1. Overview of Embedding Model

unsetunset1.1 Definition and Principleunsetunset

Embedding model is a technology that maps discrete symbolic data (such as words, sentences, images, etc.) into continuous vector space. These vectors can capture the semantics, structure and other relationships between data. Simply put, it converts symbolic data that is difficult to process directly into a numerical vector form that is easier for computers to understand and operate.

Take word embedding in natural language processing as an example. In traditional language processing methods, words are usually represented in the form of one-hot encoding, that is, one word corresponds to a long vector, in which only one position is 1 and the rest are 0, which is used to uniquely identify the word. This method has two major problems: first, the vector dimension is very high, resulting in high computational cost; second, it cannot reflect the semantic association between words. For example, "cat" and "dog" are very similar in semantics, but there is no similarity between their one-hot encoding vectors.

The Embedding model can map words into a low-dimensional vector space through training, so that semantically similar words are closer in the vector space . For example, the following example.

The principle is to learn embedding vectors based on context information. Taking the Word2Vec model as an example, it has two architectures: CBOW (Continuous Bag-of-Words) and Skip-Gram. CBOW predicts the target word based on the context words, while Skip-Gram predicts the context words based on the target word. During the training process, the model will continuously adjust the embedding vectors of the words so that the vector combination of the words that appear in the context can better predict the target word, or the vector of the target word can better predict the context word, thereby learning the semantic information of the word.

unsetunset1.2 Development Historyunsetunset

The development of the Embedding model can be traced back to the late 20th century and early 21st century. Initially, people tried to use some simple statistical methods to represent the semantics of words, such as the TF-IDF (Term Frequency-Inverse Document Frequency) method based on word frequency, but it could not capture the semantic relationship between words well.

In 2003 , Bengio et al. first proposed the Neural Probabilistic Language Model, which is the prototype of the modern word embedding model. It uses neural network methods to learn the vector representation of words, but due to limited computing resources at the time and the small scale of the model, the effect was not ideal.

It was not until 2013 that the emergence of the Word2Vec model really made word embedding technology gain widespread attention and application. Word2Vec was proposed by Mikolov and others from Google. It uses efficient training algorithms and simple model architecture to train high-quality word embedding vectors on large-scale corpora, which can well capture the semantic and grammatical relationships between words, greatly promoting the development of natural language processing.

Subsequently, various improved word embedding models have emerged. In 2014, the GloVe (Global Vectors for Word Representation) model was proposed , which combines global word frequency statistics and local context information to learn word embeddings, further improving the quality of word embeddings. In 2017, the FastText model improved Word2Vec's shortcomings in handling rare words and word form changes . By decomposing words into character n-tuples to learn word embeddings, it can better handle multilingual and language with rich word form changes.

In recent years, with the continuous development of deep learning technology, the application scope of Embedding models has expanded from natural language processing to multiple fields such as computer vision and speech recognition. For example, in computer vision, convolutional neural networks (CNNs) can map images to a feature vector space, which can be used for tasks such as image classification and target detection, which is essentially an Embedding idea.

2. Types of Embedding Models

unsetunset2.1 Word Embeddingunsetunset

Word Embedding is the most classic type of Embedding, which is mainly used to process word-level data. It maps words to a low-dimensional vector space, making semantically similar words closer in the vector space.

Word2Vec is a representative model, which has two architectures: CBOW and Skip-Gram. CBOW predicts the target word based on the context words, while Skip-Gram does the opposite. For example, when processing the sentence "The cat sat on the mat", for the target word "cat", CBOW considers the context words "The" and "sat" to predict "cat", while Skip-Gram uses "cat" to predict the context word.

The word embedding vectors trained by Word2Vec can capture the semantic relationship of words very well. Analogy relationships such as "king - man + woman ≈ queen" can be obtained through vector operations. In addition, the GloVe model learns word embeddings by combining global word frequency statistics and local context information, further improving the quality of word embeddings. FastText has improved the shortcomings of Word2Vec in dealing with rare words and word form changes. By decomposing words into character n-tuples to learn word embeddings, it can better handle multilingual and language with rich word form changes. For example, when dealing with different word forms of the French word "jouer" (play), "joue" (play, third person singular), "jouons" (play, first person plural), etc., FastText can more effectively capture their semantic associations.

unsetunset2.2 Sentence Embeddingunsetunset

Sentence Embedding maps sentences to vector space based on word embedding to capture the semantic information of sentences. It is more complex than Word Embedding because the semantics of a sentence depends not only on the words, but also on the combination and word order of words. A common approach is to use a pre-trained language model such as BERT (Bidirectional Encoder Representations from Transformers). BERT can learn rich language knowledge and semantic information by performing unsupervised learning on large-scale corpora. In Sentence Embedding, BERT can encode a sentence into a fixed-length vector that can well represent the semantics of the sentence.

