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Add TF multiple choice example (#12865)
* Add new multiple-choice example, remove old one
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Matt
GitHub
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commit
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<!---
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Copyright 2020 The HuggingFace Team. All rights reserved.
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Copyright 2021 The HuggingFace Team. All rights reserved.
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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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@ -13,26 +13,31 @@ 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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-->
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# Multiple-choice training (e.g. SWAG)
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# Multiple Choice
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This folder contains the `run_swag.py` script, showing an examples of *multiple-choice answering* with the
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🤗 Transformers library. For straightforward use-cases you may be able to use these scripts without modification,
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although we have also included comments in the code to indicate areas that you may need to adapt to your own projects.
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## Fine-tuning on SWAG
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### Multi-GPU and TPU usage
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By default, the script uses a `MirroredStrategy` and will use multiple GPUs effectively if they are available. TPUs
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can also be used by passing the name of the TPU resource with the `--tpu` argument.
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### Memory usage and data loading
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One thing to note is that all data is loaded into memory in this script. Most multiple-choice datasets are small
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enough that this is not an issue, but if you have a very large dataset you will need to modify the script to handle
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data streaming. This is particularly challenging for TPUs, given the stricter requirements and the sheer volume of data
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required to keep them fed. A full explanation of all the possible pitfalls is a bit beyond this example script and
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README, but for more information you can see the 'Input Datasets' section of
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[this document](https://www.tensorflow.org/guide/tpu).
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### Example command
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```bash
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export SWAG_DIR=/path/to/swag_data_dir
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python ./examples/multiple-choice/run_tf_multiple_choice.py \
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--task_name swag \
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--model_name_or_path bert-base-cased \
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--do_train \
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--do_eval \
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--data_dir $SWAG_DIR \
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--learning_rate 5e-5 \
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--num_train_epochs 3 \
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--max_seq_length 80 \
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--output_dir models_bert/swag_base \
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--per_gpu_eval_batch_size=16 \
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--per_device_train_batch_size=16 \
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--logging-dir logs \
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--gradient_accumulation_steps 2 \
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--overwrite_output
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python run_swag.py \
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--model_name_or_path distilbert-base-cased \
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--output_dir output \
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--do_eval \
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--do_train
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```
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459
examples/tensorflow/multiple-choice/run_swag.py
Normal file
459
examples/tensorflow/multiple-choice/run_swag.py
Normal file
@ -0,0 +1,459 @@
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#!/usr/bin/env python
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# coding=utf-8
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# Copyright The HuggingFace Team and The HuggingFace Inc. team. 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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"""
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Fine-tuning the library models for multiple choice.
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"""
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# You can also adapt this script on your own multiple choice task. Pointers for this are left as comments.
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import logging
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import os
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import sys
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import Optional
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import datasets
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import numpy as np
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import tensorflow as tf
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from datasets import load_dataset
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import transformers
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from transformers import (
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CONFIG_NAME,
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TF2_WEIGHTS_NAME,
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AutoConfig,
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AutoTokenizer,
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HfArgumentParser,
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TFAutoModelForMultipleChoice,
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TFTrainingArguments,
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create_optimizer,
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set_seed,
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)
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from transformers.utils import check_min_version
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# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
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check_min_version("4.10.0.dev0")
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logger = logging.getLogger(__name__)
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# region Helper classes and functions
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class SavePretrainedCallback(tf.keras.callbacks.Callback):
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# Hugging Face models have a save_pretrained() method that saves both the weights and the necessary
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# metadata to allow them to be loaded as a pretrained model in future. This is a simple Keras callback
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# that saves the model with this method after each epoch.
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def __init__(self, output_dir, **kwargs):
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super().__init__()
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self.output_dir = output_dir
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def on_epoch_end(self, epoch, logs=None):
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self.model.save_pretrained(self.output_dir)
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def convert_dataset_for_tensorflow(
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dataset, non_label_column_names, batch_size, dataset_mode="variable_batch", shuffle=True, drop_remainder=True
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):
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"""Converts a Hugging Face dataset to a Tensorflow Dataset. The dataset_mode controls whether we pad all batches
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to the maximum sequence length, or whether we only pad to the maximum length within that batch. The former
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is most useful when training on TPU, as a new graph compilation is required for each sequence length.
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"""
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def densify_ragged_batch(features, label=None):
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features = {
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feature: ragged_tensor.to_tensor(shape=batch_shape[feature]) for feature, ragged_tensor in features.items()
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}
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if label is None:
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return features
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else:
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return features, label
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feature_keys = list(set(dataset.features.keys()) - set(non_label_column_names + ["label"]))
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if dataset_mode == "variable_batch":
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batch_shape = {key: None for key in feature_keys}
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data = {key: tf.ragged.constant(dataset[key]) for key in feature_keys}
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elif dataset_mode == "constant_batch":
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data = {key: tf.ragged.constant(dataset[key]) for key in feature_keys}
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batch_shape = {
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key: tf.concat(([batch_size], ragged_tensor.bounding_shape()[1:]), axis=0)
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for key, ragged_tensor in data.items()
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}
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else:
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raise ValueError("Unknown dataset mode!")
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if "label" in dataset.features:
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labels = tf.convert_to_tensor(np.array(dataset["label"]))
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tf_dataset = tf.data.Dataset.from_tensor_slices((data, labels))
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else:
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tf_dataset = tf.data.Dataset.from_tensor_slices(data)
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if shuffle:
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tf_dataset = tf_dataset.shuffle(buffer_size=len(dataset))
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options = tf.data.Options()
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options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.OFF
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tf_dataset = (
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tf_dataset.with_options(options)
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.batch(batch_size=batch_size, drop_remainder=drop_remainder)
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.map(densify_ragged_batch)
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)
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return tf_dataset
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# endregion
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# region Arguments
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@dataclass
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class ModelArguments:
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"""
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Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
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"""
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model_name_or_path: str = field(
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metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
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)
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config_name: Optional[str] = field(
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default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
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)
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tokenizer_name: Optional[str] = field(
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default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
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)
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cache_dir: Optional[str] = field(
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default=None,
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metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
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)
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use_fast_tokenizer: bool = field(
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default=True,
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metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
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)
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model_revision: str = field(
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default="main",
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metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
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)
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use_auth_token: bool = field(
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default=False,
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metadata={
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"help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
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"with private models)."
