Text Classification (Generative Models)

Synopsis

Applies a Text Classification model for a given prompt

Description

Applies a Text Classification model for a given prompt. These models have been trained to predict a most likely class for new documents. For example, a sentiment classification model could predict if a sentiment is more likely to be “positive” or “negative”. Prompts can refer to data columns by putting them into double brackets, for example [[column_name]]. The result will be stored in a new column and so will the confidences for the possible classes.

Input

data (Data Table)
The data which will be injected into the prompt. Column values of the data set can get accessed with [[column_name]] in the prompt.
model (File)
The optional model directory (in your project / repository or your file system). Has to be provided if the parameter "use local model" is true. Typically, this is only necessary if you want to use your own finetuned local version of a model.

Output

data (Data Table)
The input data plus a new column (or several ones) which are the result of the prompts sent to the model.
model (File)
The model directory which has been delivered as input.

Parameters

use_local_model Indicates if a local model should be used based on a local file directory or if a model should be used from the Huggingface portal. If a local model is to be used, all task operators require a file object referencing to the model directory as a second input. If this parameter is unchecked, you will need to specify the full model name coming from the Huggingface portal for the “model” parameter. Range:
model The model from the Huggingface portal which will be used by the operator. Only used when the “use local model” parameter is unchecked. The model name needs to be the full model name as found on each model card on the Huggingface portal. Please be aware that using large models can result in downloads of many gigabytes of data and models will be stored in a local cache. Range:
name The name of the newly added column containing the predicted classes. Range:
prompt The prompt used for querying the model. Please note that you can reference the values of any of the input data columns with [[column_name]]. You may need to use a prompt prefix such as “Translate to Dutch: [[column_name]]” to tell the model what it is supposed to do. Range:
device Where the finetuning should take place. Either on a GPU, a CPU, or Apple’s MPS architecture. If set to Automatic, the training will prefer the GPU if available and will fall back to CPU otherwise. Range:
device_indices If you have multiple GPUs and computation is set up to happen on GPUs you can specify which ones are used with this parameter. Counting of devices starts with 0. The default of “0” means that the first GPU device in the system will be used, a value of “1” would refer to the second and so on. You can utilize multiple GPUs by providing a comma-separated list of device indices. For example, you could use “0,1,2,3” on a machine with four GPUs if all four should be utilized. Please note that RapidMiner performs data-parallel computation which means that the model needs to be small enough to be completely loaded on each of your GPUs. Range:
data_type Specifies the data type under which the model should be loaded. Using lower precisions can reduce memory usage while leading to slightly less accurate results in some cases. If set to “auto” the data precision is derived from the model itself. Range:
revision The specific model version to use. The default is “main”. The value can be a branch name, a tag name, or a commit id of the model in the Huggingface git repository. Range:
trust_remote_code Whether or not to allow for custom code defined on the Hub in their own modeling, configuration, tokenization or even pipeline files. Range:
conda_environment The conda environment used for this model task. Additional packages may be installed into this environment, please refer to the extension documentation for additional details on this and on version requirements. Range:

Tutorial Processes

Using a text classification model for sentiment analysis

This process uses a model which has been finetuned for sentiment analysis. It predicts the class and the confidence for that class based on the input data. You can also deliver a local model using the second operator input or specify a different model from Huggingface using the model parameter.

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