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"title": "TextBrewer: An Open-Source Knowledge Distillation Toolkit for Natural Language Processing", |
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"abstract": "In this paper, we introduce TextBrewer, an open-source knowledge distillation toolkit designed for natural language processing. It works with different neural network models and supports various kinds of supervised learning tasks, such as text classification, reading comprehension, sequence labeling. TextBrewer provides a simple and uniform workflow that enables quick setting up of distillation experiments with highly flexible configurations. It offers a set of predefined distillation methods and can be extended with custom code. As a case study, we use TextBrewer to distill BERT on several typical NLP tasks. With simple configurations, we achieve results that are comparable with or even higher than the public distilled BERT models with similar numbers of parameters. 1", |
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{ |
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"text": "In this paper, we introduce TextBrewer, an open-source knowledge distillation toolkit designed for natural language processing. It works with different neural network models and supports various kinds of supervised learning tasks, such as text classification, reading comprehension, sequence labeling. TextBrewer provides a simple and uniform workflow that enables quick setting up of distillation experiments with highly flexible configurations. It offers a set of predefined distillation methods and can be extended with custom code. As a case study, we use TextBrewer to distill BERT on several typical NLP tasks. With simple configurations, we achieve results that are comparable with or even higher than the public distilled BERT models with similar numbers of parameters. 1", |
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"text": "Large pre-trained language models, such as GPT (Radford, 2018) , BERT (Devlin et al., 2019) , RoBERTa and XLNet have achieved great success in many NLP tasks and greatly contributed to the progress of NLP research. However, one big issue of these models is the high demand for computing resources -they usually have hundreds of millions of parameters, and take several gigabytes of memory to train and inference -which makes it impractical to deploy them on mobile devices or online systems. From a research point of view, we are tempted to ask: is it necessary to have such a big model that contains hundreds of millions of parameters to achieve a high performance? Motivated by the above considerations, recently, some researchers in the NLP community have tried to design lite models (Lan et al., 2019) , or resort to knowledge 1 TextBrewer: http://textbrewer.hfl-rc.com distillation (KD) technique to compress large pretrained models to small models.", |
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"text": "(Devlin et al., 2019)", |
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"text": "KD is a technique of transferring knowledge from a teacher model to a student model, which is usually smaller than the teacher. The student model is trained to mimic the outputs of the teacher model. Before the birth of BERT, KD had been applied to several specific tasks like machine translation (Kim and Rush, 2016; Tan et al., 2019) in NLP. While the recent studies of distilling large pre-trained models focus on finding general distillation methods that work on various tasks and are receiving more and more attention (Sanh et al., 2019; Jiao et al., 2019; Sun et al., 2019a; Tang et al., 2019; Clark et al., 2019; .", |
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"text": "Though various distillation methods have been proposed, they usually share a common workflow: firstly, train a teacher model, then optimize the student model by minimizing some losses that are calculated between the outputs of the teacher and the student. Therefore it is desirable to have a reusable distillation workflow framework and treat different distillation strategies and tricks as plugins so that they could be easily and arbitrarily added to the framework. In this way, we could also achieve great flexibility in experimenting with different combinations of distillation strategies and comparing their effects.", |
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"text": "In this paper, we introduce TextBrewer, a PyTorch-based distillation toolkit for NLP that aims to provide a unified distillation workflow, save the effort of setting up experiments and help users to distill more effective models. TextBrewer provides simple-to-use APIs, a collection of distillation methods, and highly customizable configurations. It has also been proved able to distill BERT models efficiently and reproduce the state-of-theart results on typical NLP tasks. The main features of TextBrewer are:", |
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"text": "\u2022 Versatility in tasks and models. It works with a wide range of models, from the RNNbased model to the transformer-based model, and works on typical natural language understanding tasks. Its usability in tasks like text classification, reading comprehension, and sequence labeling has been fully tested.", |
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"text": "\u2022 Flexibility in configurations. The distillation process is configured by configuration objects, which can be initialized from JSON files and contain many tunable hyperparameters. Users can extend the configurations with new custom losses, schedulers, etc., if the presets do not meet their requirements.", |
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"text": "\u2022 Including various distillation methods and strategies. KD has been studied extensively in computer vision (CV) and has achieved great success. It would be worthwhile to introduce these studies to the NLP community as some of the methods in these studies could also be applied to texts. TextBrewer includes a set of methods from both CV and NLP, such as flow of solution procedure (FSP) matrix loss (Yim et al., 2017) , neuron selectivity transfer (NST) (Huang and Wang, 2017) , probability shift and dynamic temperature (Wen et al., 2019) , attention matrix loss, multi-task distillation . In our experiments, we will show the effectiveness of applying methods from CV on NLP tasks.", |