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},
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)
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@dataclass
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class DataTrainingArguments:
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"""
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Arguments pertaining to what data we are going to input our model for training and eval.
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"""
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train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
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validation_file: Optional[str] = field(
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default=None,
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metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
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)
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overwrite_cache: bool = field(
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default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
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)
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preprocessing_num_workers: Optional[int] = field(
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default=None,
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metadata={"help": "The number of processes to use for the preprocessing."},
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)
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max_seq_length: Optional[int] = field(
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default=None,
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metadata={
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"help": "The maximum total input sequence length after tokenization. If passed, sequences longer "
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"than this will be truncated, sequences shorter will be padded."
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},
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)
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pad_to_max_length: bool = field(
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default=False,
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metadata={
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"help": "Whether to pad all samples to the maximum sentence length. "
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"If False, will pad the samples dynamically when batching to the maximum length in the batch. More "
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"efficient on GPU but very bad for TPU."
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},
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)
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max_train_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": "For debugging purposes or quicker training, truncate the number of training examples to this "
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"value if set."
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},
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)
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max_eval_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
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"value if set."
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},
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)
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def __post_init__(self):
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if self.train_file is not None:
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extension = self.train_file.split(".")[-1]
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assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
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if self.validation_file is not None:
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extension = self.validation_file.split(".")[-1]
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assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
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# endregion
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def main():
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# region Argument parsing
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# See all possible arguments in src/transformers/training_args.py
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# or by passing the --help flag to this script.
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# We now keep distinct sets of args, for a cleaner separation of concerns.
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parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
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if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
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# If we pass only one argument to the script and it's the path to a json file,
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# let's parse it to get our arguments.
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model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
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else:
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model_args, data_args, training_args = parser.parse_args_into_dataclasses()
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output_dir = Path(training_args.output_dir)
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output_dir.mkdir(parents=True, exist_ok=True)
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# endregion
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# region Logging
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logging.basicConfig(
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format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
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datefmt="%m/%d/%Y %H:%M:%S",
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handlers=[logging.StreamHandler(sys.stdout)],
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)
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log_level = training_args.get_process_log_level()
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logger.setLevel(log_level)
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datasets.utils.logging.set_verbosity(log_level)
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transformers.utils.logging.set_verbosity(log_level)
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transformers.utils.logging.enable_default_handler()
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transformers.utils.logging.enable_explicit_format()
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# endregion
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# region Checkpoints
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checkpoint = None
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if len(os.listdir(training_args.output_dir)) > 0 and not training_args.overwrite_output_dir:
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if (output_dir / CONFIG_NAME).is_file() and (output_dir / TF2_WEIGHTS_NAME).is_file():
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checkpoint = output_dir
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logger.info(
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f"Checkpoint detected, resuming training from checkpoint in {training_args.output_dir}. To avoid this"
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" behavior, change the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
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)
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else:
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raise ValueError(
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f"Output directory ({training_args.output_dir}) already exists and is not empty. "
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"Use --overwrite_output_dir to continue regardless."
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)
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# endregion
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# Set seed before initializing model.
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set_seed(training_args.seed)
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# region Load datasets
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# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
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# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
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# (the dataset will be downloaded automatically from the datasets Hub).
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# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
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# 'text' is found. You can easily tweak this behavior (see below).
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# In distributed training, the load_dataset function guarantee that only one local process can concurrently
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# download the dataset.
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if data_args.train_file is not None or data_args.validation_file is not None:
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data_files = {}
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if data_args.train_file is not None:
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data_files["train"] = data_args.train_file
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if data_args.validation_file is not None:
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data_files["validation"] = data_args.validation_file
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extension = data_args.train_file.split(".")[-1]
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raw_datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
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else:
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# Downloading and loading the swag dataset from the hub.
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raw_datasets = load_dataset("swag", "regular", cache_dir=model_args.cache_dir)
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# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
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# https://huggingface.co/docs/datasets/loading_datasets.html.
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# When using your own dataset or a different dataset from swag, you will probably need to change this.
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ending_names = [f"ending{i}" for i in range(4)]
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context_name = "sent1"
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question_header_name = "sent2"
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# endregion
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# region Load model config and tokenizer
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if checkpoint is not None:
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config_path = training_args.output_dir
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elif model_args.config_name:
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config_path = model_args.config_name
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else:
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config_path = model_args.model_name_or_path
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# Distributed training:
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# The .from_pretrained methods guarantee that only one local process can concurrently
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# download model & vocab.
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config = AutoConfig.from_pretrained(
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config_path,
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cache_dir=model_args.cache_dir,
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revision=model_args.model_revision,
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use_auth_token=True if model_args.use_auth_token else None,
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)
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tokenizer = AutoTokenizer.from_pretrained(
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model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
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cache_dir=model_args.cache_dir,
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use_fast=model_args.use_fast_tokenizer,
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revision=model_args.model_revision,
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use_auth_token=True if model_args.use_auth_token else None,
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)
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# endregion
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# region Dataset preprocessing
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if data_args.max_seq_length is None:
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max_seq_length = tokenizer.model_max_length
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if max_seq_length > 1024:
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logger.warning(
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f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "
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"Picking 1024 instead. You can change that default value by passing --max_seq_length xxx."