For example, for the sentences "I love this movie" and "This movie is great", the sentence vectors generated by BERT are close in the vector space because they express similar semantics. In addition, there are other methods such as Average Word Embeddings, which takes the average of the word embedding vectors of all words in a sentence as the vector representation of the sentence, but this method ignores the order and combination information of words and is not as effective as the method based on pre-trained language models. Sentence Embedding has a wide range of applications in tasks such as text classification, semantic similarity calculation, and question-answering systems. For example, in a question-answering system, the best matching answer can be found by comparing the vector similarity between the question sentence and the candidate answer sentence.

unsetunset2.3 Document Embeddingunsetunset

Document Embedding maps documents to vector space to represent the semantic and topic information of documents. Documents usually contain multiple sentences, so Document Embedding needs to process longer text sequences. A simple method is to take the average of the sentence vectors of all sentences in the document as the vector representation of the document, but this method also ignores the structural and semantic associations between sentences.

A more effective approach is to use a hierarchical model, such as Doc2Vec (Distributed Memory Model of Paragraph Vectors). Doc2Vec is an extension of Word2Vec. It not only considers the context of words during training, but also introduces document tags as additional context information. In this way, the model can learn document-level semantic information and map documents to a low-dimensional vector space. For example, when processing news articles, Doc2Vec can map articles on different topics to different regions, so that articles with similar semantics are closer in the vector space.

Document Embedding plays an important role in tasks such as text clustering, information retrieval, and document classification. For example, in information retrieval, by calculating the vector similarity between the query document and the candidate document, documents semantically related to the query document can be quickly found.

3. Key technologies of Embedding models

unsetunset3.1 Training Methodunsetunset

There are various training methods for Embedding models, and different training methods are suitable for different scenarios and data types.

• Context-based training methods : This is one of the most commonly used training methods, especially in the field of natural language processing. Take Word2Vec as an example, it learns the embedding vector of the target word through the context words. The CBOW architecture predicts the target word based on the context words, while the Skip-Gram architecture predicts the context words based on the target word. During the training process, the model will continuously adjust the embedding vector of the word so that the vector combination of the words appearing in the context can better predict the target word, or the vector of the target word can better predict the context word. The advantage of this method is that it can capture the semantic relationship between words well, but the disadvantage is that it is less effective for rare words and languages ​​with rich word forms.
• Training method based on global statistical information : The GloVe model is a representative of this training method. It combines global word frequency statistics and local context information to learn word embedding. Specifically, the GloVe model builds a co-occurrence matrix to record the co-occurrence frequency between words, and then learns the word embedding vector by optimizing an objective function. The advantage of this method is that it can make full use of global information and further improve the quality of word embedding, but the disadvantage is that the training process is relatively complex and the computational cost is high.
• Training methods based on pre-trained language models : In recent years, with the development of deep learning technology, training methods based on pre-trained language models have gradually become mainstream. For example, the BERT model can learn rich language knowledge and semantic information by performing unsupervised learning on large-scale corpora. In Sentence Embedding, BERT can encode sentences into a fixed-length vector that can well represent the semantics of the sentence. The advantage of this method is that it can capture more complex semantic information, but the disadvantage is that the model is large in scale and the training and inference speeds are slow.

unsetunset3.2 Optimization Strategyunsetunset

In order to improve the performance and efficiency of the Embedding model, researchers have proposed a variety of optimization strategies.

