WO2021119949A1 - Procédé de formation de modèle de classification de texte, procédé et appareil de classification de texte et dispositif électronique - Google Patents

Procédé de formation de modèle de classification de texte, procédé et appareil de classification de texte et dispositif électronique Download PDF

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Publication number
WO2021119949A1
WO2021119949A1 PCT/CN2019/125747 CN2019125747W WO2021119949A1 WO 2021119949 A1 WO2021119949 A1 WO 2021119949A1 CN 2019125747 W CN2019125747 W CN 2019125747W WO 2021119949 A1 WO2021119949 A1 WO 2021119949A1
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text
classification model
text classification
sample set
prediction result
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PCT/CN2019/125747
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English (en)
Chinese (zh)
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刘园林
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深圳市欢太科技有限公司
Oppo广东移动通信有限公司
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Priority to PCT/CN2019/125747 priority Critical patent/WO2021119949A1/fr
Priority to CN201980100570.9A priority patent/CN114424186A/zh
Publication of WO2021119949A1 publication Critical patent/WO2021119949A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/35—Clustering; Classification

Definitions

  • This application relates to the field of data processing technology, and in particular to a text classification model training method, text classification method, device and electronic equipment.
  • the embodiments of the present application provide a text classification model training method, text classification method, device, and electronic equipment, so as to improve the accuracy of classifying multi-level and multi-label text.
  • an embodiment of the present application provides a text classification model training method, including:
  • the second text sample set is input into the adjusted text classification model to continue training until the prediction result of the text classification model meets the preset condition.
  • this application provides a text classification method, including:
  • the text classification model is a text classification model obtained by using the training method of the text classification model provided in the embodiment of the present application.
  • an embodiment of the present application provides a text classification model training device, including:
  • the first obtaining module is used to obtain the first text sample set
  • a prediction module configured to input the first text sample set into the text classification model for text category prediction, so as to obtain a first prediction result corresponding to the first text sample;
  • a judging module configured to compare the first prediction result with the real result, and judge whether the first prediction result meets a preset condition
  • An adjustment module configured to adjust the text classification model to obtain an adjusted text classification model if the first prediction result does not meet the preset condition
  • a processing module configured to process the target text whose first prediction result in the first text sample set does not meet a preset condition according to a preset processing mode, to obtain a second text sample set;
  • the training module is configured to input the second text sample set into the adjusted text classification model to continue training until the prediction result of the text classification model meets the preset condition.
  • an embodiment of the present application provides a text classification device, including:
  • the second acquisition module is used to acquire a text set to be classified
  • the calling module is used to call the pre-trained text classification model
  • a classification module configured to input the text set to be classified into the pre-trained text classification model to obtain a classification result of the text to be classified
  • the text classification model is a text classification model obtained by using the training method of the text classification model provided in the embodiment of the present application.
  • an embodiment of the present application provides a storage medium on which a computer program is stored, wherein, when the computer program is executed on a computer, the computer is caused to execute the text classification model training method provided in this embodiment Or text classification method.
  • an embodiment of the present application provides an electronic device including a memory and a processor, the memory stores a computer program, and the processor invokes the computer program stored in the memory to execute:
  • an embodiment of the present application provides an electronic device, including a memory and a processor, the memory stores a computer program, and the processor invokes the computer program stored in the memory to execute:
  • the text classification model is a text classification model obtained by using the training method of the text classification model provided in the embodiment of the present application.
  • FIG. 1 is a schematic diagram of the first process of a text classification model training method provided by an embodiment of the application.
  • FIG. 2 is a schematic diagram of the second process of the text classification model training method provided by an embodiment of the application.
  • FIG. 3 is a schematic diagram of the third process of the text classification model training method provided by an embodiment of the application.
  • FIG. 4 is a schematic flowchart of a text classification method provided by an embodiment of the application.
  • Fig. 5 is a schematic structural diagram of a text classification model training method provided by an embodiment of the application.
  • FIG. 6 is a schematic structural diagram of a text classification device provided by an embodiment of the application.
  • FIG. 7 is a first structural diagram of an electronic device provided by an embodiment of the present application.
