Abstract: The present disclosure discloses a method and apparatus for acquiring a pre-trained model, and relates to natural language processing and deep learning technologies in the field of artificial intelligence technologies. An implementation includes: acquiring training data, the training data including a single-modal language material and a multi-modal language material, and the multi-modal language material including a language material pair formed by a first-modal language material and a second-modal language material; and performing a multi-task training operation on a pre-trained model using the training data, the multi-task including at least one cross-modal contrastive learning task and at least one single-modal learning task; the pre-trained language model obtained in the present disclosure may learn from different forms of language materials, i.e.

Abstract: A planar light-emitting transistor device capable of surface light source emission, and a preparation method therefor and an application thereof are provided. The planar light-emitting transistor device has a charge buffer layer inserted between a semiconductor charge transport layer and a light-emitting unit, such that a prepared planar light-emitting transistor can realize stable surface light source emission, thereby effectively overcoming the defect of the emergent light of a traditional planar light-emitting transistor being linear or strip-shaped. The planar light-emitting transistor device capable of surface light source emission has a high integration level, can realize stable surface light source emission, effectively improves the aperture ratio of the transistor device, has a good gate tunable capability, high loop stability and any tunability, and can be easily miniaturized.

Abstract: A nucleoside triphosphate photoaffinity probe, method for preparing same, and applications thereof. The nucleoside triphosphate photoaffinity probe is a novel small molecule activity probe for detecting nucleoside triphosphate-binding proteins and is based on the structure of nucleoside triphosphates (GTP and ATP) connected to a smaller photoaffinity side chain modification. This probe can effectively label nucleoside triphosphate-binding proteins in cell lysates for high-throughput proteomics analysis, identify and analyze the binding sites of the probe, and can also be used to analyze the action sites of nucleoside triphosphate competitive inhibitors, thus having significant practical application value.

Abstract: The present disclosure provides a pre-training method for a sentiment analysis model and an electronic device, which relates to a field of artificial intelligence technologies. The method includes: based on a given seed sentiment dictionary, performing sentimental knowledge detection on a training corpus in a training corpus set, and determining a detection sentiment word and a detection word pair of the training corpus; according to preset mask processing rules, performing mask process on the training corpus to generate a masked corpus; performing encoding and decoding on the masked corpus by using a preset encoder and decoder to determine the detection sentiment word and the detection word pair of the training corpus; and updating the preset encoder and decoder according to a difference between prediction sentiment word and the detection sentiment word, and a difference between prediction word pair and the detection word pair.

Abstract: A method for training a model based on multi-modal data joint learning, includes: obtaining multi-modal data; in which the multi-modal data include at least one type of single-modal data and at least one type of Pair multi-modal data; inputting the single-modal data and the Pair multi-modal data into a decoupling attention Transformer network model to generate respectively Token semantic representation features and cross-modal semantic representation features; and training the decoupling attention Transformer network model based on the Token semantic representation features and the cross-modal semantic representation features.

Abstract: The present disclosure discloses a method and apparatus for acquiring a pre-trained model, and relates to natural language processing and deep learning technologies in the field of artificial intelligence technologies. An implementation includes: acquiring training data, the training data including a single-modal language material and a multi-modal language material, and the multi-modal language material including a language material pair formed by a first-modal language material and a second-modal language material; and performing a multi-task training operation on a pre-trained model using the training data, the multi-task including at least one cross-modal contrastive learning task and at least one single-modal learning task; the pre-trained language model obtained in the present disclosure may learn from different forms of language materials, i.e.

Abstract: The present disclosure provides a pre-training method for a sentiment analysis model and an electronic device, which relates to a field of artificial intelligence technologies. The method includes: based on a given seed sentiment dictionary, performing sentimental knowledge detection on a training corpus in a training corpus set, and determining a detection sentiment word and a detection word pair of the training corpus; according to preset mask processing rules, performing mask process on the training corpus to generate a masked corpus; performing encoding and decoding on the masked corpus by using a preset encoder and decoder to determine the detection sentiment word and the detection word pair of the training corpus; and updating the preset encoder and decoder according to a difference between prediction sentiment word and the detection sentiment word, and a difference between prediction word pair and the detection word pair.