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