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)
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max_seq_length = 1024
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else:
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if data_args.max_seq_length > tokenizer.model_max_length:
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logger.warning(
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f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
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f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
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)
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max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
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def preprocess_function(examples):
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first_sentences = [[context] * 4 for context in examples[context_name]]
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question_headers = examples[question_header_name]
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second_sentences = [
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[f"{header} {examples[end][i]}" for end in ending_names] for i, header in enumerate(question_headers)
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]
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# Flatten out
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first_sentences = sum(first_sentences, [])
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second_sentences = sum(second_sentences, [])
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# Tokenize
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tokenized_examples = tokenizer(first_sentences, second_sentences, truncation=True, max_length=max_seq_length)
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# Un-flatten
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data = {k: [v[i : i + 4] for i in range(0, len(v), 4)] for k, v in tokenized_examples.items()}
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return data
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if training_args.do_train:
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if "train" not in raw_datasets:
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raise ValueError("--do_train requires a train dataset")
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train_dataset = raw_datasets["train"]
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non_label_columns = [feature for feature in train_dataset.features if feature not in ("label", "labels")]
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if data_args.max_train_samples is not None:
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train_dataset = train_dataset.select(range(data_args.max_train_samples))
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with training_args.main_process_first(desc="train dataset map pre-processing"):
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train_dataset = train_dataset.map(
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preprocess_function,
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batched=True,
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num_proc=data_args.preprocessing_num_workers,
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load_from_cache_file=not data_args.overwrite_cache,
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)
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if training_args.do_eval:
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if "validation" not in raw_datasets:
|
||||
raise ValueError("--do_eval requires a validation dataset")
|
||||
eval_dataset = raw_datasets["validation"]
|
||||
if not training_args.do_train:
|
||||
non_label_columns = [feature for feature in eval_dataset.features if feature not in ("label", "labels")]
|
||||
if data_args.max_eval_samples is not None:
|
||||
eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
|
||||
with training_args.main_process_first(desc="validation dataset map pre-processing"):
|
||||
eval_dataset = eval_dataset.map(
|
||||
preprocess_function,
|
||||
batched=True,
|
||||
num_proc=data_args.preprocessing_num_workers,
|
||||
load_from_cache_file=not data_args.overwrite_cache,
|
||||
)
|
||||
# endregion
|
||||
|
||||
with training_args.strategy.scope():
|
||||
# region Build model
|
||||
if checkpoint is None:
|
||||
model_path = model_args.model_name_or_path
|
||||
else:
|
||||
model_path = checkpoint
|
||||
model = TFAutoModelForMultipleChoice.from_pretrained(
|
||||
model_path,
|
||||
config=config,
|
||||
cache_dir=model_args.cache_dir,
|
||||
revision=model_args.model_revision,
|
||||
use_auth_token=True if model_args.use_auth_token else None,
|
||||
)
|
||||
|
||||
num_replicas = training_args.strategy.num_replicas_in_sync
|
||||
total_train_batch_size = training_args.per_device_train_batch_size * num_replicas
|
||||
total_eval_batch_size = training_args.per_device_eval_batch_size * num_replicas
|
||||
if training_args.do_train:
|
||||
total_train_steps = (len(train_dataset) // total_train_batch_size) * int(training_args.num_train_epochs)
|
||||
optimizer, lr_schedule = create_optimizer(
|
||||
init_lr=training_args.learning_rate, num_train_steps=int(total_train_steps), num_warmup_steps=0
|
||||
)
|
||||
else:
|
||||
optimizer = "adam" # Just put anything in here, since we're not using it anyway
|
||||
model.compile(
|
||||
optimizer=optimizer,
|
||||
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
|
||||
metrics=[tf.keras.metrics.SparseCategoricalAccuracy(name="accuracy")],
|
||||
)
|
||||
# endregion
|
||||
|
||||
# region Training
|
||||
if training_args.do_train:
|
||||
tf_train_dataset = convert_dataset_for_tensorflow(
|
||||
train_dataset, non_label_column_names=non_label_columns, batch_size=total_train_batch_size
|
||||
)
|
||||
if training_args.do_eval:
|
||||
validation_data = convert_dataset_for_tensorflow(
|
||||
eval_dataset, non_label_column_names=non_label_columns, batch_size=total_eval_batch_size
|
||||
)
|
||||
else:
|
||||
validation_data = None
|
||||
model.fit(
|
||||
tf_train_dataset,
|
||||
validation_data=validation_data,
|
||||
epochs=int(training_args.num_train_epochs),
|
||||
callbacks=[SavePretrainedCallback(output_dir=training_args.output_dir)],
|
||||
)
|
||||
# endregion
|
||||
|
||||
# region Evaluation
|
||||
if training_args.do_eval and not training_args.do_train:
|
||||
# Do a standalone evaluation pass
|
||||
tf_eval_dataset = convert_dataset_for_tensorflow(
|
||||
eval_dataset, non_label_column_names=non_label_columns, batch_size=total_eval_batch_size
|
||||
)
|
||||
model.evaluate(tf_eval_dataset)
|
||||
# endregion
|
||||
|
||||
# region Push to hub
|
||||
if training_args.push_to_hub:
|
||||
model.push_to_hub(
|
||||
finetuned_from=model_args.model_name_or_path,
|
||||
tasks="multiple-choice",
|
||||
dataset_tags="swag",
|
||||
dataset_args="regular",
|
||||
dataset="SWAG",
|
||||
language="en",
|
||||
)
|
||||
# endregion
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -1,220 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# coding=utf-8
|
||||
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
|
||||
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
|
||||
#
|
||||
# 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.
|
||||
""" Finetuning the library models for multiple choice (Bert, Roberta, XLNet)."""