  • FIG. 8 is a schematic diagram of a second structure of an electronic device provided by an embodiment of the present application.
  • the embodiments of the present application provide a text classification model training method and text classification method.
  • the text classification model training method and text classification method are applied to electronic devices.
  • the electronic device can be a smart phone, a tablet computer, a palmtop computer, a notebook computer, or a desktop computer that is equipped with a processor and has processing capabilities.
  • FIG. 1 is a schematic diagram of a first process of a text classification model training method provided by an embodiment of the present application.
  • the text classification model training method may include the following processes:
  • the first text sample set contains multiple first texts.
  • the text to be processed can be obtained first, and then the text to be processed can be segmented to obtain multiple first texts. Encoding is performed to obtain a plurality of first tags, and the first text and the first tags corresponding to the first text form a first text sample set.
  • the text to be processed can be segmented.
  • multiple text content in the browsing page can be captured, and the text content of the text to be processed can be several hundred Words, thousands of words, or tens of thousands of words, these text contents are used as the text to be processed, and multiple first texts can be obtained by performing word segmentation processing on the text to be processed.
  • multiple database texts in the database can also be obtained, and then the text to be processed is randomly obtained from the database text, the text to be processed is processed to obtain the target database text, and the target database text and the first text are combined into the first text.
  • Set of text samples are randomly obtained from the database text, the text to be processed is processed to obtain the target database text, and the target database text and the first text are combined into the first text.
  • the first text sample set is input to the text classification model.
  • the first text classification model can recognize multiple first texts, thereby classifying the first text, and obtaining the first text corresponding to the first text.
  • One prediction result Among them, each first text has its own corresponding prediction result.
  • step 104 is entered. Able to achieve the expected text classification effect. If the training result does not meet the preset condition, go to step 105.
  • the preset condition is to determine whether the first prediction result reaches 80% of the preset result, if the first prediction result reaches 80% of the preset result, it means that the prediction result of the text classification model is accurate; if the first prediction If the result is less than 80% of the preset result, it means that the prediction result of the text classification model is not very accurate, and the text classification model still needs to be trained.
  • the text classification model has been trained.
  • additional text sample sets can be obtained, for example, obtained from a database or randomly obtained through the network, and then input into the text classification model, if the prediction result meets the preset conditions , It means that the text classification model has been trained.
  • the text classification model is not suitable for classifying the first text sample set, and it may also be that the text classification model cannot complete the first set of samples alone.
  • the classification of a text sample set needs to add other models, or delete or add part of the structure in the text classification model, so as to realize the structural adjustment of the text classification model.
  • the number of convolutional layers or pooling layers of the convolutional neural network can be adjusted to achieve different feature extraction.
  • the final adjustment is The second label classification model.
  • the loss function in the text classification model can be adjusted according to the training result to obtain the second label classification model. Specifically, the loss function can be weighted.
  • the neural network model parameters can be adjusted according to the training result, the loss function of the neural network, and the preset result. Specifically, the neural network model parameters can be adjusted according to the training result and the loss function of the neural network. And the loss value is obtained from the preset result, and then the adjustment direction of the neural network is determined according to the loss value, and finally the parameters of the neural network are adjusted to obtain the second label classification model.
  • target texts that do not meet a preset condition in the first text may be obtained, and multiple target texts may be segmented according to a preset length to obtain multiple second texts.
  • one of the first texts is "Yuanmingyuan horse head issued a physical examination report today, with white scale inside”. If the preset length is 4 characters, the first text can be divided to obtain "Yuanmingyuan horse head, For the second texts released today, physical examination report, internal attachment, and white gutter, multiple second texts can be combined into a second text sample set.
  • the preset length can be set to hundreds or thousands of words.
  • the first text can be divided according to the preset length of 300. To get multiple second texts.
  • the second text sample set can be input to the adjusted text classification model to continue to predict the second text sample set, and then obtain the second prediction corresponding to the second text As a result, the second prediction result is compared with the actual result corresponding to the second text, and it is judged whether the second prediction result meets the preset condition. If the second prediction result meets the preset condition, the training of the text classification model is stopped.
