WO2023173555A1 - Model training method and apparatus, text classification method and apparatus, device, and medium - Google Patents

Abstract

A model training method and apparatus, a text classification method and apparatus, a device, and a storage medium, relating to the technical field of artificial intelligence. The training method comprises: obtaining original training data, the original training data comprising first original data and second original data (S101); performing up-sampling processing on the second original data to obtain initial training data (S102); performing enhancement processing on the initial training data according to a preset enhancement parameter to obtain enhanced training data (S103); encoding the enhanced training data to obtain a target word embedding vector (S104); performing disturbance processing on the target word embedding vector to obtain target training data (S105); and training a preset neural network model according to the first original data and the target training data to obtain a target classification model, the target classification model being a text classification model and being used for classifying target text data (S106). The present method can improve the recognition accuracy of a model on sample text data and the training effect of the model.

Description

Model training methods, text classification methods and devices, equipment, and media

This application requests the priority of the Chinese patent application submitted to the China Patent Office on March 15, 2022, with the application number 202210253301. The entire contents are incorporated herein by reference.

Technical field

This application relates to the field of artificial intelligence technology, and in particular to a model training method, text classification method and device, equipment, and media.

Background technique

Currently, when classifying text, it is common to input relevant text data sets into a trained supervised learning model, and classify the relevant text data sets through the supervised learning model.

technical problem

The following are the technical problems of the prior art that the inventor is aware of: In related technologies, commonly used supervised learning models often cannot accurately identify minority text data, which affects the training effect of the model. Therefore, how to improve the model's recognition accuracy of sample text data to improve the model's training effect has become an urgent technical issue that needs to be solved.

Technical solutions

In the first aspect, embodiments of this application propose a model training method, which is used to train a target classification model. The method includes:

Obtain original training data, wherein the original training data includes first original data and second original data;

Perform upsampling processing on the second original data to obtain initial training data;

Perform enhancement processing on the initial training data according to preset enhancement parameters to obtain enhanced training data;

Encoding the enhanced training data to obtain a target word embedding vector;

Perform perturbation processing on the target word embedding vector to obtain target training data;

A preset neural network model is trained according to the first original data and the target training data to obtain a target classification model, wherein the target classification model is a text classification model used to classify target text data.

In the second aspect, the embodiment of this application proposes a text classification method, which method includes:

Obtain the target text data to be classified;

The target text data is input into a target classification model for label classification processing to obtain label text data, wherein the target classification model is trained according to a model training method, wherein the model training method includes: obtaining original training Data, wherein the original training data includes first original data and second original data;

Perform upsampling processing on the second original data to obtain initial training data;

Perform enhancement processing on the initial training data according to preset enhancement parameters to obtain enhanced training data;

Encoding the enhanced training data to obtain a target word embedding vector;

Perform perturbation processing on the target word embedding vector to obtain target training data;

A preset neural network model is trained according to the first original data and the target training data to obtain a target classification model, wherein the target classification model is a text classification model used to classify target text data.

In the third aspect, the embodiment of the present application proposes a model training device. The device includes:

A training data acquisition module, configured to acquire original training data, where the original training data includes first original data and second original data;

An upsampling module, used to upsample the second original data to obtain initial training data;

A data enhancement module, configured to enhance the initial training data according to preset enhancement parameters to obtain enhanced training data;

An encoding module, used to encode the enhanced training data to obtain a target word embedding vector;

A perturbation module, used to perturb the target word embedding vector to obtain target training data;

A model training module, configured to train a preset neural network model according to the first original data and the target training data to obtain a target classification model, wherein the target classification model is a text classification model, used to classify the target Text data is classified.

In the fourth aspect, the embodiment of the present application proposes a text classification device, which includes:

Text data acquisition module, used to obtain target text data to be classified;

A label classification module, used to input the target text data into a target classification model for label classification processing to obtain label text data, wherein the target classification model is trained according to a model training method, wherein the training of the model The method includes: obtaining original training data, wherein the original training data includes first original data and second original data;

Perform upsampling processing on the second original data to obtain initial training data;

Perform enhancement processing on the initial training data according to preset enhancement parameters to obtain enhanced training data;

Encoding the enhanced training data to obtain a target word embedding vector;

Perform perturbation processing on the target word embedding vector to obtain target training data;

A preset neural network model is trained according to the first original data and the target training data to obtain a target classification model, wherein the target classification model is a text classification model used to classify target text data.

In a fifth aspect, embodiments of the present application provide an electronic device. The electronic device includes a memory, a processor, a program stored on the memory and executable on the processor, and a program for implementing the processor. and a data bus for connection and communication between the memory, and when the program is executed by the processor, a model training method or a text classification method is implemented;

Wherein, the training method of the model includes:

Obtain original training data, wherein the original training data includes first original data and second original data;

Perform upsampling processing on the second original data to obtain initial training data;

Perform enhancement processing on the initial training data according to preset enhancement parameters to obtain enhanced training data;

Encoding the enhanced training data to obtain a target word embedding vector;

Perform perturbation processing on the target word embedding vector to obtain target training data;

Train a preset neural network model according to the first original data and the target training data to obtain a target classification model, wherein the target classification model is a text classification model used to classify target text data;

Wherein, the text classification method includes:

Obtain the target text data to be classified;

The target text data is input into a target classification model for label classification processing to obtain label text data, wherein the target classification model is trained according to a training method of a model, wherein the training method of the model includes: Obtain original training data, wherein the original training data includes first original data and second original data;

Perform upsampling processing on the second original data to obtain initial training data;

Perform enhancement processing on the initial training data according to preset enhancement parameters to obtain enhanced training data;

Encoding the enhanced training data to obtain a target word embedding vector;

Perform perturbation processing on the target word embedding vector to obtain target training data;

A preset neural network model is trained according to the first original data and the target training data to obtain a target classification model, wherein the target classification model is a text classification model used to classify target text data.

