--- title: Text Classification slug: text-classification docTags: createdAt: 2023-07-19T04:19:33.549Z --- The below input parameters are for different attack types. To start working with the APIs, see [Text Classification](docId\:EOqluV3UxbC2_KALISdBO). :::hint{type="warning"} Text classification is an early access with limited functionality. It is not available in AIShield pypi package. For early access, kindly contact [AIShield.Contact@bosch.com](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. [Download sample data](https://aisdocs.blob.core.windows.net/reference/upload/Text/TextClassification/IMDB_Dataset.zip) - **Model**: The model should be saved in either .h5 or TensorFlow format with full architecture along with token in .pkl format. Also there need to be a base\_model.py file which should load model and token and confire it to give prediction. All Three file base\_model.py, .h5 saved model and .pkl saved token should be zipped in a folder and uploaded. [Download sample model](https://aisdocs.blob.core.windows.net/reference/upload/Text/TextClassification/nlp_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 | Use model API to train your model instead of uploading the model as a zip file.
Yes | provide this only if use\_model\_api is "yes". | | Attack\_type | String | You can select the attack type either Blackbox or Greybox. | **Blackbox**: for performing model analysis, no information about model or data will be used. **Greybox**: information about data will be leverage for creation of attack data **Note: **only 2-5 % of data is needed | | normalize\_data | String | Model trained on Normalized data. | if model is trained on normalized data, then set this parameter as "yes" else "no". | | input\_dimensions  | String | Provide input dimension of the text (100) | the parameter should be string in the format. For example 100. | | number\_of\_attack\_queries | String | Number of attack queries that model will be
subjected to. e.g. 20000 | generally Heigher the number of attack queries, better would be the analysis. And it would take more time to process. (Range:  >0 & \<=400000) | | model\_framework | String | Original model is built with tensorflow framework. | curretly supported framework are: tensorflow, scikit-learn, keras. (Option:\[tensorflow]) | | vulnerability\_threshold | String | Number of attack queries that model will be
subjected to. e.g, 0.0 - 1 | Threshold percent of stolen model accuracy
at which defense model should be generated
(Range :  0.0 - 1) | | defense\_best\_only | String | Choose to train your model until it achieves the best results or above 95% accuracy. | when selected **"yes"**, it will train N number of model and select best model. Ofcourse this will take longer time. If **"no"**, then once defense model accuracy reached above 95% It will stop | | 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 | :::hint{type="info"} To access all sample artifacts, please visit [Artifacts](docId\:iJNEOCXoStabvvrsq11fa). - For specific artifact details, refer - Vulnerability Report : [Vulnerability Report](docId\:hL0uT2MWlCBkt8F97fr-W) - Sample Attacks : [Sample Attacks](docId:4G1mjM5lQjfm8t5WBVwpr) - Defense Report: [Defense Report](docId\:VtzlTtpja2VSF2j0STLSQ) - Defense Model: [Defense Model](docId\:xSbxmZXW4vv14-8NmBF8M) ::: :::hint{type="info"} **Note**: For Text classification, supported attack types are - Extraction :::