3654 results about "Target text" patented technology

The invention relates to a model training method and device, equipment, a storage medium and a product. The method comprises the following steps: constructing a training data set according to a target text reasoning chain obtained by converting multi-modal data; according to the training data set, performing supervision fine tuning on the pre-trained multi-modal large language model to obtain a basic reasoning model; performing optimization processing on the basic reasoning model according to reinforcement learning training of long thinking to obtain a target reasoning model; the target reasoning model is used for outputting a target answer containing a reasoning process according to the input multi-modal data. Therefore, the long text constraint can be directly used for reinforcement learning, and the training efficiency is greatly improved; and by adopting long-thinking reinforcement learning training, the model can easily learn a correct thinking process in training, so that the reasoning ability of the multi-modal large language model for processing a complex visual reasoning task is improved, and the correct thinking process is displayed in the reasoning process.

The invention provides a knowledge graph construction and intelligent question and answer method and device based on deep learning. The method includes; obtaining target text data; information extraction is conducted on the target text data, entity relation data is generated, and the entity relation data comprises key data, entity data and relation data used for indicating the relation between entities; according to the entity relationship data, importing the target text data into a graph database to form a knowledge graph; obtaining an input question statement; the question sentence classification model is used for classifying the question sentences, question sentence features in the question sentences are determined, and the question sentence features comprise question sentence types and question sentence entity data; and retrieving text data matched with the question features in the knowledge graph as answer data of the question statements. According to the method, the public text information retrieval efficiency is improved, and the accuracy of intelligent question answering is improved.

The invention provides a multi-knowledge-granularity text retrieval method and device for RAG, and the method comprises the steps: sorting an original text into a JSON file, carrying out the recursive character and semantic dicing processing, generating a target JSON file, and storing the target JSON file in a BM25 database and a vector database; when a user queries, preprocessing to generate a keyword list and a query embedded vector; searching for a target text in the BM25 database, and calculating a score of correlation with the keyword to determine a first to-be-selected text; determining a second to-be-selected text according to the query embedding vector; and performing duplicate removal processing on all the first to-be-selected texts and the second to-be-selected texts, and determining and outputting a final recommended text by utilizing a sorting model. By considering the problem of different search granularities, the keyword granularity is solved by adopting a BM25 algorithm. According to the semantic retrieval strategy combining sentence granularity and segment granularity, challenges of different text information lengths and different retrieval purposes are effectively handled, and therefore the advantages of traditional retrieval and semantic retrieval are fully utilized.

The invention discloses a large model knowledge base construction method based on multi-granularity retrieval. The method comprises the steps of obtaining a target text and analyzing a document structure, splitting and reorganizing text data according to three levels of titles, fragments and sentences, and constructing three levels of knowledge of the titles, the fragments and the sentences; using a large language model to construct a generative index as question-level knowledge according to the sentence-level knowledge; using a vector model to generate a first vector representing index keys of fragment-level knowledge and / or sentence-level knowledge, and a second vector representing index keys of title-level knowledge and / or question-level knowledge; and respectively storing the first vector and the second vector into a vector database, and storing the index values and the index types corresponding to the first vector and the second vector into a relational database. According to the method, multi-granularity document knowledge base construction is achieved, the expression modes of knowledge under different granularities are expanded, the accuracy of user query and knowledge matching is improved, and the problem that information is lost or incomplete in an existing method is solved.

The invention relates to the technical field of machine vision, and discloses a machine vision-oriented cross-modal image-text analysis method, which comprises the following steps of: partitioning an input image to generate an image block sequence; inputting the image block sequence into a visual converter for multi-scale feature extraction, and generating target visual features; encoding the input text to generate a target text feature; inputting the target visual features and the target text features into a deep reconstruction bottleneck network for compression alignment, and generating a cross-modal compression vector; and inputting the cross-modal compression vector into a large language model to generate cross-modal decoding information, so that cross-modal redundant information can be effectively filtered, compact shared semantic representation can be learned, the information integrity of the compression process is ensured through bidirectional reconstruction verification, cross-modal semantic alignment is realized, and the method has the advantages of high efficiency and high reliability. Omnibearing cross-modal content generation from the whole to details is achieved, and the requirements of different application scenes are met.