In a sixth aspect, embodiments of the present application provide a storage medium. The storage medium is a computer-readable storage medium for computer-readable storage. The storage medium stores one or more programs, and the one or more programs are stored in the storage medium. A program can be executed by one or more processors to implement a model training method or a text classification method;

Wherein, the training method of the model includes:

Obtain original training data, wherein the original training data includes first original data and second original data;

Perform upsampling processing on the second original data to obtain initial training data;

Perform enhancement processing on the initial training data according to preset enhancement parameters to obtain enhanced training data;

Encoding the enhanced training data to obtain a target word embedding vector;

Perform perturbation processing on the target word embedding vector to obtain target training data;

Train a preset neural network model according to the first original data and the target training data to obtain a target classification model, wherein the target classification model is a text classification model used to classify target text data;

Wherein, the text classification method includes:

Obtain the target text data to be classified;

The target text data is input into a target classification model for label classification processing to obtain label text data, wherein the target classification model is trained according to a training method of a model, wherein the training method of the model includes: Obtain original training data, wherein the original training data includes first original data and second original data;

Perform upsampling processing on the second original data to obtain initial training data;

Perform enhancement processing on the initial training data according to preset enhancement parameters to obtain enhanced training data;

Encoding the enhanced training data to obtain a target word embedding vector;

Perform perturbation processing on the target word embedding vector to obtain target training data;

A preset neural network model is trained according to the first original data and the target training data to obtain a target classification model, wherein the target classification model is a text classification model used to classify target text data.

beneficial effects

The model training method, text classification method and device, electronic equipment and storage medium proposed by this application obtain original training data, where the original training data includes first original data and second original data; the second original data is The upsampling process obtains the initial training data, which can effectively correct the abnormal data in the second original data and improve the rationality of the data. Furthermore, the initial training data is enhanced according to the preset enhancement parameters to obtain enhanced training data, and then the enhanced training data is encoded to obtain the target word embedding vector, and the target word embedding vector is perturbed to obtain the target training data. In this way, the target training data that meets the needs can be easily obtained, so that the obtained target training data can better highlight the characteristics of the minority class training data and improve the neural network model's attention to the minority class training data. Finally, training the preset neural network model based on the first original data and target training data can improve the model's recognition accuracy of sample text data, improve the training effect of the model, and obtain a target classification model that meets the needs, where the target The classification model is a text classification model, which can be used to classify target text data. Classifying target text data through the target classification model can improve the accuracy of text classification.

Description of the drawings

Figure 1 is a flow chart of a model training method provided by an embodiment of the present application;

Figure 2 is a flow chart of step S103 in Figure 1;

Figure 3 is another flowchart of step S103 in Figure 1;

Figure 4 is a flow chart of step S106 in Figure 1;

Figure 5 is a flow chart of the text classification method provided by the embodiment of the present application;

Figure 6 is a flow chart of step S502 in Figure 5;

Figure 7 is a schematic structural diagram of a model training device provided by an embodiment of the present application;

Figure 8 is a schematic structural diagram of a text classification device provided by an embodiment of the present application;

Figure 9 is a schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present application.

Embodiments of the invention

In order to make the purpose, technical solutions and advantages of the present application more clear, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application and are not used to limit the present application.

It should be noted that although the functional modules are divided in the device schematic diagram and the logical sequence is shown in the flow chart, in some cases, the modules can be divided into different modules in the device or the order in the flow chart can be executed. The steps shown or described. The terms "first", "second", etc. in the description, claims, and above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific sequence or sequence.

Unless otherwise defined, all technical and scientific terms used in this application have the same meaning as commonly understood by a person skilled in the technical field of this application. The terms used in this application are only for the purpose of describing the embodiments of the application and are not intended to limit the application.

First, let’s analyze some terms involved in this application:

Artificial intelligence (AI): It is a new technical science that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence; artificial intelligence is a branch of computer science, artificial intelligence Intelligence attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a manner similar to human intelligence. Research in this field includes robotics, language recognition, image recognition, natural language processing, and expert systems. Artificial intelligence can simulate the information process of human consciousness and thinking. Artificial intelligence is also a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

Natural language processing (NLP): NLP uses computers to process, understand and use human languages (such as Chinese, English, etc.). NLP is a branch of artificial intelligence and an interdisciplinary subject of computer science and linguistics. It's called computational linguistics. Natural language processing includes syntax analysis, semantic analysis, text understanding, etc. Natural language processing is commonly used in technical fields such as machine translation, handwritten and printed character recognition, speech recognition and text-to-text conversion, information intent recognition, information extraction and filtering, text classification and clustering, public opinion analysis and opinion mining, etc. It involves language Processing related data mining, machine learning, knowledge acquisition, knowledge engineering, artificial intelligence research and linguistic research related to language computing, etc.

Information Extraction (NER): Text processing technology that extracts specified types of factual information such as entities, relationships, events, etc. from natural language text and forms structured data output. Information extraction is a technique for extracting specific information from text data. Text data is composed of some specific units, such as sentences, paragraphs, and chapters. Text information is composed of some small specific units, such as words, words, phrases, sentences, paragraphs, or a combination of these specific units. . Extracting noun phrases, person names, place names, etc. from text data is text information extraction. Of course, the information extracted by text information extraction technology can be various types of information.

Data upsampling (Data SMOTE): Data upsampling refers to amplifying a small number of samples to the same number of samples as the majority of samples. For example, take one data from a few samples, find the distance between the sample and other samples, sort according to the Euclidean distance, and take out the first 5 data.

Data Augmentation: Data augmentation is also called data amplification, which means that limited data can generate value equivalent to more data without substantially increasing the data. Data augmentation can be divided into supervised data augmentation and unsupervised data augmentation methods. Among them, supervised data enhancement can be divided into single-sample data enhancement and multi-sample data enhancement methods, while unsupervised data enhancement can be divided into two directions: generating new data and learning enhancement strategies.

Encoding (Encoder): Encoding is to convert the input sequence into a fixed-length vector; decoding (decoder) is to convert the previously generated fixed vector into an output sequence; where the input sequence can be text, voice, image, or video; The output sequence can be text or images.

BERT (Bidirectional Encoder Representations from Transformers): is a language representation model. BERT uses Transformer Encoder block for connection, which is a typical bidirectional encoding model.

