transformer_processors.py

# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" GLUE processors and helpers """

import logging
import os
import copy
import csv
import json

from utils import load_jsonl

from transformers.file_utils import is_tf_available


if is_tf_available():
    import tensorflow as tf

logger = logging.getLogger(__name__)


class EvidenceExample(object):
    def __init__(self, guid, text_a, evidences, scores, text_b=None, label=None):
        self.guid = guid
        self.text_a = text_a
        self.text_b = text_b
        self.label = label
        self.evidences = evidences
        self.scores = scores

    def __repr__(self):
        return str(self.to_json_string())

    def to_dict(self):
        """Serializes this instance to a Python dictionary."""
        output = copy.deepcopy(self.__dict__)
        return output

    def to_json_string(self):
        """Serializes this instance to a JSON string."""
        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"

class InputExample(object):
    """
    A single training/test example for simple sequence classification.
    Args:
        guid: Unique id for the example.
        text_a: string. The untokenized text of the first sequence. For single
        sequence tasks, only this sequence must be specified.
        text_b: (Optional) string. The untokenized text of the second sequence.
        Only must be specified for sequence pair tasks.
        label: (Optional) string. The label of the example. This should be
        specified for train and dev examples, but not for test examples.
    """

    def __init__(self, guid, text_a, text_b=None, label=None):
        self.guid = guid
        self.text_a = text_a
        self.text_b = text_b
        self.label = label

    def __repr__(self):
        return str(self.to_json_string())

    def to_dict(self):
        """Serializes this instance to a Python dictionary."""
        output = copy.deepcopy(self.__dict__)
        return output

    def to_json_string(self):
        """Serializes this instance to a JSON string."""
        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"


class EvidenceFeatures(object):
    """
    A single set of features of data.
    Args:
        input_ids: Indices of input sequence tokens in the vocabulary.
        attention_mask: Mask to avoid performing attention on padding token indices.
            Mask values selected in ``[0, 1]``:
            Usually  ``1`` for tokens that are NOT MASKED, ``0`` for MASKED (padded) tokens.
        token_type_ids: Segment token indices to indicate first and second portions of the inputs.
        label: Label corresponding to the input
    """

    def __init__(self, input_ids, attention_mask, token_type_ids, label, score):
        self.input_ids = input_ids
        self.attention_mask = attention_mask
        self.token_type_ids = token_type_ids
        self.label = label
        self.score = score

    def __repr__(self):
        return str(self.to_json_string())

    def to_dict(self):
        """Serializes this instance to a Python dictionary."""
        output = copy.deepcopy(self.__dict__)
        return output

    def to_json_string(self):
        """Serializes this instance to a JSON string."""
        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"

class InputFeatures(object):
    """
    A single set of features of data.
    Args:
        input_ids: Indices of input sequence tokens in the vocabulary.
        attention_mask: Mask to avoid performing attention on padding token indices.
            Mask values selected in ``[0, 1]``:
            Usually  ``1`` for tokens that are NOT MASKED, ``0`` for MASKED (padded) tokens.
        token_type_ids: Segment token indices to indicate first and second portions of the inputs.
        label: Label corresponding to the input
    """

    def __init__(self, input_ids, attention_mask, token_type_ids, label):
        self.input_ids = input_ids
        self.attention_mask = attention_mask
        self.token_type_ids = token_type_ids
        self.label = label

    def __repr__(self):
        return str(self.to_json_string())

    def to_dict(self):
        """Serializes this instance to a Python dictionary."""
        output = copy.deepcopy(self.__dict__)
        return output

    def to_json_string(self):
        """Serializes this instance to a JSON string."""
        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"


class DataProcessor(object):
    """Base class for data converters for sequence classification data sets."""

