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Upenn Holiday Calendar 2023 – Open Access Policy Institutional Guidelines for Open Access Program Special Topics Editorial Process Research Ethics and Publishing Articles Awards Evidence Payment Processing

All published articles are immediately available worldwide under an open access license. No special permission is required for re-use in whole or in part of the article published by me, including figures and tables. For articles published under the Creative Commons CC BY open access license, parts of the article may be reused without permission if the original article is clearly cited. For more information, see https:///openaccess.

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Feature articles represent the most advanced research with significant potential for high impact in the field. The feature article should be a significant original article that includes several techniques or approaches, provides insight into future research directions, and describes possible research applications.

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Thematic articles are submitted by invitation or personal recommendations of scientific editors and must receive positive feedback from reviewers.

Editor's Choice articles are based on the recommendations of scientific editors of journals from around the world. The editors select a number of articles recently published in journals that they believe will be of interest to their readers, or important to their field of research. Its purpose is to provide a snapshot of some of the most exciting works published in various research fields in the journal.

Do we need a specific corpus and multiple high-performance GPUs to train a BERT model? Experiments with the COVID-19 dataset

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Received: April 28, 2022 / Revised: June 24, 2022 / Accepted: June 25, 2022 / Published: July 4, 2022

The COVID-19 pandemic has affected daily life around the world. Since 2019, the amount of literature devoted to COVID-19 has increased dramatically. However, it is almost impossible for people to read all the studies and categorize them. This paper proposes an unsupervised modeling method called zero-firing classification model, based on a trained BERT model. We used the CORD-19 dataset together with the LitCovid database to develop a new vocabulary and prepare a dataset for testing. For the downstream NLI task, we used three corpora: SNLI, MultiNLI, and MedNLI. We reduced the training time by 98.2639% to build a task-specific machine learning model, using only the Nvidia Tesla V100. The latest model can run faster and use fewer resources than its comparison. It has an accuracy of 27.84%, which is lower than the best accuracy of 6.73%, but comparable. Finally, we recognize that more specific tokens and vocabulary for COVID-19 cannot override generalities. In addition, it was found that the BART architecture affects the classification results.

Since the outbreak of COVID-19, not only the number of patients has increased every day, but also the number of studies on this disease has increased. It takes time for healthcare professionals to read all of this literature to find the research that best suits their needs. To collect information on the literature of COVID-19, the Allen Institute for Artificial Intelligence and its partners published a dataset, CORD-19 [1], which aims to connect the medical community and machine learning to find solutions to this epidemic. So far, 1669 projects have used this dataset on the Kaggle website to try to find a solution, but none of them have used the "zero learning" method [2, 3].

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Zero-shot learning is a machine learning method that can predict unseen classes in the test phase by using additional coding information to distinguish between object types. The first study using this method was presented in 2008, but it was named "classification without data [4]". This paper mainly focuses on the classification of text documents from data sets of newsgroups and Yahoo! Answer Today, data scientists can use this method in natural language processing (NLP) and computer vision (CV) [5].

Moreover, there is a new technique in NLP called "attention" [6]; This method was introduced in 2017 to overcome the limitations of previous works, such as ELMo [7], which uses a bidirectional Long-Short Term Memory (LSTM) model [8]; This model allows scientists to overcome the inability to consider the left and right context of the target word when calculating the meaning of the word. In this task, each input word is connected to a vector, which is then calculated into a matrix of questions, keys and values. Finally, the attention score is calculated by the softmax value function of the dot product of the matrix Q, by moving the matrix K divided by the square root of the key number and the product of the matrix V, where

Is the main dimension of the vector, to get the final attention score. This model is called a "transformer" model [6] and consists of encoder and decoder functions.

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In 2019, Google released a new language model, "BERT" [9], which stands for Bidirectional Encoder Representation of Transformers. BERT uses the encoder part of the transformer model [6]. The development of BERT consists of two phases: a semi-supervised phase and a supervised phase. In the first step, BERT is trained to understand linguistic context using two semi-supervised tasks on a specific corpus. The masked language model (MLM) was the first semisupervised task. In this task, the model tries to predict the hidden word by masking 15% of the words in the sentence. The second semi-supervised task is Next Sentence Prediction (NSP). Sentence B following sentence A will be predicted by the model. The second stage of the practice task depends on the task to be used, from classification to questions.

Because the method involves a lot of data and requires enormous computing resources, many models based on this technique began to rely on optimizations used to speed up training after Google released the BERT model. BERT variants based on optimization are shown in Table 1.

Some models based on BERT are targeted for use in the biomedical field. However, the techniques and inputs used in each model are different, as the goals of each model are not the same. Table 2 compares BERT-based biomedical models, including our model, from different perspectives.

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Most BERT-based models are trained on specific corpora for use in different contexts, and most corpora are derived from large datasets. As shown in Table 2, all models, except our model, use a specific corpus to refine the model to fit the purpose; This causes the BERT-based model training process to consume high computing resources due to the complexity of the pre-training process and the large size of the data source. For example, even the original BERT was trained with the corpus of BookCorpus (800 million words) [19] and the English Wikipedia (2,500 million words). Furthermore, the cost of the initial training is quite expensive; Up to four days on 4 to 16 TPUs in the cloud. Even in a different context, for example, Twitter-roBERTa [20], which aims to detect Twitter sentiment, used more than 100,000 samples for training and took 8-9 days on 8 NVIDIA V100 GPUs.

This training work and the large data size make the BERT-based model training process quite expensive. Therefore, the techniques used in this paper can be trained faster and use less computing resources.

Basically, zero-shot learning (ZSL) is the task of training a classifier on a set of labels and then evaluating it on a new set of labels it has never seen. For example, traditional null-shot learning requires some descriptor for an unseen class [21] (such as a set of visual features or just the name of the class) in order for the model to predict the class without training. data.

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An example of zero injection learning in NLP is given by Joe Davison [22], who tested this approach using Sentence-BERT [23], a new approach for generating sequences and sentences. Label embedding refines the collected BERT sequence representation for more semantic richness. ..

With this method, Joe Davison was able to achieve an F1 score of 46.9 in the Yahoo Answers topic classification task [24]. The limitation of this approach is that we need to have labeled data or annotated data for a subset of the classes we focus on.

In the Natural Language Processing (NLP) task, there is a task called Natural Language Inference (NLI). This task is to determine whether the "hypothesis" is an assumption, contradiction or neutral to a certain "premise". By adapting this work for use in null studies, Yin et al. [25] using a multi-genre NLI (MNLI) sequence pair classifier trained as a zero-box text classifier, achieved an F1 score of 37.9 on Yahoo Answers using the smallest version of BERT, fine-tuning only the MNLI corpus. In addition, Joe Davison was able to replicate this technique with a larger model, achieving an F1 score of up to 53.7.

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Since 1998, approximately 8,000 articles in ScienceDirect are related to zero-shooting classification [ 26 ]. More specifically, 2233 of these articles were published between 2020 and 2022 [27]. Most studies have focused on preparing data sets for zero photo classification [28] and Computer Vision for medical image classification [28, 29, 30, 31]. Despite the large number of articles related to this method, zero-shot models based on BERT are commonly available

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