• Negative sampling : Negative sampling is a commonly used optimization strategy in context-based training methods. Its basic idea is that during the training process, in addition to selecting the context words of the target word as positive samples, some non-context words are randomly selected as negative samples. In this way, the model can better learn the semantic relationship between words, improve training efficiency and model performance. For example, in the Skip-Gram architecture of Word2Vec, negative sampling can significantly improve the training speed of the model and the quality of word embedding.
• Learning rate adjustment : The learning rate is one of the important parameters that affect the model training effect. During the training process, reasonable adjustment of the learning rate can accelerate the convergence speed of the model and improve the performance of the model. Common learning rate adjustment strategies include fixed learning rate, learning rate decay, and adaptive learning rate. For example, the Adam optimizer is an optimization algorithm with adaptive learning rate. It can automatically adjust the learning rate according to the gradient information of the model, and has the advantages of fast convergence speed and stable performance.
• Regularization : Regularization is an optimization strategy to prevent model overfitting. In the Embedding model, common regularization methods include L1 regularization and L2 regularization. L1 regularization can make the model weights more sparse by adding the absolute value of the weights to the loss function, thereby improving the interpretability of the model. L2 regularization can limit the size of the model weights and prevent the model from overfitting by adding the square of the weights to the loss function. For example, when training the Word2Vec model, adding L2 regularization can effectively prevent the model from overfitting and improve the generalization ability of the model.
• Distributed training : As the scale of data continues to increase, single-machine training can no longer meet the needs of model training. Distributed training is an optimization strategy that decomposes model training tasks onto multiple computing nodes for parallel computing. Through distributed training, the computing resources of multiple computing nodes can be fully utilized to speed up model training. For example, when training a large-scale BERT model, using distributed training can significantly shorten the training time and improve the training efficiency of the model.

4. Application scenarios of Embedding models

unsetunset4.1 Natural Language Processingunsetunset

Embedding models have been widely and deeply applied in the field of natural language processing (NLP), which has greatly promoted the development of NLP technology. The following are some specific application scenarios and data support:

4.1.1 Machine Translation

Machine translation is one of the important tasks in NLP. The Embedding model maps words or sentences in different languages ​​to the same vector space, so that semantic information between different languages ​​can be effectively aligned and converted. For example, machine translation systems such as Google Translate use Embedding technology to achieve fast and accurate translation between multiple languages. Its translation accuracy has improved significantly in the past few years. Taking Chinese-English translation as an example, the accuracy has increased from about 60% in the early days to more than 90% today. This is largely due to the Embedding model's accurate capture and representation of semantic information.

4.1.2 Question Answering System

The question-answering system needs to understand the user's questions and find the most accurate answers from a large amount of text data. The Embedding model can map the sentence or paragraph in the question and text data into the vector space and determine the answer by calculating the similarity between the vectors. For example, some intelligent customer service systems use the Embedding model to accurately answer user questions, with a question matching accuracy of more than 85%. This not only improves the efficiency of customer service, but also improves the user experience.

4.1.3 Sentiment Analysis

Sentiment analysis is to determine the emotional tendency of text by analyzing the text content, such as positive, negative or neutral. The Embedding model can map words, sentences or documents in the text to the vector space, so that texts with similar emotions are closer in the vector space. For example, when performing sentiment analysis on user comments on social media, the analysis accuracy based on the Embedding model can reach about 90%. This enables companies to better understand users' views on products or services and make corresponding improvements.

4.1.4 Text Classification

Text classification is the process of dividing text data into different categories, such as news classification, spam identification, etc. The Embedding model can map text to a vector space and identify the features of texts of different categories by training a classification model. For example, in the news classification task, the classification accuracy based on the Embedding model can reach over 95%. This enables news websites to classify and recommend news more efficiently, improving the efficiency of users in obtaining information.

unsetunset4.2 Recommendation Systemunsetunset

Embedding models are also widely used in recommendation systems. By mapping users, items, etc. to vector space, the similarity between users and items can be calculated more effectively, thereby achieving accurate recommendations.

4.2.1 Product Recommendations

On e-commerce platforms, the Embedding model can map users’ historical purchase behaviors, browsing history and other information into the vector space, and also map the product’s feature information into the same vector space. By calculating the similarity between the user vector and the product vector, products that users may be interested in can be recommended. For example, e-commerce platforms such as Amazon use the Embedding model to increase the click-through rate of recommended products by more than 30%, significantly improving users’ shopping experience and the platform’s sales performance.

4.2.2 Content Recommendation

On content platforms such as video websites and news clients, the Embedding model can vectorize user behavior data and content feature information. For example, in video recommendations, by analyzing user viewing history and the Embedding vector of video content, the platform can recommend videos that users may be interested in, with a recommendation accuracy of more than 80%. This not only increases user stickiness to the platform, but also increases the dissemination and exposure of content.

unsetunset4.3 Image and Video Processingunsetunset

The Embedding model not only performs well in the field of text processing, but also has important applications in the fields of image and video processing.