    def get_example_from_tensor_dict(self, tensor_dict):
        """Gets an example from a dict with tensorflow tensors
        Args:
            tensor_dict: Keys and values should match the corresponding Glue
                tensorflow_dataset examples.
        """
        raise NotImplementedError()

    def get_train_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the train set."""
        raise NotImplementedError()

    def get_dev_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the dev set."""
        raise NotImplementedError()

    def get_labels(self):
        """Gets the list of labels for this data set."""
        raise NotImplementedError()

    @classmethod
    def _read_tsv(cls, input_file, quotechar=None):
        """Reads a tab separated value file."""
        with open(input_file, "r", encoding="utf-8-sig") as f:
            reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
            lines = []
            for line in reader:
                lines.append(line)
            return lines


def news_convert_example_evidence_to_features(
    examples,
    all_evidences,
    tokenizer,
    max_length=512,
    task=None,
    label_list=None,
    output_mode=None,
    pad_on_left=False,
    pad_token=0,
    pad_token_segment_id=0,
    mask_padding_with_zero=True,
):
    """
    Loads a data file into a list of ``InputFeatures``
    Args:
        examples: List of ``InputExamples`` or ``tf.data.Dataset`` containing the examples.
        tokenizer: Instance of a tokenizer that will tokenize the examples
        max_length: Maximum example length
        task: HANS
        label_list: List of labels. Can be obtained from the processor using the ``processor.get_labels()`` method
        output_mode: String indicating the output mode. Either ``regression`` or ``classification``
        pad_on_left: If set to ``True``, the examples will be padded on the left rather than on the right (default)
        pad_token: Padding token
        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)
        mask_padding_with_zero: If set to ``True``, the attention mask will be filled by ``1`` for actual values
            and by ``0`` for padded values. If set to ``False``, inverts it (``1`` for padded values, ``0`` for
            actual values)
    Returns:
        If the ``examples`` input is a ``tf.data.Dataset``, will return a ``tf.data.Dataset``
        containing the task-specific features. If the input is a list of ``InputExamples``, will return
        a list of task-specific ``InputFeatures`` which can be fed to the model.
    """
    is_tf_dataset = False
    if is_tf_available() and isinstance(examples, tf.data.Dataset):
        is_tf_dataset = True

    if task is not None:
        processor = glue_processors[task]()
        if label_list is None:
            label_list = processor.get_labels()
            logger.info("Using label list %s for task %s" % (label_list, task))
        if output_mode is None:
            output_mode = glue_output_modes[task]
            logger.info("Using output mode %s for task %s" % (output_mode, task))

    label_map = {label: i for i, label in enumerate(label_list)}

    features = []
    for (ex_index, example) in enumerate(examples):
        paired_evidences = all_evidences[ex_index]
        # if ex_index % 10000 == 0:
        #     logger.info("Writing example %d" % (ex_index))
        if is_tf_dataset:
            example = processor.get_example_from_tensor_dict(example)
            example = processor.tfds_map(example)


        claim = example.text_a
        assert(example.text_b is None)
        for e_idx, evidence in enumerate(paired_evidences):
            new_guid = example.guid + '_' + str(e_idx)

            inputs = tokenizer.encode_plus(claim, evidence, add_special_tokens=True, max_length=max_length, return_token_type_ids=True, truncation=True)
            input_ids, token_type_ids = inputs["input_ids"], inputs["token_type_ids"]

            # The mask has 1 for real tokens and 0 for padding tokens. Only real
            # tokens are attended to.
            attention_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)

            # Zero-pad up to the sequence length.
            padding_length = max_length - len(input_ids)
            if pad_on_left:
                input_ids = ([pad_token] * padding_length) + input_ids
                attention_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
                token_type_ids = ([pad_token_segment_id] * padding_length) + token_type_ids
            else:
                input_ids = input_ids + ([pad_token] * padding_length)
                attention_mask = attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
                token_type_ids = token_type_ids + ([pad_token_segment_id] * padding_length)

            assert len(input_ids) == max_length, "Error with input length {} vs {}".format(len(input_ids), max_length)
            assert len(attention_mask) == max_length, "Error with input length {} vs {}".format(
                len(attention_mask), max_length
            )
            assert len(token_type_ids) == max_length, "Error with input length {} vs {}".format(
                len(token_type_ids), max_length
            )

            if output_mode == "classification":
                label = label_map[example.label] if example.label in label_map else 0
            elif output_mode == "regression":
                label = float(example.label)
            else:
                raise KeyError(output_mode)

