adding details in readme - moving file
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thomwolf
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examples/hans/hans_processors.py
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210
examples/hans/hans_processors.py
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# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" GLUE processors and helpers """
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import logging
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import os
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from utils_hans import DataProcessor, InputExample, InputFeatures
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from transformers.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 hans_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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mask_padding_with_zero=True):
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"""
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Loads a data file into a list of ``InputFeatures``
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Args:
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examples: List of ``InputExamples`` or ``tf.data.Dataset`` containing the examples.
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tokenizer: Instance of a tokenizer that will tokenize the examples
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max_length: Maximum example length
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task: HANS
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label_list: List of labels. Can be obtained from the processor using the ``processor.get_labels()`` method
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output_mode: String indicating the output mode. Either ``regression`` or ``classification``
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pad_on_left: If set to ``True``, the examples will be padded on the left rather than on the right (default)
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pad_token: Padding token
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pad_token_segment_id: The segment ID for the padding token (It is usually 0, but can vary such as for XLNet where it is 4)
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mask_padding_with_zero: If set to ``True``, the attention mask will be filled by ``1`` for actual values
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and by ``0`` for padded values. If set to ``False``, inverts it (``1`` for padded values, ``0`` for
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actual values)
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Returns:
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If the ``examples`` input is a ``tf.data.Dataset``, will return a ``tf.data.Dataset``
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containing the task-specific features. If the input is a list of ``InputExamples``, will return
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a list of task-specific ``InputFeatures`` which can be fed to the model.
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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" % (ex_index))
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if is_tf_dataset:
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example = processor.get_example_from_tensor_dict(example)
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example = processor.tfds_map(example)
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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_length,
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)
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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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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_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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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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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_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 = label_map[example.label] if example.label in label_map else 0
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elif output_mode == "regression":
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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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pairID = str(example.pairID)
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if ex_index < 10:
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logger.info("*** Example ***")
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logger.info("text_a: %s" % (example.text_a))
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logger.info("text_b: %s" % (example.text_b))
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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("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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attention_mask=attention_mask,
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token_type_ids=token_type_ids,
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label=label, pairID=pairID))
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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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class HansProcessor(DataProcessor):
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"""Processor for the HANS data set."""
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def get_example_from_tensor_dict(self, tensor_dict):
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"""See base class."""
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return InputExample(tensor_dict['idx'].numpy(),
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tensor_dict['premise'].numpy().decode('utf-8'),
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tensor_dict['hypothesis'].numpy().decode('utf-8'),
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str(tensor_dict['label'].numpy()))
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def get_train_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(
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self._read_tsv(os.path.join(data_dir, "heuristics_train_set.txt")), "train")
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def get_dev_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(
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self._read_tsv(os.path.join(data_dir, "heuristics_evaluation_set.txt")),
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"dev")
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def get_labels(self):
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"""See base class."""
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return ["contradiction", "entailment", "neutral"]
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def _create_examples(self, lines, set_type):
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"""Creates examples for the training and dev sets."""
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examples = []
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for (i, line) in enumerate(lines):
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if i == 0:
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continue
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guid = "%s-%s" % (set_type, line[0])
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text_a = line[5]
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text_b = line[6]
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pairID = line[7][2:] if line[7].startswith('ex') else line[7]
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label = line[-1]
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examples.append(
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InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label, pairID=pairID))
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return examples
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glue_tasks_num_labels = {
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"hans": 3,
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}
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glue_processors = {
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"hans": HansProcessor,
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}
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glue_output_modes = {
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"hans": "classification",
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}
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