4.3.1 Image Recognition

In image recognition tasks, the Embedding model can map images to feature vector space, making similar images closer in the vector space. For example, a convolutional neural network (CNN) can convert images into feature vectors for tasks such as image classification and target detection. In some image recognition competitions, the recognition accuracy based on the Embedding model can reach over 99%. This enables computers to more accurately identify objects, scenes and other information in images, and is widely used in security monitoring, autonomous driving and other fields.

4.3.2 Video Retrieval

Video retrieval is to search for relevant video clips by entering keywords or descriptions. The Embedding model can map frames or clips in the video to the vector space, and convert the text description into a vector. By calculating the similarity between the text vector and the video vector, you can quickly find video clips related to the description. For example, in some video retrieval systems, the retrieval accuracy based on the Embedding model can reach more than 85%. This allows users to more easily find the video content they need, improving the efficiency and accuracy of video retrieval.

5. Comparison of mainstream embedding models

unsetunset5.1 Characteristics of different modelsunsetunset

Different Embedding models have their own characteristics and are suitable for different application scenarios and data types.

• Word2Vec
• Features : Word2Vec is one of the earliest widely used word embedding models, with a simple architecture and fast training speed. It learns word embedding vectors through contextual information, which can well capture the semantic and grammatical relationship between words. For example, the distance between the vectors of "king" and "queen" will be closer than that of "king" and "apple", and it can also reflect analogical relationships, such as "king-man + woman ≈ queen".
• Applicable scenarios : Suitable for processing large-scale text data, especially in scenarios that require rapid training and deployment. For example, in tasks such as news classification and sentiment analysis, the word embedding vectors provided by Word2Vec can be used as features to input into subsequent classification models to improve model performance.
• Limitations : It is less effective for rare words and languages ​​with rich morphological variations because it treats each word as an independent entity and cannot handle the internal structure and morphological variations of words well.
• GloVe
• Features : GloVe combines global word frequency statistics and local context information to learn word embeddings, which can make full use of global information and further improve the quality of word embeddings. It learns the word embedding vector by constructing a co-occurrence matrix, recording the co-occurrence frequency between words, and then optimizing the objective function.
• Applicable scenarios : It performs better in scenarios that require more precise word meaning representation. For example, in tasks such as semantic similarity calculation and question-answering systems, the word embedding vectors provided by GloVe can more accurately reflect the semantic relationship between words.
• Limitations : The training process is relatively complex, the computational cost is high, and it is not suitable for processing very large data sets.
• FastText
• Features : FastText improves on Word2Vec's shortcomings in handling rare words and word forms. By decomposing words into character n-tuples to learn word embedding, FastText can better handle multilingual and word-form-rich languages. For example, when dealing with different word forms of French words, FastText can more effectively capture their semantic associations.
• Applicable scenarios : Especially suitable for multilingual processing and scenarios that need to process rare words, such as cross-language translation, linguistics research, etc.
• Limitations : Due to the introduction of character-level information, the complexity of the model increases and the training speed is relatively slow.
• BERT
• Features : BERT is a pre-trained language model based on the Transformer architecture. It can learn rich language knowledge and semantic information through unsupervised learning on large-scale corpus. It can be used not only for Word Embedding, but also for Sentence Embedding and Document Embedding, and can capture more complex semantic information.
• Applicable scenarios : It performs well in many natural language processing tasks, such as question-answering systems, text classification, semantic similarity calculation, etc. For example, in a question-answering system, BERT can encode questions and candidate answer sentences into vectors and find the best matching answer by comparing vector similarities.
• Limitations : The model is large in size, the training and inference speeds are slow, and the computing resource requirements are high.

unsetunset5.2 Performance and Efficiency Analysisunsetunset

In terms of performance and efficiency, different Embedding models have their own advantages and disadvantages, and need to be selected based on specific application scenarios and resource constraints.

