644 lines
23 KiB
Python
644 lines
23 KiB
Python
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# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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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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""" GLUE processors and helpers"""
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import os
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import warnings
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from dataclasses import asdict
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from enum import Enum
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from typing import List, Optional, Union
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from ...tokenization_utils import PreTrainedTokenizer
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from ...utils import is_tf_available, logging
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from .utils import DataProcessor, InputExample, InputFeatures
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if is_tf_available():
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import tensorflow as tf
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logger = logging.get_logger(__name__)
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DEPRECATION_WARNING = (
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"This {0} will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets "
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"library. You can have a look at this example script for pointers: "
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"https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"
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)
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def glue_convert_examples_to_features(
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examples: Union[List[InputExample], "tf.data.Dataset"],
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tokenizer: PreTrainedTokenizer,
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max_length: Optional[int] = None,
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task=None,
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label_list=None,
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output_mode=None,
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):
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"""
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Loads a data file into a list of `InputFeatures`
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Args:
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examples: List of `InputExamples` or `tf.data.Dataset` containing the examples.
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tokenizer: Instance of a tokenizer that will tokenize the examples
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max_length: Maximum example length. Defaults to the tokenizer's max_len
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task: GLUE task
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label_list: List of labels. Can be obtained from the processor using the `processor.get_labels()` method
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output_mode: String indicating the output mode. Either `regression` or `classification`
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Returns:
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If the `examples` input is a `tf.data.Dataset`, will return a `tf.data.Dataset` containing the task-specific
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features. If the input is a list of `InputExamples`, will return a list of task-specific `InputFeatures` which
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can be fed to the model.
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"""
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warnings.warn(DEPRECATION_WARNING.format("function"), FutureWarning)
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if is_tf_available() and isinstance(examples, tf.data.Dataset):
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if task is None:
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raise ValueError("When calling glue_convert_examples_to_features from TF, the task parameter is required.")
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return _tf_glue_convert_examples_to_features(examples, tokenizer, max_length=max_length, task=task)
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return _glue_convert_examples_to_features(
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examples, tokenizer, max_length=max_length, task=task, label_list=label_list, output_mode=output_mode
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)
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if is_tf_available():
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def _tf_glue_convert_examples_to_features(
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examples: tf.data.Dataset,
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tokenizer: PreTrainedTokenizer,
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task=str,
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max_length: Optional[int] = None,
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) -> tf.data.Dataset:
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"""
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Returns:
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A `tf.data.Dataset` containing the task-specific features.
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"""
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processor = glue_processors[task]()
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examples = [processor.tfds_map(processor.get_example_from_tensor_dict(example)) for example in examples]
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features = glue_convert_examples_to_features(examples, tokenizer, max_length=max_length, task=task)
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label_type = tf.float32 if task == "sts-b" else tf.int64
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def gen():
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for ex in features:
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d = {k: v for k, v in asdict(ex).items() if v is not None}
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label = d.pop("label")
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yield (d, label)
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input_names = tokenizer.model_input_names
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return tf.data.Dataset.from_generator(
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gen,
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({k: tf.int32 for k in input_names}, label_type),
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({k: tf.TensorShape([None]) for k in input_names}, tf.TensorShape([])),
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)
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def _glue_convert_examples_to_features(
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examples: List[InputExample],
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tokenizer: PreTrainedTokenizer,
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max_length: Optional[int] = None,
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task=None,
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label_list=None,
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output_mode=None,
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):
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if max_length is None:
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max_length = tokenizer.model_max_length
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if task is not None:
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processor = glue_processors[task]()
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if label_list is None:
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label_list = processor.get_labels()
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logger.info(f"Using label list {label_list} for task {task}")
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if output_mode is None:
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output_mode = glue_output_modes[task]
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logger.info(f"Using output mode {output_mode} for task {task}")
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label_map = {label: i for i, label in enumerate(label_list)}
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def label_from_example(example: InputExample) -> Union[int, float, None]:
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if example.label is None:
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return None
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if output_mode == "classification":
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return label_map[example.label]
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elif output_mode == "regression":
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return float(example.label)
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raise KeyError(output_mode)
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labels = [label_from_example(example) for example in examples]
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batch_encoding = tokenizer(
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[(example.text_a, example.text_b) for example in examples],
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max_length=max_length,
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padding="max_length",
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truncation=True,
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)
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features = []
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for i in range(len(examples)):
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inputs = {k: batch_encoding[k][i] for k in batch_encoding}
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feature = InputFeatures(**inputs, label=labels[i])
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features.append(feature)
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for i, example in enumerate(examples[:5]):
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logger.info("*** Example ***")
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logger.info(f"guid: {example.guid}")
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logger.info(f"features: {features[i]}")
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return features
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class OutputMode(Enum):
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classification = "classification"
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regression = "regression"
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class MrpcProcessor(DataProcessor):
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"""Processor for the MRPC data set (GLUE version)."""