  • the text classification model can already accurately classify the input files.
  • step 105 If the second prediction result still does not meet the preset condition, then go to step 105 to continue training the text classification model.
  • the prediction result of the text classification model meets the preset condition, it means that the text classification model has been trained.
  • the trained text classification model can accurately classify and recognize multi-label text.
  • the text classification model In the process of training the text classification model, by adjusting and optimizing the input text samples, and at the same time adjusting and optimizing the text classification model, the text classification model can accurately achieve the text classification effect in the process of predicting multi-label text. .
  • the first sample set is input into the text classification model for text category prediction; the obtained first prediction result is compared with the real result, and it is judged whether the first prediction result satisfies the prediction.
  • Set conditions if the preset conditions are not met, the text classification model is adjusted to obtain the adjusted text classification model; according to the preset processing method, the first prediction result in the first text sample set does not meet the preset conditions for the target text Processing is performed to obtain a second text sample set; the second text sample set is input into the adjusted text classification model to continue training until the prediction result of the text classification model meets the preset condition.
  • the trained text classification model can classify multi-label texts and at the same time improve the accuracy of text classification.
  • FIG. 2 is a schematic diagram of the second process of the training method of the text classification model provided by the embodiment of the present application.
  • the training method of the text classification model includes the following processes:
  • the acquired text to be processed may be words, words, paragraphs, articles, etc., and the number of words in the text to be processed may be hundreds, thousands, or tens of thousands.
  • Text segmentation processing for example, you can divide the words in the paragraph, or divide the sentences in the article, and so on. You can also perform word segmentation processing on the text to be processed through the word segmenter. After the word segmentation process is performed on the text to be processed, the text after the word segmentation is the first text, and the first text includes multiple ones.
  • the text to be processed can be processed according to the model specifically adopted by the text classification model.
  • the text classification model is a model that does not require word segmentation, such as the BERT model
  • the text does not need to be segmented.
  • each first text needs to be coded, so that each first text has its own corresponding label, so that the multiple first texts can be distinguished.
  • the tags corresponding to the first text can be integrated, and multiple tags can be spliced and integrated into one tag.
  • the target first labels below the lowest label level can be obtained, and these target first labels are processed in subsequent steps.
  • labels below the lowest label level can be obtained.
  • the preset label level is 5, but there are 7 first labels of the same type.
  • the seven first labels are arranged from high to low as A, B, C, D, E, F, G.
  • label F and label G are lower than the lowest label level.
  • the label F and the label G can be determined to be the target first label, and the target first label F and the target first label G can be classified into the first label E of the lowest label level.
  • the tail tag is processed in this way.
  • tags of the same type can be integrated according to a preset tag hierarchy.
  • tags of the same type can be obtained from multiple first tags, where tags of the same type are A, B, C, D, E
  • the five first labels are integrated according to the preset label hierarchy.
  • the corresponding texts of the five first labels of A, B, C, D, and E are "current affairs", “domestic current affairs”, and “ “venue Current Affairs”, “Policies”, “San Nong”, if the default label level is 5 levels, and the first label needs to be spliced from high level to low level, then the combined label will be "ABCDE".
  • first labels of the same type can be integrated to form multiple integrated labels to reduce the number of labels.
  • Tag coding may be performed on multiple integrated tags. For example, onehot encoding may be used to code the integrated tags, and finally the integrated tags and the text corresponding to the integrated tags form the first text sample set.
  • FIG. 3 is a schematic diagram of the third process of the training method of the text classification model provided by the embodiment of the present application.
  • the training method of the text classification model may include the following processes:
  • invalid characters in the text to be processed can be filtered and deleted to ensure the authenticity of the text to be processed, and then the filtered text to be processed is segmented to obtain multiple first texts.
  • Multiple first texts are text-encoded to obtain multiple first tags.
  • tags of the same type can be integrated to form an integrated tag, and finally multiple integrated tags are tag-encoded, and finally the integrated tags
  • the text corresponding to the integrated label forms a first text sample set.
  • the first text sample set is input to the text classification model.
  • the first text classification model can recognize multiple first texts, thereby classifying the first text, and obtaining the first text corresponding to the first text.
