Contents

Semantic Dependency Parsers

BiaffineSemanticDependencyParser

class supar.parsers.sdp.BiaffineSemanticDependencyParser(*args, **kwargs)[source]

The implementation of Biaffine Semantic Dependency Parser [Dozat & Manning 2018].

MODEL

alias of supar.models.sdp.BiaffineSemanticDependencyModel

train(train, dev, test, buckets=32, batch_size=5000, update_steps=1, verbose=True, **kwargs)[source]
Parameters

  • train/dev/test (list[list] or str) – Filenames of the train/dev/test datasets.

  • buckets (int) – The number of buckets that sentences are assigned to. Default: 32.

  • batch_size (int) – The number of tokens in each batch. Default: 5000.

  • update_steps (int) – Gradient accumulation steps. Default: 1.

  • verbose (bool) – If True, increases the output verbosity. Default: True.

  • kwargs (dict) – A dict holding unconsumed arguments for updating training configs.

evaluate(data, buckets=8, batch_size=5000, verbose=True, **kwargs)[source]
Parameters

  • data (str) – The data for evaluation, both list of instances and filename are allowed.

  • buckets (int) – The number of buckets that sentences are assigned to. Default: 32.

  • batch_size (int) – The number of tokens in each batch. Default: 5000.

  • verbose (bool) – If True, increases the output verbosity. Default: True.

  • kwargs (dict) – A dict holding unconsumed arguments for updating evaluation configs.

Returns

The loss scalar and evaluation results.

predict(data, pred=None, lang=None, buckets=8, batch_size=5000, verbose=True, **kwargs)[source]
Parameters

  • data (list[list] or str) – The data for prediction, both a list of instances and filename are allowed.

  • pred (str) – If specified, the predicted results will be saved to the file. Default: None.

  • lang (str) – Language code (e.g., en) or language name (e.g., English) for the text to tokenize. None if tokenization is not required. Default: None.

  • buckets (int) – The number of buckets that sentences are assigned to. Default: 32.

  • batch_size (int) – The number of tokens in each batch. Default: 5000.

  • prob (bool) – If True, outputs the probabilities. Default: False.

  • verbose (bool) – If True, increases the output verbosity. Default: True.

  • kwargs (dict) – A dict holding unconsumed arguments for updating prediction configs.

Returns

A Dataset object that stores the predicted results.

classmethod load(path, reload=False, src='github', **kwargs)[source]

Loads a parser with data fields and pretrained model parameters.

Parameters

  • path (str) –

    • a string with the shortcut name of a pretrained model defined in supar.MODEL to load from cache or download, e.g., 'biaffine-sdp-en'.

    • a local path to a pretrained model, e.g., ./<path>/model.

  • reload (bool) – Whether to discard the existing cache and force a fresh download. Default: False.

  • src (str) – Specifies where to download the model. 'github': github release page. 'hlt': hlt homepage, only accessible from 9:00 to 18:00 (UTC+8). Default: 'github'.

  • kwargs (dict) – A dict holding unconsumed arguments for updating training configs and initializing the model.

Examples

>>> from supar import Parser
>>> parser = Parser.load('biaffine-sdp-en')
>>> parser = Parser.load('./dm.biaffine.sdp.lstm.char')
classmethod build(path, min_freq=7, fix_len=20, **kwargs)[source]

Build a brand-new Parser, including initialization of all data fields and model parameters.

Parameters

  • path (str) – The path of the model to be saved.

  • min_freq (str) – The minimum frequency needed to include a token in the vocabulary. Default:7.

  • fix_len (int) – The max length of all subword pieces. The excess part of each piece will be truncated. Required if using CharLSTM/BERT. Default: 20.

  • kwargs (dict) – A dict holding the unconsumed arguments.

VISemanticDependencyParser

class supar.parsers.sdp.VISemanticDependencyParser(*args, **kwargs)[source]

The implementation of Semantic Dependency Parser using Variational Inference [Wang et al. 2019].

MODEL

alias of supar.models.sdp.VISemanticDependencyModel

train(train, dev, test, buckets=32, batch_size=5000, update_steps=1, verbose=True, **kwargs)[source]
Parameters

  • train/dev/test (list[list] or str) – Filenames of the train/dev/test datasets.

  • buckets (int) – The number of buckets that sentences are assigned to. Default: 32.

  • batch_size (int) – The number of tokens in each batch. Default: 5000.

  • update_steps (int) – Gradient accumulation steps. Default: 1.

  • verbose (bool) – If True, increases the output verbosity. Default: True.

  • kwargs (dict) – A dict holding unconsumed arguments for updating training configs.

evaluate(data, buckets=8, batch_size=5000, verbose=True, **kwargs)[source]
Parameters

  • data (str) – The data for evaluation, both list of instances and filename are allowed.

  • buckets (int) – The number of buckets that sentences are assigned to. Default: 32.

  • batch_size (int) – The number of tokens in each batch. Default: 5000.

  • verbose (bool) – If True, increases the output verbosity. Default: True.

  • kwargs (dict) – A dict holding unconsumed arguments for updating evaluation configs.

Returns

The loss scalar and evaluation results.

predict(data, pred=None, lang=None, buckets=8, batch_size=5000, verbose=True, **kwargs)[source]
Parameters

  • data (list[list] or str) – The data for prediction, both a list of instances and filename are allowed.

  • pred (str) – If specified, the predicted results will be saved to the file. Default: None.

  • lang (str) – Language code (e.g., en) or language name (e.g., English) for the text to tokenize. None if tokenization is not required. Default: None.

  • buckets (int) – The number of buckets that sentences are assigned to. Default: 32.

  • batch_size (int) – The number of tokens in each batch. Default: 5000.

  • prob (bool) – If True, outputs the probabilities. Default: False.

  • verbose (bool) – If True, increases the output verbosity. Default: True.

  • kwargs (dict) – A dict holding unconsumed arguments for updating prediction configs.

Returns

A Dataset object that stores the predicted results.

classmethod load(path, reload=False, src='github', **kwargs)[source]

Loads a parser with data fields and pretrained model parameters.

Parameters

  • path (str) –

    • a string with the shortcut name of a pretrained model defined in supar.MODEL to load from cache or download, e.g., 'vi-sdp-en'.

    • a local path to a pretrained model, e.g., ./<path>/model.

  • reload (bool) – Whether to discard the existing cache and force a fresh download. Default: False.

  • src (str) – Specifies where to download the model. 'github': github release page. 'hlt': hlt homepage, only accessible from 9:00 to 18:00 (UTC+8). Default: 'github'.

  • kwargs (dict) – A dict holding unconsumed arguments for updating training configs and initializing the model.

Examples

>>> from supar import Parser
>>> parser = Parser.load('vi-sdp-en')
>>> parser = Parser.load('./dm.vi.sdp.lstm.char')

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