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Version: v0.22.x

Datasets

Overview​

The Datasets page lets you manage all datasets within your current project. From here, you can view, search, filter, and take actions on individual datasets — including launching experiments directly from the dataset view.

You can access Datasets from:

  • The top navigation tabs, or
  • The Datasets card on the homepage of a project

view-datasets

List datasets​

When you open the Datasets page, you’ll see a table showing all datasets in the current project.

Table columns​

Each row represents one dataset and includes:

  • Name
  • Number of rows
  • File size (e.g. KB, MB)
  • File type (e.g. CSV, Parquet)
  • Created date

You can:

  • Search by name
  • Search by file type
  • Customize columns using the column visibility toggle (top-right of the table) toggle-dataset-view

Edit dataset​

Click the Edit button (top-left) to:

  • Select multiple datasets
  • Bulk delete selected datasets

edit-dataset

Use the Cancel button (bottom of the screen) to exit edit mode.

Row actions​

Each dataset row includes a dropdown menu with:

  • Rename — opens a rename dialog
  • Delete — removes the dataset from the project

Add new dataset​

Click the ➕ New Dataset button (top of the page) to upload your own data.

In the New Dataset screen, you can:

  • Name your dataset (optional — a default name will be auto-generated if left blank)
  • Select a Problem Type from the dropdown
  • Upload a file via drag-and-drop or file picker

Problem Type dropdown

Problem type​

The Problem Type dropdown determines how the dataset is interpreted during training and which settings are shown when you create an experiment from the Experiments page.

Supported problem types and dataset requirements:

Causal LM​

  • Requires an input column.
  • The output column is optional for conversation-style datasets.
  • If your dataset contains only input text (without labels), you can generate outputs using Data Generation before fine-tuning.
  • In experiment setup, this uses text fields (for example, input_column and optional output_column).

Classification LM​

  • Requires an input column and an output (label) column.
  • Output labels are validated as classification labels during training.
  • In experiment setup, this uses text-based input_column and output_column fields.

Image Classification​

  • Requires an image column containing base64-encoded or binary image data.
  • Requires an output (class-label) column.
  • Unlike object detection, this does not require a COCO annotation column.
  • In experiment setup, this uses image-specific fields such as Image Column instead of text-only input fields.

Object Detection​

  • Requires an image column and a single annotation output column.
  • Each annotation must include bbox and category fields (COCO-style structure).
  • The output column must be a single column name (not a list).
  • In experiment setup, this uses image_column and output_column (annotation) fields, not text input fields.

Multimodal Causal LM​

  • Requires an input column, an output column, and an image column.
  • The optional system column is supported if provided.
  • In experiment setup, this uses both text and image fields.

Tabular​

  • Used for tabular prediction tasks (classification and regression) powered by TabH2O.
  • Requires a target column to predict.
  • Tabular datasets do not support training — predictions are run directly using a foundation model from the Predictions page.
  • Supported use cases include binary classification, multi-class classification, and regression.

Supported file formats​

  • .csv — Comma-separated values
  • .parquet or .pq — Parquet format
  • .json — JSON structured data

Once uploaded, you’ll be returned to the Datasets page and your new dataset will appear in the list.

Use a demo dataset​

If you don’t have a dataset handy, you can start with one of our pre-configured demo datasets.

On the New Dataset screen, scroll down to find:

“…or get started quickly with our demo datasets”

Clicking any of the demos will immediately import that dataset into your project.

demo-datasets

Available demo datasets​

DatasetProblem TypeSizeDescription
Chatbot ConversationsCausal LM14 MB • ~12K rowsInstruction-output pairs for training conversational AI models (OASST2)
Text-to-SQLCausal LM3.7 MB • 3K rowsQuestion-SQL pairs for training models to convert natural language to SQL
Sentiment ClassificationClassification LM20 MB • ~25K rowsIMDB movie reviews for binary sentiment classification
Multi-Label Text ClassificationClassification LM5.1 MB • ~10K rowsFinancial news with multiple category labels (Reuters-21578)
Note

The specific demos available may change over time — they’re designed to help you quickly test and explore LLM fine-tuning in the platform.

After importing a demo, you’ll be redirected back to the Datasets list where you can explore and use it like any other dataset.

Dataset overview page​

Clicking on a dataset opens its Overview page.

Header actions​

At the top of the page:

  • Rename the dataset via the edit icon next to the name
  • Create Experiment to kick off a fine-tuning run using this dataset
  • Delete the dataset

Metadata summary​

Right below the header, you’ll see metadata about the dataset:

  • File size
  • Row and column count
  • Token count (total tokens in the dataset)
  • Created date
  • Number of experiments launched using this dataset

Column details​

Scroll down to view the Column Details section:

  • Each column is listed with:
    • Column name
    • Max token length for that column (useful for prompt planning)
  • You can collapse/expand this section as needed

Dataset sample​

Below the column details is the Dataset Sample table:

  • Shows up to 20 preview rows
  • You can expand individual rows to view full text if it’s truncated
  • Especially helpful for verifying formatting and text content in large-language tasks
Feedback

Related content

Data generation
Experiments