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Improvements and Changes


  • Replace AnnotatedDocument common data format to List of List format borrowed from Anago.
  • Removes dependency on NLTK
  • Model
    • NERModel -- base class extends ClassifierMixin, so exposes predict() instead of transform().
    • DictionaryNER
      • similar to ExactMatchDictionaryNER except
        • takes Anago style IO
        • handles multiple classes (as well as single class as special case)
        • can handle Anago style input via fit(X, y, combine_tokens=True) and dictionary style input via fit(X, y, combine_tokens=False).
    • CrfNER
      • similar to CRF except
        • takes Anago style IO (native IO format to wrapped model sklearn_crfsuite.CRF)
        • replaces dependency on nltk.tokenize_pos() to SpaCy
        • allows features to be directly passed to fit() using is_featurized=False.
    • SpacyNER
      • similar to SpacyStatisticalNER, except
        • takes Anago style IO
        • more robust to large data sizes, uses mini-batches for training
    • BiLstmCrfNER
      • similar to BidirectionalLSTM except
        • takes Anago style IO
        • works against most recent Anago API changes
        • does not give timestep size errors
    • ElmoNER
      • New, available in Anago DEV repo, same API as Anago's BiLSTMCRF
    • FlairNER
    • TransformerNER
      • New, provides support for transformer based NERs using choice of BERT, RoBERTa, DistilBERT, CamemBERT, and XLM-RoBERTa language models, uses the SimpleTransformers library.
    • EnsembleNER
      • simpler interface
      • weights from each classifier
      • fit() and predict() can use multiple parallel jobs (n_jobs).
  • Utils
    • Thin wrapper over anago's load_data_and_labels
    • flatten_list and unflatten_list to convert between list(list(str)) produced by NERDS models and list(str) required by sklearn, scikit-learn metrics can be used.
    • tokens_to_spans and spans_to_tokens -- utility functions to convert between sentence and span format (used by the other 2 of 5 provided models) from and to BIO format.
  • Converters
    • Converter from BRAT (.txt and .ann) to IOB format
  • Miscellaneous
    • replaced deprecated sklearn.external.joblib -> joblib
    • True Scikit-Learn interoperability -- moved parameters to constructor. However, sklearn.utils.check_estimator still fails, most likely because the parameters to fit() and predict() are list(list(str)) rather than list(str).
    • Docs converted to Numpy Docstring format.


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