When a Convolutional Neural Network was able to win the ImageNet competition for the first time, attention was drawn to machine learning and deep learning. So many companies sought to solve problems using intelligent algorithms. while they were unaware that the convolutional neural network participating in this competition was trained based on a large amount of data; In case, to solve many problems, users do not have access to such a volume of information. On the other hand, training a deep network with a lot of data is not something that all companies have the ability to do. Because it requires a large amount of data and systems that have the ability to process information. It was at this point that the use of pre-trained models came to the aid of businesses and individuals who had limited data and processing power. In such a situation, it is sufficient to prepare a network for our work using various features such as feature extraction and fine-tuning, etc. and train it specifically.

 

The beginning of the formation of the BERT network

The BERT model, named Bidirectional Encoder Representations from Transformers, is a Deep Language Model based on the Transformers architecture, which was developed in 2018 by Google's artificial intelligence team. Brett is able to generate a fixed-size feature vector for a word or sentence that can be used in natural language processing tasks, such as emotion recognition, machine translation, and question answering. The most important feature of Brett's model is that it is a two-way language model, which means that to predict each word in a sentence, it looks at all the words before and after it and uses the information contained in them. This feature makes Burt's model efficient for all natural language processing tasks, including emotion recognition, machine translation, and question answering.

 

 

How does Burt's model work?

This model is actually a combination of two different approaches called Unsupervised Language Model and Simultaneous Multi-Task Learning.

 

In the unsupervised learning language model approach, the network starts the natural language learning process by using big data and without data labeling. At this stage, the network can learn unsupervised what each word in a sentence means and how it relates to other words in the sentence. In the simultaneous learning approach, the network is trained simultaneously for several different tasks, including sentence order recognition, question and answer recognition, sentence type recognition, and entity recognition. By doing this, the network can learn the best features for each of these tasks and combine these features to perform other tasks.

 

By combining these two approaches and having a large amount of data and the ability to perform several tasks simultaneously, Brett's model is able to perform the assigned tasks in the most accurate way possible. This issue has made BERT one of the best and most powerful natural language processing models.

 

In fact, the BERT model is taught in two different sizes, which are called BERTBASE and BERTLARGE(Figure 1).

 

 

 

(Figure 1).

 

 

Burt model is actually a group of transformer model encoders

(Transformer Model) that have been trained. Both Burt models include encoder layers. For example, the basic Brett model has 12 encoder layers and the larger model has 24 encoder layers. The basic model has a total of 110 million parameters and the large model has 345 million parameters, each of which takes four days to train (provided you have powerful hardware). The basic model has 768 and the larger model has 1024 hidden nodes in its feedforward network layer, and the number of attention layers in the first one is 12 and in the second one is 16 (Figure 2).

 

 

 

 

(Figure 2).

 

 

In the above model, the first input token is provided to the model with a special token called CLS, which is very similar to the transformer architecture encoder. More precisely, a sequence of words is provided to the model as input. These move along the encoder layers. Each encoder layer has a self-attention layer and a feed-forward network layer through which the inputs pass and then enter the next encoder layer (Figure 3).

(Figure 3).

 

Each position vector represents the nodes of the hidden layer in the output. For example, in the basic Brett model, the size of the hidden layer is 768, so we will have 768 vectors in the output. In the classification problem, only the first output vector is important, whose input is the CLS token (Figure 4). This output vector is entered in the classification problem as an input to the classification layer so that it can show the result in the output (Figure 5).

(Figure 4).

(Figure 5).

 

 

Brett model training

Brett's model is taught based on unsupervised approach and transfer learning. In this approach, the model is trained using a large dataset collected from various sources such as the web, without the need for precise labels. In the first step, each sentence is divided into several smaller pieces to train the model. Then, for each segment, a feature vector is created that contains all the words and their position in the sentence. In the next step, using transformer networks for each feature, a new vector called Masked Language Model is created. In this step, synonyms or special symbols are used for some words in each paragraph, and then the model tries to predict alternative words based on other words in the same sentence. In the last step, using the masked language model approach, the model creates a new feature vector for all new sentences and texts. Then, using these feature vectors, the model is trained for other tasks such as entity recognition and question and answer based on the transfer learning approach.

 

Methods of using Burt's model

Due to its very high capabilities in natural language processing, Burt's model is used in performing various types of tasks related to natural language processing. Some of the methods of using Burt's model are as follows:

 

Recognition of entities: Brett's model is able to recognize the names of persons, places and companies in sentences.

Questions and answers: Burt's model can be used to answer questions for which there is an exact answer. In this method, the model automatically finds the best answer by receiving a question. This is the same technique that JPT Chat uses.

Recognition of emotions: using Brett's model, positive and negative emotions can be well recognized in texts.

Text summarization: Using Burt's model, long texts can be summarized and the most important information can be extracted.

Machine translation: Burt's model is also used for machine translation and is used due to its high capabilities in understanding the language and more accurate translations. Today, tools like Google Translate and Microsoft Bing use this feature to translate texts into different languages.

Natural Language Processing in Information Retrieval: Using Brett's model, it is possible to find the best web pages to answer users' questions.