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"text": "\u2022 Being non-intrusive and simple to use. Nonintrusive means there is no need to modify the existing code that defines the models. Users can re-use the most parts of their existing training scripts, such as model definition and initialization, data preprocessing and task evaluation. Only some preparatory work (see Section 3.3) are additionally required to use TextBrewer to perform the distillation.", |
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"text": "TextBrewer also provides some useful utilities such as model size analysis and data augmentation to help model design and distillation.", |
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"text": "Recently some distilled BERT models have been released, such as DistilBERT (Sanh et al., 2019) , TinyBERT (Jiao et al., 2019) , and ERNIE Slim 2 . DistilBERT performs distillation on the pretraining task, i.e., masked language modeling.", |
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"text": "2 https://github.com/PaddlePaddle/ERNIE TinyBERT performs transformer distillation at both the pre-training and task-specific learning stages. ERNIE Slim distills ERNIE (Sun et al., 2019b ,c)on a sentiment classification task. Their distillation code is publicly available, and users can replicate their experiments easily. However, it is laborious and error-prone to change the distillation method or adapt the distillation code for some other models and tasks, since the code is not written for general distillation purposes.", |
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"text": "(Sun et al., 2019b", |
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"text": "There also exist some libraries for general model compression. Distiller (Zmora et al., 2018) and PaddleSlim 3 are two versatile libraries supporting pruning, quantization and knowledge distillation. They focus on models and tasks in computer vision. In comparison, TextBrewer is more focused on knowledge distillation on NLP tasks, more flexible, and offers more functionalities. Based on PyTorch, It provides simple APIs and rich customization for fast and clean implementations of experiments.", |
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"text": "3 Architecture and Design Figure 1 shows an overview of the main functionalities and architecture of TextBrewer. To support different models and different tasks and meanwhile stay flexible and extensible, TextBrewer provides distillers to conduct the actual experiments and configuration classes to configure the behaviors of the distillers.", |
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"text": "Distillers are the cores of TextBrewer. They automatically train and save models and support custom evaluation functions. Five distillers have been implemented: BasicDistiller is used for single-task single-teacher distillation; GeneralDistiller in addition supports more advanced intermediate loss functions; MultiTeacherDistiller distills an ensemble of teacher models into a single student model; MultiTaskDistiller distills multiple teacher models of different tasks into a single multi-task student model (Clark et al., 2019; . We also have implemented BasicTrainer for training teachers on labeled data to unify the workflows of supervised learning and distillation. All the distillers share the same interface and usage. They can be replaced by each other easily. ", |
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"section": "Distillers", |
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"sec_num": "3.1" |
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"text": "The general training settings and the distillation method settings of a distiller are specified by two configurations: TrainingConfig and DistillationConfig.", |
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"text": "TrainingConfig defines the settings that are general to deep learning experiments, including the directory where logs and student model are stored (log dir, output dir), the device to use (device), the frequency of storing and evaluating student model (ckpt frequencey), etc.", |
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"text": "DistillationConfig defines the settings that are pertinent to distillation, where various distillation methods could be configured or enabled.", |
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"sec_num": "3.2" |
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"text": "It includes the type of KD loss (kd loss type), the temperature and weight of KD loss (temperature and kd loss weight), the weight of hard-label loss (hard label weight), probability shift switch, schedulers and intermediate losses, etc. Intermediate losses are used for computing the losses between the intermediate states of teacher and student, and they could be freely combined and added to the distillers. Schedulers are used to adjust loss weight or temperature dynamically.", |
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"sec_num": "3.2" |
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"text": "The available values of configuration options such as loss functions and schedulers are defined as dictionaries in presets. For example, the loss function dictionary includes hidden state loss, cosine similarity loss, FSP loss, NST loss, etc.", |
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"sec_num": "3.2" |
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"text": "All the configurations can be initialized from JSON files. In Figure 3 we show an example of DistillationConfig for distilling BERT BASE , to a 4-layer transformers. See Section 4 for more details. ", |
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"text": "Figure 3", |
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"sec_num": "3.2" |
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"text": "Before distilling a teacher model using TextBrewer, some preparatory works have to be done:", |
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"sec_num": "3.3" |
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"text": "1. Train a teacher model on a labeled dataset.", |
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"sec_num": "3.3" |
