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__init__.py
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__init__.py
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from .models import print_text_classifiers, print_text_regression_models, text_classifier, text_regression_model
from .data import texts_from_folder, texts_from_csv, texts_from_df, texts_from_array
from .ner.data import entities_from_gmb, entities_from_conll2003, entities_from_txt, entities_from_df, entities_from_array
from .ner.models import sequence_tagger, print_sequence_taggers
from .eda import get_topic_model
from .textutils import extract_filenames, load_text_files, filter_by_id
from .preprocessor import Transformer, TransformerEmbedding
from .summarization import TransformerSummarizer
from .zsl import ZeroShotClassifier
from .translation import EnglishTranslator, Translator
from . import shallownlp
from .qa import SimpleQA
from . import textutils
import pickle
__all__ = [
'text_classifier', 'text_regression_model',
'print_text_classifiers', 'print_text_regression_models',
'texts_from_folder', 'texts_from_csv', 'texts_from_df', 'texts_from_array',
'entities_from_gmb',
'entities_from_conll2003',
'entities_from_txt',
'entities_from_array',
'entities_from_df',
'sequence_tagger',
'print_sequence_taggers',
'get_topic_model',
'Transformer',
'TransformerEmbedding',
'shallownlp',
'TransformerSummarizer',
'ZeroShotClassifier',
'EnglishTranslator',
'Translator',
'SimpleQA',
'extract_filenames',
'load_text_files',
]
def load_topic_model(fname):
"""
Load saved TopicModel object
Args:
fname(str): base filename for all saved files
"""
with open(fname+'.tm_vect', 'rb') as f:
vectorizer = pickle.load(f)
with open(fname+'.tm_model', 'rb') as f:
model = pickle.load(f)
with open(fname+'.tm_params', 'rb') as f:
params = pickle.load(f)
tm = get_topic_model(n_topics=params['n_topics'],
n_features = params['n_features'],
verbose = params['verbose'])
tm.model = model
tm.vectorizer = vectorizer
return tm
seqlen_stats = Transformer.seqlen_stats