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# coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# 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.
"""Defines a supervised NLP task."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import abc
from typing import List, Tuple
import configure_finetuning
from finetune import feature_spec
from finetune import scorer
from model import modeling
class Example(object):
__metaclass__ = abc.ABCMeta
def __init__(self, task_name):
self.task_name = task_name
class Task(object):
"""Override this class to add a new fine-tuning task."""
__metaclass__ = abc.ABCMeta
def __init__(self, config: configure_finetuning.FinetuningConfig, name):
self.config = config
self.name = name
def get_test_splits(self):
return ["test"]
@abc.abstractmethod
def get_examples(self, split):
pass
@abc.abstractmethod
def get_scorer(self) -> scorer.Scorer:
pass
@abc.abstractmethod
def get_feature_specs(self) -> List[feature_spec.FeatureSpec]:
pass
@abc.abstractmethod
def featurize(self, example: Example, is_training: bool,
log: bool=False):
pass
@abc.abstractmethod
def get_prediction_module(
self, bert_model: modeling.BertModel, features: dict, is_training: bool,
percent_done: float) -> Tuple:
pass
def __repr__(self):
return "Task(" + self.name + ")"
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