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"text": "Users usually train the teacher model with their own training scripts. TextBrewer also provides BasicTrainer for supervised training on a labeled dataset.", |
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"section": "Workflow", |
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"text": "2. Define and initialize the student model.", |
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"text": "3. Build a dataloader of the dataset for distillation and initialize the optimizer and learning rate scheduler.", |
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"text": "The above steps are usually common to all deep learning experiments. To perform distillation, take the following additional steps:", |
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"text": "1. Initialize training and distillation configurations, and construct a distiller.", |
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"text": "2. Define adaptors and a callback function.", |
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"text": "3. Call the train method of the distiller.", |
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"text": "A code snippet that shows the minimal workflow is presented in Figure 2 . The concepts of callback and adaptor will be explained below.", |
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{ |
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"end": 71, |
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"text": "Figure 2", |
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"ref_id": "FIGREF1" |
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"section": "Workflow", |
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"sec_num": "3.3" |
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}, |
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"text": "An example of distillation configuration. This configuration is used to distill a 12-layer BERT BASE to a 4-layer T4-tiny.", |
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"section": "Figure 3:", |
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"text": "To monitor the performance of the student model during training, people usually evaluate the student model on a development set at some checkpoints besides logging the loss curve. For example, in the early stopping strategy, users choose the best model weights checkpoint based on the performance of the student model on the development set at the end of each epoch. TextBrewer supports such functionality by providing the callback function argument in the train method, as shown in line 24 of Figure 2 . The callback function takes two arguments: the student model and the current training step. At each checkpoint step (determined by num train epochs and ckpt frequencey), the distiller saves the student model and then calls the callback function.", |
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"text": "Since it is impractical to implement evaluation metrics and evaluation procedures for all NLP tasks, we encourage users to implement their own evaluation functions as the callbacks for the best practice.", |
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"text": "The distiller is model-agnostic. It needs a translator to translate the model outputs into meaningful data. Adaptor plays the role of translator. An Adaptor is an interface and responsible for explaining the inputs and outputs of the teacher and student for the distiller.", |
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"text": "Adaptor takes two arguments: the model inputs and the model outputs. It is expected to return a dictionary with some specific keys. Each key explains the meaning of the corresponding value, as shown in Figure 1 (b) . For example, logits is the logits of final outputs, hidden is intermediate hidden states, attention is the attention matrices, inputs mask is used to mask padding positions. The distiller only takes necessary elements from the outputs of adaptors according to its distillation configurations. A minimal adaptor only needs to explain logits, as shown in lines 11-14 of Figure 2 .", |
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"text": "TextBrewer also works with users' custom modules. New loss functions and schedulers can be easily added to the toolkit. For example, to use a custom loss function, one first implements the loss function with a compatible interface, then adds it to the loss function dictionary in the presets with a custom name, so that the new loss function becomes available as a new option value of the configuration and can be recognized by distillers.", |
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"text": "In this section, we conduct several experiments to show TextBrewer's ability to distill large pretrained models on different NLP tasks and achieve results are comparable with or even higher than the public distilled BERT models with similar numbers of parameters. 4", |
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"text": "Datasets and tasks. We conduct experiments on both English and Chinese datasets. For English datasets, We use MNLI for text classification task, SQuAD1.1 (Rajpurkar et al., 2016) for span-extraction machine reading comprehension (MRC) task and CoNLL-2003 (Tjong Kim Sang and De Meulder, 2003) for named entity recognition (NER) task. For Chinese datasets, we use the Chinese part of XNLI (Conneau et al., 2018) , LCQMC , CMRC 2018 (Cui et al., 2019b) and DRCD (Shao et al., 2018) . XNLI is the multilingual version of MNLI. LCQMC is a large-scale Chinese question matching corpus. CMRC 2018 and DRCD are two span-extraction machine reading comprehension datasets similar to SQuAD. The statistics of the datasets are listed in Table 1 . Models. All the teachers are BERT BASE -based models. For English tasks, teachers are initialized with the weights released by Google 5 and converted into PyTorch format via Transformers 6 . For Chinese tasks, teacher is initialized with the pre-trained RoBERTa-wwm-ext 7 (Cui et al., 2019a) . We test the performance of the following student models:", |
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"text": "(Rajpurkar et al., 2016)", |
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"ref_id": "BIBREF13" |
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"start": 255, |
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"end": 292, |