Embedding: Embedding is a vector representation, which refers to using a low-dimensional vector to represent an object. The object can be a word, a product, a movie, etc.; the nature of this embedding vector is that it can Objects corresponding to vectors with similar distances have similar meanings. For example, the distance between embedding (Avengers) and embedding (Iron Man) will be very close, but the distance between embedding (Avengers) and embedding (Gone with the Wind) It will be further away. Embedding is essentially a mapping, a mapping from semantic space to vector space, while maintaining the relationship between the original sample and the semantic space in the vector space as much as possible. For example, the positions of two words with close semantics are also relatively close in the vector space. Embedding can encode objects with low-dimensional vectors and retain their meaning. It is often used in machine learning. In the process of building a machine learning model, the object is encoded into a low-dimensional dense vector and then passed to the DNN to improve efficiency.

Softmax classifier: It is a general induction of multiple classifications faced by the logistic regression classifier, and the output is the probability value belonging to different categories.

At present, when classifying text, it is often used to input relevant text data sets into a trained supervised learning model, and then classify the relevant text data sets through the supervised learning model; because the training effect of the supervised learning model often depends on The quantity and quality of the training set. In text classification scenarios, there is a widespread problem of imbalanced training data. The sample categories that need attention are often minority sample categories, and the minority sample categories account for a relatively small proportion in the entire data set. If the data is directly Input model training, the model often tends to predict all samples as the majority class, and has poor recognition accuracy for minority class sample data. Therefore, how to improve the model's recognition accuracy of sample text data to improve the model's training effect has become an urgent technical issue that needs to be solved.

Based on this, embodiments of the present application provide a model training method, text classification method and device, equipment, and media, aiming to improve the model's recognition accuracy of sample text data, thereby improving the training effect of the model.

The model training method, text classification method and device, equipment, and medium provided by the embodiments of the present application are specifically described through the following embodiments. First, the model training method in the embodiment of the present application is described.

The embodiments of this application can obtain and process relevant data based on artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. .

Basic artificial intelligence technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation/interaction systems, mechatronics and other technologies. Artificial intelligence software technology mainly includes computer vision technology, robotics technology, biometric technology, speech processing technology, natural language processing technology, and machine learning/deep learning.

The model training method, text classification method and device, equipment, and media provided by the embodiments of this application relate to the field of artificial intelligence technology. The model training method, text classification method and device, equipment, and media provided by the embodiments of the present application can be applied to terminals or servers, or can be software running in terminals or servers. In some embodiments, the terminal can be a smartphone, a tablet, a laptop, a desktop computer, etc.; the server can be configured as an independent physical server, or as a server cluster or distributed system composed of multiple physical servers. A cloud that can be configured to provide basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. Server; software can be an application that implements text classification methods, etc., but is not limited to the above forms.

The application may be used in a variety of general or special purpose computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, including Distributed computing environment for any of the above systems or devices, etc. The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including storage devices.

Figure 1 is an optional flow chart of the model training method provided by the embodiment of the present application. The method in Figure 1 may include, but is not limited to, steps S101 to S106.

Step S101, obtain original training data, wherein the original training data includes first original data and second original data;

Step S102, perform upsampling processing on the second original data to obtain initial training data;

Step S103, perform enhancement processing on the initial training data according to preset enhancement parameters to obtain enhanced training data;

Step S104: Encode the enhanced training data to obtain a target word embedding vector;

Step S105, perform perturbation processing on the target word embedding vector to obtain target training data;

Step S106: Train a preset neural network model according to the first original data and the target training data to obtain a target classification model, where the target classification model is a text classification model used to perform target text data classification. Classification.

Steps S101 to S106 illustrated in the embodiment of the present application obtain initial training data by upsampling the second original data, which can effectively correct abnormal data in the second original data and improve the rationality of the data. The initial training data is enhanced according to the preset enhancement parameters to obtain enhanced training data, and then the enhanced training data is encoded to obtain the target word embedding vector. The target word embedding vector is perturbed to obtain the target training data, which can be convenient The target training data that meets the needs can be obtained accurately, so that the obtained target training data can better highlight the characteristics of the minority class training data and improve the neural network model's attention to the minority class training data. Training the preset neural network model based on the first original data and target training data can improve the model's recognition accuracy of sample text data, improve the training effect of the model, and obtain a target classification model that meets the needs.

In step S101 of some embodiments, sample data can be obtained by writing a web crawler, setting the data source, and then crawling data in a targeted manner. Sample data can also be obtained through other methods and is not limited to this. It should be noted that this sample data is text data with text category labels. According to the preset proportion parameters, the sample data is divided into original training data, original verification data and original test data. In order to improve the training effect of the model, it is necessary to perform data enhancement processing on the original training data. Specifically, first perform data statistics on the original training data to obtain the number of samples of each text category in the original training data. According to the samples corresponding to each text category label quantity, the original training data is divided into the first original data and the second original data, that is, the first original data and the second original data can be distinguished according to the text category labels on the original training data. The first original data is when the number of samples is greater than the predetermined number. The original training data with a quantity threshold is labeled as majority class sample data label 0, while the second original data is the original training data with a sample number less than or equal to the preset quantity threshold, labeled as minority class sample data label 1, where, for the majority class The class sample data label 0 (i.e., the first original data) does not undergo data enhancement processing. The minority class sample data label 1 (i.e., the second original data) needs to undergo data enhancement processing. For example, if the majority class sample data label 0 (i.e., the second original data) The number of samples of the first original data) is m, and the number of samples of the minority class sample data label 1 (i.e., the second original data) is n. Then it is necessary to perform data enhancement on the minority class sample data label 1 (i.e., the second original data), and we get m-n sample data, so that the sample number ratio between the enhanced second original data and the first original data is 1:1.

In step S102 of some embodiments, the minority class sample data label 1 (i.e., the second original data) that needs to be enhanced is randomly upsampled, and the number of samples is the majority class sample data label 0 (i.e., the first original data). Therefore, The second original data after sampling will generate m-n repeated sample data, thereby obtaining new training data, which is recorded as initial training data.

Referring to Figure 2, in some embodiments, the enhancement parameter includes a first disturbance ratio, and step S103 may include but is not limited to steps S201 to S203:

Step S201, obtain the first sentence length of the initial training data;

Step S202, calculate the first disturbance amount based on the first sentence length and the first disturbance ratio;

Step S203: Delete the initial training data according to the first disturbance amount to obtain enhanced training data.

In step S201 of some embodiments, count the first sentence length s1 of each text sentence in the initial training data set in units of characters. For example, if a certain text sentence consists of five words and three punctuation marks, then the text The first sentence length s1 of the sentence is 8.

