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Version: v1.3.0

Import dataset settings: Text regression

Dataset name​

Name of the dataset.

Problem type​

Defines the problem type of the experiment, which also defines the settings H2O Hydrogen Torch displays for the experiment.

Note
  • The selected problem category (in the Problem category setting) determines the available problem types.
  • The selected problem type and experience level determine the settings H2O Hydrogen Torch displays for the experiment.

Train dataframe​

Defines a .csv or .pq file containing a dataframe with training records that H2O Hydrogen Torch uses to train the model.

note
  • The records are combined into mini-batches when training the model.
  • If a validation dataframe is provided, a fold column is not needed in the train dataframe.
  • You can now import datasets for inference only. To do so, when defining the setting for an experiment, set the Train dataframe setting to None while setting the Test dataframe setting to the relevant dataframe (as a result, H2O Hydrogen Torch utilizes the relevant dataset for predictions and not for training).

Validation dataframe​

Defines a .csv or .pq file containing a dataframe with validation records that H2O Hydrogen Torch uses to evaluate the model during training.

Note
  • To set a Validation dataframe requires the Validation strategy to be set to Custom holdout validation. In this case, H2O Hydrogen Torch fully respects the choice of a separate validation dataframe and does not perform any internal cross-validation. In other words, the model is trained on the full provided train dataframe, and model performance is evaluated on the provided validation dataframe.
  • The validation dataframe should have the same format as the train dataframe but does not require a fold column.

Test dataframe​

Defines a .csv or .pq file containing a dataframe with test records that H2O Hydrogen Torch uses to test the model.

note
  • The test dataframe should have the same format as the train dataframe but does not require a label column.
  • You can now import datasets for inference only. To do so, when defining the setting for an experiment, set the Train dataframe setting to None while setting the Test dataframe setting to the relevant dataframe (as a result, H2O Hydrogen Torch utilizes the relevant dataset for predictions and not for training).

Unlabeled dataframe​

Defines a separate .csv or .pq file (depending on the problem type) containing a dataframe with unlabeled records that H2O Hydrogen Torch utilizes to generate pseudo labels. H2O Hydrogen Torch first trains the model with the provided labeled data (Train dataframe). Right after, the model predicts pseudo labels for the provided unlabeled dataframe before doing another training run that combines the original labels and pseudo labels.

note
  • Image regression | Image classification | Image object detection
    • The unlabeled dataframe just needs to contain a single image column
  • Text regression | Text classification
    • The unlabeled dataframe just needs to contain a single text column
  • Audio regression | Audio classification | Speech recognition
    • The unlabeled dataframe just needs to contain a single audio column
  • Image regression | Image classification | Image object detection | Audio regression | Audio classification | Speech recognition
    • Assets (images or audios) need to be located in the Data folder (setting)
  • All supported problem types
    • The training time can significantly increase depending on the size of the unlabeled data
tip

As labeling can be expensive, having additional unlabeled data is quite common. Providing this unlabeled data in H2O Hydrogen Torch trains the model semi-supervised, potentially improving the model quality in contrast to only training on labeled data.

Label columns​

Defines the name(s) of the dataframe column(s) that refer to the target value(s) H2O Hydrogen Torch aims to predict.

Text column​

Defines the dataset column(s) containing the input text H2O Hydrogen Torch uses during model training. H2O Hydrogen Torch concatenates multiple text columns with a specific separator token.

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