            # if ex_index < 10:
            #     logger.info("*** Example ***")
            #     logger.info("text_a: %s" % (claim))
            #     logger.info("text_b: %s" % (evidence))
            #     logger.info("guid: %s" % (new_guid))
            #     logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
            #     logger.info("attention_mask: %s" % " ".join([str(x) for x in attention_mask]))
            #     logger.info("token_type_ids: %s" % " ".join([str(x) for x in token_type_ids]))
            #     logger.info("label: %s (id = %d)" % (example.label, label))

            features.append(
                InputFeatures(
                    input_ids=input_ids,
                    attention_mask=attention_mask,
                    token_type_ids=token_type_ids,
                    label=label
                )
            )

    if is_tf_available() and is_tf_dataset:

        def gen():
            for ex in features:
                yield (
                    {
                        "input_ids": ex.input_ids,
                        "attention_mask": ex.attention_mask,
                        "token_type_ids": ex.token_type_ids,
                    },
                    ex.label,
                )

        return tf.data.Dataset.from_generator(
            gen,
            ({"input_ids": tf.int32, "attention_mask": tf.int32, "token_type_ids": tf.int32}, tf.int64),
            (
                {
                    "input_ids": tf.TensorShape([None]),
                    "attention_mask": tf.TensorShape([None]),
                    "token_type_ids": tf.TensorShape([None]),
                },
                tf.TensorShape([]),
            ),
        )

    return features

def news_convert_examples_to_features(
    examples,
    tokenizer,
    max_length=512,
    task=None,
    label_list=None,
    output_mode=None,
    pad_on_left=False,
    pad_token=0,
    pad_token_segment_id=0,
    mask_padding_with_zero=True,
):
    """
    Loads a data file into a list of ``InputFeatures``
    Args:
        examples: List of ``InputExamples`` or ``tf.data.Dataset`` containing the examples.
        tokenizer: Instance of a tokenizer that will tokenize the examples
        max_length: Maximum example length
        task: HANS
        label_list: List of labels. Can be obtained from the processor using the ``processor.get_labels()`` method
        output_mode: String indicating the output mode. Either ``regression`` or ``classification``
        pad_on_left: If set to ``True``, the examples will be padded on the left rather than on the right (default)
        pad_token: Padding token
        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)
        mask_padding_with_zero: If set to ``True``, the attention mask will be filled by ``1`` for actual values
            and by ``0`` for padded values. If set to ``False``, inverts it (``1`` for padded values, ``0`` for
            actual values)
    Returns:
        If the ``examples`` input is a ``tf.data.Dataset``, will return a ``tf.data.Dataset``
        containing the task-specific features. If the input is a list of ``InputExamples``, will return
        a list of task-specific ``InputFeatures`` which can be fed to the model.
    """
    is_tf_dataset = False
    if is_tf_available() and isinstance(examples, tf.data.Dataset):
        is_tf_dataset = True

    if task is not None:
        processor = glue_processors[task]()
        if label_list is None:
            label_list = processor.get_labels()
            logger.info("Using label list %s for task %s" % (label_list, task))
        if output_mode is None:
            output_mode = glue_output_modes[task]
            logger.info("Using output mode %s for task %s" % (output_mode, task))

    label_map = {label: i for i, label in enumerate(label_list)}

    features = []
    for (ex_index, example) in enumerate(examples):
        if ex_index % 10000 == 0:
            logger.info("Writing example %d" % (ex_index))
        if is_tf_dataset:
            example = processor.get_example_from_tensor_dict(example)
            example = processor.tfds_map(example)

        inputs = tokenizer.encode_plus(example.text_a, example.text_b, add_special_tokens=True, max_length=max_length, truncation=True, return_token_type_ids=True)
        input_ids, token_type_ids = inputs["input_ids"], inputs["token_type_ids"]

