lmp.dset._wnli

WNLI dataset.

class lmp.dset._wnli.WNLIDset(*, ver: Optional[str] = None)

Bases: BaseDset

Winograd NLI dataset.

Winograd NLI is a relaxation of the Winograd Schema Challenge 1 proposed as part of the GLUE 2 benchmark. This dataset only extract sentences from WNLI and no NLI labels were used.

Here are the statistics of each supported version. Tokens are separated by whitespaces.

Version	Number of samples	Maximum number of tokens	Minimum number of tokens
dev	142	63	4
test	292	60	4
train	1270	63	3

Parameters

ver (Optional[str], default: None) – Version of the dataset. Set to None to use the default version self.__class__.df_ver.

df_ver

Default version is train.

Type

ClassVar[str]

dset_name

CLI name of WNLI dataset is WNLI.

Type

ClassVar[str]

spls

All samples in the dataset.

Type

list[str]

ver

Version of the dataset.

Type

str

vers

Supported versions including 'train', 'dev' and 'test'.

Type

ClassVar[list[str]]

Examples

>>> from lmp.dset import WNLIDset
>>> dset = WNLIDset(ver='test')
>>> dset[0]
Mark was timid .

classmethod download_dataset() → None

Download WNLI dataset.

Download zip file from https://dl.fbaipublicfiles.com/glue/data/WNLI.zip and extract raw files from zip file. Raw files are named as wnli.ver.tsv, where ver is the version of the dataset. After extracting raw files the downloaded zip file will be deleted.

Return type

None

static download_file(mode: str, download_path: str, url: str) → None

Download file from url.

Parameters

• mode (str) – Can only be 'binary' or 'text'.

• download_path (str) – File path of the downloaded file.

• url (str) – URL of the file to be downloaded.

Return type

None

static norm(txt: str) → str

Text normalization.

Text will be NFKC normalized. Whitespaces are collapsed and strip from both ends.

Parameters

txt (str) – Text to be normalized.

Returns

Normalized text.

Return type

str

See also

unicodedata.normalize

Python built-in unicode normalization.

Examples

>>> from lmp.dset import BaseDset
>>> BaseDset.norm('１２３４５６７８９')
'123456789'

1

Hector Levesque, Ernest Davis, and Leora Morgenstern. The winograd schema challenge. In Thirteenth international conference on the principles of knowledge representation and reasoning. 2012. URL: https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html.

2

Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. GLUE: a multi-task benchmark and analysis platform for natural language understanding. In Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, 353–355. Brussels, Belgium, November 2018. Association for Computational Linguistics. URL: https://aclanthology.org/W18-5446, doi:10.18653/v1/W18-5446.