hanlp_restful¶

class hanlp_restful.HanLPClient(url: str, auth: str = None, language=None, timeout=5)[source]¶

Parameters

url (str) – An API endpoint to a service provider.
auth (str) – An auth key licenced from a service provider.
language (str) – The default language for each parse() call. Contact the service provider for the list of languages supported. Conventionally, zh is used for Chinese and mul for multilingual. Leave None to use the default language on server.
timeout (int) – Maximum waiting time in seconds for a request.

__call__(text: Union[str, List[str]] = None, tokens: List[List[str]] = None, tasks: Optional[Union[str, List[str]]] = None, skip_tasks: Optional[Union[str, List[str]]] = None, language: str = None) → hanlp_common.document.Document [source]¶: A shortcut of parse().

about() → Dict[str, Any][source]¶

Get the information about server and your client.

Returns: A dict containing your rate limit and server version etc.

coreference_resolution(text: Optional[str] = None, tokens: Optional[List[List[str]]] = None, speakers: Optional[List[str]] = None, language: Optional[str] = None) → Union[Dict[str, Union[List[str], List[List[Tuple[str, int, int]]]]], List[List[Tuple[str, int, int]]]][source]¶

Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities.

Parameters

text – A piece of text, usually a document without tokenization.
tokens – A list of sentences where each sentence is a list of tokens.
speakers – A list of speakers where each speaker is a str representing the speaker’s ID, e.g., Tom.
language – The language of input text. None to use the default language.

Examples:

HanLP.coreference_resolution('我姐送我她的猫。我很喜欢它。')
{'clusters': [
              [['我', 0, 1], ['我', 3, 4], ['我', 8, 9]], # 指代说话人
              [['我姐', 0, 2], ['她', 4, 5]],             # 指代说话人的姐姐
              [['她的猫', 4, 7], ['它', 11, 12]]],        # 指代说话人的姐姐的猫
 'tokens': ['我', '姐', '送', '我', '她', '的', '猫', '。', '我', '很', '喜欢', '它', '。']}

HanLP.coreference_resolution(tokens=[['我', '姐', '送', '我', '她', '的', '猫', '。'],
                                     ['我', '很', '喜欢', '它', '。']])
             [
              [['我', 0, 1], ['我', 3, 4], ['我', 8, 9]], # 指代说话人
              [['我姐', 0, 2], ['她', 4, 5]],             # 指代说话人的姐姐
              [['她的猫', 4, 7], ['它', 11, 12]]],        # 指代说话人的姐姐的猫

Returns: When text is specified, return the clusters and tokens. Otherwise just the clusters, In this case, you need to sum(tokens, []) in order to match the span indices with tokens

parse(text: Union[str, List[str]] = None, tokens: List[List[str]] = None, tasks: Optional[Union[str, List[str]]] = None, skip_tasks: Optional[Union[str, List[str]]] = None, language: str = None) → hanlp_common.document.Document [source]¶

Parse a piece of text.

Parameters

text – A document (str), or a list of sentences (List[str]).
tokens – A list of sentences where each sentence is a list of tokens.
tasks – The tasks to predict.
skip_tasks – The tasks to skip.
language – The language of input text or tokens. None to use the default language on server.

Returns

A Document.

Raises

HTTPError – Any errors happening on the Internet side or the server side. Refer to the code and msg of the exception for more details. A list of common errors :

400 Bad Request indicates that the server cannot process the request due to a client fault (e.g., text too long, language unsupported).
401 Unauthorized indicates that the request lacks valid auth credentials for the API.
422 Unprocessable Entity indicates that the content type of the request entity is not in proper json format.
429 Too Many Requests indicates the user has sent too many requests in a given amount of time (“rate limiting”).

semantic_textual_similarity(text: Union[Tuple[str, str], List[Tuple[str, str]]], language: str = None) → Union[float, List[float]][source]¶

Semantic textual similarity deals with determining how similar two pieces of texts are.

Parameters

text – A pair or pairs of text.
language – The language of input text. None to use the default language.

Examples:

HanLP.semantic_textual_similarity([
    ('看图猜一电影名', '看图猜电影'),
    ('无线路由器怎么无线上网', '无线上网卡和无线路由器怎么用'),
    ('北京到上海的动车票', '上海到北京的动车票'),
])
# Output:
[
    0.9764469861984253,   # Similarity of ('看图猜一电影名', '看图猜电影')
    0.0,                  # Similarity of ('无线路由器怎么无线上网', '无线上网卡和无线路由器怎么用')
    0.003458738327026367  # Similarity of ('北京到上海的动车票', '上海到北京的动车票')
]

Returns: Similarities.

text_style_transfer(text: Union[str, List[str]], target_style: str, language: str = None) → Union[str, List[str]][source]¶

Text style transfer aims to change the style of the input text to the target style while preserving its content.

Parameters

text – Source text.
target_style – Target style.
language – The language of input text. None to use the default language.

Examples:

HanLP.text_style_transfer(['国家对中石油抱有很大的期望.', '要用创新去推动高质量的发展。'],
                          target_style='gov_doc')
# Output:
[
    '国家对中石油寄予厚望。',
    '要以创新驱动高质量发展。'
]

HanLP.text_style_transfer('我看到了窗户外面有白色的云和绿色的森林', target_style='modern_poetry')
# Output:
'我看见窗外的白云绿林'

Returns: Text or a list of text of the target style.

tokenize(text: Union[str, List[str]], coarse: Optional[bool] = None, language=None) → List[List[str]][source]¶

Split a document into sentences and tokenize them. Note that it is always faster to tokenize a whole document than to tokenize each sentence one by one. So avoid calling this method sentence by sentence but put sentences into a list and pass them to the text argument.

Parameters

text – A document (str), or a list of sentences (List[str]).
coarse – Whether to perform coarse-grained or fine-grained tokenization.
language – The language of input text. None to use the default language.

Examples:

# Avoid tokenizing sentence by sentence, it is expensive:
HanLP.tokenize('商品和服务。')
[['商品', '和', '服务', '。']]
HanLP.tokenize('阿婆主来到北京立方庭参观自然语义科技公司')
[['阿婆主', '来到', '北京', '立方庭', '参观', '自然', '语义', '科技', '公司']]

# Instead, the following codes are much faster:
HanLP.tokenize('商品和服务。阿婆主来到北京立方庭参观自然语义科技公司')
[['商品', '和', '服务', '。'],
 ['阿婆主', '来到', '北京', '立方庭', '参观', '自然', '语义', '科技', '公司']]

# To tokenize with coarse-grained standard:
HanLP.tokenize('商品和服务。阿婆主来到北京立方庭参观自然语义科技公司', coarse=True)
[['商品', '和', '服务', '。'],
 ['阿婆主', '来到', '北京', '立方庭', '参观', '自然语义科技公司']]

# To tokenize pre-segmented sentences:
HanLP.tokenize(['商品和服务。', '当下雨天地面积水分外严重'])
[['商品', '和', '服务', '。'],
 ['当', '下雨天', '地面', '积水', '分', '外', '严重']]

# Multilingual tokenization by specifying language='mul':
HanLP.tokenize(
    ['In 2021, HanLPv2.1 delivers state-of-the-art multilingual NLP techniques
     'to production environment.',
     '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。'], language='mul')
[['In', '2021', ',', 'HanLPv2.1', 'delivers', 'state-of-the-art', 'multilingual',
  'NLP', 'techniques', 'to', 'production', 'environment', '.'],
 ['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', '。']]

Returns: A list of tokenized sentences.

HanLP Documentation

hanlp_restful¶