        # The mask has 1 for real tokens and 0 for padding tokens. Only real
        # tokens are attended to.
        attention_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)

        # Zero-pad up to the sequence length.
        padding_length = max_length - len(input_ids)
        if pad_on_left:
            input_ids = ([pad_token] * padding_length) + input_ids
            attention_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
            token_type_ids = ([pad_token_segment_id] * padding_length) + token_type_ids
        else:
            input_ids = input_ids + ([pad_token] * padding_length)
            attention_mask = attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
            token_type_ids = token_type_ids + ([pad_token_segment_id] * padding_length)

        assert len(input_ids) == max_length, "Error with input length {} vs {}".format(len(input_ids), max_length)
        assert len(attention_mask) == max_length, "Error with input length {} vs {}".format(
            len(attention_mask), max_length
        )
        assert len(token_type_ids) == max_length, "Error with input length {} vs {}".format(
            len(token_type_ids), max_length
        )

        if output_mode == "classification":
            label = label_map[example.label] if example.label in label_map else 0
        elif output_mode == "regression":
            label = float(example.label)
        else:
            raise KeyError(output_mode)

        if ex_index < 10:
            logger.info("*** Example ***")
            logger.info("text_a: %s" % (example.text_a))
            logger.info("text_b: %s" % (example.text_b))
            logger.info("guid: %s" % (example.guid))
            logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
            logger.info("attention_mask: %s" % " ".join([str(x) for x in attention_mask]))
            logger.info("token_type_ids: %s" % " ".join([str(x) for x in token_type_ids]))
            logger.info("label: %s (id = %d)" % (example.label, label))

        features.append(
            InputFeatures(
                input_ids=input_ids,
                attention_mask=attention_mask,
                token_type_ids=token_type_ids,
                label=label
            )
        )

    if is_tf_available() and is_tf_dataset:

        def gen():
            for ex in features:
                yield (
                    {
                        "input_ids": ex.input_ids,
                        "attention_mask": ex.attention_mask,
                        "token_type_ids": ex.token_type_ids,
                    },
                    ex.label,
                )

        return tf.data.Dataset.from_generator(
            gen,
            ({"input_ids": tf.int32, "attention_mask": tf.int32, "token_type_ids": tf.int32}, tf.int64),
            (
                {
                    "input_ids": tf.TensorShape([None]),
                    "attention_mask": tf.TensorShape([None]),
                    "token_type_ids": tf.TensorShape([None]),
                },
                tf.TensorShape([]),
            ),
        )

    return features


def newsevidence_convert_examples_to_features(
    examples,
    tokenizer,
    max_length=512,
    task=None,
    label_list=None,
    output_mode=None,
    pad_on_left=False,
    pad_token=0,
    pad_token_segment_id=0,
    mask_padding_with_zero=True,
):
    """
    Loads a data file into a list of ``InputFeatures``
    Args:
        examples: List of ``InputExamples`` or ``tf.data.Dataset`` containing the examples.
        tokenizer: Instance of a tokenizer that will tokenize the examples
        max_length: Maximum example length
        task: HANS
        label_list: List of labels. Can be obtained from the processor using the ``processor.get_labels()`` method
        output_mode: String indicating the output mode. Either ``regression`` or ``classification``
        pad_on_left: If set to ``True``, the examples will be padded on the left rather than on the right (default)
        pad_token: Padding token
        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)
        mask_padding_with_zero: If set to ``True``, the attention mask will be filled by ``1`` for actual values
            and by ``0`` for padded values. If set to ``False``, inverts it (``1`` for padded values, ``0`` for
            actual values)
    Returns:
        If the ``examples`` input is a ``tf.data.Dataset``, will return a ``tf.data.Dataset``
        containing the task-specific features. If the input is a list of ``InputExamples``, will return
        a list of task-specific ``InputFeatures`` which can be fed to the model.
    """
    is_tf_dataset = False
    if is_tf_available() and isinstance(examples, tf.data.Dataset):
        is_tf_dataset = True

