Artificial intelligence data labeling method and device
An artificial intelligence and data technology, applied in the field of data processing, can solve the problems that the scale of annotation is difficult to keep consistent, the subjective influence of annotators and reviewers is large, and the accuracy is not high. The effect of labeling errors
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[0065] Example one
[0066] See figure 2 , figure 2 This is a schematic diagram of the artificial intelligence data labeling process in the embodiment of this application. The specific steps are:
[0067] Step 201: Obtain a data set to be labeled.
[0068] Step 202: Obtain the AI tag with the highest probability score of each piece of data to be labeled and the corresponding probability score based on the established AI model.
[0069] In specific implementation, one or more established AI models can also be used to obtain the AI tag with the highest probability score for each piece of data to be labeled, and the corresponding probability score.
[0070] Taking M AI models as an example, based on the established AI model, the AI tag with the highest probability score for each piece of data to be labeled and the probability score are obtained, including:
[0071] For the data to be labeled, the probability score corresponding to each AI tag corresponding to the model is obtained ...
Example Embodiment
[0088] Example two
[0089] See image 3 , image 3 In this embodiment of the present application, the data annotated by the AI model is used as a schematic flow chart of the data sample for training the AI model. The specific steps are:
[0090] Step 301: Obtain a data set to be labeled.
[0091] Step 302: Obtain the AI tag with the highest probability score of each piece of data to be labeled and the corresponding probability score based on the established AI model.
[0092] Step 303: For any data to be labeled, determine whether the probability score is greater than a first preset threshold.
[0093] Step 304: When it is determined that the probability score is greater than the first preset threshold, and it is determined to randomly check the data to be labeled, an artificial label is labeled for the data to be labeled.
[0094] In step 305, it is determined whether the artificial tag is consistent with the obtained AI tag, if so, step 309 is executed; otherwise, step 308 is e...
Example Embodiment
[0102] Example three
[0103] See Figure 4 , Figure 4 This is a schematic diagram of the process of determining whether to update the first threshold according to the accuracy rate in an embodiment of this application. The specific steps are:
[0104] Step 401: Obtain a data set to be labeled.
[0105] Step 402: Obtain the AI tag with the highest probability score of each piece of data to be labeled and the corresponding probability score based on the established AI model.
[0106] Step 403: For any data to be labeled, determine whether the probability score is greater than a first preset threshold.
[0107] Step 404: When it is determined that the probability score is greater than the first preset threshold, and it is determined that the data to be labeled is randomly checked, an artificial label is labeled for the data to be labeled; and whether the artificial label for the data is consistent with the obtained AI label, Step 406 is executed.
[0108] Step 405: When it is determin...
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