  • One prediction result Among them, each first text has its own corresponding prediction result, and the sigmoid function can be used to calculate the accuracy value of the first prediction result.
  • the first prediction result can be compared with the actual result corresponding to the first text to determine whether the first prediction result meets a preset condition. For example, it can be determined whether the accuracy of the first prediction result reaches The preset accuracy rate, if the accuracy rate of the first prediction result does not reach the preset accuracy rate, the text classification model is adjusted.
  • the parameters of the text classification model can be adjusted.
  • the loss function of the text classification model can be obtained, and the first prediction result and the real result can be input into the loss function to obtain the loss value, which is adjusted according to the target loss value.
  • the parameters of the text classification model are adjusted so that the loss value of the text classification model is less than or equal to the target loss value.
  • the text classification model can also be concatenated with a preset model to adjust the text classification model.
  • the text classification model before the adjustment uses the BERT model, and the BERT model can also be concatenated.
  • Neural network model to form a new text classification model can also be concatenated.
  • the structure of the text classification model can be adjusted.
  • the text classification model uses an integrated model composed of a BERT model and a convolutional neural network model.
  • the convolutional neural network model can be multiplied.
  • the scale convolution kernel may implement multiple different pooling operations, and the number of layers of the convolutional neural network can also be changed.
  • the settings of some parameters in the text classification model before the adjustment are correct, and there is no need to adjust them.
  • These parameters that do not need to be adjusted are preset parameters.
  • Setting parameters can continue to adjust the text classification model, and the finally adjusted text classification model can continue to be used for the next training until the prediction result output by the text classification model meets the preset conditions.
  • the target tags are integrated through a preset label level to obtain the tail tags of the target tags, and some of the tail tags of the target tags can be selected to be included in the target tags of the lowest tag level.
  • the segmented text is encoded to obtain the target label, the target label is integrated through the preset label level, and the tail label of the target label is obtained.
  • the tail labels of some target labels can be selected to be included in the target label of the lowest label level.
  • high-confidence text and target text can be selected from the database and combined into a second text sample set.
  • the second text sample set can be input to the adjusted text classification model to continue to predict the second text sample set, and then obtain the second prediction corresponding to the second text As a result, the second prediction result is compared with the actual result corresponding to the second text, and it is judged whether the second prediction result meets the preset condition. If the second prediction result meets the preset condition, the training of the text classification model is stopped.
  • the text classification model can already accurately classify the input files.
  • the trained text classification model can accurately classify and recognize multi-label text.
  • the text category prediction is performed by inputting the first text sample set into the text classification model to obtain the first prediction result corresponding to the first text sample; if the first prediction result does not meet the preset condition
  • adjust the text classification model obtain the preset parameters of the text classification model, and set the preset parameters in the adjusted model; perform text segmentation on the target text according to the preset length to obtain multiple segmented texts;
  • a plurality of segmented texts are encoded to obtain a second text sample set; the second text sample set is input into the adjusted text classification model to continue training until the prediction result of the text classification model meets the prediction condition.
  • a trained text classification model is obtained.
  • the trained text classification model can classify multi-level and multi-label text, and the prediction accuracy of the text classification model can also achieve the expected effect.
  • FIG. 4 is a text classification method provided by an embodiment of the present application.
  • the method includes the following processes:
  • the invalid characters in the text to be classified can be filtered and deleted to ensure the authenticity of the text to be classified.
  • the text encoding of multiple texts to be classified can obtain multiple text labels to be classified. Considering the large number of texts to be classified, you can Tag integration is performed on tags of the same type to form a text-to-be-categorized integrated label, and finally a plurality of text-to-be-categorized integration tags are tag-encoded, and finally the text-to-be-categorized integration tags and text to be classified form a text-to-be-categorized set.
  • the text classification model can accurately predict most of the text.
  • the text classification sub-model can also be concatenated after the text classification model. The classification sub-model can adjust the accuracy of text classification according to actual needs.
  • the accuracy of the classification result of the text to be classified is not very high, but it has reached a high degree of accuracy and can be used for multi-level and multi-label The text to be classified is classified.