    if task is not None:
        processor = glue_processors[task]()
        if label_list is None:
            label_list = processor.get_labels()
            logger.info("Using label list %s for task %s" % (label_list, task))
        if output_mode is None:
            output_mode = glue_output_modes[task]
            logger.info("Using output mode %s for task %s" % (output_mode, task))

    label_map = {label: i for i, label in enumerate(label_list)}

    features = []
    for (ex_index, example) in enumerate(examples):
        if ex_index % 10000 == 0:
            logger.info("Writing example %d" % (ex_index))
        if is_tf_dataset:
            example = processor.get_example_from_tensor_dict(example)
            example = processor.tfds_map(example)
        for e_idx, e in enumerate(example.evidences):
            inputs = tokenizer.encode_plus(example.text_a, e, add_special_tokens=True, max_length=max_length, return_token_type_ids=True, truncation=True)
            input_ids, token_type_ids = inputs["input_ids"], inputs["token_type_ids"]

            # The mask has 1 for real tokens and 0 for padding tokens. Only real
            # tokens are attended to.
            attention_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)

            # Zero-pad up to the sequence length.
            padding_length = max_length - len(input_ids)
            if pad_on_left:
                input_ids = ([pad_token] * padding_length) + input_ids
                attention_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
                token_type_ids = ([pad_token_segment_id] * padding_length) + token_type_ids
            else:
                input_ids = input_ids + ([pad_token] * padding_length)
                attention_mask = attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
                token_type_ids = token_type_ids + ([pad_token_segment_id] * padding_length)

            assert len(input_ids) == max_length, "Error with input length {} vs {}".format(len(input_ids), max_length)
            assert len(attention_mask) == max_length, "Error with input length {} vs {}".format(
                len(attention_mask), max_length
            )
            assert len(token_type_ids) == max_length, "Error with input length {} vs {}".format(
                len(token_type_ids), max_length
            )
            score = float(example.scores[e_idx])
            if output_mode == "classification":
                label = label_map[example.label] if example.label in label_map else 0
            elif output_mode == "regression":
                label = float(example.label)
            else:
                raise KeyError(output_mode)

            if ex_index < 10:
                logger.info("*** Example ***")
                logger.info("text_a: %s" % (example.text_a))
                logger.info("text_b: %s" % (example.text_b))
                logger.info("guid: %s" % (example.guid))
                logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
                logger.info("attention_mask: %s" % " ".join([str(x) for x in attention_mask]))
                logger.info("token_type_ids: %s" % " ".join([str(x) for x in token_type_ids]))
                logger.info("label: %s (id = %d)" % (example.label, label))

            features.append(
                EvidenceFeatures(
                    input_ids=input_ids,
                    attention_mask=attention_mask,
                    token_type_ids=token_type_ids,
                    label=label,
                    score=score
                )
            )

    if is_tf_available() and is_tf_dataset:

        def gen():
            for ex in features:
                yield (
                    {
                        "input_ids": ex.input_ids,
                        "attention_mask": ex.attention_mask,
                        "token_type_ids": ex.token_type_ids,
                    },
                    ex.label,
                )

        return tf.data.Dataset.from_generator(
            gen,
            ({"input_ids": tf.int32, "attention_mask": tf.int32, "token_type_ids": tf.int32}, tf.int64),
            (
                {
                    "input_ids": tf.TensorShape([None]),
                    "attention_mask": tf.TensorShape([None]),
                    "token_type_ids": tf.TensorShape([None]),
                },
                tf.TensorShape([]),
            ),
        )