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
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def get_example_from_tensor_dict(self, tensor_dict):
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"""See base class."""
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return InputExample(
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tensor_dict["idx"].numpy(),
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tensor_dict["sentence1"].numpy().decode("utf-8"),
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tensor_dict["sentence2"].numpy().decode("utf-8"),
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str(tensor_dict["label"].numpy()),
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)
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def get_train_examples(self, data_dir):
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"""See base class."""
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logger.info(f"LOOKING AT {os.path.join(data_dir, 'train.tsv')}")
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return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
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def get_dev_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
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def get_test_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
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def get_labels(self):
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"""See base class."""
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return ["0", "1"]
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def _create_examples(self, lines, set_type):
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"""Creates examples for the training, dev and test sets."""
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examples = []
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for i, line in enumerate(lines):
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if i == 0:
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continue
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guid = f"{set_type}-{i}"
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text_a = line[3]
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text_b = line[4]
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label = None if set_type == "test" else line[0]
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examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
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return examples
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class MnliProcessor(DataProcessor):
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"""Processor for the MultiNLI data set (GLUE version)."""
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
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def get_example_from_tensor_dict(self, tensor_dict):
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"""See base class."""
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return InputExample(
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tensor_dict["idx"].numpy(),
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tensor_dict["premise"].numpy().decode("utf-8"),
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tensor_dict["hypothesis"].numpy().decode("utf-8"),
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str(tensor_dict["label"].numpy()),
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)
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def get_train_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
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def get_dev_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev_matched.tsv")), "dev_matched")
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def get_test_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(self._read_tsv(os.path.join(data_dir, "test_matched.tsv")), "test_matched")
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def get_labels(self):
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"""See base class."""
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return ["contradiction", "entailment", "neutral"]
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def _create_examples(self, lines, set_type):
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"""Creates examples for the training, dev and test sets."""
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examples = []
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for i, line in enumerate(lines):
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if i == 0:
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continue
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guid = f"{set_type}-{line[0]}"
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text_a = line[8]
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text_b = line[9]
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label = None if set_type.startswith("test") else line[-1]
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examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
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return examples
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class MnliMismatchedProcessor(MnliProcessor):
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"""Processor for the MultiNLI Mismatched data set (GLUE version)."""
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
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def get_dev_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev_mismatched.tsv")), "dev_mismatched")
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def get_test_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(self._read_tsv(os.path.join(data_dir, "test_mismatched.tsv")), "test_mismatched")
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class ColaProcessor(DataProcessor):
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"""Processor for the CoLA data set (GLUE version)."""
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
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def get_example_from_tensor_dict(self, tensor_dict):
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"""See base class."""
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return InputExample(
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tensor_dict["idx"].numpy(),
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tensor_dict["sentence"].numpy().decode("utf-8"),
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None,
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str(tensor_dict["label"].numpy()),
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)
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def get_train_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
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def get_dev_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
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def get_test_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
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def get_labels(self):
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"""See base class."""
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return ["0", "1"]
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def _create_examples(self, lines, set_type):
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"""Creates examples for the training, dev and test sets."""
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test_mode = set_type == "test"
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if test_mode:
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lines = lines[1:]
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text_index = 1 if test_mode else 3
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examples = []
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for i, line in enumerate(lines):
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guid = f"{set_type}-{i}"
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text_a = line[text_index]
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label = None if test_mode else line[1]
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examples.append(InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
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return examples
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class Sst2Processor(DataProcessor):
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"""Processor for the SST-2 data set (GLUE version)."""
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
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def get_example_from_tensor_dict(self, tensor_dict):
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"""See base class."""
