in official/nlp/data/squad_lib_sp.py [0:0]
def postprocess_output(all_examples,
all_features,
all_results,
n_best_size,
max_answer_length,
do_lower_case,
version_2_with_negative=False,
null_score_diff_threshold=0.0,
xlnet_format=False,
verbose=False):
"""Postprocess model output, to form predicton results."""
del do_lower_case, verbose
example_index_to_features = collections.defaultdict(list)
for feature in all_features:
example_index_to_features[feature.example_index].append(feature)
unique_id_to_result = {}
for result in all_results:
unique_id_to_result[result.unique_id] = result
_PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name
"PrelimPrediction",
["feature_index", "start_index", "end_index", "start_logit", "end_logit"])
all_predictions = collections.OrderedDict()
all_nbest_json = collections.OrderedDict()
scores_diff_json = collections.OrderedDict()
for (example_index, example) in enumerate(all_examples):
features = example_index_to_features[example_index]
prelim_predictions = []
# keep track of the minimum score of null start+end of position 0
score_null = 1000000 # large and positive
min_null_feature_index = 0 # the paragraph slice with min mull score
null_start_logit = 0 # the start logit at the slice with min null score
null_end_logit = 0 # the end logit at the slice with min null score
for (feature_index, feature) in enumerate(features):
if feature.unique_id not in unique_id_to_result:
logging.info("Skip eval example %s, not in pred.", feature.unique_id)
continue
result = unique_id_to_result[feature.unique_id]
# if we could have irrelevant answers, get the min score of irrelevant
if version_2_with_negative:
if xlnet_format:
feature_null_score = result.class_logits
else:
feature_null_score = result.start_logits[0] + result.end_logits[0]
if feature_null_score < score_null:
score_null = feature_null_score
min_null_feature_index = feature_index
null_start_logit = result.start_logits[0]
null_end_logit = result.end_logits[0]
doc_offset = 0 if xlnet_format else feature.tokens.index("[SEP]") + 1
for (start_index, start_logit,
end_index, end_logit) in _get_best_indexes_and_logits(
result=result,
n_best_size=n_best_size,
xlnet_format=xlnet_format):
# We could hypothetically create invalid predictions, e.g., predict
# that the start of the span is in the question. We throw out all
# invalid predictions.
if start_index - doc_offset >= len(feature.tok_start_to_orig_index):
continue
if end_index - doc_offset >= len(feature.tok_end_to_orig_index):
continue
if not feature.token_is_max_context.get(start_index, False):
continue
if end_index < start_index:
continue
length = end_index - start_index + 1
if length > max_answer_length:
continue
prelim_predictions.append(
_PrelimPrediction(
feature_index=feature_index,
start_index=start_index - doc_offset,
end_index=end_index - doc_offset,
start_logit=start_logit,
end_logit=end_logit))
if version_2_with_negative and not xlnet_format:
prelim_predictions.append(
_PrelimPrediction(
feature_index=min_null_feature_index,
start_index=-1,
end_index=-1,
start_logit=null_start_logit,
end_logit=null_end_logit))
prelim_predictions = sorted(
prelim_predictions,
key=lambda x: (x.start_logit + x.end_logit),
reverse=True)
_NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name
"NbestPrediction", ["text", "start_logit", "end_logit"])
seen_predictions = {}
nbest = []
for pred in prelim_predictions:
if len(nbest) >= n_best_size:
break
feature = features[pred.feature_index]
if pred.start_index >= 0 or xlnet_format: # this is a non-null prediction
tok_start_to_orig_index = feature.tok_start_to_orig_index
tok_end_to_orig_index = feature.tok_end_to_orig_index
start_orig_pos = tok_start_to_orig_index[pred.start_index]
end_orig_pos = tok_end_to_orig_index[pred.end_index]
paragraph_text = example.paragraph_text
final_text = paragraph_text[start_orig_pos:end_orig_pos + 1].strip()
if final_text in seen_predictions:
continue
seen_predictions[final_text] = True
else:
final_text = ""
seen_predictions[final_text] = True
nbest.append(
_NbestPrediction(
text=final_text,
start_logit=pred.start_logit,
end_logit=pred.end_logit))
# if we didn't inlude the empty option in the n-best, include it
if version_2_with_negative and not xlnet_format:
if "" not in seen_predictions:
nbest.append(
_NbestPrediction(
text="", start_logit=null_start_logit,
end_logit=null_end_logit))
# In very rare edge cases we could have no valid predictions. So we
# just create a nonce prediction in this case to avoid failure.
if not nbest:
nbest.append(
_NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
assert len(nbest) >= 1
total_scores = []
best_non_null_entry = None
for entry in nbest:
total_scores.append(entry.start_logit + entry.end_logit)
if not best_non_null_entry:
if entry.text:
best_non_null_entry = entry
probs = _compute_softmax(total_scores)
nbest_json = []
for (i, entry) in enumerate(nbest):
output = collections.OrderedDict()
output["text"] = entry.text
output["probability"] = probs[i]
output["start_logit"] = entry.start_logit
output["end_logit"] = entry.end_logit
nbest_json.append(output)
assert len(nbest_json) >= 1
if not version_2_with_negative:
all_predictions[example.qas_id] = nbest_json[0]["text"]
else:
assert best_non_null_entry is not None
if xlnet_format:
score_diff = score_null
scores_diff_json[example.qas_id] = score_diff
all_predictions[example.qas_id] = best_non_null_entry.text
else:
# predict "" iff the null score - the score of best non-null > threshold
score_diff = score_null - best_non_null_entry.start_logit - (
best_non_null_entry.end_logit)
scores_diff_json[example.qas_id] = score_diff
if score_diff > null_score_diff_threshold:
all_predictions[example.qas_id] = ""
else:
all_predictions[example.qas_id] = best_non_null_entry.text
all_nbest_json[example.qas_id] = nbest_json
return all_predictions, all_nbest_json, scores_diff_json