    return features


class NewsEvidenceProcessor(DataProcessor):
    def __init__(self, input_key, label_key):
        super(NewsEvidenceProcessor, self).__init__()
        self.input_key = input_key
        self.label_key = label_key


    def get_example_from_tensor_dict(self, tensor_dict):
        """See base class."""
        return InputExample(
            tensor_dict["idx"].numpy(),
            tensor_dict["premise"].numpy().decode("utf-8"),
            tensor_dict["hypothesis"].numpy().decode("utf-8"),
            str(tensor_dict["label"].numpy()),
        )

    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(load_jsonl(os.path.join(data_dir, "train.jsonl")), "train")

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(load_jsonl(os.path.join(data_dir, "dev.jsonl")), "dev")

    def get_labels(self):
        """See base class."""
        return ["0", "1"]

    def _create_examples(self, raw_examples, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, example) in enumerate(raw_examples):
            if 'link' in example.keys():
                ex_id = example['link']
            elif 'id' in example.keys():
                ex_id = example['id']
            elif 'origin_line_number' in example.keys():
                ex_id = "%s-%s" % (example['origin_line_number'], i)
            else:
                ex_id = i
            guid = "%s-%s" % (set_type, ex_id)
            text_a = example[self.input_key]
            # For lucene virification
            text_a = ' '.join(text_a.strip().split())
            if len(text_a) == 0:
                continue
            label = example[self.label_key]
            if type(label) is not str:
                label = str(label)
            try:
                evidences = [' '.join(e.strip().split())  if e is not None else None for e in example['evidences']]
            except:
                print(example)
                exit()
            scores = example['scores']
            examples.append(EvidenceExample(guid=guid, text_a=text_a, evidences=evidences, scores=scores, text_b=None, label=label))
        return examples

class NewsProcessor(DataProcessor):
    """Processor for the NELA data set."""


    def __init__(self, input_key, label_key):
        super(NewsProcessor, self).__init__()
        self.input_key = input_key
        self.label_key = label_key


    def get_example_from_tensor_dict(self, tensor_dict):
        """See base class."""
        return InputExample(
            tensor_dict["idx"].numpy(),
            tensor_dict["premise"].numpy().decode("utf-8"),
            tensor_dict["hypothesis"].numpy().decode("utf-8"),
            str(tensor_dict["label"].numpy()),
        )

    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(load_jsonl(os.path.join(data_dir, "train.jsonl")), "train")

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(load_jsonl(os.path.join(data_dir, "dev.jsonl")), "dev")

    def get_labels(self):
        """See base class."""
        return ["0", "1"]

    def _create_examples(self, raw_examples, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, example) in enumerate(raw_examples):
            if 'link' in example.keys():
                ex_id = example['link']
            elif 'id' in example.keys():
                ex_id = example['id']
            elif 'origin_line_number' in example.keys():
                ex_id = "%s-%s" % (example['origin_line_number'], i)
            else:
                ex_id = i
            guid = "%s-%s" % (set_type, ex_id)
            text_a = example[self.input_key]
            # For lucene virification
            text_a = ' '.join(text_a.strip().split())
            if len(text_a) == 0:
                continue
            label = example[self.label_key]
            if type(label) is not str:
                label = str(label)
            examples.append(InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
        return examples

class News3Processor(NewsProcessor):
    """Processor for the NELA data set."""

    def get_labels(self):
        """See base class."""
        return ["0", "1", "2"]

glue_tasks_num_labels = {
    "news": 2,
    "news3": 3,
    "newsevidence": 2
}

glue_processors = {
    "news": NewsProcessor,
    "news3": News3Processor,
    "newsevidence": NewsEvidenceProcessor,
}

glue_output_modes = {
    "news": "classification",
    "news3": "classification",
    "newsevidence": "classification",
}