• Performance comparison
• Word sense representation accuracy : BERT performs best in word sense representation accuracy and can capture more complex semantic information, such as context-related word sense changes. GloVe is second, and can provide more accurate word sense representation by combining global information. Word2Vec and FastText have relatively low word sense representation accuracy, but can also meet most basic semantic analysis needs.
• Semantic similarity calculation : BERT and GloVe perform better in semantic similarity calculation and can more accurately reflect the semantic similarity between words, sentences or documents. Word2Vec and FastText may have certain errors in semantic similarity calculation, but they can also achieve good results for some simple similarity judgments.
• Multilingual processing capability : FastText has a clear advantage in multilingual processing and can better handle word form changes and semantic associations between different languages. BERT also supports multilingual versions, but may not be as flexible as FastText in processing the details of specific languages.
• Efficiency comparison
• Training speed : Word2Vec has the fastest training speed and is suitable for processing large-scale data sets. GloVe has a relatively slow training speed, especially when the data scale is large, the training process is more complicated. FastText's training speed is between Word2Vec and GloVe. Although it introduces character-level information, its training efficiency is still high. BERT has the slowest training speed. Due to its large model size, the training process requires a lot of computing resources and time.
• Inference speed : In the inference stage, Word2Vec and FastText have faster inference speeds and can quickly generate word embedding vectors. GloVe's inference speed is also relatively fast, but it may be affected by data preprocessing. BERT's inference speed is slow, especially when processing long texts, which requires more computing resources and time.
• Resource consumption : BERT has the highest demand for computing resources and memory, and requires high-performance GPU or TPU support. GloVe and FastText have relatively low resource requirements and can be run on ordinary servers. Word2Vec has the lowest resource requirements and can even be trained and inferred on a personal computer.

6. Challenges and future trends of Embedding models

unsetunset6.1 Current Challengesunsetunset

Although the Embedding model has achieved remarkable results in many fields, it still faces some challenges that restrict its further development and application.

6.1.1 Model complexity and efficiency issues

• High computing resource requirements : Pre-trained language models such as BERT are large in scale, and the training and inference processes require a lot of computing resources. For example, the BERT model contains hundreds of millions of parameters, and training once may take weeks and requires high-performance GPU or TPU support. This makes it difficult for many small businesses and research institutions to afford its high computing costs, limiting the widespread application of these models.
• Slow inference speed : In practical applications, the inference speed of the model directly affects the user experience. Models such as BERT have a slow inference speed when processing long texts, and it is difficult to meet the scenarios with high real-time requirements, such as online question-answering systems. For example, when processing an article containing thousands of words, BERT's inference time may reach several seconds or even longer, which obviously cannot meet the user's demand for instant feedback.

6.1.2 Data quality and annotation issues

• Data noise : When training an embedding model, data quality is crucial. However, real-world data often contains noise, such as typos, grammatical errors, and irrelevant information in text data. These noisy data can affect the learning effect of the model and cause the quality of the generated embedding vector to deteriorate. For example, in social media data, the text posted by users may contain a large number of typos and irregular expressions. If used directly for training, this may cause the model to have a biased understanding of the semantics of the words.
• Scarcity of labeled data : For some tasks that require supervised learning, such as sentiment analysis and text classification, the cost of obtaining labeled data is high. High-quality labeled data requires professional labelers, which is not only time-consuming and labor-intensive, but also costly. For example, in text classification tasks in the medical field, medical experts are required to label a large amount of medical text, which is very difficult in actual operations, resulting in a scarcity of labeled data and limiting the performance improvement of the model.

6.1.3 Difficulties of Multimodal Fusion

• Large modality differences : In multimodal learning, data of different modalities (such as text, image, speech, etc.) have different features and semantic information. It is a huge challenge to effectively fuse these different modal data. For example, text data is a discrete sequence of symbols, while image data is a continuous pixel matrix. There is a big difference in feature representation between the two. How to map them into a unified vector space and effectively fuse them is an urgent problem to be solved.
• Semantic alignment is difficult : Even if data from different modalities are mapped to the same vector space, it is still difficult to ensure that they are semantically aligned. For example, in the image description generation task, the visual information of the image needs to be aligned with the semantic information of the text to generate an accurate image description. However, due to the differences between the modalities, it is difficult to find an effective alignment method so that the generated description can accurately reflect the content of the image.