The invention discloses a contract auditing method, apparatus and device, and a computer readable storage medium. The method comprises the steps of obtaining an image file corresponding to a contract file; inputting the image file into a multi-modal analysis model for model identification so as to obtain text information of each text area corresponding to the image file; performing screening operation on the text information based on a preset keyword to obtain target text information; and auditing the target text information by adopting a large language model based on an entity recognition rule to obtain an auditing result. According to the method, the text information is screened, the target text information is audited on the basis of the large language model, intelligent audit of the contract file is achieved, meaningless input of the large model and the data annotation process can be reduced as much as possible through keyword screening and entity recognition rule matching, and the user experience is improved. The efficiency and accuracy of contract auditing are improved.

The invention relates to a text division method and device and electronic equipment. The method comprises the steps that sentences in a target text are recognized, the sentences are converted into sentence vectors, position codes for representing the positions of the sentences in the target text are constructed, the mapping relation among the sentence vectors, the sentences and the position codes is established, semantic clustering is conducted on the sentence vectors, multiple theme class clusters are generated, and a preset large language model is used for generating a plurality of theme class clusters based on the sentence vectors in the theme class clusters. The method comprises the following steps: generating a subject tag corresponding to each subject class cluster, converting the subject tag of the subject class cluster into a corresponding subject tag vector for each subject class cluster, calculating the similarity between the subject tag vector and a sentence vector in the subject class cluster, and dividing a target text based on the similarity and position codes. According to the scheme provided by the invention, the advantages of the semantic clustering algorithm and the large language model can be fully utilized, the recognition accuracy of each theme in the text is improved, and the accurate division of the text according to the theme is realized.

The invention discloses a document question and answer processing method and system, electronic equipment, a storage medium and a computer program product, and relates to the technical field of large model technologies and document question and answer technologies. The method comprises the following steps: according to an input question, obtaining a recalled document and a key value cache corresponding to the recalled document; generating an initial text sequence by using the input question and the recalled document; based on a mixed key value strategy, key value filling calculation is carried out on the initial text sequence, a target text sequence is obtained, the mixed key value strategy is used for representing key value confirmation mechanisms corresponding to a plurality of lexical elements in different model layers, and the key value confirmation mechanisms comprise one of a key value multiplexing mechanism based on key value cache and a key value reusing mechanism based on key value cache; a hybrid mechanism of a key value multiplexing mechanism and a key value calculation mechanism; and performing reasoning calculation by using the target text sequence to obtain a target answer. The technical problem that a model is difficult to consider the reasoning speed and the reasoning precision due to a calculation mode completely depending on key value multiplexing in related technologies is solved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of medical health, financial science and technology, culture and art and the like, and discloses a feature fusion-based audio analysis method, which comprises the following steps of: obtaining a target audio in a target field and a target text associated with the target audio, extracting a music feature vector of the target audio, extracting a text semantic feature vector of the target text, fusing the music feature vector and the text semantic feature vector to generate fusion features, constructing a knowledge graph containing knowledge nodes of the target domain, inputting the fusion features into the knowledge graph for semantic analysis, and generating an analysis result. According to the method, deep semantic analysis is performed by fusing audio and text features and combining a knowledge graph, so that multi-modal understanding of audio data is realized, the accuracy and interpretability of an analysis result are improved, and the relevance between the analysis result and industry knowledge is enhanced; therefore, the applicability of the audio analysis technology in the fields of culture and art, medical health, finance and the like is improved.

The invention discloses a knowledge graph construction method and apparatus, and a medium. The method comprises the steps of collecting a to-be-processed text for constructing a knowledge graph in a specified field; screening out a target text from the to-be-processed text, wherein the correlation between the target text and the specified field meets a preset condition; processing the target text through a pre-constructed target large language model to construct an initial triple; filtering the initial triple to obtain a target triple; and constructing the knowledge graph through the target triple. Therefore, through strict correlation screening conditions, texts irrelevant to the specified field are removed, the influence of noise data on knowledge graph construction is reduced, and the quality and accuracy of the knowledge graph are improved. On the basis of the target texts, the knowledge triples are efficiently and accurately extracted from a large number of target texts by means of the powerful language understanding and generating capacity of the large language model, the labor cost overhead of manual extraction is reduced, and the knowledge graph construction efficiency is improved.

