update internal glue processors
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thomwolf
parent
0f091062d4
commit
f71758f7a4
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@ -278,7 +278,11 @@ def load_and_cache_examples(args, task, tokenizer, evaluate=False):
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# HACK(label indices are swapped in RoBERTa pretrained model)
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label_list[1], label_list[2] = label_list[2], label_list[1]
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examples = processor.get_dev_examples(args.data_dir) if evaluate else processor.get_train_examples(args.data_dir)
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features = convert_examples_to_features(examples, label_list, args.max_seq_length, tokenizer, output_mode,
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features = convert_examples_to_features(examples,
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label_list,
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args.max_seq_length,
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tokenizer,
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output_mode,
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pad_on_left=bool(args.model_type in ['xlnet']), # pad on the left for xlnet
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pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0],
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pad_token_segment_id=4 if args.model_type in ['xlnet'] else 0,
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@ -292,14 +296,14 @@ def load_and_cache_examples(args, task, tokenizer, evaluate=False):
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# Convert to Tensors and build dataset
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all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
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all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)
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all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)
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all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long)
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all_token_type_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long)
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if output_mode == "classification":
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all_label_ids = torch.tensor([f.label_id for f in features], dtype=torch.long)
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all_labels = torch.tensor([f.label for f in features], dtype=torch.long)
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elif output_mode == "regression":
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all_label_ids = torch.tensor([f.label_id for f in features], dtype=torch.float)
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all_labels = torch.tensor([f.label for f in features], dtype=torch.float)
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dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
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dataset = TensorDataset(all_input_ids, all_attention_mask, all_token_type_ids, all_labels)
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return dataset
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@ -19,11 +19,18 @@ import logging
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import os
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from .utils import DataProcessor, InputExample, InputFeatures
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from ...file_utils import is_tf_available
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if is_tf_available():
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import tensorflow as tf
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logger = logging.getLogger(__name__)
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def glue_convert_examples_to_features(examples, label_list, max_seq_length,
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tokenizer, output_mode,
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def glue_convert_examples_to_features(examples, tokenizer,
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max_length=512,
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task=None,
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label_list=None,
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output_mode=None,
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pad_on_left=False,
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pad_token=0,
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pad_token_segment_id=0,
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@ -31,46 +38,63 @@ def glue_convert_examples_to_features(examples, label_list, max_seq_length,
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"""
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Loads a data file into a list of `InputBatch`s
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"""
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is_tf_dataset = False
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if is_tf_available() and isinstance(examples, tf.data.Dataset):
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is_tf_dataset = True
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if task is not None:
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processor = glue_processors[task]()
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if label_list is None:
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label_list = processor.get_labels()
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logger.info("Using label list %s for task %s" % (label_list, task))
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if output_mode is None:
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output_mode = glue_output_modes[task]
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logger.info("Using output mode %s for task %s" % (output_mode, task))
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label_map = {label: i for i, label in enumerate(label_list)}
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features = []
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for (ex_index, example) in enumerate(examples):
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if ex_index % 10000 == 0:
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logger.info("Writing example %d of %d" % (ex_index, len(examples)))
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logger.info("Writing example %d" % (ex_index))
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if is_tf_dataset:
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example = InputExample(example['idx'].numpy(),
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example['sentence1'].numpy().decode('utf-8'),
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example['sentence2'].numpy().decode('utf-8'),
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str(example['label'].numpy()))
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inputs = tokenizer.encode_plus(
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example.text_a,
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example.text_b,
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add_special_tokens=True,
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max_length=max_seq_length,
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truncate_first_sequence=True # We're truncating the first sequence as a priority
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max_length=max_length,
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truncate_first_sequence=True # We're truncating the first sequence in priority
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)
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input_ids, segment_ids = inputs["input_ids"], inputs["token_type_ids"]
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input_ids, token_type_ids = inputs["input_ids"], inputs["token_type_ids"]
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# The mask has 1 for real tokens and 0 for padding tokens. Only real
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# tokens are attended to.
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input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)
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attention_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)
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# Zero-pad up to the sequence length.