Fine-tuning in Brett model

Fine-tuning the Burt model means retraining the model to perform a specific task using labeled data. In this method, the Burt model is first trained without labels, and in the fine-tuning part, it is retrained to perform a specific task using labeled data. Specific tasks can be emotion recognition, entity recognition, or question and answer and interaction with users. Next, optimization algorithms such as Adam are used to retrain the model, and the model parameters are updated using the labeled data. Also, techniques such as Dropout and L2 regularization are used to avoid overfitting.

 

Feature extraction in BER model

Feature extraction in Brett's model means using parts of the model that are already trained, without the need to perform fine-tuning again to extract features from necessary texts. In this method, the Brett model is not trained with labeled data and instead, pre-trained segments are used to extract features from texts.

 

For feature extraction, texts are first divided into smaller pieces and then a new feature vector is created for each feature vector using the pre-trained parts of Burt's model. These feature vectors are commonly used for tasks such as sentiment analysis, entity recognition, and text summarization. The main advantage of feature extraction in Burt's model is that there is no need to retrain the model for each specific task. This method is used as a fast and effective method in the field of natural language processing in systems that need to process a lot of information, such as search engines and natural language processing systems.

 

Masking technique is also used here, which means hiding part of the inputs in the pre-training process. In this method, for each input sentence, part of the words are randomly hidden and the model tries to predict the hidden words according to other input words.

 

More precisely, in the masking technique, for each input sentence, 15% of the words are randomly hidden. Then, in the pre-training phase, the model tries to predict the corresponding word for each hidden word. For example, in the sentence "I went to university and took my books with me", the word "went" is randomly hidden in this technique and the model tries to predict the corresponding word "to". The masking technique in the model helps to improve the performance of the model in natural language processing tasks because the model has dealt with hidden words in pre-training. Also, this technique makes the model pay attention to the order of words and the relationship between them for better understanding of sentences.

 

Key features of Burt's model

Burt's model is of interest to machine learning and natural language processing engineers due to its key features. Among these features, the following should be mentioned:

 

It is based on transformer architecture: Brett's model is designed on the basis of transformer architecture, which is an advanced neural network

It is for natural language processing and it is possible to simultaneously process two directions of word sequences.

Pre-training with a large dataset: The Brett model is pre-trained using a large dataset that contains various unlabeled texts. This pre-training allows Brett's model to better understand the meaning of words and sentences.

Ability to recognize entities: Burt's model works very well in tasks of recognizing entities such as person, organization, product, place, etc.

Ability to analyze sentiments: Brett's model has a very powerful solution for sentiment analysis of texts and is even able to recognize positive, negative and neutral sentiments in a sentence.

Calculation of similar sentences: using techniques such as Multi-Head Attention, Burt's model has the possibility of calculating the similarity between two sentences or answering text questions.

Ability to interpret: Burt's model has the ability to interpret the results it produces and can establish the closest natural language conversation with humans due to the weighting of words.

 

Among other features of Brett's model, we can mention teaching using the Masked Language Model method. In this method, to train the model, some words in the sentences are replaced with other words and the model must be able to identify the replacement words. This training method makes Brett's model better able to understand in what contexts words are related, which plays an effective role in answering questions and machine translation more accurately. At present, Burt's model is one of the most popular and efficient language models in the field of natural language processing and is used by many companies and organizations to solve natural language processing problems. Due to these features, Burt's model is considered as one of the best natural language processing models that performs very well in many tasks such as sentiment analysis, entity recognition, question and answer, and text summarization.

 

 

 

What is the main application of BERT model?

The main application of Burt's model is in the field of natural language processing, because it provides the best result. For example, in the discussion of entity recognition, Brett's model can best recognize entities such as names of people, companies, places, buildings, etc. Also, in the text question and answer, Burt's model can answer various questions and suggest questions related to the user's main question based on the principle of inference. In text summarization, this model can convert a long text into a short summary. Also, it works very well in machine translation and can translate texts into another language in an accurate and readable way.

 

What other models in natural language processing is the Brett model comparable to?

Today, there is a hot debate in scientific circles about the comparison of Brett's model with other natural language processing models, and some believe that this model performs better than its competitors. Some of the models that are comparable to Burt's model are as follows:

 

ELMo: The ELMo model is one of the efficient models in the field of natural language processing that was introduced in 2018. Investigations show that Burt's model outperforms ELMo in many natural language processing tasks.

GPT-2: The GPT-2 model is another model in the field of natural language processing that was introduced in 2019. Compared to the BERT model, GPT-2 performs better on tasks such as text generation and machine translation, but on tasks such as sentiment analysis and entity recognition, BERT performs better.

Transformer-XL: The Transformer-XL model is another flagship model in the field of natural language processing that was introduced in 2019. Compared to Burt's model, Transformer-XL performs better on tasks such as text question and answer, but Burt still outperforms on tasks such as sentiment analysis and entity recognition.

last word

 

Burt's model performs very well in many natural language processing tasks, including entity recognition, sentiment analysis, text question and answer, text summarization, and machine translation, and is considered one of the most popular and widely used natural language processing models. According to many experts, Bert is a powerful language model that is considered a turning point in the field of natural language processing. Brett's model has made it possible to use the transfer learning technique in the field of natural language processing and has provided good performance in many tasks in this field. Without a doubt, Brett will be helping us with a wide variety of tasks in the near future.