def test_read_annotated_subset(src_filename):
src_filename = os.path.join(annotations_home, src_filename)
if 'mismatched' in src_filename:
split = 'validation_mismatched'
else:
split = 'validation_matched'
data = nli.read_annotated_subset(src_filename, mnli[split])
assert len(data) == 495
assess_reader=None,
random_state=42,
vectorize=False)
# The return value of `nli.experiment` contains the information we need to make predictions on new examples.
#
# Next, we load in the 'matched' condition annotations ('mismatched' would work as well):
# In[31]:
matched_ann_filename = os.path.join(ANNOTATIONS_HOME,
"multinli_1.0_matched_annotations.txt")
# In[32]:
matched_ann = nli.read_annotated_subset(matched_ann_filename, MULTINLI_HOME)
# The following function uses `rnn_multinli_experiment` to make predictions on annotated examples, and harvests some other information that is useful for error analysis:
# In[33]:
def predict_annotated_example(ann, experiment_results):
model = experiment_results['model']
phi = experiment_results['phi']
ex = ann['example']
prem = ex.sentence1_parse
hyp = ex.sentence2_parse
feats = phi(prem, hyp)
pred = model.predict([feats])[0]
gold = ex.gold_label
def test_read_annotated_subset(src_filename):
src_filename = os.path.join(annotations_home, src_filename)
data = nli.read_annotated_subset(src_filename, multinli_home)
assert len(data) == 495