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return InputExample(
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tensor_dict["idx"].numpy(),
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tensor_dict["sentence"].numpy().decode("utf-8"),
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None,
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str(tensor_dict["label"].numpy()),
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)
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def get_train_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
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def get_dev_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
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def get_test_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
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def get_labels(self):
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"""See base class."""
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return ["0", "1"]
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|
|
def _create_examples(self, lines, set_type):
|
||
|
|
"""Creates examples for the training, dev and test sets."""
|
||
|
|
examples = []
|
||
|
|
text_index = 1 if set_type == "test" else 0
|
||
|
|
for i, line in enumerate(lines):
|
||
|
|
if i == 0:
|
||
|
|
continue
|
||
|
|
guid = f"{set_type}-{i}"
|
||
|
|
text_a = line[text_index]
|
||
|
|
label = None if set_type == "test" else line[1]
|
||
|
|
examples.append(InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
|
||
|
|
return examples
|
||
|
|
|
||
|
|
|
||
|
|
class StsbProcessor(DataProcessor):
|
||
|
|
"""Processor for the STS-B data set (GLUE version)."""
|
||
|
|
|
||
|
|
def __init__(self, *args, **kwargs):
|
||
|
|
super().__init__(*args, **kwargs)
|
||
|
|
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
|
||
|
|
|
||
|
|
def get_example_from_tensor_dict(self, tensor_dict):
|
||
|
|
"""See base class."""
|
||
|
|
return InputExample(
|
||
|
|
tensor_dict["idx"].numpy(),
|
||
|
|
tensor_dict["sentence1"].numpy().decode("utf-8"),
|
||
|
|
tensor_dict["sentence2"].numpy().decode("utf-8"),
|
||
|
|
str(tensor_dict["label"].numpy()),
|
||
|
|
)
|
||
|
|
|
||
|
|
def get_train_examples(self, data_dir):
|
||
|
|
"""See base class."""
|
||
|
|
return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
|
||
|
|
|
||
|
|
def get_dev_examples(self, data_dir):
|
||
|
|
"""See base class."""
|
||
|
|
return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
|
||
|
|
|
||
|
|
def get_test_examples(self, data_dir):
|
||
|
|
"""See base class."""
|
||
|
|
return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
|
||
|
|
|
||
|
|
def get_labels(self):
|
||
|
|
"""See base class."""
|
||
|
|
return [None]
|
||
|
|
|
||
|
|
def _create_examples(self, lines, set_type):
|
||
|
|
"""Creates examples for the training, dev and test sets."""
|
||
|
|
examples = []
|
||
|
|
for i, line in enumerate(lines):
|
||
|
|
if i == 0:
|
||
|
|
continue
|
||
|
|
guid = f"{set_type}-{line[0]}"
|
||
|
|
text_a = line[7]
|
||
|
|
text_b = line[8]
|
||
|
|
label = None if set_type == "test" else line[-1]
|
||
|
|
examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
|
||
|
|
return examples
|
||
|
|
|
||
|
|
|
||
|
|
class QqpProcessor(DataProcessor):
|
||
|
|
"""Processor for the QQP data set (GLUE version)."""
|
||
|
|
|
||
|
|
def __init__(self, *args, **kwargs):
|
||
|
|
super().__init__(*args, **kwargs)
|
||
|
|
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
|
||
|
|
|
||
|
|
def get_example_from_tensor_dict(self, tensor_dict):
|
||
|
|
"""See base class."""
|
||
|
|
return InputExample(
|
||
|
|
tensor_dict["idx"].numpy(),
|
||
|
|
tensor_dict["question1"].numpy().decode("utf-8"),
|
||
|
|
tensor_dict["question2"].numpy().decode("utf-8"),
|
||
|
|
str(tensor_dict["label"].numpy()),
|
||
|
|
)
|
||
|
|
|
||
|
|
def get_train_examples(self, data_dir):
|
||
|
|
"""See base class."""
|
||
|
|
return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
|
||
|
|
|
||
|
|
def get_dev_examples(self, data_dir):
|
||
|
|
"""See base class."""
|
||
|
|
return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
|
||
|
|
|
||
|
|
def get_test_examples(self, data_dir):
|
||
|
|
"""See base class."""
|
||
|
|
return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
|
||
|
|
|
||
|
|
def get_labels(self):
|
||
|
|
"""See base class."""
|
||
|
|
return ["0", "1"]
|
||
|
|
|
||
|
|
def _create_examples(self, lines, set_type):
|
||
|
|
"""Creates examples for the training, dev and test sets."""