The present application relates to the technical field of artificial intelligence, and in particular to a question answering method based on a large model, and an electronic device. The electronic device comprises a communication interface, a memory, and a processor, the memory is configured to store a computer instruction, and the processor is configured to execute a computer program to enable the electronic device to: determine, on the basis of each pre-stored text block, a target text block matched with a question to be answered; obtain, if non-text data including a picture and / or a table is pre-stored for the target text block in advance, stored summary text of the non-text data; and input the question to be answered, the target text block, and the summary text into a first large model on the basis of a preset format to obtain answer information. Since the summary text is text that summarizes all content in the non-text data, the first large model does not need to process the picture and / or the table and still can ensure that the content recorded in the picture and / or the table is taken into consideration during generation of the answer information, thereby improving the model-based question answering accuracy.

The embodiment of the invention relates to the technical field of natural language processing, and provides a text error correction method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a to-be-corrected target text, and carrying out the semantic boundary recognition, and obtaining all semantic boundaries; segmenting the target text into a plurality of clauses, distributing IDs according to the appearing sequence of each clause, and generating structured data with sequence IDs; inputting the structured data into a language model, and calling a target domain knowledge base corresponding to the target text as a constraint condition to perform semantic analysis, and identifying an error type; adjusting an error correction path based on the error type so as to re-identify error information, the error information including error content, the error type and the error position; and recombining a plurality of clauses based on the sequence ID, the error content, the error type and the error position, and generating an error correction result containing the error position, the error type and the correction suggestion. Therefore, collaborative detection and correction of grammar, semantic and logic errors are realized.

A computer-implemented method is provided that detects jailbreak attempts against generative models, which may involve a shift between benign and malicious content. The method includes determining a probability-based metric for each of a plurality of tokens in a target text using a language model, the probability-based metric being based on a probability at least one preceding token. The probability-based metrics are processed to identify a subset of the plurality of tokens having a change in the probability-based metric with respect to others of the plurality of the tokens not within the subset of the plurality of tokens. A jailbreak attempt in the target text is detected in response to identifying the change in the probability-based metric in the subset of the plurality of tokens.

The invention discloses a relation extraction method and system based on a graph neural network, and belongs to the technical field of natural language processing. A target text is obtained, word segmentation, part-of-speech tagging and named entity recognition are carried out, and an entity set is extracted; constructing a text graph structure containing multiple edge types based on the entity set; performing feature coding on nodes in the graph to generate an initial feature vector fusing semantic, part-of-speech and position information; inputting the graph into the graph neural network model, and obtaining high-order node representation through multi-layer message passing and aggregation; modeling the entity pair in combination with the structure path and the context information, and inputting a multi-channel classification network to predict the relationship type of the multi-channel classification network; and finally, outputting an entity relationship triple according to a prediction result. The method has stronger semantic modeling ability and structure expression ability in a relation extraction task, and is suitable for scenes such as knowledge graph construction and information extraction systems.

The invention relates to a vertical domain relation extraction method and device, electronic equipment and a storage medium, and the method comprises the steps: constructing a knowledge base of a target vertical domain, carrying out the entity relation extraction of a target text through combining with the knowledge base of the target vertical domain, and obtaining a first entity relation set; performing entity relationship extraction on the target text by utilizing a trained entity relationship extraction model to obtain a second entity relationship set, and performing correction processing on the second entity relationship set by utilizing a knowledge base to obtain a fourth entity relationship set, and screening out a target entity relationship set from the first entity relationship set and the fourth entity relationship set in combination with the knowledge base of the target vertical field. By introducing the vertical field knowledge base, external data support is provided for a large model, the defects of the prior art in the aspects of long-tail relation processing and vertical field reasoning capability are overcome, and meanwhile, the filtering capability of noise samples is enhanced and the extraction quality and efficiency are improved by combining a combined comparison screening mechanism, a knowledge base automatic updating mechanism and a health management mechanism.