6.1.4 Insufficient model interpretability

• Black box models : Many embedding models, especially those based on deep learning, are considered "black box" models. The internal working mechanisms of these models are complex, and it is difficult to explain how the generated embedding vectors capture the semantic information of the data. For example, the BERT model learns the embedding vectors of words through a multi-layer Transformer architecture, but it is difficult to understand the specific role of each layer and how to obtain the final semantic representation through learning through these layers. This makes it difficult for users to understand and trust the decision-making process of the model in practical applications.
• Lack of intuitive explanation : For some application scenarios that require interpretability, such as medical diagnosis, financial risk assessment, etc., the interpretability of the model is crucial. However, the current Embedding model still has great deficiencies in this regard. For example, in medical diagnosis, doctors need to understand how the model generates diagnostic results based on the patient's symptoms and examination results, but the current model cannot provide intuitive explanations, which limits its application in these fields.

unsetunset6.2 Future Development Directionunsetunset

Although the Embedding model faces many challenges, its future development prospects are still broad with the continuous advancement of technology. The following are some possible future development directions:

6.2.1 Model optimization and lightweighting

• Model compression technology : In order to reduce the complexity of the model and the computing resource requirements, more efficient model compression technologies may appear in the future. For example, through pruning, quantization and other methods, redundant parameters and computing units in the model can be removed to reduce the size of the model while maintaining the performance of the model as much as possible. Researchers are already exploring some model compression methods, such as pruning the BERT model to reduce its parameters by half while maintaining high performance, which will make the model easier to deploy and apply.
• Lightweight model design : Develop lightweight embedding models so that they can run more efficiently while maintaining high performance. For example, some research teams are exploring the design of smaller-scale Transformer architectures, or combining other lightweight neural network structures, such as MobileNet, to build embedding models suitable for mobile devices and edge computing. These lightweight models will be able to better meet the needs of real-time and resource-constrained scenarios.

6.2.2 Data Augmentation and Self-Supervised Learning

• Data enhancement technology : In order to improve the robustness and generalization ability of the model, data enhancement technology will be more widely used. Through data enhancement, more diverse training data can be generated and the impact of data noise can be reduced. For example, in text data, data enhancement can be performed through synonym replacement, sentence reorganization, etc.; in image data, more image samples can be generated through operations such as rotation, scaling, and cropping. These enhanced data can enable the model to learn richer features and semantic information, improving the performance of the model.
• Self-supervised learning : Self-supervised learning is a learning method that does not require a large amount of labeled data. It allows the model to learn useful features and semantic information from a large amount of unlabeled data by designing some pre-training tasks. In the future, self-supervised learning will play a more important role in Embedding models. For example, by designing some prediction tasks, such as predicting the next word in a text, predicting the missing part in an image, etc., the model is pre-trained on unlabeled data, and then fine-tuned on a small amount of labeled data, thereby improving the performance and generalization ability of the model.

6.2.3 Deepening of Multimodal Fusion

• Modality alignment technology : In the future, more effective modality alignment technology will emerge to solve the difficulties of semantic alignment of multimodal data. For example, by designing some cross-modal alignment objective functions or introducing some alignment constraint mechanisms, data of different modalities can be better aligned in the vector space. Researchers are already exploring some alignment methods based on attention mechanisms, which can achieve more accurate semantic alignment by calculating the attention weights between different modal data, which will promote the development of multimodal learning.
• Multimodal pre-training models : Develop more powerful multimodal pre-training models that can process data from multiple modalities at the same time and learn richer semantic information. For example, the CLIP model is a typical multimodal pre-training model that learns the semantic association between images and text by jointly training image and text data. In the future, more similar multimodal pre-training models may appear, and these models will play an important role in multimodal applications, such as cross-modal retrieval, multimodal question answering, etc.

6.2.4 Improving model interpretability

• Innovation in explanation methods : In order to improve the interpretability of models, more innovative explanation methods will emerge in the future. For example, through visualization technology, the internal working mechanism and decision-making process of the model can be intuitively displayed; or some rule-based explanation systems can be developed to generate easy-to-understand explanation rules based on the output of the model. Researchers are already exploring some visualization methods, such as drawing attention weight maps to show the model's attention to different words or image regions, which will help users better understand the model's decision-making process.
• Interpretable model design : In the model design stage, consider interpretability factors and develop some interpretable embedding models. For example, design some models based on symbolic logic, or combine some traditional statistical methods and machine learning methods to make the model's decision-making process more transparent and interpretable. These interpretable models will be widely used in some fields with high interpretability requirements, such as medicine, finance, etc.