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import collections
import numpy as np
from tqdm.auto import tqdm
def postprocess_token_span_predictions(
features,
examples,
raw_predictions,
tokenizer,
n_best_size=20,
max_answer_length=30,
squad_v2=False,
):
all_start_logits, all_end_logits, token_logits = raw_predictions
# Build a map example to its corresponding features.
example_id_to_index = {k: i for i, k in enumerate(list(examples["id"]))}
features_per_example = collections.defaultdict(list)
for i, feature in enumerate(features):
features_per_example[example_id_to_index[feature["example_id"]]].append(i)
# The dictionaries we have to fill.
predictions = collections.OrderedDict()
# Logging.
print(
f"Post-processing {len(examples)} example predictions split into {len(features)} features."
)
# Let's loop over all the examples!
for example_index in tqdm(range(len(examples))):
# Those are the indices of the features associated to the current example.
feature_indices = features_per_example[example_index]
min_null_score = None # Only used if squad_v2 is True.
valid_answers = []
context = examples[example_index]["context"]
# Looping through all the features associated to the current example.
for feature_index in feature_indices:
# We grab the predictions of the model for this feature.
start_logits = all_start_logits[feature_index]
end_logits = all_end_logits[feature_index]
# This is what will allow us to map some the positions in our logits to span of texts in the original
# context.
offset_mapping = features[feature_index]["offset_mapping"]
# Update minimum null prediction.
cls_index = features[feature_index]["input_ids"].index(
tokenizer.cls_token_id
)
feature_null_score = start_logits[cls_index] + end_logits[cls_index]
if min_null_score is None or min_null_score < feature_null_score:
min_null_score = feature_null_score
# Go through all possibilities for the `n_best_size` greater start and end logits.
start_indexes = np.argsort(start_logits)[
-1 : -n_best_size - 1 : -1
].tolist()
end_indexes = np.argsort(end_logits)[-1 : -n_best_size - 1 : -1].tolist()
for start_index in start_indexes:
for end_index in end_indexes:
# Don't consider out-of-scope answers, either because the indices are out of bounds or correspond
# to part of the input_ids that are not in the context.
if (
start_index >= len(offset_mapping)
or end_index >= len(offset_mapping)
or offset_mapping[start_index] is None
or offset_mapping[end_index] is None
):
continue
# Don't consider answers with a length that is either < 0 or > max_answer_length.
if (
end_index < start_index
or end_index - start_index + 1 > max_answer_length
):
continue
start_char = offset_mapping[start_index][0]
end_char = offset_mapping[end_index][1]
valid_answers.append(
{
"qa_score": (
start_logits[start_index] + end_logits[end_index]
)
/ 2,
"token_score": np.mean(
[
token_logits[example_index][token_index][1]
for token_index in range(start_index, end_index + 1)
]
),
"score": (start_logits[start_index] + end_logits[end_index])
/ 2
+ np.mean(
[
token_logits[example_index][token_index][1]
for token_index in range(start_index, end_index + 1)
]
),
"text": context[start_char:end_char],
"start": start_char,
"end": end_char,
}
)
if len(valid_answers) > 0:
sorted_answers = sorted(
valid_answers, key=lambda x: x["score"], reverse=True
)
else:
# In the very rare edge case we have not a single non-null prediction, we create a fake prediction to avoid
# failure.
sorted_answers = [{"text": "", "score": 0.0, "start": None, "end": None}]
# Let's pick our final answer: the best one or the null answer (only for squad_v2)
if sorted_answers[0]["score"] <= min_null_score:
sorted_answers = [
{"text": "", "score": min_null_score, "start": None, "end": None},
] + sorted_answers
predictions[examples[example_index]["id"]] = sorted_answers
return predictions
def postprocess_multi_span_predictions(
features,
examples,
raw_predictions,
tokenizer,
n_best_size=20,
max_answer_length=30,
squad_v2=False,
):
all_start_logits, all_end_logits = raw_predictions
# Build a map example to its corresponding features.
example_id_to_index = {k: i for i, k in enumerate(list(examples["id"]))}
features_per_example = collections.defaultdict(list)
for i, feature in enumerate(features):
features_per_example[example_id_to_index[feature["example_id"]]].append(i)
# The dictionaries we have to fill.
predictions = collections.OrderedDict()
# Logging.
print(
f"Post-processing {len(examples)} example predictions split into {len(features)} features."
)
# Let's loop over all the examples!
for example_index in tqdm(range(len(examples))):
# Those are the indices of the features associated to the current example.
feature_indices = features_per_example[example_index]
min_null_score = None # Only used if squad_v2 is True.
valid_answers = []
context = examples[example_index]["context"]
# Looping through all the features associated to the current example.
for feature_index in feature_indices:
# We grab the predictions of the model for this feature.
start_logits = all_start_logits[feature_index]
end_logits = all_end_logits[feature_index]
# This is what will allow us to map some the positions
# in our logits to span of texts in the original context.
offset_mapping = features[feature_index]["offset_mapping"]
# Update minimum null prediction.
cls_index = features[feature_index]["input_ids"].index(
tokenizer.cls_token_id
)
feature_null_score = start_logits[cls_index] + end_logits[cls_index]
if min_null_score is None or min_null_score < feature_null_score:
min_null_score = feature_null_score
# Go through all possibilities for the `n_best_size` greater start and end logits.
start_indexes = np.argsort(start_logits)[
-1 : -n_best_size - 1 : -1
].tolist()
end_indexes = np.argsort(end_logits)[-1 : -n_best_size - 1 : -1].tolist()
for start_index in start_indexes:
for end_index in end_indexes:
# Don't consider out-of-scope answers,
# either because the indices are out of bounds or correspond
# to part of the input_ids that are not in the context.
if (
start_index >= len(offset_mapping)
or end_index >= len(offset_mapping)
or offset_mapping[start_index] is None
or offset_mapping[end_index] is None
):
continue
# Don't consider answers with a length that
# is either < 0 or > max_answer_length.
if (
end_index < start_index
or end_index - start_index + 1 > max_answer_length
):
continue
start_char = offset_mapping[start_index][0]
end_char = offset_mapping[end_index][1]
valid_answers.append(
{
"score": start_logits[start_index] + end_logits[end_index],
"text": context[start_char:end_char],
"start": start_char,
"end": end_char,
}
)
if len(valid_answers) > 0:
sorted_answers = sorted(
valid_answers, key=lambda x: x["score"], reverse=True
)
else:
# In the very rare edge case we have not a single non-null prediction,
# we create a fake prediction to avoid failure.
sorted_answers = [{"text": "", "score": 0.0, "start": None, "end": None}]
# Let's pick our final answer: the best one or the null answer (only for squad_v2)
if sorted_answers[0]["score"] <= min_null_score:
sorted_answers = [
{"text": "", "score": min_null_score, "start": None, "end": None},
] + sorted_answers
predictions[examples[example_index]["id"]] = sorted_answers
return predictions |