In step S202 of some embodiments, the first disturbance ratio may be set according to actual requirements. For example, if the first disturbance ratio r1 is set to 0.1, then the first disturbance amount d1 is calculated based on the first sentence length s1 and the first disturbance ratio r1. The first disturbance amount d1 can be the corresponding value when s1*r1 is rounded, that is The first disturbance amount d1 is int(s1*r1).

In step S203 of some embodiments, int (s1*r1) positions are randomly selected from the current text sentence as replacement positions, and the characters at these replacement positions are replaced with nulls, thereby realizing the text sentence of the initial training data. Deletion processing to obtain enhanced training data.

Referring to Figure 3, in other embodiments, the enhancement parameter includes a second disturbance ratio, and step S103 may include but is not limited to steps S301 to S303:

Step S301, obtain the second sentence length of the initial training data;

Step S302, calculate the second disturbance amount based on the second sentence length and the second disturbance ratio;

Step S303: Expand the initial training data according to the second disturbance amount and preset punctuation marks to obtain enhanced training data.

In step S301 of some embodiments, count the second sentence length s2 of each text sentence in the initial training data set in units of characters. For example, if a certain text sentence consists of six words and two punctuation marks, then the text The second sentence length s2 of the sentence is 8.

In step S302 of some embodiments, the second disturbance ratio may be set according to actual requirements. For example, if the second disturbance ratio r2 is set to 0.1, then the second disturbance amount d2 is calculated based on the second sentence length s2 and the second disturbance ratio r2. The second disturbance amount d2 can be the corresponding value when s2*r2 is rounded, that is The second disturbance amount d2 is int(s2*r2).

In step S303 of some embodiments, the preset punctuation mark p is a neutral mark, such as a comma, a comma, a colon, a semicolon, a period, an ellipsis, etc. Randomly select int(s2*r2) positions from the current text sentence as replacement positions, randomly extract int(s2*r2) symbols from p (repeated extraction is allowed), and replace the characters at the replacement positions with punctuation marks. In this way, the text sentences of the initial training data are expanded and processed, and enhanced training data is obtained.

It should be noted that the first perturbation ratio and the second perturbation ratio can be understood as enhancement ratios, which are used to determine the proportion of the number of characters that need to be modified in a certain text sentence. The first perturbation amount and the second perturbation amount can be understood as The number of enhanced characters is used to determine the number of characters that need to be modified in a certain text sentence.

Taking steps S201 to S203 as an example, setting the first disturbance ratio r1 to 0.1 means that 10% of the characters need to be modified in a certain text sentence. If the length of a certain text sentence is 10, then the first disturbance amount d1 is int. (10*0.1)=1, the text sentence needs to modify 1 character, then randomly select a position in the text sentence as the replacement position, and replace the character at this replacement position with empty, thereby realizing the The text sentences are deleted to obtain enhanced training data.

It should be noted that when performing data enhancement processing on the initial training data, the above two data enhancement methods can be selected for data enhancement at the same time, or one of the data enhancement methods can be used alone for data enhancement. For example, in order to improve the efficiency of data enhancement, select the above two data enhancement methods to enhance the initial training data at the same time, and set the proportion of one of the data enhancement methods to k, then there are (m-n)*k in the initial training data The sample data is enhanced using this method, while other sample data in the initial training data is enhanced using another data enhancement method. For example, the (m-n)*k sample data are deleted through the above-mentioned steps S201 to step S203, and the other sample data excluding the m-n)*k sample data are expanded through the above-mentioned steps S301 to step S303, thereby obtaining enhancement. training data.

In step S104 in some embodiments, a BERT encoder may be used to encode the enhanced training data to obtain a target word embedding vector. Because BERT uses Transformer Encoder block for connection, it is a typical bidirectional encoding model. Therefore, the enhanced training data can be bidirectionally encoded through the BERT encoder, that is, the enhanced training data can be encoded from left to right and from right to left, respectively, to obtain the target word embedding vector (token embedding).

In step S105 of some embodiments, when performing perturbation processing on the target word embedding vector, perturbation can be added to the target word embedding vector (token embedding) along the gradient direction according to a preset perturbation factor. The preset perturbation factor can be Represented as a word embedding weight matrix, that is, the target word embedding vector and the preset word embedding weight matrix are matrix multiplied along the gradient direction to obtain the target training data.

Referring to Figure 4, in some embodiments, step S106 may include, but is not limited to, steps S401 to S403:

Step S401: Perform perturbation calculation on the first original data and target training data through a preset function to obtain the text perturbation value;

Step S402: Calculate the loss function of the neural network model based on the text disturbance value to obtain the loss value;

Step S403: Use the loss value as a backpropagation amount to adjust the model parameters of the neural network model to train the neural network model and obtain a text classification model.

In step S401 of some embodiments, first input the first original data and target training data into the preset neural network model, and set the number of iterations (epoches_num) and data batch size (batch size) of the neural network model, and Divide the first original data and target training data into multiple batches according to the data batch size to obtain batch data. Among them, the preset function is the cross-entropy function.

Specifically, in each iteration process, cross entropy is used to obtain the loss value loss1 of each batch of data, and the parameter gradient of the batch of data is calculated, and the original gradient value of each original parameter β _i of the batch of data is calculated grad_β _i is divided by the norm L ₂ of the original parameters and multiplied by a hyperparameter α to obtain the text perturbation value, and the text perturbation value is added to the original parameters to obtain the intermediate parameter β′ _i of each batch of data. The calculation process is shown in formula (1):

Among them, norm

The text perturbation value is

The value range of hyperparameter α is (0,1]. If you want the above target word embedding vector to add greater disturbance, set the hyperparameter α to a larger value. After many verifications, when the hyperparameter α is 0.1 to 0.3 , the model training effect is better.

Further, calculate the absolute value r _i of the difference between the intermediate parameter β′ _i and the original parameter β _i , and set a threshold ε, where the value range of ε is (0,1], thereby controlling whether the disturbance is added to the original parameters.

For example, if r _i >ε, then ε*r _i /Norm(r _i ) is added to the original parameter β _i as a disturbance amount to obtain the final target parameter β″ _i . The calculation formulas are as follows: formula (2) and formula ( 3) as shown:

r _i =abs(β′ _i -β _i ) Formula (2)

It should be noted that the larger the value of ε, the more difficult it is to add the disturbance amount to the parameter matrix of the original parameters. After many verifications, when ε is 0.8 to 1, the training effect of the model is better.

