Abstract: A semantic classification model training method includes that a sample query template and a label category of at least one category to be predicted in the sample query template are acquired, where the sample query template is constructed according to a sample query statement and a number of the at least one category to be predicted; the sample query template is input to the pre-constructed semantic classification model to obtain a sample semantic category of the at least one category to be predicted; and the semantic classification model is trained according to the sample semantic category and the label category of the at least one category to be predicted.

Abstract: The method for identifying noise samples, includes: obtaining an original sample set; obtaining a target sample set by adding masks to original training corpora in the original sample set using a preset adjustment rule; performing mask prediction on a plurality of target training corpora in the target sample set using a pre-trained language model to obtain a first mask prediction character corresponding to each target training corpus; matching the first mask prediction character corresponding to each target training corpus with a preset condition; and according to target training corpora of which first mask prediction characters do not match the preset condition in the target sample set, determining corresponding original training corpora in the original sample set as noise samples.

Abstract: Embodiments of the present disclosure provide a sample processing method, a sample processing device, an apparatus and a computer readable storage medium. The sample processing method includes the following. A feature representation of samples included in a sample set is determined. Each of the samples has a pre-annotated category. A clustering is performed on the samples to determine a cluster including one or more of the samples based on the feature representation. A purity of the cluster is determined based on categories of samples included in the cluster. The purity indicates a chaotic degree of the categories of samples included in the cluster. Filtered samples are determined from the samples included in the cluster based on the purity.