|
||||
|
||||
|
||||
import logging
|
||||
import os
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Dict, Optional
|
||||
|
||||
import numpy as np
|
||||
|
||||
from transformers import (
|
||||
AutoConfig,
|
||||
AutoTokenizer,
|
||||
EvalPrediction,
|
||||
HfArgumentParser,
|
||||
TFAutoModelForMultipleChoice,
|
||||
TFTrainer,
|
||||
TFTrainingArguments,
|
||||
set_seed,
|
||||
)
|
||||
from transformers.utils import logging as hf_logging
|
||||
from utils_multiple_choice import Split, TFMultipleChoiceDataset, processors
|
||||
|
||||
|
||||
hf_logging.set_verbosity_info()
|
||||
hf_logging.enable_default_handler()
|
||||
hf_logging.enable_explicit_format()
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def simple_accuracy(preds, labels):
|
||||
return (preds == labels).mean()
|
||||
|
||||
|
||||
@dataclass
|
||||
class ModelArguments:
|
||||
"""
|
||||
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
|
||||
"""
|
||||
|
||||
model_name_or_path: str = field(
|
||||
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
|
||||
)
|
||||
config_name: Optional[str] = field(
|
||||
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
|
||||
)
|
||||
tokenizer_name: Optional[str] = field(
|
||||
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
|
||||
)
|
||||
cache_dir: Optional[str] = field(
|
||||
default=None,
|
||||
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
class DataTrainingArguments:
|
||||
"""
|
||||
Arguments pertaining to what data we are going to input our model for training and eval.
|
||||
"""
|
||||
|
||||
task_name: str = field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys())})
|
||||
data_dir: str = field(metadata={"help": "Should contain the data files for the task."})
|
||||
max_seq_length: int = field(
|
||||
default=128,
|
||||
metadata={
|
||||
"help": "The maximum total input sequence length after tokenization. Sequences longer "
|
||||
"than this will be truncated, sequences shorter will be padded."
|
||||
},
|
||||
)
|
||||
overwrite_cache: bool = field(
|
||||
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
|
||||
)
|
||||
|
||||
|
||||
def main():
|
||||
# See all possible arguments in src/transformers/training_args.py
|
||||
# or by passing the --help flag to this script.
|
||||
# We now keep distinct sets of args, for a cleaner separation of concerns.
|
||||
|
||||
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
|
||||
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
|
||||
|
||||
if (
|
||||
os.path.exists(training_args.output_dir)
|
||||
and os.listdir(training_args.output_dir)
|
||||
and training_args.do_train
|
||||
and not training_args.overwrite_output_dir
|
||||
):
|
||||
raise ValueError(
|
||||
f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome."
|
||||
)
|
||||
|
||||
# Setup logging
|
||||
logging.basicConfig(
|
||||
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
||||
datefmt="%m/%d/%Y %H:%M:%S",
|
||||
level=logging.INFO,
|
||||
)
|
||||
logger.warning(
|
||||
f"device: {training_args.device}, n_replicas: {training_args.n_replicas}, "
|
||||
f"16-bits training: {training_args.fp16}"
|
||||
)
|
||||
logger.info(f"Training/evaluation parameters {training_args}")
|
||||
|
||||
# Set seed
|
||||
set_seed(training_args.seed)
|
||||
|
||||
try:
|
||||
processor = processors[data_args.task_name]()
|
||||
label_list = processor.get_labels()
|
||||
num_labels = len(label_list)
|
||||
except KeyError:
|
||||
raise ValueError(f"Task not found: {data_args.task_name}")
|
||||
|
||||
# Load pretrained model and tokenizer
|
||||
#
|
||||
# Distributed training:
|
||||
# The .from_pretrained methods guarantee that only one local process can concurrently
|
||||
# download model & vocab.
|
||||
config = AutoConfig.from_pretrained(
|
||||
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
|
||||
num_labels=num_labels,
|
||||
finetuning_task=data_args.task_name,
|
||||
cache_dir=model_args.cache_dir,
|
||||
)
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
|
||||
cache_dir=model_args.cache_dir,
|
||||
)
|
||||
with training_args.strategy.scope():
|
||||
model = TFAutoModelForMultipleChoice.from_pretrained(
|
||||
model_args.model_name_or_path,
|
||||
from_pt=bool(".bin" in model_args.model_name_or_path),
|
||||
config=config,
|
||||
cache_dir=model_args.cache_dir,
|
||||
)
|
||||
# Get datasets
|
||||
train_dataset = (
|
||||
TFMultipleChoiceDataset(
|
||||
data_dir=data_args.data_dir,
|
||||
tokenizer=tokenizer,
|
||||
task=data_args.task_name,
|
||||
max_seq_length=data_args.max_seq_length,
|
||||
overwrite_cache=data_args.overwrite_cache,
|
||||
mode=Split.train,
|
||||
)
|
||||
if training_args.do_train
|
||||
else None
|
||||
)
|
||||
eval_dataset = (
|
||||
TFMultipleChoiceDataset(
|
||||
data_dir=data_args.data_dir,
|
||||
tokenizer=tokenizer,
|
||||
task=data_args.task_name,
|
||||
max_seq_length=data_args.max_seq_length,
|
||||
overwrite_cache=data_args.overwrite_cache,
|
||||
mode=Split.dev,
|
||||
)
|
||||
if training_args.do_eval
|
||||
else None
|
||||
)
|
||||
|
||||
def compute_metrics(p: EvalPrediction) -> Dict:
|
||||
preds = np.argmax(p.predictions, axis=1)
|
||||
return {"acc": simple_accuracy(preds, p.label_ids)}
|
||||
|
||||
# Initialize our Trainer
|
||||
trainer = TFTrainer(
|
||||
model=model,
|
||||
args=training_args,
|
||||
train_dataset=train_dataset.get_dataset() if train_dataset else None,
|
||||
eval_dataset=eval_dataset.get_dataset() if eval_dataset else None,
|
||||
compute_metrics=compute_metrics,
|
||||
)
|
||||
|
||||
# Training
|
||||
if training_args.do_train:
|
||||
trainer.train()
|
||||
trainer.save_model()
|
||||
tokenizer.save_pretrained(training_args.output_dir)
|
||||
# Evaluation
|
||||
results = {}
|
||||
if training_args.do_eval:
|
||||
logger.info("*** Evaluate ***")
|
||||
|
||||
result = trainer.evaluate()
|
||||
|
||||
output_eval_file = os.path.join(training_args.output_dir, "eval_results.txt")
|
||||
with open(output_eval_file, "w") as writer:
|
||||
logger.info("***** Eval results *****")
|
||||
for key, value in result.items():
|
||||
logger.info(f" {key} = {value}")
|
||||
writer.write(f"{key} = {value}\n")
|
||||
|
||||
results.update(result)
|
||||
|
||||
return results
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -1,573 +0,0 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