Furthermore, in order to improve the training effect of the model, parameter k is set to control the number of disturbances, and the above-mentioned intermediate parameter calculation process and target parameter calculation process are cycled k times. Since excessive number of perturbations will bring too much noise and affect the prediction accuracy of the neural network model on each text category, the number of perturbations is generally set to 2 or 3 times to obtain the final text perturbation value.

In step S402 of some embodiments, the loss function of the neural network model is calculated based on the final text perturbation value to obtain the loss value. Specifically, the loss function corresponding to the fully connected layer of the neural network model can be calculated to obtain the loss value.

In step S403 of some embodiments, the loss value is used as a backpropagation amount to adjust the model parameters of the neural network model to train the neural network model and obtain a text classification model, so that the labeled text data generated by the neural network model is more accurate. Improve the recognition accuracy of neural network models for minority text data.

The model training method of the embodiment of the present application obtains original training data, where the original training data includes first original data and second original data; performs upsampling processing on the second original data to obtain initial training data, which can effectively Correct the abnormal data in the second original data to improve the rationality of the data. Furthermore, the initial training data is enhanced according to the preset enhancement parameters to obtain enhanced training data, and then the enhanced training data is encoded to obtain the target word embedding vector, and the target word embedding vector is perturbed to obtain the target training data. In this way, the target training data that meets the needs can be easily obtained, so that the obtained target training data can better highlight the characteristics of the minority class training data and improve the neural network model's attention to the minority class training data. Finally, training the preset neural network model based on the first original data and target training data can improve the model's recognition accuracy of sample text data, improve the training effect of the model, and obtain a target classification model that meets the needs.

Figure 5 is an optional flow chart of the text classification method provided by the embodiment of the present application. The method in Figure 5 may include, but is not limited to, steps S501 to S502.

Step S501, obtain the target text data to be classified;

Step S502: Input the target text data into a target classification model for label classification processing to obtain label text data, wherein the target classification model is trained according to the training method of the embodiment of the first aspect.

In step S501 of some embodiments, the target text data to be classified can be obtained by writing a web crawler, setting the data source, and then crawling data in a targeted manner. Sample data can also be obtained through other methods and is not limited to this. It should be noted that the target text data can be articles, text fields, text segments, etc.

In step S502 of some embodiments, the target text data is input into the target classification model, the target text data is mapped to a preset vector space through the target classification model, the target text vector is obtained, and the target text vector is obtained through the preset classification function. The text vector is subjected to label classification processing to obtain label text data.

Referring to Figure 6, in some embodiments, step S502 may also include, but is not limited to, steps S601 to S602:

Step S601, map the target text data to a preset vector space through the fully connected layer of the target classification model to obtain the target text vector;

Step S602: Perform label classification processing on the target text vector through the classification function of the fully connected layer and the preset text category label to obtain label text data.

In step S601 of some embodiments, the feature dimensions of the preset text category labels are obtained, and the target text data is mapped from semantic space to vector space through the MLP network of the fully connected layer, and the target text data is mapped to the preset text. The feature dimensions of the category labels are the same in the vector space to obtain the target text vector.

In step S602 of some embodiments, the classification function may be a softmax function. For example, a probability distribution is created on each text category label through the softmax function to obtain a predicted probability value that the target text vector belongs to each text category. Finally, according to the size of the classification probability value, the text category judgment and labeling processing are performed on the target text vector to obtain label text data.

It should be noted that the preset text category labels can be set according to actual needs, and the text category labels in different business scenarios can be different. For example, in the application scenario of classifying books, the preset text category labels include classical literature, foreign literature, prose, novels, poetry collections, etc. In daily life scenarios, preset text category labels can include transportation, weather conditions, time information, etc.

The text classification method of the embodiment of the present application obtains the target text data to be classified and inputs the target text data into the target classification model for label classification processing. The target classification model has good recognition accuracy for minority text data. The target classification model can identify target text data of different categories, and classify the target text data according to different category labels to obtain labeled text data, which improves the accuracy of text classification.

Please refer to Figure 7. This embodiment of the present application also provides a model training device that can implement the above model training method. The model training device includes:

The training data acquisition module 701 is used to acquire original training data, where the original training data includes first original data and second original data;

The upsampling module 702 is used to perform upsampling processing on the second original data to obtain initial training data;

The data enhancement module 703 is used to enhance the initial training data according to preset enhancement parameters to obtain enhanced training data;

Encoding module 704 is used to encode the enhanced training data to obtain the target word embedding vector;

The perturbation module 705 is used to perturb the target word embedding vector to obtain target training data;

The model training module 706 is used to train a preset neural network model according to the first original data and the target training data to obtain a target classification model, where the target classification model is a text classification model, used for Target text data is classified.

In some embodiments, data enhancement module 703 includes:

The first sentence length acquisition unit is used to obtain the first sentence length of the initial training data;

A first disturbance amount calculation unit, configured to calculate the first disturbance amount based on the first sentence length and the first disturbance ratio;

The data deletion unit is used to delete the initial training data according to the first disturbance amount to obtain enhanced training data.

In other embodiments, the data enhancement module 703 includes:

The second sentence length acquisition unit is used to obtain the second sentence length of the initial training data;

a second disturbance amount calculation unit, configured to calculate the second disturbance amount based on the second sentence length and the second disturbance ratio;

The data expansion unit is used to expand the initial training data according to the second disturbance amount and preset punctuation marks to obtain enhanced training data.

In some embodiments, model training module 706 includes:

A perturbation calculation unit, used to perform perturbation calculation on the first original data and target training data through a preset function to obtain the text perturbation value;

The loss value calculation unit is used to calculate the loss function of the neural network model based on the text disturbance value to obtain the loss value;

The training unit is used to use the loss value as a backpropagation amount to adjust the model parameters of the neural network model to train the neural network model and obtain a text classification model.

The model training device in the embodiment of the present application is used to perform the model training method in the above embodiment. The specific processing process is the same as the model training method in the above embodiment, and will not be described again here.