|
||
|
|
test_mode = set_type == "test"
|
||
|
|
q1_index = 1 if test_mode else 3
|
||
|
|
q2_index = 2 if test_mode else 4
|
||
|
|
examples = []
|
||
|
|
for i, line in enumerate(lines):
|
||
|
|
if i == 0:
|
||
|
|
continue
|
||
|
|
guid = f"{set_type}-{line[0]}"
|
||
|
|
try:
|
||
|
|
text_a = line[q1_index]
|
||
|
|
text_b = line[q2_index]
|
||
|
|
label = None if test_mode else line[5]
|
||
|
|
except IndexError:
|
||
|
|
continue
|
||
|
|
examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
|
||
|
|
return examples
|
||
|
|
|
||
|
|
|
||
|
|
class QnliProcessor(DataProcessor):
|
||
|
|
"""Processor for the QNLI data set (GLUE version)."""
|
||
|
|
|
||
|
|
def __init__(self, *args, **kwargs):
|
||
|
|
super().__init__(*args, **kwargs)
|
||
|
|
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
|
||
|
|
|
||
|
|
def get_example_from_tensor_dict(self, tensor_dict):
|
||
|
|
"""See base class."""
|
||
|
|
return InputExample(
|
||
|
|
tensor_dict["idx"].numpy(),
|
||
|
|
tensor_dict["question"].numpy().decode("utf-8"),
|
||
|
|
tensor_dict["sentence"].numpy().decode("utf-8"),
|
||
|
|
str(tensor_dict["label"].numpy()),
|
||
|
|
)
|
||
|
|
|
||
|
|
def get_train_examples(self, data_dir):
|
||
|
|
"""See base class."""
|
||
|
|
return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
|
||
|
|
|
||
|
|
def get_dev_examples(self, data_dir):
|
||
|
|
"""See base class."""
|
||
|
|
return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
|
||
|
|
|
||
|
|
def get_test_examples(self, data_dir):
|
||
|
|
"""See base class."""
|
||
|
|
return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
|
||
|
|
|
||
|
|
def get_labels(self):
|
||
|
|
"""See base class."""
|
||
|
|
return ["entailment", "not_entailment"]
|
||
|
|
|
||
|
|
def _create_examples(self, lines, set_type):
|
||
|
|
"""Creates examples for the training, dev and test sets."""
|
||
|
|
examples = []
|
||
|
|
for i, line in enumerate(lines):
|
||
|
|
if i == 0:
|
||
|
|
continue
|
||
|
|
guid = f"{set_type}-{line[0]}"
|
||
|
|
text_a = line[1]
|
||
|
|
text_b = line[2]
|
||
|
|
label = None if set_type == "test" else line[-1]
|
||
|
|
examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
|
||
|
|
return examples
|
||
|
|
|
||
|
|
|
||
|
|
class RteProcessor(DataProcessor):
|
||
|
|
"""Processor for the RTE data set (GLUE version)."""
|
||
|
|
|
||
|
|
def __init__(self, *args, **kwargs):
|
||
|
|
super().__init__(*args, **kwargs)
|
||
|
|
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
|
||
|
|
|
||
|
|
def get_example_from_tensor_dict(self, tensor_dict):
|
||
|
|
"""See base class."""
|
||
|
|
return InputExample(
|
||
|
|
tensor_dict["idx"].numpy(),
|
||
|
|
tensor_dict["sentence1"].numpy().decode("utf-8"),
|
||
|
|
tensor_dict["sentence2"].numpy().decode("utf-8"),
|
||
|
|
str(tensor_dict["label"].numpy()),
|
||
|
|
)
|
||
|
|
|
||
|
|
def get_train_examples(self, data_dir):
|
||
|
|
"""See base class."""
|
||
|
|
return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
|
||
|
|
|
||
|
|
def get_dev_examples(self, data_dir):
|
||
|
|
"""See base class."""
|
||
|
|
return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
|
||
|
|
|
||
|
|
def get_test_examples(self, data_dir):
|
||
|
|
"""See base class."""
|
||
|
|
return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
|
||
|
|
|
||
|
|
def get_labels(self):
|
||
|
|
"""See base class."""