The invention discloses a task planning method and device for a large language model, a storage medium and equipment. The method comprises the steps that firstly, a target field to which a target task text belongs is determined; then, M text blocks similar to the target task text are screened out from the knowledge document of the target domain; inputting the key information of the M text blocks into a preset large language model in combination with the first prompt on the basis of a multi-dimensional prompt text abstract generation mode to obtain a text abstract of the M text blocks, and inputting the text abstract of the M text blocks into the large language model in combination with the second prompt to obtain N subtasks corresponding to the target text. According to a pre-constructed tool description mapping relation, tools for executing the N sub-tasks are called through a large language model, and processing results of the N sub-tasks are obtained by executing the tools so as to form a final processing result of the target task. Therefore, the overall efficiency and quality of executing the target task by the large language model are improved.

The invention discloses a voice interaction task execution method and device based on a large model, equipment and a medium, and relates to the field of artificial intelligence, and the method comprises the steps: carrying out the voice recognition of voice features, obtaining a text character sequence, and carrying out the optimization of the text character sequence through a preset large language model, and obtaining a target text character sequence; performing entity recognition on the target text character sequence by using a preset large language model to obtain an entity recognition result, determining a relationship type among entities in the target text character sequence according to the entity recognition result, and constructing a knowledge graph according to the relationship type among the entities; generating an initial triple based on the knowledge graph, and optimizing the initial triple by using a preset large language model to obtain a target triple; and fusing the target triple with the initial knowledge graph, and executing a voice interaction task in the target voice interaction scene based on the updated knowledge graph. According to the method and the device, the efficiency and the accuracy of extracting the structured knowledge from the Chinese speech are improved.

The invention provides a cross-modal retrieval model training method and a remote sensing image text retrieval method, and is applied to the technical field of remote sensing cross-modal retrieval. The remote sensing image text retrieval method comprises the following steps: performing feature extraction on a preprocessed remote sensing image by utilizing a trained cross-modal retrieval model to obtain image global features and image local features, and performing feature extraction on a preprocessed target text to obtain text global features and phrase global features; calculating the global similarity between the image global features and the text global features by using the trained cross-modal retrieval model, and calculating the local similarity between the image local features and the word group global features; and carrying out weighted calculation on the global similarity and the local similarity by utilizing the trained cross-modal retrieval model, and screening a matching result between the remote sensing image and the target text based on the weighted similarity to obtain a retrieval result between the remote sensing image and the target text.

The invention discloses a Tibetan language multi-dialect real-time semantic conversion method based on a cross-language BERT model, and the method comprises the following steps: S1, collecting original text corpora of each dialect of the Tibetan language, and constructing a standardized training corpus set; s2, performing parameter initialization on the mBERT model, and preliminarily training the mBERT model; s3, constructing a semantic modeling model, and performing fine adjustment on the semantic modeling model; s4, receiving to-be-converted text input, and encoding; s5, obtaining an intermediate semantic representation vector of the text through a semantic coding sub-module; s6, inputting the intermediate semantic representation vector into a semantic generation sub-module, and generating target text output; and S7, executing syntactic consistency correction and language fluency correction. According to the method, mBERT modeling and an adversarial optimization mechanism are fused, real-time semantic consistency conversion of multiple dialects of the Tibetan language is achieved, and the method has the advantages of being high in accuracy, high in robustness and low in response delay.

The invention provides a depth map generation method and device based on a large model, a three-dimensional reconstruction method and device, electronic equipment and a storage medium, relates to the technical field of artificial intelligence, in particular to the technical fields of computer vision, deep learning, large models and the like, can be applied to real-time road scene depth perception, environment three-dimensional reconstruction and obstacle avoidance, and can be applied to real-time road scene depth perception. And virtual and real scene fusion and other scenes can be realized. The specific implementation scheme is as follows: performing visual coding on a monocular image to obtain a coded image; inputting the coded image and the target text into a pre-trained large language model for fusion to obtain fusion features; generating global guide features based on the fusion features, wherein the global guide features comprise joint semantic information of visual features and text features; adding noise to the color image of the monocular image to obtain a noise feature sequence; de-noising the noise feature sequence under the condition of the global guide feature, and generating an implicit feature matched with the joint semantic information; a depth map is generated based on the implicit features.

