def _download_pretrained_model(): """Downloads the pre-trained BIST model if non-existent.""" if not path.isfile(path.join(SpacyBISTParser.dir, 'bist.model')): print('Downloading pre-trained BIST model...') zip_path = path.join(SpacyBISTParser.dir, 'bist-pretrained.zip') makedirs(SpacyBISTParser.dir, exist_ok=True) download_unlicensed_file('https://s3-us-west-1.amazonaws.com/nervana-modelzoo/parse/', 'bist-pretrained.zip', zip_path) print('Unzipping...') uncompress_file(zip_path, outpath=SpacyBISTParser.dir) remove(zip_path) print('Done.')
def _download_pretrained_model(): """Downloads the pre-trained BIST model if non-existent.""" if not path.isfile(SpacyBISTParser.dir / 'bist.model'): print('Downloading pre-trained BIST model...') zip_path = SpacyBISTParser.dir / 'bist-pretrained.zip' makedirs(SpacyBISTParser.dir, exist_ok=True) download_unlicensed_file( 'https://s3-us-west-2.amazonaws.com/nlp-architect-data/models/dep_parse/', 'bist-pretrained.zip', zip_path) print('Unzipping...') uncompress_file(zip_path, outpath=str(SpacyBISTParser.dir)) remove(zip_path) print('Done.')
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
def _download_pretrained_model(): """Downloads the pre-trained BIST model if non-existent.""" if not path.isfile(SpacyBISTParser.dir / "bist.model"): print("Downloading pre-trained BIST model...") zip_path = SpacyBISTParser.dir / "bist-pretrained.zip" makedirs(SpacyBISTParser.dir, exist_ok=True) download_unlicensed_file( "https://d2zs9tzlek599f.cloudfront.net/models/dep_parse/", "bist-pretrained.zip", zip_path, ) print("Unzipping...") uncompress_file(zip_path, outpath=str(SpacyBISTParser.dir)) remove(zip_path) print("Done.")
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
parser.add_argument('--op_thresh', type=int, default=2) parser.add_argument('--max_iter', type=int, default=3) parser.add_argument('--large', type=str, default="no") args = parser.parse_args() # Download ABSA dependencies including spacy parser and glove embeddings from spacy.cli.download import download as spacy_download from nlp_architect.utils.io import uncompress_file from nlp_architect.models.absa import TRAIN_OUT, LEXICONS_OUT spacy_download('en') GLOVE_ZIP = os.path.join(args.data_folder, 'clothing_data/glove.840B.300d.zip') EMBEDDING_PATH = TRAIN_OUT / 'word_emb_unzipped' / 'glove.840B.300d.txt' uncompress_file(GLOVE_ZIP, Path(EMBEDDING_PATH).parent) clothing_train = os.path.join(args.data_folder, 'clothing_data/clothing_absa_train_small.csv') if args.large == 'yes': print(f'Using large dataset: clothing_data/clothing_absa_train.csv') clothing_train = os.path.join(args.data_folder, 'clothing_data/clothing_absa_train.csv') else: print(f'Using small dataset: clothing_data/clothing_absa_train_small.csv') clothing_train = os.path.join( args.data_folder, 'clothing_data/clothing_absa_train_small.csv') os.makedirs('outputs', exist_ok=True)