def test_predict(nlp: Pipeline):
text_1 = ("We are great fans of Slack, but we wish the subscriptions "
"were more accessible to small startups.")
text_2 = "We are great fans of Slack"
aspect = "Slack"
examples = [Example(text_1, aspect), Example(text_2, aspect)]
tokenized_examples = nlp.tokenize(examples)
input_batch = nlp.encode(tokenized_examples)
output_batch = nlp.predict(input_batch)
assert output_batch.scores.shape == [2, 3]
assert output_batch.hidden_states.shape == [2, 13, 25, 768]
assert output_batch.attentions.shape == [2, 12, 12, 25, 25]
assert output_batch.attention_grads.shape == [2, 12, 12, 25, 25]
scores = output_batch.scores.numpy()
assert np.argmax(scores, axis=-1).tolist() == [2, 2]
def test_review(nlp: Pipeline):
text_1 = ("We are great fans of Slack, but we wish the subscriptions "
"were more accessible to small startups.")
text_2 = "The Slack often has bugs."
aspect = "slack"
examples = [Example(text_1, aspect), Example(text_2, aspect)]
tokenized_examples = nlp.tokenize(examples)
input_batch = nlp.encode(tokenized_examples)
output_batch = nlp.predict(input_batch)
predictions = nlp.review(tokenized_examples, output_batch)
predictions = list(predictions)
labeled_1, labeled_2 = predictions
assert labeled_1.sentiment == Sentiment.positive
assert labeled_2.sentiment == Sentiment.negative
assert isinstance(labeled_1, PredictedExample)
assert isinstance(labeled_1.scores, list)
assert isinstance(labeled_1.review, Review)
assert not labeled_1.review.is_reference
assert not labeled_1.review.patterns