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"text": "(Tjong Kim Sang and De Meulder, 2003)", |
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"ref_id": "BIBREF21" |
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"end": 410, |
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"text": "(Conneau et al., 2018)", |
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"ref_id": "BIBREF1" |
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"end": 450, |
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"text": "(Cui et al., 2019b)", |
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"ref_id": "BIBREF3" |
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"end": 479, |
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"text": "(Shao et al., 2018)", |
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"ref_id": "BIBREF15" |
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"end": 1027, |
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"text": "(Cui et al., 2019a)", |
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"ref_id": "BIBREF2" |
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} |
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"text": "Table 1", |
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"ref_id": "TABREF2" |
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"sec_num": "4.1" |
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{ |
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"text": "\u2022 T6 and T3 are BERT BASE with fewer layers of transformers. Especially, T6 has the same structure as DistilBERT (Sanh et al., 2019) .", |
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"start": 113, |
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"end": 132, |
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"text": "(Sanh et al., 2019)", |
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"text": "\u2022 T3-small is a 3-layer BERT with half BERTbase's hidden size and feed-forward size.", |
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"text": "\u2022 T4-tiny is the same as TinyBERT, a 4-layer model with an even smaller hidden size and feedforward size.", |
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"text": "\u2022 BiGRU is a single-layer bidirectional GRU. Its word embeddings are taken from BERT BASE .", |
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"text": "T3-small and T4-tiny are initialized randomly. The model structures of the teacher and students are summarized in Table 3 . Training settings. To keep experiments simple, we directly distill the teacher model that has been trained on the task, while we do not perform task-irrelevant language modeling distillation in advance. The number of epochs ranges from 30 to 60, and the learning rate of students is 1e-4 for all distillation experiments. Distillation settings. Temperature is set to 8 for all experiments. We add intermediate losses uniformly distributed among all the layers between teacher and student (except BiGRU). The loss functions we choose are hidden mse loss which computes the mean square loss between two hidden states, and NST loss which is an effective method in CV. In Figure 3 we show an example of distillation configuration for distilling BERT BASE to a T4-tiny. Since their hidden sizes are different, we use proj option to add linear layers to match the dimensions. The linear layers will be trained together with the student automatically. We experiment with two kinds of distillers: GeneralDistiller and MultiTeacherDistiller .", |
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"end": 121, |
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"text": "Table 3", |
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"ref_id": "TABREF6" |
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"start": 792, |
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"end": 800, |
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"text": "Figure 3", |
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"text": "We list the public results (DistilBERT and Tiny-BERT) and our distillation results obtained by GeneralDistiller in Table 2 . We have the following observations.", |
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"section": "Results on English Datasets", |
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"sec_num": "4.2" |
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"text": "First, teachers can be distilled to T6 models with minor losses in performance. All the T6 models achieve 99% performance of the teachers, higher than the DistilBERT.", |
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"text": "Second, T4-tiny outperforms TinyBERT though they share the same structure. This is attributed to the NST losses in the distillation configuration. This result proves the effectiveness of applying KD method developed in CV on NLP tasks.", |
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"text": "Third, although T4-tiny has less parameters than T3-small, T4-tiny outperforms T3-small in most T6 6 768 3072 65M 60% T3 3 768 3072 44M 41% T3-small 3 384 1536 17M 16% T4-tiny 4 312 1200 14M 13% BiGRU 1 768 -31M 29% cases. It may be a hint that narrow-and-deep models are better than wide-and-shallow models. Finally, data augmentation (DA) is critical. For the experiments in the last line in Table 2 , we use additional datasets during distillation: a subset of NewsQA (Trischler et al., 2017) training set is used in SQuAD; passages from the HotpotQA (Yang et al., 2018) training set is used in CoNLL-2003. The augmentation datasets significantly improve the performance, especially when the size of the training set is small, like CoNLL-2003. We next show the effectiveness of MultiTeacherDistiller, which distills an ensemble of teachers to a single student model. For each task, we train three BERT BASE teacher models with different seeds. The student is also a BERT BASE model. The temperature is set to 8, and intermediate losses are not used. As Table 4 shows, for each task, the student achieves the best performance, even higher than the ensemble result.", |
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"end": 523, |
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"text": "(Trischler et al., 2017)", |
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"end": 601, |
