Model Training Method for Intention Identification Accuracy
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Solution Overview
Problem
Existing intention identification models struggle with accurately distinguishing between different text expressions, leading to poor accuracy in identifying intention categories.
Innovation Solution
A model training method that processes text to obtain positive and negative samples, builds batches of training sets with a preset ratio of positive to negative samples, and adjusts the model using overall loss values calculated from label information and loss functions to improve intention identification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional intention identification models are used, then the model structure is simple, but the accuracy of intention identification for texts with different expressions is poor
Solution Approach 1:
The training process is segmented into multiple batches, where each batch focuses on specific positive and negative samples. This segmentation allows the model to progressively learn different aspects of intention identification without overwhelming complexity in a single training pass.
Solution Approach 2:
Positive and negative samples are pre-processed and organized before training. The samples are prepared with specific formatting and labeling in advance, allowing the model to focus on learning patterns rather than processing raw data during training, thereby improving accuracy while managing complexity.
2Measurement precision
If more sample data is used for training, then the intention identification accuracy improves, but the training time and computational resources increase
Solution Approach 1:
Instead of using all available samples in a single training pass, the method uses partial samples organized in multiple batches. Each batch contains carefully selected positive and negative samples that are sufficient to learn the intended patterns, avoiding the need to process excessive data while maintaining high accuracy.
Solution Approach 2:
The training is conducted in periodic batches rather than continuously processing all samples. This periodic approach allows the model to learn from structured subsets of data repeatedly, improving accuracy through iterative refinement without the linear time cost of processing all samples in one pass.
3Measurement precision
If the model is trained to distinguish subtle text differences, then the intention identification accuracy improves, but the training difficulty and computational complexity increase
Solution Approach 1:
The training focuses on local qualities of text samples by using specifically crafted positive and negative samples that highlight subtle differences. Each batch emphasizes particular linguistic patterns or expression variations, allowing the model to learn fine-grained distinctions without needing to analyze all possible text variations simultaneously.
Data Source
AI summary
The present application relates to a model training method and an apparatus. The model training method includes: processing a text in a corpus to obtain a plurality of samples, where the plurality of samples includes a plurality of positive samples and a plurality of negative samples; building a plurality batches of training sets; inputting each batch of training set into a model for training, and obtaining a category of each sample in each batch of training set; and obtaining an overall loss value according to label information and the category corresponding to each sample, a sample quantity and an overall loss function in each batch of training set, and adjusting the model according to the overall loss value to obtain a trained model.