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import numpy as np
from .constants import (
QUESTION_COLUMN_NAME,
CONTEXT_COLUMN_NAME,
ANSWER_COLUMN_NAME,
ANSWERABLE_COLUMN_NAME,
ID_COLUMN_NAME
)
def get_sketch_features(
tokenizer,
mode,
data_args
):
"""
Get the features for sketch model.
Args:
tokenizer (Tokenizer): Tokenizer for tokenizing input examples.
mode (str): Mode of operation ("train", "eval", or "test").
data_args (dict): Additional arguments for data loading.
Returns:
tuple: A tuple containing the function for preparing features and a boolean value indicating if labels are required.
"""
pad_on_right = tokenizer.padding_side == "right"
max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
def tokenize_fn(examples):
"""
Tokenize input examples.
Args:
examples (dict): Input examples.
Returns:
dict: Tokenized examples.
"""
# Tokenize the input examples using the provided tokenizer.
# The tokenizer is configured to truncate sequences to a maximum length.
# The tokenizer also returns the overflowing tokens, offsets mapping, and token type IDs.
# The padding strategy is determined by the data_args.pad_to_max_length parameter.
# tokenized_examples = tokenizer(
# examples[QUESTION_COLUMN_NAME if pad_on_right else CONTEXT_COLUMN_NAME],
# examples[CONTEXT_COLUMN_NAME if pad_on_right else QUESTION_COLUMN_NAME],
# truncation="only_second" if pad_on_right else "only_first",
# truncation=True,
# max_length=max_seq_length,
# stride=data_args.doc_stride,
# return_overflowing_tokens=True,
# return_offsets_mapping=False,
# return_token_type_ids=data_args.return_token_type_ids,
# padding="max_length" if data_args.pad_to_max_length else False,
# )
# Strip leading and trailing whitespaces from questions and contexts
questions = [q.strip() for q in examples[QUESTION_COLUMN_NAME if pad_on_right else CONTEXT_COLUMN_NAME]]
contexts = [c.strip() for c in examples[CONTEXT_COLUMN_NAME if pad_on_right else QUESTION_COLUMN_NAME]]
# Now, apply the tokenizer
tokenized_examples = tokenizer(
questions,
contexts,
truncation="only_second" if pad_on_right else "only_first",
max_length=max_seq_length,
stride=data_args.doc_stride,
return_overflowing_tokens=True,
return_offsets_mapping=True,
return_token_type_ids=data_args.return_token_type_ids,
padding="max_length" if data_args.pad_to_max_length else False,
)
return tokenized_examples
def prepare_train_features(examples):
"""
Prepare training features by tokenizing the input examples and adding labels.
Args:
examples (dict): Input examples.
Returns:
dict: Tokenized and labeled examples.
"""
# Tokenize the input examples using the provided tokenizer.
tokenized_examples = tokenize_fn(examples)
sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
# Add labels to the tokenized examples.
# The label is 0 for answerable and 1 for not answerable.
tokenized_examples["labels"] = []
for i in range(len(tokenized_examples["input_ids"])):
sample_index = sample_mapping[i]
# Determine if the example is answerable or not.
is_impossible = examples[ANSWERABLE_COLUMN_NAME][sample_index]
tokenized_examples["labels"].append(1 if is_impossible else 0)
return tokenized_examples
def prepare_eval_features(examples):
"""
Prepare evaluation features by tokenizing the input examples and adding labels.
Args:
examples (dict): Input examples.
Returns:
dict: Tokenized and labeled examples.
"""
# Tokenize the input examples using the provided tokenizer.
tokenized_examples = tokenize_fn(examples)
sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
# Add example ids and labels to the tokenized examples.
tokenized_examples["example_id"] = []
tokenized_examples["labels"] = []
for i in range(len(tokenized_examples["input_ids"])):
# Determine the sample index.
sample_index = sample_mapping[i]
# Extract the example id.
id_col = examples[ID_COLUMN_NAME][sample_index]
tokenized_examples["example_id"].append(id_col)
# Determine the label.
# answerable: 0, not answerable: 1.
is_impossible = examples[ANSWERABLE_COLUMN_NAME][sample_index]
tokenized_examples["labels"].append(1 if is_impossible else 0)
return tokenized_examples
def prepare_test_features(examples):
"""
Prepare test features by tokenizing the input examples and adding example ids.