Please refer to Figure 8. This embodiment of the present application also provides a text classification device that can implement the above text classification method. The text classification device includes:

Text data acquisition module 801, used to acquire target text data to be classified;

The label classification module 802 is configured to input the target text data into a target classification model for label classification processing to obtain label text data, wherein the target classification model is trained according to the training method of any one of the embodiments of the first aspect.

In some embodiments, tag classification module 802 includes:

The mapping unit is used to map the target text data to the preset vector space through the fully connected layer of the target classification model to obtain the target text vector;

The label classification unit is used to perform label classification processing on the target text vector through the classification function of the fully connected layer and the preset text category label to obtain label text data.

The text classification device in the embodiment of the present application is used to perform the text classification method in the above embodiment. Its specific processing process is the same as the text classification method in the above embodiment, and will not be described again here.

Embodiments of the present application also provide an electronic device. The electronic device includes: a memory, a processor, a program stored on the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory. , when the program is executed by the processor, a model training method or a text classification method is implemented, wherein the model training method includes: obtaining original training data, wherein the original training data includes first original data and second original data; Perform upsampling processing on the second original data to obtain initial training data; perform enhancement processing on the initial training data according to preset enhancement parameters to obtain enhanced training data; perform encoding processing on the enhanced training data to obtain the target word embedding vector; performing perturbation processing on the target word embedding vector to obtain target training data; training a preset neural network model according to the first original data and the target training data to obtain a target classification model, wherein, The target classification model is a text classification model, used to classify target text data; wherein the text classification method includes: obtaining target text data to be classified; inputting the target text data into the target classification model for label classification processing, Obtain labeled text data, in which the target classification model is trained according to the training method of the model. The electronic device can be any smart terminal including a tablet computer, a vehicle-mounted computer, etc.

Please refer to Figure 9, which illustrates the hardware structure of an electronic device according to another embodiment. The electronic device includes:

The processor 901 can be implemented by a general CPU (Central Processing Unit, central processing unit), a microprocessor, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement The technical solutions provided by the embodiments of this application;

The memory 902 can be implemented in the form of read-only memory (ReadOnlyMemory, ROM), static storage device, dynamic storage device, or random access memory (RandomAccessMemory, RAM). The memory 902 can store operating systems and other application programs. When implementing the technical solutions provided by the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 902 and called by the processor 901 to execute the implementation of this application. Example model training methods or text classification methods;

Input/output interface 903, used to implement information input and output;

Communication interface 904 is used to realize communication interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wirelessly (such as mobile network, WIFI, Bluetooth, etc.);

Bus 905, which transmits information between various components of the device (such as processor 901, memory 902, input/output interface 903, and communication interface 904);

The processor 901, the memory 902, the input/output interface 903 and the communication interface 904 implement communication connections between each other within the device through the bus 905.

Embodiments of the present application also provide a storage medium. The storage medium is a computer-readable storage medium for computer-readable storage. The storage medium stores one or more programs, and the one or more programs can be processed by one or more The processor is executed to implement a model training method or a text classification method, wherein the model training method includes: obtaining original training data, wherein the original training data includes first original data and second original data; The second original data is subjected to upsampling processing to obtain initial training data; the initial training data is enhanced according to preset enhancement parameters to obtain enhanced training data; the enhanced training data is encoded to obtain the target word Embedding vectors; performing perturbation processing on the target word embedding vector to obtain target training data; training a preset neural network model according to the first original data and the target training data to obtain a target classification model, wherein The target classification model is a text classification model, used to classify target text data; wherein, the text classification method includes: obtaining target text data to be classified; inputting the target text data into the target classification model for label classification processing, and obtaining Labeled text data, in which the target classification model is trained according to the training method of the model. In addition, computer-readable storage media may be non-volatile or volatile.

As a non-transitory computer-readable storage medium, memory can be used to store non-transitory software programs and non-transitory computer executable programs. In addition, the memory may include high-speed random access memory and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include memory located remotely from the processor, and the remote memory may be connected to the processor via a network. Examples of the above-mentioned networks include but are not limited to the Internet, intranets, local area networks, mobile communication networks and combinations thereof.

The model training method, model training device, text classification method, text classification device, electronic device and storage medium provided by the embodiments of the present application obtain original training data, where the original training data includes first original data and second Original data; perform upsampling processing on the second original data to obtain initial training data, which can effectively correct abnormal data in the second original data and improve the rationality of the data. Furthermore, the initial training data is enhanced according to the preset enhancement parameters to obtain enhanced training data, and then the enhanced training data is encoded to obtain the target word embedding vector, and the target word embedding vector is perturbed to obtain the target training data. In this way, the target training data that meets the needs can be easily obtained, so that the obtained target training data can better highlight the characteristics of the minority class training data and improve the neural network model's attention to the minority class training data. Finally, training the preset neural network model based on the first original data and target training data can improve the model's recognition accuracy of sample text data, improve the training effect of the model, and obtain a target classification model that meets the needs, where the target The classification model is a text classification model, which can be used to classify target text data. Classifying target text data through the target classification model can improve the accuracy of text classification.

The embodiments described in the embodiments of the present application are for the purpose of more clearly illustrating the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art will know that with the evolution of technology and new technologies, As application scenarios arise, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

Those skilled in the art can understand that the technical solutions shown in Figures 1-4 and 5-6 do not limit the embodiments of the present application, and may include more or fewer steps than those shown in the figures, or a combination of certain some steps, or different steps.

The device embodiments described above are only illustrative, and the units described as separate components may or may not be physically separate, that is, they may be located in one place, or they may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of this embodiment.

Those of ordinary skill in the art can understand that all or some steps, systems, and functional modules/units in the devices disclosed above can be implemented as software, firmware, hardware, and appropriate combinations thereof.

The terms "first", "second", "third", "fourth", etc. (if present) in the description of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe specific objects. Sequence or sequence. It is to be understood that the data so used are interchangeable under appropriate circumstances so that the embodiments of the application described herein can be practiced in sequences other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, e.g., a process, method, system, product, or apparatus that encompasses a series of steps or units and need not be limited to those explicitly listed. Those steps or elements may instead include other steps or elements not expressly listed or inherent to the process, method, product or apparatus.

