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* add mctest and mctaco * Update mctaco.py * add task to suppported tasks
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Original file line number | Diff line number | Diff line change |
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@@ -0,0 +1,116 @@ | ||
import numpy as np | ||
import torch | ||
from dataclasses import dataclass | ||
from typing import List | ||
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||
from jiant.tasks.core import ( | ||
BaseExample, | ||
BaseTokenizedExample, | ||
BaseDataRow, | ||
BatchMixin, | ||
Task, | ||
TaskTypes, | ||
) | ||
from jiant.tasks.lib.templates.shared import double_sentence_featurize, labels_to_bimap | ||
from jiant.utils.python.io import read_file_lines | ||
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||
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@dataclass | ||
class Example(BaseExample): | ||
guid: str | ||
sentence_question: str | ||
answer: str | ||
label: str | ||
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def tokenize(self, tokenizer): | ||
return TokenizedExample( | ||
guid=self.guid, | ||
sentence_question=tokenizer.tokenize(self.sentence_question), | ||
answer=tokenizer.tokenize(self.answer), | ||
label_id=MCTACOTask.LABEL_TO_ID[self.label], | ||
) | ||
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||
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@dataclass | ||
class TokenizedExample(BaseTokenizedExample): | ||
guid: str | ||
sentence_question: List | ||
answer: List | ||
label_id: int | ||
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||
def featurize(self, tokenizer, feat_spec): | ||
return double_sentence_featurize( | ||
guid=self.guid, | ||
input_tokens_a=self.sentence_question, | ||
input_tokens_b=self.answer, | ||
label_id=self.label_id, | ||
tokenizer=tokenizer, | ||
feat_spec=feat_spec, | ||
data_row_class=DataRow, | ||
) | ||
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@dataclass | ||
class DataRow(BaseDataRow): | ||
guid: str | ||
input_ids: np.ndarray | ||
input_mask: np.ndarray | ||
segment_ids: np.ndarray | ||
label_id: int | ||
tokens: list | ||
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@dataclass | ||
class Batch(BatchMixin): | ||
input_ids: torch.LongTensor | ||
input_mask: torch.LongTensor | ||
segment_ids: torch.LongTensor | ||
label_id: torch.LongTensor | ||
tokens: list | ||
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class MCTACOTask(Task): | ||
Example = Example | ||
TokenizedExample = TokenizedExample | ||
DataRow = DataRow | ||
Batch = Batch | ||
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TASK_TYPE = TaskTypes.CLASSIFICATION | ||
LABELS = ["yes", "no"] | ||
LABEL_TO_ID, ID_TO_LABEL = labels_to_bimap(LABELS) | ||
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def get_train_examples(self): | ||
return self._create_examples( | ||
lines=read_file_lines(self.train_path, strip_lines=True), set_type="train" | ||
) | ||
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def get_val_examples(self): | ||
return self._create_examples( | ||
lines=read_file_lines(self.val_path, strip_lines=True), set_type="val" | ||
) | ||
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def get_test_examples(self): | ||
return self._create_examples( | ||
lines=read_file_lines(self.test_path, strip_lines=True), set_type="test" | ||
) | ||
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@classmethod | ||
def _create_examples(cls, lines, set_type): | ||
# noinspection DuplicatedCode | ||
examples = [] | ||
last_question = "" | ||
question_count = -1 | ||
for (i, line) in enumerate(lines): | ||
sentence, question, answer, label, category = line.split("\t") | ||
if last_question != question: | ||
question_count += 1 | ||
last_question = question | ||
examples.append( | ||
Example( | ||
guid="%s-q%s-%s" % (set_type, question_count, i), | ||
sentence_question=sentence + question, | ||
answer=answer, | ||
label=label if set_type != "test" else cls.LABELS[-1], | ||
) | ||
) | ||
return examples |
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Original file line number | Diff line number | Diff line change |
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from dataclasses import dataclass | ||
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from jiant.tasks.lib.templates.shared import labels_to_bimap | ||
from jiant.tasks.lib.templates import multiple_choice as mc_template | ||
from jiant.utils.python.io import read_file_lines | ||
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@dataclass | ||
class Example(mc_template.Example): | ||
@property | ||
def task(self): | ||
return MCTestTask | ||
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@dataclass | ||
class TokenizedExample(mc_template.TokenizedExample): | ||
pass | ||
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@dataclass | ||
class DataRow(mc_template.DataRow): | ||
pass | ||
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@dataclass | ||
class Batch(mc_template.Batch): | ||
pass | ||
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class MCTestTask(mc_template.AbstractMultipleChoiceTask): | ||
Example = Example | ||
TokenizedExample = TokenizedExample | ||
DataRow = DataRow | ||
Batch = Batch | ||
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CHOICE_KEYS = ["A", "B", "C", "D"] | ||
CHOICE_TO_ID, ID_TO_CHOICE = labels_to_bimap(CHOICE_KEYS) | ||
NUM_CHOICES = len(CHOICE_KEYS) | ||
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def get_train_examples(self): | ||
return self._create_examples( | ||
lines=read_file_lines(self.train_path, strip_lines=True), | ||
ans_lines=read_file_lines(self.path_dict["train_ans"], strip_lines=True), | ||
set_type="train", | ||
) | ||
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def get_val_examples(self): | ||
return self._create_examples( | ||
lines=read_file_lines(self.val_path, strip_lines=True), | ||
ans_lines=read_file_lines(self.path_dict["val_ans"], strip_lines=True), | ||
set_type="val", | ||
) | ||
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def get_test_examples(self): | ||
return self._create_examples( | ||
lines=read_file_lines(self.test_path, strip_lines=True), | ||
ans_lines=None, | ||
set_type="test", | ||
) | ||
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@classmethod | ||
def _create_examples(cls, lines, ans_lines, set_type): | ||
examples = [] | ||
if ans_lines is None: | ||
ans_lines = ["\t".join([cls.CHOICE_KEYS[-1]] * 4) for line in lines] | ||
for i, (line, ans) in enumerate(zip(lines, ans_lines)): | ||
line = line.split("\t") | ||
ans = ans.split("\t") | ||
for j in range(4): | ||
examples.append( | ||
Example( | ||
guid="%s-%s" % (set_type, i * 4 + j), | ||
prompt=line[2].replace("\\newline", " ") + " " + line[3 + j * 5], | ||
choice_list=line[4 + j * 5 : 8 + j * 5], | ||
label=ans[j], | ||
) | ||
) | ||
return examples |
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