8results about How to "Improve fine-tuning" patented technology

The invention discloses an efficient fine tuning method and device for a pre-training model, a server and a storage medium, and the method comprises the steps: obtaining a first text feature and an image prompt feature, the first text feature being generated by coding according to preset entity category text information and learnable text prompt information, the image prompt features are generated by encoding entity category image blocks corresponding to the first input image and preset learnable visual prompt parameters; performing mask processing on the image prompt feature to generate a first mask image feature, and processing the first mask image feature and the first text feature by using a preset multi-mode mask reconstruction network to generate a reconstructed image; and calculating loss values of the first input image and the reconstructed image, and finely adjusting parameters of the pre-training model according to the loss values. Through the method and the device, the problems of low accuracy and weak migration ability in downstream tasks of a parameter fine tuning method of a pre-training visual language model based on prompt in the related technology are solved.

The utility model provides an external gate opening measuring device for a hydraulic hoist, which comprises a first fixed slide rail hoop, the first fixed slide rail hoop is fixedly connected with the front end of a cylinder body of the hydraulic hoist, and an external sliding opening device is selectively mounted on the first fixed slide rail hoop in a matched manner through a slide rail ring. A rope collecting device is installed at the front end of the external sliding opening device and fixedly connected with an extension rod through a steel wire rope, the bottom of the extension rod is installed on a second fixed sliding rail hoop in a sliding fit mode, and the second fixed sliding rail hoop is fixedly connected with the front end of a hydraulic hoist push-pull rod. The external opening degree measuring device has the advantages that the external opening degree measuring device can be longitudinally and transversely adjusted by adjusting the installation position, the problem of accurate measurement of the opening degree of the gate can be effectively solved, reliability is improved, and meanwhile due to the fact that the opening degree measuring device is external, maintenance time can be effectively shortened.

This application relates to a data processing method, apparatus, and model training method, apparatus, and electronic device. The data processing method includes: obtaining first features of m training data points in a training dataset, where m is an integer greater than or equal to 1; obtaining n target data points with second features, so as to adjust the model parameters of a pre-trained model based on at least a portion of the n target data points, where n is an integer greater than or equal to 1; wherein the first and second features satisfy a similarity requirement, the pre-trained model is trained based on the training dataset, and the target data points may be unlabeled or labeled. This application can improve the applicability of training data, thereby improving the model training effect.

The invention relates to the technical field of chemical engineering and natural language processing, in particular to an efficient parameter fine tuning method for a large model in the chemical engineering field. According to the method, aiming at the defect that'fine-tuning granularity ', 'parameter flexibility' and'resource efficiency 'are difficult to consider in an existing parameter efficient fine-tuning technology, a double-Hadamard-product mechanism is introduced to realize fine-granularity adjustment of model parameters, and key parameters in a chemical task are dynamically identified and optimized in combination with an adaptive parameter selection strategy; therefore, a high-efficiency fine tuning frame with low parameter quantity and high precision is constructed. According to the method, finer-grained model adaptation can be realized under the condition that only a small number of parameters are trained, and important parameters are adaptively selected according to the complexity of different chemical tasks, so that the fine tuning performance is ensured, and the parameter utilization efficiency and the task adaptation flexibility are remarkably improved.

The invention discloses an accelerated construction method of an embedded table, an embedded vector retrieval method, a DSA (Digital Subscriber Amplifier) and equipment. The accelerated construction method of the embedded table comprises the following steps: segmenting each embedded vector in N embedded vectors into M sub-vectors, and collecting the sub-vectors at the same segmentation position into the same sub-vector set to obtain M sub-vector sets; loading N sub-vectors in each sub-vector set into a plurality of matrix multiplication operation units, and carrying out parallel calculation on P clustering centers corresponding to each sub-vector set; loading each segmented embedded vector and the P * M clustering centers into a plurality of matrix multiplication operation units, and carrying out parallel calculation on the nearest clustering center of each sub-vector in each embedded vector; and constructing an index table matched with the N embedded vectors according to the nearest clustering center of each sub-vector in each embedded vector. The invention provides a method for constructing the embedded table adaptive to the hardware multi-thread capability of the DSA and retrieving the embedded vector, so that the retrieval speed is increased.

The utility model relates to the field of carbon fiber production, and discloses a carbon fiber guide system and a carbon fiber production line. The carbon fiber filament guiding system comprises a carbon fiber filament guiding device and a tension adjusting device, the carbon fiber filament guiding device and the tension adjusting device are sequentially arranged from front to back in the running direction of a precursor filament, and a tension monitoring mechanism used for detecting the tension of a filament bundle is arranged on the tension adjusting device. The carbon fiber guide device is connected with the tension monitoring mechanism; the carbon fiber guiding device can adjust multiple strands of carbon fiber precursors with different lengths according to the tension information detected by the tension monitoring mechanism, so that the lengths of the multiple strands of carbon fiber precursors adjusted by the carbon fiber guiding device after the carbon fiber guiding device is located behind the carbon fiber guiding device are consistent. According to the carbon fiber guiding system, the carbon fiber guiding device can accurately regulate and control the lengths of the multiple strands of precursors, so that the paths of the adjusted multiple strands of precursors running to the oxidation furnace are the same, and roller winding does not occur on the roller surfaces of metal rollers at an inlet and an outlet of the oxidation furnace.

The embodiment of the present invention discloses a method, device, equipment and storage medium for fine-tuning a pre-trained model. The method includes: obtaining first sample data associated with a target downstream task; performing data domain analysis on the first sample data based on a preset statistical language model, and determining second sample data matching the target downstream domain from the first sample data based on the domain analysis result; fine-tuning the pre-trained model based on the actual sample data corresponding to the target downstream task to determine the first fine-tuning model; denoising the second sample data based on the first fine-tuning model to obtain denoised third sample data; fine-tuning the first fine-tuning model again based on the third sample data and the actual sample data to determine the second fine-tuning model, and determining the target downstream task model based on the second fine-tuning model. Through the technical solution of the embodiment of the present invention, the quality of the expanded sample data can be improved, thereby improving the fine-tuning effect.

The invention provides a few-sample underwater acoustic target identification method and system based on label-free sample assistance, and the method comprises the steps: obtaining a large-scale base data set, pre-training a model on the large-scale base data set, and completing the updating of network parameters in the pre-training process of the model; network parameters are used as initial parameters, and a label sample in a support set # imgabs0 # and a label-free sample fine tuning model of a current domain are used; and a feature extractor f phi, an environment feature adaptation module # imgabs 1 # and a classifier g theta are used to predict samples in the query set # imgabs 2 #, and an underwater acoustic target recognition result is obtained. The system comprises an obtaining unit and a processing unit, the obtaining unit obtains a label-free sample, the label-free sample is input into the processing unit, a model in the processing unit predicts the label-free sample, and an underwater acoustic target recognition result is output. According to the method, the fine tuning performance of the model in a few-sample scene is enhanced by using the label-free sample in the target domain; the model is helped to select features by comparing the features with environmental information in a feature extraction stage.