Beijing Baidu Netcom Science and Technology Co., Ltd.

Beijing Baidu Netcom Science Technology Co., Ltd

Beijing Etag Technology Company., Ltd.

Chao Pang

Yu Sun

Shuohuan Wang

Haifeng Wang

Hua Wu

Hao Tian

Siyu Ding

Yanbin Zhao

Junyuan Shang

Zhi Hong Li

Shikun Feng

Xuan Ouyang

Xingfu Zhou

Jiping Ren

Dongge Hu

A method for pre-training a language model includes: constructing a pre-training language data set, in which the pre-training language data set comprises unsupervised language data and supervised language data; generating a hierarchical multi-template and multi-task language data set based on the pre-training language data set; and pre-training the language model based on the hierarchical multi-template and multi-task language data set.

Method for pre-training language model

A method and apparatus for pre-training a model, a device, a storage medium, and a program product. An embodiment of the method includes: acquiring a sample natural language text; generating N types of prompt words based on the sample natural language text, where N is a positive integer; generating sample input data based on the sample natural language text and the N types of prompt words; and training an initial language model based on the sample input data, to obtain a pre-trained language model.

Method for pre-training model, device, and storage medium

The present application provides a method of training a natural language processing model, which relates to a field of artificial intelligence, and in particular to a field of natural language processing. A specific implementation scheme includes: performing a semantic learning for multi-tasks on an input text, so as to obtain a semantic feature for the multi-tasks, wherein the multi-tasks include a plurality of branch tasks; performing a feature learning for each branch task based on the semantic feature, so as to obtain a first output result for each branch task; calculating a loss for each branch task according to the first output result for the branch task; and adjusting a parameter of the natural language processing model according to the loss for each branch task. The present application further provides a method of processing a natural language, an electronic device, and a storage medium.

Method and apparatus of training natural language processing model, and method and apparatus of processing natural language

The present disclosure provides a multi-lingual model training method, apparatus, electronic device and readable storage medium and relates to the technical field of deep learning and natural language processing. A technical solution of the present disclosure when training the multi-lingual model is: obtaining training corpuses comprising a plurality of bilingual corpuses and a plurality of monolingual corpuses; training a multi-lingual model with a first training task by using the plurality of bilingual corpuses; training the multi-lingual model with a second training task by using the plurality of monolingual corpuses; and completing the training of the multi-lingual model in a case of determining that loss functions of the first training task and second training task converge. In the present disclosure, the multi-lingual model can be enabled to achieve semantic interaction between different languages and improve the accuracy of the multi-lingual model in learning the semantic representations of the multi-lingual model.

Multi-lingual model training method, apparatus, electronic device and readable storage medium

A method, an apparatus, a device and a storage medium for learning a knowledge representation are provided. The method can include: sampling a sub-graph of a knowledge graph from a knowledge base; serializing the sub-graph of the knowledge graph to obtain a serialized text; and reading using a pre-trained language model the serialized text in an order in the sub-graph of the knowledge graph, to perform learning to obtain a knowledge representation of each word in the serialized text. The knowledge representation learning in this embodiment is performed for entity and relationship representation learning in the knowledge base.

Method, apparatus, device, and storage medium for learning knowledge representation

The present invention discloses a livestock tracing system and method. The system includes: a feeding module, an adopting and purchasing module, a weighing module, a server, a first commutation module and a second communication module; the feeding module is configured to provide food intake information of livestock and transmit the food intake information to the server via the first communication module; the weighing module is configured to provide weight information of the livestock and transmit the weight information to the server via the second communication module; the adopting and purchasing module is configured to provide vaccine information of the livestock and transmit the vaccine information to the server; and the server is configured to store the food intake information, the weight information and the vaccine information, and obtain corresponding tracing information according to a tracing look-up instruction.

Livestock tracing system and method

A method for generating a vector representation of a text includes dividing the text into text segments. Each text segment is represented as a segment vector corresponding to the respective text segment by employing a first-level semantic model. The segment vector is configured to indicate a semantics of the text segment. Text semantics recognition is performed on the segment vector of each text segment by employing a second-level semantic model to obtain a text vector for indicating a topic of the text.

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Beijing, China

Chao Pang