Source code for nlp_architect.models.pretrained_models

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from nlp_architect.utils.io import uncompress_file, zipfile_list
from nlp_architect.utils.file_cache import cached_path

from nlp_architect import LIBRARY_OUT

S3_PREFIX = "https://s3-us-west-2.amazonaws.com/nlp-architect-data/"


[docs]class PretrainedModel: """ Generic class to download the pre-trained models Usage Example: chunker = ChunkerModel.get_instance() chunker2 = ChunkerModel.get_instance() print(chunker, chunker2) print("Local File path = ", chunker.get_file_path()) files_models = chunker2.get_model_files() for idx, file_name in enumerate(files_models): print(str(idx) + ": " + file_name) """ def __init__(self, model_name, sub_path, files): if isinstance(self, (BistModel, ChunkerModel, MrcModel, IntentModel, AbsaModel, NerModel)): if self._instance is not None: # pylint: disable=no-member raise Exception("This class is a singleton!") self.model_name = model_name self.base_path = S3_PREFIX + sub_path self.files = files self.download_path = LIBRARY_OUT / 'pretrained_models' / self.model_name self.model_files = []
[docs] @classmethod # pylint: disable=no-member def get_instance(cls): """ Static instance access method Args: cls (Class name): Calling class """ if cls._instance is None: cls() # pylint: disable=no-value-for-parameter return cls._instance
[docs] def get_file_path(self): """ Return local file path of downloaded model files """ for filename in self.files: cached_file_path, need_downloading = cached_path( self.base_path + filename, self.download_path) if filename.endswith('zip'): if need_downloading: print('Unzipping...') uncompress_file(cached_file_path, outpath=self.download_path) print('Done.') return self.download_path
[docs] def get_model_files(self): """ Return individual file names of downloaded models """ for fileName in self.files: cached_file_path, need_downloading = cached_path( self.base_path + fileName, self.download_path) if fileName.endswith('zip'): if need_downloading: print('Unzipping...') uncompress_file(cached_file_path, outpath=self.download_path) print('Done.') self.model_files.extend(zipfile_list(cached_file_path)) else: self.model_files.extend([fileName]) return self.model_files
# Model-specific classes developers instantiate where model has to be used
[docs]class BistModel(PretrainedModel): """ Download and process (unzip) pre-trained BIST model """ _instance = None sub_path = 'models/dep_parse/' files = ['bist-pretrained.zip'] def __init__(self): super().__init__('bist', self.sub_path, self.files) BistModel._instance = self
[docs]class IntentModel(PretrainedModel): """ Download and process (unzip) pre-trained Intent model """ _instance = None sub_path = 'models/intent/' files = ['model_info.dat', 'model.h5'] def __init__(self): super().__init__('intent', self.sub_path, self.files) IntentModel._instance = self
[docs]class MrcModel(PretrainedModel): """ Download and process (unzip) pre-trained MRC model """ _instance = None sub_path = 'models/mrc/' files = ['mrc_data.zip', 'mrc_model.zip'] def __init__(self): super().__init__('mrc', self.sub_path, self.files) MrcModel._instance = self
[docs]class NerModel(PretrainedModel): """ Download and process (unzip) pre-trained NER model """ _instance = None sub_path = 'models/ner/' files = ['model_v4.h5', 'model_info_v4.dat'] def __init__(self): super().__init__('ner', self.sub_path, self.files) NerModel._instance = self
[docs]class AbsaModel(PretrainedModel): """ Download and process (unzip) pre-trained ABSA model """ _instance = None sub_path = 'models/absa/' files = ['rerank_model.h5'] def __init__(self): super().__init__('absa', self.sub_path, self.files) AbsaModel._instance = self
[docs]class ChunkerModel(PretrainedModel): """ Download and process (unzip) pre-trained Chunker model """ _instance = None sub_path = 'models/chunker/' files = ['model.h5', 'model_info.dat.params'] def __init__(self): super().__init__('chunker', self.sub_path, self.files) ChunkerModel._instance = self