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"text": "(Yang et al., 2018)", |
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"ref_id": "BIBREF26" |
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"start": 763, |
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"end": 774, |
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"text": "CoNLL-2003.", |
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"ref_id": null |
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} |
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"start": 96, |
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"end": 243, |
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"text": "T6 6 768 3072 65M 60% T3 3 768 3072 44M 41% T3-small 3 384 1536 17M 16% T4-tiny 4 312 1200 14M 13% BiGRU 1 768 -31M 29%", |
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"ref_id": "TABREF2" |
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}, |
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"start": 422, |
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"end": 429, |
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"text": "Table 2", |
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"ref_id": "TABREF4" |
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"start": 1084, |
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"end": 1091, |
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"text": "Table 4", |
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"ref_id": "TABREF7" |
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} |
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], |
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"section": "Results on English Datasets", |
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"sec_num": "4.2" |
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}, |
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{ |
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"text": "The results on Chinese datasets are presented in Table 5 . We notice that T4-tiny still outperforms T3-small on all tasks, which is consistent with their performance on English tasks. In the experiments with DA, CMRC 2018 and DRCD take each other's dataset as data augmentation. We observe that since CMRC 2018 has a relatively small training set, DA has a much more significant effect.", |
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"cite_spans": [], |
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"start": 49, |
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"end": 56, |
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"text": "Table 5", |
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"ref_id": "TABREF9" |
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} |
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"section": "Results on Chinese Datasets", |
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"sec_num": "5" |
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}, |
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{ |
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"text": "In this paper, we present TextBrewer, a flexible PyTorch-based distillation toolkit for NLP research and applications. TextBrewer provides rich customization options for users to compare different distillation methods and build their strategies. We have conducted a series of experiments. The results show that the distilled models can achieve state-of-the-art results with simple settings. TextBrewer also has its limitations. For example, its usability in generation tasks such as machine translation has not been tested. We will keep adding more examples and tests to expand TextBrewer's scope of application.", |
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"section": "Conclusion and Future Work", |
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"sec_num": "6" |
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}, |
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{ |
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"text": "Apart from the distillation strategies, the model structure also affects the performance. In the future, we aim to integrate neural architecture search into the toolkit to automate the searching for model structures.", |
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"section": "Conclusion and Future Work", |
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"sec_num": "6" |
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}, |
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{ |
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"text": "https://github.com/PaddlePaddle/PaddleSlim", |
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"cite_spans": [], |
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"eq_spans": [], |
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"section": "", |
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"sec_num": null |
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}, |
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{ |
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"text": "More results are presented in the online documentation: https://textbrewer.readthedocs.io", |
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}, |
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{ |
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"text": "https://github.com/google-research/bert 6 https://github.com/huggingface/transformers 7 https://github.com/ymcui/Chinese-BERT-wwm", |
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], |
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"back_matter": [ |
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{ |
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"text": "We would like to thank all anonymous reviewers for their valuable comments on our work. This work was supported by the National Natural Science Foundation of China (NSFC) via grant 61976072, 61632011, and 61772153.", |
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"section": "Acknowledgments", |
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}, |
|
"TABREF9": { |
|
"num": null, |
|
"text": "Development set results for the teacher and various students on Chinese tasks.", |
|
"type_str": "table", |
|
"html": null, |
|
"content": "<table/>" |
|
} |
|
} |
|
} |
|
} |