|
||||
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
|
||||
#
|
||||
# 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.
|
||||
""" Multiple choice fine-tuning: utilities to work with multiple choice tasks of reading comprehension """
|
||||
|
||||
|
||||
import csv
|
||||
import glob
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
from dataclasses import dataclass
|
||||
from enum import Enum
|
||||
from typing import List, Optional
|
||||
|
||||
import tqdm
|
||||
|
||||
from filelock import FileLock
|
||||
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class InputExample:
|
||||
"""
|
||||
A single training/test example for multiple choice
|
||||
|
||||
Args:
|
||||
example_id: Unique id for the example.
|
||||
question: string. The untokenized text of the second sequence (question).
|
||||
contexts: list of str. The untokenized text of the first sequence (context of corresponding question).
|
||||
endings: list of str. multiple choice's options. Its length must be equal to contexts' length.
|
||||
label: (Optional) string. The label of the example. This should be
|
||||
specified for train and dev examples, but not for test examples.
|
||||
"""
|
||||
|
||||
example_id: str
|
||||
question: str
|
||||
contexts: List[str]
|
||||
endings: List[str]
|
||||
label: Optional[str]
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class InputFeatures:
|
||||
"""
|
||||
A single set of features of data.
|
||||
Property names are the same names as the corresponding inputs to a model.
|
||||
"""
|
||||
|
||||
example_id: str
|
||||
input_ids: List[List[int]]
|
||||
attention_mask: Optional[List[List[int]]]
|
||||
token_type_ids: Optional[List[List[int]]]
|
||||
label: Optional[int]
|
||||
|
||||
|
||||
class Split(Enum):
|
||||
train = "train"
|
||||
dev = "dev"
|
||||
test = "test"
|
||||
|
||||
|
||||
if is_torch_available():
|
||||
import torch
|
||||
from torch.utils.data.dataset import Dataset
|
||||
|
||||
class MultipleChoiceDataset(Dataset):
|
||||
"""
|
||||
This will be superseded by a framework-agnostic approach
|
||||
soon.
|
||||
"""
|
||||
|
||||
features: List[InputFeatures]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
data_dir: str,
|
||||
tokenizer: PreTrainedTokenizer,
|
||||
task: str,
|
||||
max_seq_length: Optional[int] = None,
|
||||
overwrite_cache=False,
|
||||
mode: Split = Split.train,
|
||||
):
|
||||
processor = processors[task]()
|
||||
|
||||
cached_features_file = os.path.join(
|
||||
data_dir, f"cached_{mode.value}_{tokenizer.__class__.__name__}_{max_seq_length}_{task}"
|
||||
)
|
||||
|
||||
# Make sure only the first process in distributed training processes the dataset,
|
||||
# and the others will use the cache.
|
||||
lock_path = cached_features_file + ".lock"
|
||||
with FileLock(lock_path):
|
||||
|
||||
if os.path.exists(cached_features_file) and not overwrite_cache:
|
||||
logger.info(f"Loading features from cached file {cached_features_file}")
|
||||
self.features = torch.load(cached_features_file)
|
||||
else:
|
||||
logger.info(f"Creating features from dataset file at {data_dir}")
|
||||
label_list = processor.get_labels()
|
||||
if mode == Split.dev:
|
||||
examples = processor.get_dev_examples(data_dir)
|
||||
elif mode == Split.test:
|
||||
examples = processor.get_test_examples(data_dir)
|
||||
else:
|
||||
examples = processor.get_train_examples(data_dir)
|
||||
logger.info(f"Training examples: {len(examples)}")
|
||||
self.features = convert_examples_to_features(
|
||||
examples,
|
||||
label_list,
|
||||
max_seq_length,
|
||||
tokenizer,
|
||||
)
|
||||
logger.info(f"Saving features into cached file {cached_features_file}")
|
||||
torch.save(self.features, cached_features_file)
|
||||
|
||||
def __len__(self):
|
||||
return len(self.features)
|
||||
|
||||
def __getitem__(self, i) -> InputFeatures:
|
||||
return self.features[i]
|
||||
|
||||
|
||||
if is_tf_available():
|
||||
import tensorflow as tf
|
||||
|
||||
class TFMultipleChoiceDataset:
|
||||
"""
|
||||
This will be superseded by a framework-agnostic approach
|
||||
soon.
