Text Recommendation

4 min

the below input parameters are for different attack types to start working with the apis, see docid\ pf0 v4ug62 akwbxclkoz text recommendation is an early access with limited functionality it is not available in aishield pypi package for early access, kindly contact mailto\ aishield contact\@bosch com file upload format data the processed data, ready to be passed to the model for prediction, should be saved in a folder https //aisdocs blob core windows net/reference/upload/text/textrecommendation/data zip model the model should be saved in either h5 or tensorflow format https //aisdocs blob core windows net/reference/upload/text/textrecommendation/model zip common parameters the below table parameters are common for extraction attack type parameter data type description remark model id string model id received during model registration we need to provide this model id in query parameter in url you have to do model registration only once for a model to perform model analysis this will help you track the no of api call made, and it's success metric request body (json format) model api details string if use model api is yes, then provide api details of hosted model as encrypted json string is mandatory provide this only if use model api is "yes" use model api string use model api to train your model instead of uploading the model as a zip file when this parameter is yes, you don't have to upload model as zip you can pass api url along with other verification credential in json file model framework string original model is built with tensorflow framework curretly supported framework are tensorflow, scikit learn, keras (option \[tensorflow]) title string movie title for the recommendation movie title for which the recommendation is requested from model encryption strategy int choose a encryption strategy for you model if model is uploaded directly as a zip pick 0, 1 if model is encryted as pyc and uploaded as a zip ignore if use model api is yes select 0 pass tensorflow model as it is, select 1 pass encrypted model it could be pyc file to access all sample artifacts, please visit docid\ ijneocxostabvvrsq11fa for specific artifact details, refer vulnerability report docid\ hl0ut2mwlcbkt8f97fr w note for text recommendation, supported attack types are extraction