It should be understood that in this application, "at least one (item)" refers to one or more, and "plurality" refers to two or more. "And/or" is used to describe the relationship between associated objects, indicating that there can be three relationships. For example, "A and/or B" can mean: only A exists, only B exists, and A and B exist simultaneously. , where A and B can be singular or plural. The character "/" generally indicates that the related objects are in an "or" relationship. “At least one of the following” or similar expressions thereof refers to any combination of these items, including any combination of a single item (items) or a plurality of items (items). For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c" ”, where a, b, c can be single or multiple.

In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the above units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or may be Integrated into another system, or some features can be ignored, or not implemented. On the other hand, the coupling or direct coupling or communication connection between each other shown or discussed may be through some interfaces, and the indirect coupling or communication connection of the devices or units may be in electrical, mechanical or other forms.

The units described above as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place, or they may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or software functional units.

Integrated units may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as independent products. Based on this understanding, the technical solution of the present application is essentially or contributes to the existing technology, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium , including multiple instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc. that can store programs. medium.

The preferred embodiments of the embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of rights of the embodiments of the present application. Any modifications, equivalent substitutions and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of the present application shall be within the scope of rights of the embodiments of the present application.

Claims (20)

1. A model training method, wherein the method is used to train a target classification model, the method includes:

   Obtain original training data, wherein the original training data includes first original data and second original data;

   Perform upsampling processing on the second original data to obtain initial training data;

   Perform enhancement processing on the initial training data according to preset enhancement parameters to obtain enhanced training data;

   Encoding the enhanced training data to obtain a target word embedding vector;

   Perform perturbation processing on the target word embedding vector to obtain target training data;

   A preset neural network model is trained according to the first original data and the target training data to obtain a target classification model, wherein the target classification model is a text classification model used to classify target text data.
2. The training method according to claim 1, wherein the enhancement parameters include a first disturbance ratio, and the step of performing enhancement processing on the initial training data according to the preset enhancement parameters to obtain enhanced training data includes:

   Obtain the first sentence length of the initial training data;

   Calculate a first perturbation amount based on the first sentence length and the first perturbation ratio;

   The initial training data is deleted according to the first disturbance amount to obtain the enhanced training data.
3. The training method according to claim 1, wherein the enhancement parameters include a second disturbance ratio, and the step of performing enhancement processing on the initial training data according to the preset enhancement parameters to obtain enhanced training data includes:

   Obtain the second sentence length of the initial training data;

   Calculate a second perturbation amount based on the second sentence length and the second perturbation ratio;

   The initial training data is expanded according to the second disturbance amount and preset punctuation marks to obtain the enhanced training data.
4. The training method according to any one of claims 1 to 3, wherein the step of training a preset neural network model according to the first original data and the target training data to obtain a target classification model includes :

   Perform perturbation calculation on the first original data and the target training data through a preset function to obtain a text perturbation value;

   Calculate the loss function of the neural network model according to the text perturbation value to obtain a loss value;

   The loss value is used as a back propagation amount, and the model parameters of the neural network model are adjusted to train the neural network model to obtain the text classification model.
5. A text classification method, wherein the method includes:

   Obtain the target text data to be classified;

   The target text data is input into a target classification model for label classification processing to obtain label text data, wherein the target classification model is trained according to a model training method, wherein the model training method includes: obtaining original training Data, wherein the original training data includes first original data and second original data;

   Perform upsampling processing on the second original data to obtain initial training data;

   Perform enhancement processing on the initial training data according to preset enhancement parameters to obtain enhanced training data;

   Encoding the enhanced training data to obtain a target word embedding vector;

   Perform perturbation processing on the target word embedding vector to obtain target training data;

   A preset neural network model is trained according to the first original data and the target training data to obtain a target classification model, wherein the target classification model is a text classification model used to classify target text data.
6. The text classification method according to claim 5, wherein the enhancement parameters include a first disturbance ratio, and the step of performing enhancement processing on the initial training data according to the preset enhancement parameters to obtain enhanced training data includes:

   Obtain the first sentence length of the initial training data;

   Calculate a first perturbation amount based on the first sentence length and the first perturbation ratio;

   The initial training data is deleted according to the first disturbance amount to obtain the enhanced training data.
7. The text classification method according to claim 5, wherein the enhancement parameters include a second disturbance ratio, and the step of performing enhancement processing on the initial training data according to the preset enhancement parameters to obtain enhanced training data includes:

   Obtain the second sentence length of the initial training data;

   Calculate a second perturbation amount based on the second sentence length and the second perturbation ratio;

   The initial training data is expanded according to the second disturbance amount and preset punctuation marks to obtain the enhanced training data.
8. The text classification method according to any one of claims 5 to 7, wherein the step of training a preset neural network model according to the first original data and the target training data to obtain a target classification model, include:

   Perform perturbation calculation on the first original data and the target training data through a preset function to obtain a text perturbation value;

   Calculate the loss function of the neural network model according to the text perturbation value to obtain a loss value;

   The loss value is used as a back propagation amount, and the model parameters of the neural network model are adjusted to train the neural network model to obtain the text classification model.
9. The text classification method according to claim 5, wherein the step of inputting the target text data into a target classification model for label classification processing to obtain label text data includes:

   Map the target text data to a preset vector space through the fully connected layer of the target classification model to obtain a target text vector;

   The target text vector is subjected to label classification processing through the classification function of the fully connected layer and the preset text category label to obtain the label text data.
10. A model training device, wherein the device includes:

    A training data acquisition module, configured to acquire original training data, where the original training data includes first original data and second original data;

    An upsampling module, used to upsample the second original data to obtain initial training data;

    A data enhancement module, configured to enhance the initial training data according to preset enhancement parameters to obtain enhanced training data;

    An encoding module, used to encode the enhanced training data to obtain a target word embedding vector;

    A perturbation module, used to perturb the target word embedding vector to obtain target training data;

    A model training module, configured to train a preset neural network model according to the first original data and the target training data to obtain a target classification model, wherein the target classification model is a text classification model, used to classify the target Text data is classified.
11. A text classification device, wherein the device includes:

    Text data acquisition module, used to obtain target text data to be classified;

    A label classification module, used to input the target text data into a target classification model for label classification processing to obtain label text data, wherein the target classification model is trained according to a model training method, wherein the training of the model The method includes: obtaining original training data, wherein the original training data includes first original data and second original data;

    Perform upsampling processing on the second original data to obtain initial training data;

