hanlp 2.1.3
HanLP: Han Language Processing
License: Apache License 2.0
Requires Python: >=3.6
Keywords: corpus, machine-learning, NLU, NLP

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<h2 align="center">HanLP: Han Language Processing</h2>

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<h4 align="center">
    <a href="https://github.com/hankcs/HanLP/tree/doc-zh">中文</a> |
    <a href="https://github.com/hankcs/HanLP/tree/doc-ja">日本語</a> |
    <a href="https://hanlp.hankcs.com/docs/">Docs</a> |
    <a href="https://bbs.hankcs.com/">Forum</a>
</h4>

HanLP is the multilingual NLP library designed for researchers and enterprises, built on PyTorch and TensorFlow 2.x to advance state-of-the-art deep learning techniques in 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](https://hanlp.hankcs.com/docs/api/hanlp/pretrained/mtl.html#hanlp.pretrained.mtl.UD_ONTONOTES_TOK_POS_LEM_FEA_NER_SRL_DEP_SDP_CON_MMINILMV2L6): 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.

<details>
  <summary>Click to expand tutorials for RESTful APIs</summary>

  ### 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') # Support en, ja, zh, mul
  ```

  ### Java

  Insert the following dependency into your `pom.xml`.

  ```xml
  <dependency>
    <groupId>com.hankcs.hanlp.restful</groupId>
    <artifactId>hanlp-restful</artifactId>
    <version>0.0.15</version>
  </dependency>
  ```

  Create a client with our API endpoint and your auth.

  ```java
  HanLPClient HanLP = new HanLPClient("https://hanlp.hankcs.com/api", null, "mul"); // Support en, ja, zh, 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.

</details>


## Native APIs

```bash
pip install hanlp
```

HanLP requires Python 3.6 or higher. While GPU or TPU acceleration is recommended, it is 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.
- To process English, Chinese or Japanese, HanLP provides mono-lingual models in each language which significantly outperform the
  multilingual model. See [docs](https://hanlp.hankcs.com/docs/api/hanlp/pretrained/index.html) for the list of models.

## 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_96.7'
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.

## Performance

The performance of multi-task learning models is shown in the following table.

<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>

- 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 of [a single-task mono-lingual model](https://hanlp.hankcs.com/docs/api/hanlp/pretrained/index.html) if you are
  targeting at high accuracy instead of faster speed.
- A state-of-the-art [AMR model](https://hanlp.hankcs.com/docs/api/hanlp/pretrained/amr.html) has been released.

## Citing

If you use HanLP in your research, please cite [our EMNLP paper](https://aclanthology.org/2021.emnlp-main.451):

```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