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Load datasets from Hugging Face Hub into Pixeltable tables for processing with AI models.

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Problem

You want to use a dataset from Hugging Face Hub—for fine-tuning, evaluation, or analysis. You need to load it into a format where you can add computed columns, embeddings, or AI transformations.

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Solution

What’s in this recipe:
  • Import Hugging Face datasets directly into tables
  • Handle datasets with multiple splits (train/test/validation)
  • Work with image datasets
You use pxt.create_table() with a Hugging Face dataset as the source parameter. Pixeltable automatically maps HF types to Pixeltable column types.

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Setup

%pip install -qU pixeltable datasets

import pixeltable as pxt
from datasets import load_dataset

# Create a fresh directory
pxt.drop_dir('hf_demo', force=True)
pxt.create_dir('hf_demo')

Created directory ‘hf_demo’.
<pixeltable.catalog.dir.Dir at 0x31e39d8d0>

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Import a single split

Load a specific split from a dataset: 
# Load a small subset for demo (first 100 rows of rotten_tomatoes)
hf_dataset = load_dataset(
    'cornell-movie-review-data/rotten_tomatoes', split='train[:100]'
)

# Import into Pixeltable
reviews = pxt.create_table('hf_demo/reviews', source=hf_dataset)

Created table ‘reviews’.
Inserting rows into `reviews`: 100 rows [00:00, 14781.69 rows/s]
Inserted 100 rows with 0 errors.
# View imported data
reviews.head(5)

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Import multiple splits

Load a DatasetDict with multiple splits and track which split each row came from: 
# Load dataset with multiple splits (small subset for demo)
hf_dataset_dict = load_dataset(
    'cornell-movie-review-data/rotten_tomatoes',
    split={'train': 'train[:50]', 'test': 'test[:50]'},
)

# Import each split separately for clarity
train_data = pxt.create_table(
    'hf_demo/reviews_train', source=hf_dataset_dict['train']
)
test_data = pxt.create_table(
    'hf_demo/reviews_test', source=hf_dataset_dict['test']
)

Created table ‘reviews_train’.
Inserting rows into `reviews_train`: 50 rows [00:00, 10150.29 rows/s]
Inserted 50 rows with 0 errors.
Created table ‘reviews_test’.
Inserting rows into `reviews_test`: 50 rows [00:00, 9883.37 rows/s]
Inserted 50 rows with 0 errors.
# View training data
train_data.head(5)

# View test data
test_data.head(3)

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Add AI-powered computed columns

Enrich the dataset with AI models: 
# Add a computed column for text length
reviews.add_computed_column(
    text_length=reviews.text.apply(len, col_type=pxt.Int)
)

Added 100 column values with 0 errors.
100 rows updated, 200 values computed.
# View with computed column
reviews.select(reviews.text, reviews.label, reviews.text_length).head(5)

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Type mapping

Pixeltable automatically maps Hugging Face types to Pixeltable types:
Use schema_overrides to customize type mapping when needed.

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Explanation

Why import Hugging Face datasets into Pixeltable:
  1. Add computed columns - Enrich data with embeddings, AI analysis, or transformations
  2. Incremental processing - Add new rows without reprocessing existing data
  3. Persistent storage - Keep processed results across sessions
  4. Query capabilities - Filter, aggregate, and join with other tables
Working with large datasets: For very large datasets, consider loading in batches or using streaming mode in the datasets library before importing.

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See also

Last modified on May 1, 2026