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padding_length = max_seq_length - len(input_ids)
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padding_length = max_length - len(input_ids)
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if pad_on_left:
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input_ids = ([pad_token] * padding_length) + input_ids
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input_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
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segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids
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attention_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
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token_type_ids = ([pad_token_segment_id] * padding_length) + token_type_ids
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else:
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input_ids = input_ids + ([pad_token] * padding_length)
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input_mask = input_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
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segment_ids = segment_ids + ([pad_token_segment_id] * padding_length)
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attention_mask = attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
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token_type_ids = token_type_ids + ([pad_token_segment_id] * padding_length)
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assert len(input_ids) == max_seq_length
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assert len(input_mask) == max_seq_length
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assert len(segment_ids) == max_seq_length
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assert len(input_ids) == max_length, "Error with input length {} vs {}".format(len(input_ids), max_length)
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assert len(attention_mask) == max_length, "Error with input length {} vs {}".format(len(attention_mask), max_length)
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assert len(token_type_ids) == max_length, "Error with input length {} vs {}".format(len(token_type_ids), max_length)
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if output_mode == "classification":
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label_id = label_map[example.label]
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label = label_map[example.label]
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elif output_mode == "regression":
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label_id = float(example.label)
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label = float(example.label)
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else:
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raise KeyError(output_mode)
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@ -78,15 +102,34 @@ def glue_convert_examples_to_features(examples, label_list, max_seq_length,
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logger.info("*** Example ***")
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logger.info("guid: %s" % (example.guid))
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logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
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logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
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logger.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
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logger.info("label: %s (id = %d)" % (example.label, label_id))
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logger.info("attention_mask: %s" % " ".join([str(x) for x in attention_mask]))
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logger.info("token_type_ids: %s" % " ".join([str(x) for x in token_type_ids]))
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logger.info("label: %s (id = %d)" % (example.label, label))
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features.append(
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InputFeatures(input_ids=input_ids,
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input_mask=input_mask,
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segment_ids=segment_ids,
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label_id=label_id))
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attention_mask=attention_mask,
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token_type_ids=token_type_ids,
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label=label))
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if is_tf_available() and is_tf_dataset:
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def gen():
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for ex in features:
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yield ({'input_ids': ex.input_ids,
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'attention_mask': ex.attention_mask,
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'token_type_ids': ex.token_type_ids},
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ex.label)
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return tf.data.Dataset.from_generator(gen,
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({'input_ids': tf.int32,
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'attention_mask': tf.int32,
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'token_type_ids': tf.int32},
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tf.int64),
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({'input_ids': tf.TensorShape([None]),
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'attention_mask': tf.TensorShape([None]),
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'token_type_ids': tf.TensorShape([None])},
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tf.TensorShape([])))
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return features
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@ -16,6 +16,7 @@
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import csv
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import sys
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import copy
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class InputExample(object):
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"""A single training/test example for simple sequence classification."""
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@ -36,15 +37,39 @@ class InputExample(object):
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self.text_b = text_b
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self.label = label
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def __repr__(self):
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return str(self.to_json_string())
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def to_dict(self):
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"""Serializes this instance to a Python dictionary."""
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output = copy.deepcopy(self.__dict__)
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return output
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def to_json_string(self):
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"""Serializes this instance to a JSON string."""
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return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
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class InputFeatures(object):
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"""A single set of features of data."""
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def __init__(self, input_ids, input_mask, segment_ids, label_id):
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def __init__(self, input_ids, attention_mask, token_type_ids, label):
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self.input_ids = input_ids
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self.input_mask = input_mask
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self.segment_ids = segment_ids
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self.label_id = label_id
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self.attention_mask = attention_mask
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self.token_type_ids = token_type_ids
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self.label = label
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def __repr__(self):
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return str(self.to_json_string())
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def to_dict(self):
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"""Serializes this instance to a Python dictionary."""
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output = copy.deepcopy(self.__dict__)
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return output
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def to_json_string(self):
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"""Serializes this instance to a JSON string."""
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return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
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class DataProcessor(object):
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