    Perform enhancement processing on the initial training data according to preset enhancement parameters to obtain enhanced training data;

    Encoding the enhanced training data to obtain a target word embedding vector;

    Perform perturbation processing on the target word embedding vector to obtain target training data;

    A preset neural network model is trained according to the first original data and the target training data to obtain a target classification model, wherein the target classification model is a text classification model used to classify target text data.
12. An electronic device, wherein the electronic device includes a memory, a processor, a program stored on the memory and executable on the processor, and a connection between the processor and the memory A data bus for communication, when the program is executed by the processor, the steps of implementing a model training method or a text classification method are implemented;

    Wherein, the training method of the model includes:

    Obtain original training data, wherein the original training data includes first original data and second original data;

    Perform upsampling processing on the second original data to obtain initial training data;

    Perform enhancement processing on the initial training data according to preset enhancement parameters to obtain enhanced training data;

    Encoding the enhanced training data to obtain a target word embedding vector;

    Perform perturbation processing on the target word embedding vector to obtain target training data;

    Train a preset neural network model according to the first original data and the target training data to obtain a target classification model, wherein the target classification model is a text classification model used to classify target text data;

    Wherein, the text classification method includes:

    Obtain the target text data to be classified;

    The target text data is input into a target classification model for label classification processing to obtain label text data, wherein the target classification model is trained according to a training method of a model, wherein the training method of the model includes: Obtain original training data, wherein the original training data includes first original data and second original data;

    Perform upsampling processing on the second original data to obtain initial training data;

    Perform enhancement processing on the initial training data according to preset enhancement parameters to obtain enhanced training data;

    Encoding the enhanced training data to obtain a target word embedding vector;

    Perform perturbation processing on the target word embedding vector to obtain target training data;

    A preset neural network model is trained according to the first original data and the target training data to obtain a target classification model, wherein the target classification model is a text classification model used to classify target text data.
13. The electronic device according to claim 12, wherein the enhancement parameters include a first disturbance ratio, and the step of performing enhancement processing on the initial training data according to the preset enhancement parameters to obtain enhanced training data includes:

    Obtain the first sentence length of the initial training data;

    Calculate a first perturbation amount based on the first sentence length and the first perturbation ratio;

    The initial training data is deleted according to the first disturbance amount to obtain the enhanced training data.
14. The electronic device according to claim 12, wherein the enhancement parameters include a second disturbance ratio, and the step of performing enhancement processing on the initial training data according to the preset enhancement parameters to obtain enhanced training data includes:

    Obtain the second sentence length of the initial training data;

    Calculate a second perturbation amount based on the second sentence length and the second perturbation ratio;

    The initial training data is expanded according to the second disturbance amount and preset punctuation marks to obtain the enhanced training data.
15. The electronic device according to any one of claims 12 to 14, wherein the step of training a preset neural network model according to the first original data and the target training data to obtain a target classification model includes :

    Perform perturbation calculation on the first original data and the target training data through a preset function to obtain a text perturbation value;

    Calculate the loss function of the neural network model according to the text perturbation value to obtain a loss value;

    The loss value is used as a back propagation amount, and the model parameters of the neural network model are adjusted to train the neural network model to obtain the text classification model.
16. The electronic device according to claim 12, wherein the step of inputting the target text data into a target classification model for tag classification processing to obtain the tag text data includes:

    Map the target text data to a preset vector space through the fully connected layer of the target classification model to obtain a target text vector;

    The target text vector is subjected to label classification processing through the classification function of the fully connected layer and the preset text category label to obtain the label text data.
17. A storage medium, the storage medium is a computer-readable storage medium for computer-readable storage, wherein the storage medium stores one or more programs, and the one or more programs can be used by one or more The processor executes the steps to implement a model training method or a text classification method:

    Wherein, the training method of the model includes:

    Obtain original training data, wherein the original training data includes first original data and second original data;

    Perform upsampling processing on the second original data to obtain initial training data;

    Perform enhancement processing on the initial training data according to preset enhancement parameters to obtain enhanced training data;

    Encoding the enhanced training data to obtain a target word embedding vector;

    Perform perturbation processing on the target word embedding vector to obtain target training data;

    Train a preset neural network model according to the first original data and the target training data to obtain a target classification model, wherein the target classification model is a text classification model used to classify target text data;

    Wherein, the text classification method includes:

    Obtain the target text data to be classified;

    The target text data is input into a target classification model for label classification processing to obtain label text data, wherein the target classification model is trained according to a training method of a model, wherein the training method of the model includes: Obtain original training data, wherein the original training data includes first original data and second original data;

    Perform upsampling processing on the second original data to obtain initial training data;

    Perform enhancement processing on the initial training data according to preset enhancement parameters to obtain enhanced training data;

    Encoding the enhanced training data to obtain a target word embedding vector;

    Perform perturbation processing on the target word embedding vector to obtain target training data;

    A preset neural network model is trained according to the first original data and the target training data to obtain a target classification model, wherein the target classification model is a text classification model used to classify target text data.
18. The storage medium according to claim 17, wherein the enhancement parameters include a first disturbance ratio, and the step of performing enhancement processing on the initial training data according to the preset enhancement parameters to obtain enhanced training data includes:

    Obtain the first sentence length of the initial training data;

    Calculate a first perturbation amount based on the first sentence length and the first perturbation ratio;

    The initial training data is deleted according to the first disturbance amount to obtain the enhanced training data.
19. The storage medium according to claim 17, wherein the enhancement parameters include a second perturbation ratio, and the step of performing enhancement processing on the initial training data according to the preset enhancement parameters to obtain enhanced training data includes:

    Obtain the second sentence length of the initial training data;

    Calculate a second perturbation amount based on the second sentence length and the second perturbation ratio;

    The initial training data is expanded according to the second disturbance amount and preset punctuation marks to obtain the enhanced training data.
20. The storage medium according to any one of claims 17 to 19, wherein the step of training a preset neural network model according to the first original data and the target training data to obtain a target classification model includes :

    Perform perturbation calculation on the first original data and the target training data through a preset function to obtain a text perturbation value;

    Calculate the loss function of the neural network model according to the text perturbation value to obtain a loss value;

    The loss value is used as a back propagation amount, and the model parameters of the neural network model are adjusted to train the neural network model to obtain the text classification model.

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