  • the lowest-level tail label of the text label to be classified can be input into the text classification sub-model, so as to realize the text classification corresponding to the text corresponding to the lowest-level tail label of the text label to be classified, thereby achieving more accurate text classification. effect.
  • the text set to be classified is obtained; the pre-trained text classification model is invoked; the text set to be classified is input to the pre-trained text classification model to obtain the classification result of the text to be classified; The text corresponding to the lowest-level label, and the text corresponding to the lowest label-level label continues to be text categorized.
  • the classification of multi-level and multi-label text is realized, and the accuracy of text classification is improved.
  • FIG. 5 is a schematic structural diagram of a text classification model training device provided by an embodiment of the present application.
  • the text classification model training device 500 includes: a first acquisition module 510, a prediction module 520, a judgment module 530, and an adjustment module 540, a processing module 550, and a training module 560.
  • the first obtaining module 510 is configured to obtain a first text sample set
  • the prediction module 520 is configured to input the first text sample set into the text classification model to perform text category prediction, so as to obtain a first prediction result corresponding to the first text sample;
  • the judging module 530 is configured to compare the first prediction result with the real result, and determine whether the first prediction result meets a preset condition
  • the adjustment module 540 is configured to adjust the text classification model to obtain an adjusted text classification model if the first prediction result does not meet the preset condition;
  • the processing module 550 is configured to process the target text in the first text sample set whose first prediction result does not meet the preset condition according to a preset processing mode, to obtain a second text sample set;
  • the training module 560 is configured to input the second text sample set into the adjusted text classification model to continue training until the prediction result of the text classification model meets the preset condition.
  • the first obtaining module 510 is specifically configured to obtain a text to be processed, perform word segmentation processing on the text to be processed to obtain a plurality of the first texts; and encode the first text to obtain all the texts.
  • a first label corresponding to the first text; and a plurality of the first labels are integrated according to a preset label level to obtain the first text sample set.
  • the first obtaining module 510 is specifically configured to obtain a target first label whose label level is lower than the lowest label level among the preset label levels; and classify the target first label into the lowest label Among the first tags in the hierarchy; multiple first tags of the same type are integrated according to the preset tag hierarchy to obtain a first text sample set.
  • the adjustment module 540 is specifically configured to input the first prediction result and the real result into the loss function of the text classification model to obtain a loss value; The parameters of the text classification model are adjusted.
  • the adjustment module 540 is specifically configured to adjust the network structure of the text classification model according to the first prediction result and the real result; connect the adjusted text classification model in series with a preset model .
  • the adjustment module 540 is specifically configured to obtain the loss function of the text classification model; and perform weighting processing on the loss function according to the first prediction result and the real result.
  • the adjustment module 540 is specifically configured to obtain preset parameters of the text classification model; and set the preset parameters in the adjusted text classification model.
  • the first sample set is obtained by the first obtaining module 510, and the prediction module 520 inputs the first sample set into the text classification model for text category prediction; the judgment module 530 will obtain the first sample set.
  • the prediction result is compared with the actual result to determine whether the first prediction result meets the preset condition; the adjustment module 540 adjusts the text classification model when the first prediction result does not meet the preset condition to obtain the adjusted text classification model
  • the processing module 550 processes the target text in the first text sample set whose first prediction result does not meet the preset conditions according to a preset processing method to obtain a second text sample set; the training module 560 inputs the second text sample set to the adjustment
  • the subsequent text classification model continues to train until the prediction result of the text classification model meets the preset conditions.
  • the trained text classification model can accurately classify multi-level and multi-label text.
  • FIG. 6 is a schematic structural diagram of a text classification device provided by an embodiment of the present application.
  • the text classification device 600 specifically includes: a second acquisition module 610, a calling module 620, and a classification module 630.
  • the second obtaining module 610 is used to obtain a target text set
  • the calling module 620 is used to call a pre-trained text classification model
  • the classification module 630 is configured to input the target text set into the pre-trained text classification model to obtain a classification result of the target text;
  • the text classification model is a text classification model obtained by the training method of the text classification model provided in the embodiment of the application.
