Merge branch 'master' into run_multiple_choice_merge
# Please enter a commit message to explain why this merge is necessary, # especially if it merges an updated upstream into a topic branch. # # Lines starting with '#' will be ignored, and an empty message aborts # the commit.
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@@ -13,7 +13,7 @@
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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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""" BERT classification fine-tuning: utilities to work with GLUE tasks """
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""" BERT multiple choice fine-tuning: utilities to work with multiple choice tasks of reading comprehension """
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from __future__ import absolute_import, division, print_function
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@@ -38,11 +38,10 @@ class InputExample(object):
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"""Constructs a InputExample.
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Args:
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guid: Unique id for the example.
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text_a: string. The untokenized text of the first sequence. For single
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sequence tasks, only this sequence must be specified.
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text_b: (Optional) string. The untokenized text of the second sequence.
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Only must be specified for sequence pair tasks.
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example_id: Unique id for the example.
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contexts: list of str. The untokenized text of the first sequence (context of corresponding question).
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question: string. The untokenized text of the second sequence (qustion).
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endings: list of str. multiple choice's options. Its length must be equal to contexts' length.
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label: (Optional) string. The label of the example. This should be
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specified for train and dev examples, but not for test examples.
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"""
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@@ -73,7 +72,7 @@ class InputFeatures(object):
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class DataProcessor(object):
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"""Base class for data converters for sequence classification data sets."""
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"""Base class for data converters for multiple choice data sets."""
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def get_train_examples(self, data_dir):
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"""Gets a collection of `InputExample`s for the train set."""
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@@ -84,7 +83,7 @@ class DataProcessor(object):
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raise NotImplementedError()
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def get_test_examples(self, data_dir):
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"""Gets a collection of `InputExample`s for the dev set."""
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"""Gets a collection of `InputExample`s for the test set."""
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raise NotImplementedError()
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def get_labels(self):
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@@ -93,7 +92,7 @@ class DataProcessor(object):
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class RaceProcessor(DataProcessor):
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"""Processor for the MRPC data set (GLUE version)."""
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"""Processor for the RACE data set."""
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def get_train_examples(self, data_dir):
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"""See base class."""
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@@ -152,13 +151,13 @@ class RaceProcessor(DataProcessor):
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InputExample(
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example_id=race_id,
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question=question,
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contexts=[article, article, article, article],
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contexts=[article, article, article, article], # this is not efficient but convenient
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endings=[options[0], options[1], options[2], options[3]],
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label=truth))
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return examples
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class SwagProcessor(DataProcessor):
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"""Processor for the MRPC data set (GLUE version)."""
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"""Processor for the SWAG data set."""
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def get_train_examples(self, data_dir):
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"""See base class."""
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@@ -172,9 +171,12 @@ class SwagProcessor(DataProcessor):
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def get_test_examples(self, data_dir):
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"""See base class."""
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logger.info("LOOKING AT {} test".format(data_dir))
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logger.info("LOOKING AT {} dev".format(data_dir))
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raise ValueError(
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"For swag testing, the input file does not contain a label column. It can not be tested in current code"
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"setting!"
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)
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return self._create_examples(self._read_csv(os.path.join(data_dir, "test.csv")), "test")
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def get_labels(self):
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"""See base class."""
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return ["0", "1", "2", "3"]
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@@ -213,7 +215,7 @@ class SwagProcessor(DataProcessor):
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class ArcProcessor(DataProcessor):
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"""Processor for the MRPC data set (GLUE version)."""
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"""Processor for the ARC data set (request from allennlp)."""
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def get_train_examples(self, data_dir):
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"""See base class."""
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@@ -242,6 +244,7 @@ class ArcProcessor(DataProcessor):
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def _create_examples(self, lines, type):
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"""Creates examples for the training and dev sets."""
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#There are two types of labels. They should be normalized
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def normalize(truth):
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if truth in "ABCD":
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return ord(truth) - ord("A")
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@@ -256,6 +259,7 @@ class ArcProcessor(DataProcessor):
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four_choice = 0
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five_choice = 0
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other_choices = 0
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# we deleted example which has more than or less than four choices
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for line in tqdm.tqdm(lines, desc="read arc data"):
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data_raw = json.loads(line.strip("\n"))
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if len(data_raw["question"]["choices"]) == 3:
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@@ -274,7 +278,6 @@ class ArcProcessor(DataProcessor):
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question = question_choices["stem"]
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id = data_raw["id"]
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options = question_choices["choices"]
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if len(options) == 4:
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examples.append(
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InputExample(
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@@ -328,13 +331,16 @@ def convert_examples_to_features(examples, label_list, max_seq_length,
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tokens_a = tokenizer.tokenize(context)
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tokens_b = None
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if example.question.find("_") != -1:
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#this is for cloze question
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tokens_b = tokenizer.tokenize(example.question.replace("_", ending))
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else:
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tokens_b = tokenizer.tokenize(example.question)
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tokens_b += [sep_token]
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if sep_token_extra:
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tokens_b += [sep_token]
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tokens_b += tokenizer.tokenize(ending)
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tokens_b = tokenizer.tokenize(example.question + " " + ending)
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# you can add seq token between quesiotn and ending. This does not make too much difference.
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# tokens_b = tokenizer.tokenize(example.question)
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# tokens_b += [sep_token]
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# if sep_token_extra:
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# tokens_b += [sep_token]
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# tokens_b += tokenizer.tokenize(ending)
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special_tokens_count = 4 if sep_token_extra else 3
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_truncate_seq_pair(tokens_a, tokens_b, max_seq_length - special_tokens_count)
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@@ -427,15 +433,20 @@ def _truncate_seq_pair(tokens_a, tokens_b, max_length):
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# one token at a time. This makes more sense than truncating an equal percent
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# of tokens from each, since if one sequence is very short then each token
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# that's truncated likely contains more information than a longer sequence.
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# However, since we'd better not to remove tokens of options and questions, you can choose to use a bigger
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# length or only pop from context
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while True:
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total_length = len(tokens_a) + len(tokens_b)
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if total_length <= max_length:
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break
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# if len(tokens_a) > len(tokens_b):
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# tokens_a.pop()
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# else:
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# tokens_b.pop()
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tokens_a.pop()
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if len(tokens_a) > len(tokens_b):
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tokens_a.pop()
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else:
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logger.info('Attention! you are removing from token_b (swag task is ok). '
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'If you are training ARC and RACE (you are poping question + options), '
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'you need to try to use a bigger max seq length!')
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tokens_b.pop()
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processors = {
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