|
||||
"""
|
||||
|
||||
features: List[InputFeatures]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
data_dir: str,
|
||||
tokenizer: PreTrainedTokenizer,
|
||||
task: str,
|
||||
max_seq_length: Optional[int] = 128,
|
||||
overwrite_cache=False,
|
||||
mode: Split = Split.train,
|
||||
):
|
||||
processor = processors[task]()
|
||||
|
||||
logger.info(f"Creating features from dataset file at {data_dir}")
|
||||
label_list = processor.get_labels()
|
||||
if mode == Split.dev:
|
||||
examples = processor.get_dev_examples(data_dir)
|
||||
elif mode == Split.test:
|
||||
examples = processor.get_test_examples(data_dir)
|
||||
else:
|
||||
examples = processor.get_train_examples(data_dir)
|
||||
logger.info(f"Training examples: {len(examples)}")
|
||||
|
||||
self.features = convert_examples_to_features(
|
||||
examples,
|
||||
label_list,
|
||||
max_seq_length,
|
||||
tokenizer,
|
||||
)
|
||||
|
||||
def gen():
|
||||
for (ex_index, ex) in tqdm.tqdm(enumerate(self.features), desc="convert examples to features"):
|
||||
if ex_index % 10000 == 0:
|
||||
logger.info(f"Writing example {ex_index} of {len(examples)}")
|
||||
|
||||
yield (
|
||||
{
|
||||
"example_id": 0,
|
||||
"input_ids": ex.input_ids,
|
||||
"attention_mask": ex.attention_mask,
|
||||
"token_type_ids": ex.token_type_ids,
|
||||
},
|
||||
ex.label,
|
||||
)
|
||||
|
||||
self.dataset = tf.data.Dataset.from_generator(
|
||||
gen,
|
||||
(
|
||||
{
|
||||
"example_id": tf.int32,
|
||||
"input_ids": tf.int32,
|
||||
"attention_mask": tf.int32,
|
||||
"token_type_ids": tf.int32,
|
||||
},
|
||||
tf.int64,
|
||||
),
|
||||
(
|
||||
{
|
||||
"example_id": tf.TensorShape([]),
|
||||
"input_ids": tf.TensorShape([None, None]),
|
||||
"attention_mask": tf.TensorShape([None, None]),
|
||||
"token_type_ids": tf.TensorShape([None, None]),
|
||||
},
|
||||
tf.TensorShape([]),
|
||||
),
|
||||
)
|
||||
|
||||
def get_dataset(self):
|
||||
self.dataset = self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features)))
|
||||
|
||||
return self.dataset
|
||||
|
||||
def __len__(self):
|
||||
return len(self.features)
|
||||
|
||||
def __getitem__(self, i) -> InputFeatures:
|
||||
return self.features[i]
|
||||
|
||||
|
||||
class DataProcessor:
|
||||
"""Base class for data converters for multiple choice data sets."""
|
||||
|
||||
def get_train_examples(self, data_dir):
|
||||
"""Gets a collection of `InputExample`s for the train set."""
|
||||
raise NotImplementedError()
|
||||
|
||||
def get_dev_examples(self, data_dir):
|
||||
"""Gets a collection of `InputExample`s for the dev set."""
|
||||
raise NotImplementedError()
|
||||
|
||||
def get_test_examples(self, data_dir):
|
||||
"""Gets a collection of `InputExample`s for the test set."""
|
||||
raise NotImplementedError()
|
||||
|
||||
def get_labels(self):
|
||||
"""Gets the list of labels for this data set."""
|
||||
raise NotImplementedError()
|
||||
|
||||
|
||||
class RaceProcessor(DataProcessor):
|
||||
"""Processor for the RACE data set."""
|
||||
|
||||
def get_train_examples(self, data_dir):
|
||||
"""See base class."""
|
||||
logger.info(f"LOOKING AT {data_dir} train")
|
||||
high = os.path.join(data_dir, "train/high")
|
||||
middle = os.path.join(data_dir, "train/middle")
|
||||
high = self._read_txt(high)
|
||||
middle = self._read_txt(middle)
|
||||
return self._create_examples(high + middle, "train")
|
||||
|
||||
def get_dev_examples(self, data_dir):
|
||||
"""See base class."""
|
||||
logger.info(f"LOOKING AT {data_dir} dev")
|
||||
high = os.path.join(data_dir, "dev/high")
|
||||
middle = os.path.join(data_dir, "dev/middle")
|
||||
high = self._read_txt(high)
|
||||
middle = self._read_txt(middle)
|
||||
return self._create_examples(high + middle, "dev")
|
||||
|
||||
def get_test_examples(self, data_dir):
|
||||
"""See base class."""
|
||||
logger.info(f"LOOKING AT {data_dir} test")
|
||||
high = os.path.join(data_dir, "test/high")
|
||||
middle = os.path.join(data_dir, "test/middle")
|
||||
high = self._read_txt(high)
|
||||
middle = self._read_txt(middle)
|
||||
return self._create_examples(high + middle, "test")
|
||||
|
||||
def get_labels(self):
|
||||
"""See base class."""
|
||||
return ["0", "1", "2", "3"]
|
||||
|
||||
def _read_txt(self, input_dir):
|
||||
lines = []
|
||||
files = glob.glob(input_dir + "/*txt")
|
||||
for file in tqdm.tqdm(files, desc="read files"):
|
||||
with open(file, "r", encoding="utf-8") as fin:
|
||||
data_raw = json.load(fin)
|
||||
data_raw["race_id"] = file
|
||||
lines.append(data_raw)
|
||||
return lines
|
||||
|
||||
def _create_examples(self, lines, set_type):
|
||||
"""Creates examples for the training and dev sets."""
|
||||
examples = []
|
||||
for (_, data_raw) in enumerate(lines):
|
||||
race_id = f"{set_type}-{data_raw['race_id']}"
|
||||
article = data_raw["article"]
|
||||
for i in range(len(data_raw["answers"])):
|
||||
truth = str(ord(data_raw["answers"][i]) - ord("A"))
|
||||
question = data_raw["questions"][i]
|
||||
options = data_raw["options"][i]
|
||||
|
||||
examples.append(
|
||||
InputExample(
|
||||
example_id=race_id,
|
||||
question=question,
|
||||
contexts=[article, article, article, article], # this is not efficient but convenient
|
||||
endings=[options[0], options[1], options[2], options[3]],
|
||||
label=truth,
|
||||
)
|
||||
)
|
||||
return examples
|
||||
|
||||
|
||||
class SynonymProcessor(DataProcessor):
|
||||
"""Processor for the Synonym data set."""