Args:
examples (dict): Input examples.
Returns:
dict: Tokenized and labeled examples.
"""
# Tokenize the input examples using the provided tokenizer.
tokenized_examples = tokenize_fn(examples)
sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
# Add example ids to the tokenized examples.
tokenized_examples["example_id"] = []
for i in range(len(tokenized_examples["input_ids"])):
# Determine the sample index.
sample_index = sample_mapping[i]
# Extract the example id.
id_col = examples[ID_COLUMN_NAME][sample_index]
# Add the example id to the tokenized examples.
tokenized_examples["example_id"].append(id_col)
return tokenized_examples
if mode == "train":
get_features_fn = prepare_train_features
elif mode == "eval":
get_features_fn = prepare_eval_features
elif mode == "test":
get_features_fn = prepare_test_features
return get_features_fn, True
def get_intensive_features(
tokenizer,
mode,
data_args
):
"""
Generate intensive features for training, evaluation, or testing.
Args:
tokenizer (Tokenizer): The tokenizer used to tokenize the input examples.
mode (str): The mode of operation. Must be one of "train", "eval", or "test".
data_args (DataArguments): The data arguments containing the configuration for tokenization.
Returns:
tuple: A tuple containing the function to prepare the features and a boolean indicating if the tokenizer is beam-based.
Raises:
ValueError: If the mode is not one of "train", "eval", or "test".
"""
pad_on_right = tokenizer.padding_side == "right"
max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
beam_based = data_args.intensive_model_type in ["xlnet", "xlm"]
def tokenize_fn(examples):
"""
Tokenize input examples.
Args:
examples (dict): Input examples.
Returns:
dict: Tokenized examples.
"""
# Tokenize the input examples using the provided tokenizer.
# The tokenizer is configured to truncate sequences to a maximum length.
# The tokenizer also returns the overflowing tokens, offsets mapping, and token type IDs.
# The padding strategy is determined by the data_args.pad_to_max_length parameter.
tokenized_examples = tokenizer(
examples[QUESTION_COLUMN_NAME if pad_on_right else CONTEXT_COLUMN_NAME],
examples[CONTEXT_COLUMN_NAME if pad_on_right else QUESTION_COLUMN_NAME],
truncation="only_second" if pad_on_right else "only_first",
max_length=max_seq_length,
stride=data_args.doc_stride,
return_overflowing_tokens=True,
return_offsets_mapping=True,
return_token_type_ids=data_args.return_token_type_ids,
padding="max_length" if data_args.pad_to_max_length else False,
)
return tokenized_examples
def prepare_train_features(examples):
"""
Prepare training features by tokenizing the input examples and adding labels.
Args:
examples (dict): Input examples.
Returns:
dict: Tokenized and labeled examples.
"""
# Tokenize the input examples using the provided tokenizer.
tokenized_examples = tokenize_fn(examples)
sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
offset_mapping = tokenized_examples.pop("offset_mapping")
# Add start positions, end positions, and is_impossibles to the tokenized examples.
tokenized_examples["start_positions"] = []
tokenized_examples["end_positions"] = []
tokenized_examples["is_impossibles"] = []
if beam_based:
# Add cls_index and p_mask to the tokenized examples if beam_based.
tokenized_examples["cls_index"] = []
tokenized_examples["p_mask"] = []
for i, offsets in enumerate(offset_mapping):
# We will label impossible answers with the index of the CLS token.
# Get the input_ids and cls_index for the current example.
input_ids = tokenized_examples["input_ids"][i]
cls_index = input_ids.index(tokenizer.cls_token_id)
# Get the sequence_ids for the current example.
sequence_ids = tokenized_examples.sequence_ids(i)