The invention discloses a text video retrieval method based on human brain scene memory path inspiration. The method comprises the following steps: performing content representation extraction on target text data or target video data by utilizing a content coding component to obtain multi-scale target representation; performing context representation extraction on the target text data or the target video data by using a context coding component to obtain a target token; inputting the multi-scale target representation and the target token into a hyperbolic graph neural network to obtain a target scene representation; taking the target scene representation as a target index; calculating the similarity between the representation of the to-be-retrieved text or video and the target index, and screening the to-be-retrieved text or video according to the similarity to obtain a target retrieval result; according to the method, multi-level semantic features are comprehensively captured through multi-granularity information fusion, multi-modal and multi-granularity high-order information is fused through hyperbolic graph convolution operation, the complex relation between the text and the video can be better captured, and the precision and efficiency of text and video retrieval are remarkably improved.

The invention provides a video production method and device for automatically explaining static PPT by a digital human, computer equipment and a computer storage medium, and relates to the technical field of video processing. Comprising the following steps: extracting first target text information in each sub-image from a sequence image set of static PPT; converting the first target text information in each subimage into voice, and synthesizing the obtained initial audio files corresponding to each subimage to obtain a target audio file for the static PPT; wherein a preset pause duration is added between the initial audio files in the target audio file, and a corresponding relation between the audio time of each initial audio file and the display time of each sub-image is constructed; adding a PPT transition animation with a preset pause duration between the sub-images to obtain an initial PPT video for the static PPT; and based on the target audio file, controlling the virtual digital human to explain first target text information in the initial PPT video to obtain a target PPT video. According to the method, the video playing fluency can be improved.

The invention discloses a multi-modal emotion analysis method and device, and relates to the technical field of emotion analysis. The method comprises the following steps: preprocessing each modal emotion of multi-modal emotion data, performing dimension alignment, and determining a target text feature, an aligned video feature and an aligned audio feature; a single-mode encoder is adopted to encode the aligned video features and the aligned audio features, and target video features and target audio features are correspondingly obtained; performing cross-membrane attention mechanism operation on the basis of the target text features, the target audio features and the target video features through a cross-modal fusion network and a cross-membrane conversion network, inputting the target text features, the target audio features and the target video features into a Transform network, performing self-attention operation, performing feature fusion, and determining emotion fusion features; and performing sentiment classification on the sentiment fusion features by adopting a sentiment classification layer, and outputting a sentiment analysis result. Based on the scheme, information loss during modal interaction is reduced, and the reliability of multi-modal emotion analysis is improved.

The invention provides a method for editing facial image attributes based on a text of a diffusion model. The method realizes flexible facial editing with high quality and identity consistency. The method comprises the following steps: constructing a description control face diffusion model comprising a noise prediction network and a variational auto-encoder, wherein the noise prediction network comprises a plurality of text alignment face Transform modules and an optimization residual feature module; the method comprises the following steps: inputting an original image and target text description based on a pre-training model, optimizing a target embedding generated by the input target text description by a text alignment face Transform module to obtain an optimized embedding, and performing model fine tuning after optimization is completed; and performing linear interpolation on the target embedding and the optimized embedding to obtain an initial editing result, introducing ArcFace-Loss as identity loss, extracting face features of an original image and the initial editing result through a pre-training model, calculating feature similarity and minimizing the loss, and ensuring consistency of face identities after editing.

Provided are a video generation method, an electronic device, and a computer readable storage medium, relating to the technical fields of computers and video processing. The video generation method comprises: acquiring a target text, wherein the target text is used for describing video content to be generated (S21); and using a target video generation model to perform video generation processing on the target text to obtain a target video, wherein the target video generation model is a model obtained by performing model alignment on an initial video generation model and a preset reward model in a fine-tuning manner (S22). The present invention solves the technical problems in the related art that a video generated by a video generation model obtained by training based on network data has poor quality and does not meet user expectations.

