def test_train_charlm__nocache_load_use_classifier():
corpus = NLPTaskDataFetcher.fetch_data(NLPTask.IMDB)
label_dict = corpus.make_label_dictionary()
glove_embedding: TokenEmbeddings = CharLMEmbeddings('news-forward-fast', use_cache=False)
document_embeddings: DocumentLSTMEmbeddings = DocumentLSTMEmbeddings([glove_embedding], 128, 1, False, 64,
False,
False)
model = TextClassifier(document_embeddings, label_dict, False)
trainer = TextClassifierTrainer(model, corpus, label_dict, False)
trainer.train('./results', max_epochs=2)
sentence = Sentence("Berlin is a really nice city.")
for s in model.predict(sentence):
for l in s.labels:
assert (l.value is not None)
assert (0.0 <= l.score <= 1.0)
assert (type(l.score) is float)
loaded_model = TextClassifier.load_from_file('./results/final-model.pt')
sentence = Sentence('I love Berlin')
sentence_empty = Sentence(' ')
loaded_model.predict(sentence)
loaded_model.predict([sentence, sentence_empty])
loaded_model.predict([sentence_empty])
# clean up results directory
shutil.rmtree('./results')
def test_train_load_use_classifier(results_base_path, tasks_base_path):
corpus = NLPTaskDataFetcher.fetch_data(NLPTask.IMDB,
base_path=tasks_base_path)
label_dict = corpus.make_label_dictionary()
glove_embedding: WordEmbeddings = WordEmbeddings('en-glove')
document_embeddings: DocumentLSTMEmbeddings = DocumentLSTMEmbeddings(
[glove_embedding], 128, 1, False, 64, False, False)
model = TextClassifier(document_embeddings, label_dict, False)
trainer = TextClassifierTrainer(model, corpus, label_dict, test_mode=True)
trainer.train(str(results_base_path), max_epochs=2)
sentence = Sentence("Berlin is a really nice city.")
for s in model.predict(sentence):
for l in s.labels:
assert (l.value is not None)
assert (0.0 <= l.score <= 1.0)
assert (type(l.score) is float)
loaded_model = TextClassifier.load_from_file(results_base_path /
'final-model.pt')
sentence = Sentence('I love Berlin')
sentence_empty = Sentence(' ')
loaded_model.predict(sentence)
loaded_model.predict([sentence, sentence_empty])
loaded_model.predict([sentence_empty])
# clean up results directory
shutil.rmtree(results_base_path)