context_index = 1 if pad_on_right else 0
# Build the p_mask: non special tokens and context gets 0.0, the others get 1.0.
# The cls token gets 0.0 too (for predictions of empty answers).
# Inspired by XLNet.
if beam_based:
tokenized_examples["cls_index"].append(cls_index)
tokenized_examples["p_mask"].append(
[
0.0 if s == context_index or k == cls_index else 1.0
for s, k in enumerate(sequence_ids)
]
)
# Get the sample_index, answers, and is_impossible for the current example.
sample_index = sample_mapping[i]
answers = examples[ANSWER_COLUMN_NAME][sample_index]
is_impossible = examples[ANSWERABLE_COLUMN_NAME][sample_index]
# If no answers are given, set the cls_index as answer.
if is_impossible or len(answers["answer_start"]) == 0:
tokenized_examples["start_positions"].append(cls_index)
tokenized_examples["end_positions"].append(cls_index)
tokenized_examples["is_impossibles"].append(1.0) # unanswerable
else:
# Start and end token index of the current span in the text.
start_char = answers["answer_start"][0]
end_char = start_char + len(answers["text"][0])
# sequence_ids: 0 for question, 1 for context, None for others
# Start token index of the current span in the tokenized context.
token_start_index = 0
while sequence_ids[token_start_index] != context_index:
token_start_index += 1
# End token index of the current span in the tokenized context.
token_end_index = len(input_ids) - 1
while sequence_ids[token_end_index] != context_index:
token_end_index -= 1
# Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).
if not (offsets[token_start_index][0] <= start_char and
offsets[token_end_index][1] >= end_char
):
tokenized_examples["start_positions"].append(cls_index)
tokenized_examples["end_positions"].append(cls_index)
tokenized_examples["is_impossibles"].append(1.0) # answerable
else:
# Otherwise move the token_start_index and token_end_index to the two ends of the answer.
# Note: we could go after the last offset if the answer is the last word (edge case).
while (token_start_index < len(offsets) and
offsets[token_start_index][0] <= start_char):
token_start_index += 1
tokenized_examples["start_positions"].append(token_start_index - 1)
while offsets[token_end_index][1] >= end_char:
token_end_index -= 1
tokenized_examples["end_positions"].append(token_end_index + 1)
tokenized_examples["is_impossibles"].append(0.0) # answerable
return tokenized_examples
def prepare_eval_features(examples):
"""
Prepare evaluation features by tokenizing the input examples and adding labels.
Args:
examples (dict): Input examples.
Returns:
dict: Tokenized and labeled examples.
"""
# Tokenize the input examples using the provided tokenizer.
tokenized_examples = tokenize_fn(examples)
sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
# Add example ids to the tokenized examples.
tokenized_examples["example_id"] = []
if beam_based:
# Add cls_index and p_mask to the tokenized examples if beam_based.
tokenized_examples["cls_index"] = []
tokenized_examples["p_mask"] = []
for i, input_ids in enumerate(tokenized_examples["input_ids"]):
# Find the CLS index in the input_ids.
cls_index = input_ids.index(tokenizer.cls_token_id)
sequence_ids = tokenized_examples.sequence_ids(i)
context_index = 1 if pad_on_right else 0
if beam_based:
# Build the p_mask: non special tokens and context gets 0.0, the others get 1.0.
# The cls token gets 0.0 too (for predictions of empty answers).
# Inspired by XLNet.
tokenized_examples["cls_index"].append(cls_index)
tokenized_examples["p_mask"].append(
[
0.0 if s == context_index or k == cls_index else 1.0
for s, k in enumerate(sequence_ids)
]
)
sample_index = sample_mapping[i]
id_col = examples[ID_COLUMN_NAME][sample_index]
tokenized_examples["example_id"].append(id_col)
# Set to None the offset mapping that are not part of the context
# so it's easy to determine if a token position is part of the context or not.
tokenized_examples["offset_mapping"][i] = [
(o if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples["offset_mapping"][i])
]
return tokenized_examples
if mode == "train":
get_features_fn = prepare_train_features
elif mode == "eval":
get_features_fn = prepare_eval_features
elif mode == "test":
get_features_fn = prepare_eval_features
return get_features_fn, True
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