|
||
|
|
return ["entailment", "not_entailment"]
|
||
|
|
|
||
|
|
def _create_examples(self, lines, set_type):
|
||
|
|
"""Creates examples for the training, dev and test sets."""
|
||
|
|
examples = []
|
||
|
|
for i, line in enumerate(lines):
|
||
|
|
if i == 0:
|
||
|
|
continue
|
||
|
|
guid = f"{set_type}-{line[0]}"
|
||
|
|
text_a = line[1]
|
||
|
|
text_b = line[2]
|
||
|
|
label = None if set_type == "test" else line[-1]
|
||
|
|
examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
|
||
|
|
return examples
|
||
|
|
|
||
|
|
|
||
|
|
class WnliProcessor(DataProcessor):
|
||
|
|
"""Processor for the WNLI data set (GLUE version)."""
|
||
|
|
|
||
|
|
def __init__(self, *args, **kwargs):
|
||
|
|
super().__init__(*args, **kwargs)
|
||
|
|
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
|
||
|
|
|
||
|
|
def get_example_from_tensor_dict(self, tensor_dict):
|
||
|
|
"""See base class."""
|
||
|
|
return InputExample(
|
||
|
|
tensor_dict["idx"].numpy(),
|
||
|
|
tensor_dict["sentence1"].numpy().decode("utf-8"),
|
||
|
|
tensor_dict["sentence2"].numpy().decode("utf-8"),
|
||
|
|
str(tensor_dict["label"].numpy()),
|
||
|
|
)
|
||
|
|
|
||
|
|
def get_train_examples(self, data_dir):
|
||
|
|
"""See base class."""
|
||
|
|
return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
|
||
|
|
|
||
|
|
def get_dev_examples(self, data_dir):
|
||
|
|
"""See base class."""
|
||
|
|
return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
|
||
|
|
|
||
|
|
def get_test_examples(self, data_dir):
|
||
|
|
"""See base class."""
|
||
|
|
return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
|
||
|
|
|
||
|
|
def get_labels(self):
|
||
|
|
"""See base class."""
|
||
|
|
return ["0", "1"]
|
||
|
|
|
||
|
|
def _create_examples(self, lines, set_type):
|
||
|
|
"""Creates examples for the training, dev and test sets."""
|
||
|
|
examples = []
|
||
|
|
for i, line in enumerate(lines):
|
||
|
|
if i == 0:
|
||
|
|
continue
|
||
|
|
guid = f"{set_type}-{line[0]}"
|
||
|
|
text_a = line[1]
|
||
|
|
text_b = line[2]
|
||
|
|
label = None if set_type == "test" else line[-1]
|
||
|
|
examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
|
||
|
|
return examples
|
||
|
|
|
||
|
|
|
||
|
|
glue_tasks_num_labels = {
|
||
|
|
"cola": 2,
|
||
|
|
"mnli": 3,
|
||
|
|
"mrpc": 2,
|
||
|
|
"sst-2": 2,
|
||
|
|
"sts-b": 1,
|
||
|
|
"qqp": 2,
|
||
|
|
"qnli": 2,
|
||
|
|
"rte": 2,
|
||
|
|
"wnli": 2,
|
||
|
|
}
|
||
|
|
|
||
|
|
glue_processors = {
|
||
|
|
"cola": ColaProcessor,
|
||
|
|
"mnli": MnliProcessor,
|
||
|
|
"mnli-mm": MnliMismatchedProcessor,
|
||
|
|
"mrpc": MrpcProcessor,
|
||
|
|
"sst-2": Sst2Processor,
|
||
|
|
"sts-b": StsbProcessor,
|
||
|
|
"qqp": QqpProcessor,
|
||
|
|
"qnli": QnliProcessor,
|
||
|
|
"rte": RteProcessor,
|
||
|
|
"wnli": WnliProcessor,
|
||
|
|
}
|
||
|
|
|
||
|
|
glue_output_modes = {
|
||
|
|
"cola": "classification",
|
||
|
|
"mnli": "classification",
|
||
|
|
"mnli-mm": "classification",
|
||
|
|
"mrpc": "classification",
|
||
|
|
"sst-2": "classification",
|
||
|
|
"sts-b": "regression",
|
||
|
|
"qqp": "classification",
|
||
|
|
"qnli": "classification",
|
||
|
|
"rte": "classification",
|
||
|
|
"wnli": "classification",
|
||
|
|
}
|