  • the classification module 630 is also used to input the lowest-level tail label of the text label to be classified into the text classification sub-model, so as to realize the text classification of the text corresponding to the lowest-level tail label of the text label to be classified, thereby achieving more accurate text Classification effect.
  • the second acquisition module 610 acquires the text set to be classified; the calling module 620 calls the pre-trained text classification model; the classification module 630 inputs the text set to be classified into the pre-trained text classification model to The classification result of the text to be classified is obtained; the classification module 630 may also obtain the lowest-level label corresponding to the text to be classified, and continue to perform text classification on the text corresponding to the label of the lowest label level. In this way, the classification of multi-level and multi-label text is realized, and the accuracy of text classification is improved.
  • the image attribute recognition device provided in this embodiment of the application belongs to the same concept as the image attribute recognition method in the above embodiment, and the image attribute that can be run on the image attribute recognition device is any one provided in the method embodiment.
  • the specific implementation process of the method please refer to the image processing method embodiment, which will not be repeated here.
  • the embodiment of the present application provides a computer-readable storage medium on which a computer program is stored.
  • the computer executes the network model training method or image provided in the embodiment of the present application. ⁇ Treatment methods.
  • the storage medium may be a magnetic disk, an optical disc, a read only memory (Read Only Memory, ROM,), or a random access device (Random Access Memory, RAM), etc.
  • An embodiment of the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory.
  • the processor is configured to execute the computer program stored in the memory by calling the computer program stored in the memory.
  • Example provides the training method of the network model or the image attribute recognition method.
  • the above-mentioned electronic device may be a mobile terminal such as a tablet computer or a smart phone.
  • a mobile terminal such as a tablet computer or a smart phone.
  • FIG. 7 is a schematic diagram of the first structure of an electronic device provided by an embodiment of this application.
  • the electronic device 700 may include components such as a memory 701 and a processor 702. Those skilled in the art can understand that the structure of the electronic device shown in FIG. 7 does not constitute a limitation on the electronic device, and may include more or fewer components than shown in the figure, or a combination of certain components, or different component arrangements.
  • the memory 701 may be used to store software programs and modules.
  • the processor 702 executes various functional applications and data processing by running the computer programs and modules stored in the memory 701.
  • the memory 701 may mainly include a storage program area and a storage data area.
  • the storage program area may store an operating system, a computer program required by at least one function (such as a sound playback function, an image playback function, etc.), etc.; Data created by the use of electronic equipment, etc.
  • the processor 702 is the control center of the electronic device. It uses various interfaces and lines to connect the various parts of the entire electronic device. It executes the electronic device by running or executing the application program stored in the memory 701 and calling the data stored in the memory 701. The various functions and processing data of the electronic equipment can be used to monitor the electronic equipment as a whole.
  • the memory 701 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.
  • the memory 701 may further include a memory controller to provide the processor 702 with access to the memory 701.
  • the processor 702 in the electronic device will load the executable code corresponding to the process of one or more application programs into the memory 701 according to the following instructions, and the processor 702 will run and store the executable code in the memory 701.
  • processor 702 when the processor 702 adjusts the text classification model, it may execute:
  • the parameters of the text classification model are adjusted according to the loss value.
  • processor 702 when the processor 702 adjusts the text classification model, it may execute:
  • a preset model is connected in series with the adjusted text classification model.
  • processor 702 when the processor 702 adjusts the text classification model, it may execute:
  • processor 702 when it adjusts the text classification model, it may execute:
  • the preset parameters are set in the adjusted text classification model.
  • the processor 702 when the processor 702 processes the target text whose first prediction result in the first text sample set does not meet a preset condition according to a preset processing manner, it may execute:
  • the processor 702 may execute:
  • the processor 702 when the processor 702 executes to integrate a plurality of the first tags according to a preset tag level to obtain the first text sample set, it may execute:
  • the multiple first tags of the same type are integrated according to the preset tag level to obtain a first text sample set.
  • the processor 702 in the electronic device will load the executable code corresponding to the process of one or more application programs into the memory 701 according to the following instructions, and the processor 702 will run and store the executable code in the memory 701.
  • the text classification model is a text classification model obtained by the training method of the text classification model provided in the embodiment of the application.
