# nlp_architect.pipelines package

## Submodules

## nlp_architect.pipelines.spacy_bist module

class `nlp_architect.pipelines.spacy_bist.``SpacyBISTParser`(verbose=False, spacy_model='en', bist_model=None)

  Bases: `object`

  Main class which handles parsing with Spacy-BIST parser.

  Parameters: | verbose (bool, optional) – Controls output verbosity. spacy_model (str, optional) – Spacy model to use https ((see) – //spacy.io/api/top-level#spacy.load). bist_model (str, optional) – Path to a .model file to load. Defaults pre-trained model’.

  `dir` = PosixPath('/Users/pizsak/nlp-architect/cache/bist-pretrained')
  `parse`(doc_text, show_tok=True, show_doc=True)

    Parse a raw text document.

    Parameters: | doc_text (str) – show_tok (bool, optional) – Specifies whether to include token text in output. show_doc (bool, optional) – Specifies whether to include document text in output.
    Returns: | The annotated document.
    Return type: | CoreNLPDoc

  `to_conll`(doc_text)

    Converts a document to CoNLL format with spacy POS tags.

    Parameters: | doc_text (str) – raw document text.
    Yields: | list of ConllEntry – The next sentence in the document in CoNLL format.

## nlp_architect.pipelines.spacy_np_annotator module

class `nlp_architect.pipelines.spacy_np_annotator.``NPAnnotator`(model, word_vocab, char_vocab, chunk_vocab, batch_size: int = 32)

  Bases: `object`

  Spacy based NP annotator - uses models.SequenceChunker model for annotation

  Parameters: | model (SequenceChunker) – a chunker model word_vocab (Vocabulary) – word-id vocabulary of the model char_vocab (Vocabulary) – char id vocabulary of words of the model chunk_vocab (Vocabulary) – chunk tag vocabulary of the model batch_size (int, optional) – inference batch size

  classmethod `load`(model_path: str, parameter_path: str, batch_size: int = 32, use_cudnn: bool = False)

    Load a NPAnnotator annotator

    Parameters: | model_path (str) – path to trained model parameter_path (str) – path to model parameters batch_size (int, optional) – inference batch_size use_cudnn (bool, optional) – use gpu for inference (cudnn cells)
    Returns: | NPAnnotator class with loaded model

class `nlp_architect.pipelines.spacy_np_annotator.``SpacyNPAnnotator`(model_path, settings_path, spacy_model='en', batch_size=32, use_cudnn=False)

  Bases: `object`

  Simple Spacy pipe with NP extraction annotations

`nlp_architect.pipelines.spacy_np_annotator.``get_noun_phrases`(doc: spacy.tokens.doc.Doc) → [<class 'spacy.tokens.span.Span'>]

  Get noun phrase tags from a spacy annotated document.

  Parameters: | doc (Doc) – a spacy type document
  Returns: | a list of noun phrase Span objects

`nlp_architect.pipelines.spacy_np_annotator.``set_noun_phrases`(doc: spacy.tokens.doc.Doc, nps: [<class 'spacy.tokens.span.Span'>]) → None

  Set noun phrase tags

  Parameters: | doc (Doc) – a spacy type document nps ([Span]) – a list of Spans

## Module contents