>73.63</td><td>-</td><td>92.60</td><td>96.04</td><td>81.19</td><td>85.08</td><td>82.13</td><td>-</td></tr><tr><td rowspan="5">zh</td><td rowspan="2">open</td><td>small</td><td>97.25</td><td>-</td><td>96.66</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>95.00</td><td>84.57</td><td>87.62</td><td>73.40</td><td>84.57</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>base</td><td>97.50</td><td>-</td><td>97.07</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>96.04</td><td>87.11</td><td>89.84</td><td>77.78</td><td>87.11</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="3">close</td><td>small</td><td>96.70</td><td>95.93</td><td>96.87</td><td>97.56</td><td>95.05</td><td>-</td><td>96.22</td><td>95.74</td><td>76.79</td><td>84.44</td><td>88.13</td><td>75.81</td><td>74.28</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>base</td><td>97.52</td><td>96.44</td><td>96.99</td><td>97.59</td><td>95.29</td><td>-</td><td>96.48</td><td>95.72</td><td>77.77</td><td>85.29</td><td>88.57</td><td>76.52</td><td>73.76</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>ernie</td><td>96.95</td><td>97.29</td><td>96.76</td><td>97.64</td><td>95.22</td><td>-</td><td>97.31</td><td>96.47</td><td>77.95</td><td>85.67</td><td>89.17</td><td>78.51</td><td>74.10</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr></tbody></table>
        
        - AMR models will be released soon.
        
        ## Citing
        
        If you use HanLP in your research, please cite this repository. 
        
        ```bibtex
        @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.",
        }
        ```
        
        ## License
        
        ### Codes
        
        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.
        
        ### Models
        
        Unless otherwise specified, all models in HanLP are licensed under  [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). 
        
        ## References
        
        https://hanlp.hankcs.com/docs/references.html
        
        
Keywords: corpus,machine-learning,NLU,NLP
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Development Status :: 3 - Alpha
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Text Processing :: Linguistic
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: full