  • FIG. 8 is a schematic diagram of a second structure of an electronic device provided by an embodiment of the application.
  • the electronic device further includes a camera component 703, a radio frequency circuit 704, an audio circuit 705, and Power supply 706.
  • the display 703, the radio frequency circuit 704, the audio circuit 705, and the power supply 706 are electrically connected to the processor 702, respectively.
  • the display 703 may be used to display information input by the user or information provided to the user, and various graphical user interfaces. These graphical user interfaces may be composed of graphics, text, icons, videos, and any combination thereof.
  • the display 703 may include a display panel.
  • the display panel may be configured in the form of a liquid crystal display (LCD) or an organic light-emitting diode (OLED).
  • LCD liquid crystal display
  • OLED organic light-emitting diode
  • the radio frequency circuit 704 may be used to transmit and receive radio frequency signals to establish wireless communication with network equipment or other electronic equipment through wireless communication, and to transmit and receive signals with the network equipment or other electronic equipment.
  • the audio circuit 705 may be used to provide an audio interface between the user and the electronic device through a speaker or a microphone.
  • the power supply 706 can be used to power various components of the electronic device 600.
  • the power supply 706 may be logically connected to the processor 702 through a power management system, so that functions such as charging, discharging, and power consumption management can be managed through the power management system.
  • the electronic device 600 may also include a camera component, a Bluetooth module, etc.
  • the camera component may include an image processing circuit, which may be implemented by hardware and/or software components, and may include defining image signal processing (Image Signal Processing) various processing units of the pipeline.
  • the image processing circuit may at least include: multiple cameras, an image signal processor (Image Signal Processor, ISP processor), a control logic, an image memory, a display, and the like.
  • Each camera can include at least one or more lenses and image sensors.
  • the image sensor may include a color filter array (such as a Bayer filter). The image sensor can obtain the light intensity and wavelength information captured by each imaging pixel of the image sensor, and provide a set of raw image data that can be processed by the image signal processor.
  • the training device/text classification device of the text classification model provided by the embodiment of the application belongs to the same concept as the training method/text classification method of the text classification model in the above embodiments. Any method provided in the training method/text classification method embodiment of the text classification model can be run on the classification device. For the specific implementation process, please refer to the training method/image processing method embodiment of the network model. Go into details again.
  • the training method/text classification method of the text classification model described in the embodiment of the present application a person of ordinary skill in the art can understand all of the training method/image processing method of the network model described in the embodiment of the present application. Or part of the process can be accomplished by controlling the relevant hardware through a computer program.
  • the computer program can be stored in a computer readable storage medium, such as in a memory, and executed by at least one processor. May include the flow of the embodiment of the training method of the network model/the image processing method.
  • the storage medium may be a magnetic disk, an optical disc, a read only memory (ROM, Read Only Memory), a random access memory (RAM, Random Access Memory), etc.
  • the network model training device/image processing device of the embodiment of the present application its functional modules can be integrated into one processing chip, or each module can exist alone physically, or two or more modules can be used. Integrated in a module.
  • the above-mentioned integrated modules can be implemented in the form of hardware or software functional modules. If the integrated module is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer readable storage medium, such as a read-only memory, a magnetic disk or an optical disk, etc. .

Abstract

Procédé de formation de modèle de classification de texte, procédé et appareil de classification de texte et dispositif électronique. Le procédé de formation consiste : à obtenir un premier ensemble d'échantillons de texte (101) ; à entrer le premier ensemble d'échantillons de texte dans un modèle de classification de texte afin d'obtenir un premier résultat de prédiction (102) ; si le premier résultat de prédiction ne satisfait pas une condition préétablie, à ajuster le modèle de classification de texte (105) ; à entrer un second ensemble d'échantillons dans le modèle de classification de texte ajusté jusqu'à ce que le résultat de prédiction du modèle de classification de texte satisfasse la condition préétablie (107).
PCT/CN2019/125747 2019-12-16 2019-12-16 Procédé de formation de modèle de classification de texte, procédé et appareil de classification de texte et dispositif électronique WO2021119949A1 (fr)

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