91 lines
3.2 KiB
Python
91 lines
3.2 KiB
Python
#
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# Copyright (c) 2019, The Board of Trustees of the Leland Stanford Junior University
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# All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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#
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# * Redistributions of source code must retain the above copyright notice, this
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# list of conditions and the following disclaimer.
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#
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# * Redistributions in binary form must reproduce the above copyright notice,
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# this list of conditions and the following disclaimer in the documentation
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# and/or other materials provided with the distribution.
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#
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# * Neither the name of the copyright holder nor the names of its
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# contributors may be used to endorse or promote products derived from
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# this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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from . import generic_dataset
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class BaseTask:
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"""
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Base class for all tasks.
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Includes all the code to handle generic tasks
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"""
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def __init__(self, name, args):
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self.name = name
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@property
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def default_question(self):
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return ''
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@property
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def default_context(self):
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return ''
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def get_splits(self, field, root, **kwargs):
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"""
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Load the train, test, eval datasets for this task
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:param field: the torchtext.Field to use for tokenization, preprocessing and vocabulary construction
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:param root: the base directory where data is stored
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:param kwargs: other arguments to pass to the Dataset
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:return: a list of torchtext.Dataset
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"""
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return generic_dataset.JSON.splits(
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fields=field, root=root, name=self.name, **kwargs)
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def preprocess_example(self, ex, train=False, max_context_length=None):
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"""
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Preprocess a given example, in a task specific way.
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The example should be modified in place.
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Return False if the example should be dropped from the dataset
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:param ex: the torchtext.Example to preprocess
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:return: True if the example is valid, False otherwise
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"""
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return True
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@property
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def metrics(self):
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"""
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What metrics to evaluate this task on.
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This property must return a non-empty list.
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The first entry in the list will be the metric to use to compute the decascore.
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:return: a list of metric names
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"""
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return ['em', 'nem', 'nf1']
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tokenize = None
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detokenize = None
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