The invention discloses a machine generated text detection method, a terminal, a medium and a program product in the field of natural language processing, and the method comprises the steps: inputting a to-be-detected target text into a trained text detection model, and obtaining a detection result outputted by the text detection model; the text detection model comprises a first semantic feature extraction network, a second semantic feature extraction network and a fusion classification network; the first semantic feature extraction network is used for extracting semantic features of keywords in a target text to generate a first representation vector; the second semantic feature extraction network is used for extracting overall semantic features of the target text and generating a second representation vector; the fusion classification network processes the first representation vector and the second representation vector to generate a detection result about whether the target text is a machine-generated text; the training of the text detection model adopts a multi-task learning strategy. According to the method, multi-task training is introduced, so that the accuracy and robustness of machine generated text detection are effectively improved.

The invention discloses an intelligent retrieval method and system for electronic archives, and belongs to the field of electric digital data processing.The intelligent retrieval method for the electronic archives comprises the following steps that search content input by a user is received, spelling errors are automatically corrected, and related words are expanded; preliminarily screening the candidate text electronic archive set based on a reverse index mechanism to obtain an initial sorting result, performing correlation resorting and extension recall on the initial sorting result through a semantic similarity model, and outputting a final sorting result; and after determining the target text electronic archive in the final sorting result, according to the uploading information of the target text electronic archive, the method has the beneficial effects that the non-text electronic archives are matched through the determined uploading information of the target text electronic archive, and the non-text electronic archives are sorted according to the association strength, so that the efficiency of sorting the non-text electronic archives is improved. The non-text format content can be identified during keyword retrieval; and the tendency of the user is predicted, so that the use experience of the user in downloading or checking the electronic file is improved.

The invention belongs to the technical field of three-dimensional reconstruction, and discloses a three-dimensional digital human generation method and system capable of voice interaction. According to the invention, brand new speaking audios in different languages are automatically generated according to different languages of the input target text and the sampled human voice audios; the sequential stability and detail reduction capability of three-dimensional human motion are guaranteed by using multi-model joint estimation and a sequential loss function, and facial expression details and hand postures in the image can be accurately estimated. After the high-precision three-dimensional human body model is obtained through estimation, human body action and expression generation is carried out based on voice driving, accurate synchronization of actions and expressions generated through voice is achieved, and facial expression movement and body posture movement, namely a whole-body three-dimensional human body model, conforming to brand-new speaking audio are accurately generated; and finally, rendering the whole-body three-dimensional human body model into a real digital human capable of voice interaction by using a three-dimensional neural rendering model. According to the invention, the realization of single person picture input, high-precision three-dimensional digital person generation and voice interaction is facilitated.

The invention discloses a video generation method and device, a readable medium, electronic equipment and a program product, and the method comprises the steps: displaying a video editing interface, and displaying a target text in the video editing interface; in response to a split mirror generation operation in the video editing interface, a split mirror list is displayed based on the target text, the split mirror list comprises a plurality of split mirror cards, and a split mirror picture and split mirror subtitles obtained by splitting the target text are displayed in each split mirror card; and in response to a video generation operation in the video editing interface, generating a video based on the split list. Therefore, the video can be automatically generated based on the target text of the video editing interface, so that the complexity of video generation operation is simplified, and the video generation difficulty is reduced. Besides, as the split card comprises the split picture and the split subtitle, whether the split picture is matched with the picture described by the split subtitle or not can be quickly identified in an image-text combination form, and the accuracy and creation efficiency of the video content are improved.

The invention provides an electric power image-text cross-modal retrieval method based on Transform and a related device. The method comprises the steps of obtaining a target text; performing text feature extraction based on the target text to obtain target text features; retrieving in a to-be-processed image data set based on the target text features, and determining a target image conforming to the target text semantics, including: performing image feature extraction on the to-be-processed image in the to-be-processed image data set based on a trained retrieval model to obtain image features; performing matching based on the target text features and the image features to obtain target image features; and determining the target image based on the target image feature.