公开(公告)号：US12283085B2

公开(公告)日：2025-04-22

申请号：US17902323

申请日：2022-09-02

Applicant: Beijing Baidu Netcom Science Technology Co., Ltd.

Inventor： Siqi Xu ,  Ke Sun ,  Jian Gong ,  Xu Pan ,  Zhiqun Xia ,  Zhe Yang ,  Zecheng Zhuo

IPC: G06V10/762 ,  G06F16/28 ,  G06V10/74 ,  G06V10/764

Abstract: Provided is a data labeling method based on artificial intelligence, an apparatus, and a storage medium relating to the field of artificial intelligence, particularly data labeling, image recognition, and natural language processing. The method includes: determining a plurality of samples involved in clustering; performing a plurality of following operations circularly to realize iterative processing, until a convergence condition is satisfied or a quantity of iterations reaches a number threshold, comprising: pre-clustering the plurality of samples according to a vector representation of the respective samples to obtain a plurality of class clusters, each class cluster containing at least one sample; receiving labeling information for the respective class clusters and re-determining the plurality of samples according to the labeling information; and determining a clustering result according to the labeling information for the respective class clusters.

公开(公告)号：US12277398B2

公开(公告)日：2025-04-15

申请号：US17694034

申请日：2022-03-14

Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

Inventor： Jian Gong ,  Yu Sun ,  Hao Tian ,  Hua Wu ,  Haifeng Wang ,  Qiaoqiao She

IPC: G06F40/40 ,  G06F40/205 ,  G06F40/284

Abstract: A model training method, a model training platform, an electronic device and a storage medium are provided, which can be used in the field of artificial intelligence, particularly the fields of natural language processing and deep learning. The model training method includes: receiving an input; determining, based on the input, a user-oriented prefabricated function; determining, based on the input, a model training function; determining, based on the input, a pre-trained model; determining, based on the input, a network structure associated with the pre-trained model so as to support use of the pre-trained model; training, based on the input, the model by using the prefabricated function, the model training function, and the pre-trained model; and providing an output associated with a trained model.