|
||||
|
||||
def get_train_examples(self, data_dir):
|
||||
"""See base class."""
|
||||
logger.info(f"LOOKING AT {data_dir} train")
|
||||
return self._create_examples(self._read_csv(os.path.join(data_dir, "mctrain.csv")), "train")
|
||||
|
||||
def get_dev_examples(self, data_dir):
|
||||
"""See base class."""
|
||||
logger.info(f"LOOKING AT {data_dir} dev")
|
||||
return self._create_examples(self._read_csv(os.path.join(data_dir, "mchp.csv")), "dev")
|
||||
|
||||
def get_test_examples(self, data_dir):
|
||||
"""See base class."""
|
||||
logger.info(f"LOOKING AT {data_dir} dev")
|
||||
|
||||
return self._create_examples(self._read_csv(os.path.join(data_dir, "mctest.csv")), "test")
|
||||
|
||||
def get_labels(self):
|
||||
"""See base class."""
|
||||
return ["0", "1", "2", "3", "4"]
|
||||
|
||||
def _read_csv(self, input_file):
|
||||
with open(input_file, "r", encoding="utf-8") as f:
|
||||
return list(csv.reader(f))
|
||||
|
||||
def _create_examples(self, lines: List[List[str]], type: str):
|
||||
"""Creates examples for the training and dev sets."""
|
||||
|
||||
examples = [
|
||||
InputExample(
|
||||
example_id=line[0],
|
||||
question="", # in the swag dataset, the
|
||||
# common beginning of each
|
||||
# choice is stored in "sent2".
|
||||
contexts=[line[1], line[1], line[1], line[1], line[1]],
|
||||
endings=[line[2], line[3], line[4], line[5], line[6]],
|
||||
label=line[7],
|
||||
)
|
||||
for line in lines # we skip the line with the column names
|
||||
]
|
||||
|
||||
return examples
|
||||
|
||||
|
||||
class SwagProcessor(DataProcessor):
|
||||
"""Processor for the SWAG data set."""
|
||||
|
||||
def get_train_examples(self, data_dir):
|
||||
"""See base class."""
|
||||
logger.info(f"LOOKING AT {data_dir} train")
|
||||
return self._create_examples(self._read_csv(os.path.join(data_dir, "train.csv")), "train")
|
||||
|
||||
def get_dev_examples(self, data_dir):
|
||||
"""See base class."""
|
||||
logger.info(f"LOOKING AT {data_dir} dev")
|
||||
return self._create_examples(self._read_csv(os.path.join(data_dir, "val.csv")), "dev")
|
||||
|
||||
def get_test_examples(self, data_dir):
|
||||
"""See base class."""
|
||||
logger.info(f"LOOKING AT {data_dir} dev")
|
||||
raise ValueError(
|
||||
"For swag testing, the input file does not contain a label column. It can not be tested in current code"
|
||||
"setting!"
|
||||
)
|
||||
return self._create_examples(self._read_csv(os.path.join(data_dir, "test.csv")), "test")
|
||||
|
||||
def get_labels(self):
|
||||
"""See base class."""
|
||||
return ["0", "1", "2", "3"]
|
||||
|
||||
def _read_csv(self, input_file):
|
||||
with open(input_file, "r", encoding="utf-8") as f:
|
||||
return list(csv.reader(f))
|
||||
|
||||
def _create_examples(self, lines: List[List[str]], type: str):
|
||||
"""Creates examples for the training and dev sets."""
|
||||
if type == "train" and lines[0][-1] != "label":
|
||||
raise ValueError("For training, the input file must contain a label column.")
|
||||
|
||||
examples = [
|
||||
InputExample(
|
||||
example_id=line[2],
|
||||
question=line[5], # in the swag dataset, the
|
||||
# common beginning of each
|
||||
# choice is stored in "sent2".
|
||||
contexts=[line[4], line[4], line[4], line[4]],
|
||||
endings=[line[7], line[8], line[9], line[10]],
|
||||
label=line[11],
|
||||
)
|
||||
for line in lines[1:] # we skip the line with the column names
|
||||
]
|
||||
|
||||
return examples
|
||||
|
||||
|
||||
class ArcProcessor(DataProcessor):
|
||||
"""Processor for the ARC data set (request from allennlp)."""
|
||||
|
||||
def get_train_examples(self, data_dir):
|
||||
"""See base class."""
|
||||
logger.info(f"LOOKING AT {data_dir} train")
|
||||
return self._create_examples(self._read_json(os.path.join(data_dir, "train.jsonl")), "train")
|
||||
|
||||
def get_dev_examples(self, data_dir):
|
||||
"""See base class."""
|
||||
logger.info(f"LOOKING AT {data_dir} dev")
|
||||
return self._create_examples(self._read_json(os.path.join(data_dir, "dev.jsonl")), "dev")
|
||||
|
||||
def get_test_examples(self, data_dir):
|
||||
logger.info(f"LOOKING AT {data_dir} test")
|
||||
return self._create_examples(self._read_json(os.path.join(data_dir, "test.jsonl")), "test")
|
||||
|
||||
def get_labels(self):
|
||||
"""See base class."""
|
||||
return ["0", "1", "2", "3"]
|
||||
|
||||
def _read_json(self, input_file):
|
||||
with open(input_file, "r", encoding="utf-8") as fin:
|
||||
lines = fin.readlines()
|
||||
return lines
|
||||
|
||||
def _create_examples(self, lines, type):
|
||||
"""Creates examples for the training and dev sets."""
|
||||
|
||||
# There are two types of labels. They should be normalized
|
||||
def normalize(truth):
|
||||
if truth in "ABCD":
|
||||
return ord(truth) - ord("A")
|
||||
elif truth in "1234":
|
||||
return int(truth) - 1
|
||||
else:
|
||||
logger.info(f"truth ERROR! {truth}")
|
||||
return None
|
||||
|
||||
examples = []
|
||||
three_choice = 0
|
||||
four_choice = 0
|
||||
five_choice = 0
|
||||
other_choices = 0
|
||||
# we deleted example which has more than or less than four choices
|
||||
for line in tqdm.tqdm(lines, desc="read arc data"):
|
||||
data_raw = json.loads(line.strip("\n"))
|
||||
if len(data_raw["question"]["choices"]) == 3:
|
||||
three_choice += 1
|
||||
continue
|
||||
elif len(data_raw["question"]["choices"]) == 5:
|
||||
five_choice += 1
|
||||
continue
|
||||
elif len(data_raw["question"]["choices"]) != 4:
|
||||
other_choices += 1
|
||||
continue
|
||||
four_choice += 1
|
||||
truth = str(normalize(data_raw["answerKey"]))
|
||||
assert truth != "None"
|
||||
question_choices = data_raw["question"]
|
||||
question = question_choices["stem"]
|
||||
id = data_raw["id"]
|
||||
options = question_choices["choices"]
|
||||
if len(options) == 4:
|
||||
examples.append(
|
||||
InputExample(
|
||||
example_id=id,
|
||||
question=question,
|
||||
contexts=[
|
||||
options[0]["para"].replace("_", ""),
|
||||
options[1]["para"].replace("_", ""),
|
||||
options[2]["para"].replace("_", ""),
|
||||
options[3]["para"].replace("_", ""),
|
||||
],
|
||||
endings=[options[0]["text"], options[1]["text"], options[2]["text"], options[3]["text"]],
|
||||
label=truth,
|
||||
)
|
||||
)
|
||||
|
||||
if type == "train":
|
||||
assert len(examples) > 1
|
||||
assert examples[0].label is not None
|
||||
logger.info(f"len examples: {len(examples)}")
|
||||
logger.info(f"Three choices: {three_choice}")
|
||||
logger.info(f"Five choices: {five_choice}")
|
||||
logger.info(f"Other choices: {other_choices}")
|
||||
logger.info(f"four choices: {four_choice}")
|
||||
|
||||
return examples
|
||||
|
||||
|
||||
def convert_examples_to_features(
|
||||
examples: List[InputExample],
|
||||
label_list: List[str],
|
||||
max_length: int,
|
||||
tokenizer: PreTrainedTokenizer,
|
||||
) -> List[InputFeatures]:
|
||||
"""
|
||||
Loads a data file into a list of `InputFeatures`
|
||||
"""
|
||||
|
||||
label_map = {label: i for i, label in enumerate(label_list)}
|
||||
|
||||
features = []
|
||||
for (ex_index, example) in tqdm.tqdm(enumerate(examples), desc="convert examples to features"):
|
||||
if ex_index % 10000 == 0:
|
||||
logger.info(f"Writing example {ex_index} of {len(examples)}")
|
||||
choices_inputs = []
|
||||
for ending_idx, (context, ending) in enumerate(zip(example.contexts, example.endings)):
|
||||
text_a = context
|
||||
if example.question.find("_") != -1:
|
||||
# this is for cloze question
|
||||
text_b = example.question.replace("_", ending)
|
||||
else:
|
||||
text_b = example.question + " " + ending
|
||||
|
||||
inputs = tokenizer(
|
||||
text_a,
|
||||
text_b,
|
||||
add_special_tokens=True,
|
||||
max_length=max_length,
|
||||
padding="max_length",
|
||||
truncation=True,
|
||||
return_overflowing_tokens=True,
|
||||
)
|
||||
if "num_truncated_tokens" in inputs and inputs["num_truncated_tokens"] > 0:
|
||||
logger.info(
|
||||
"Attention! you are cropping tokens (swag task is ok). "
|
||||
"If you are training ARC and RACE and you are poping question + options,"
|
||||
"you need to try to use a bigger max seq length!"
|
||||
)
|
||||
|
||||
choices_inputs.append(inputs)
|
||||
|
||||
label = label_map[example.label]
|
||||
|
||||
input_ids = [x["input_ids"] for x in choices_inputs]
|
||||
attention_mask = (
|
||||
[x["attention_mask"] for x in choices_inputs] if "attention_mask" in choices_inputs[0] else None
|
||||
)
|
||||
token_type_ids = (
|
||||
[x["token_type_ids"] for x in choices_inputs] if "token_type_ids" in choices_inputs[0] else None
|
||||
)
|
||||
|
||||
features.append(
|
||||
InputFeatures(
|
||||
example_id=example.example_id,
|
||||
input_ids=input_ids,
|
||||
attention_mask=attention_mask,
|
||||
token_type_ids=token_type_ids,
|
||||
label=label,
|
||||
)
|
||||
)
|
||||
|
||||
for f in features[:2]:
|
||||
logger.info("*** Example ***")
|
||||
logger.info("feature: {f}")
|
||||
|
||||
return features
|
||||
|
||||
|
||||
processors = {"race": RaceProcessor, "swag": SwagProcessor, "arc": ArcProcessor, "syn": SynonymProcessor}
|
||||
MULTIPLE_CHOICE_TASKS_NUM_LABELS = {"race", 4, "swag", 4, "arc", 4, "syn", 5}
|
||||
Loading…
Reference in New Issue
Block a user