Import from Hugging Face

HoneyHive supports flexible dataset schemas, making it easy to import datasets from Hugging Face or any other data source.

Prerequisites

HoneyHive API key
An existing project
datasets library installed: pip install datasets

Import a Dataset

Install dependencies

pip install honeyhive datasets

Load and import

Python

import os
from datasets import load_dataset
from honeyhive import HoneyHive
from honeyhive.models import (
    CreateDatasetRequest,
    AddDatapointsToDatasetRequest,
    DatapointMapping,
)

client = HoneyHive(api_key=os.environ["HH_API_KEY"])

# Load HuggingFace dataset
hf_dataset = load_dataset("lhoestq/demo1", split="train[:100]")

# Step 1: Create an empty dataset
dataset = client.datasets.create(CreateDatasetRequest(
    name="HuggingFace Demo Dataset",
    description="Imported from lhoestq/demo1",
))
dataset_id = dataset.result.insertedId

# Step 2: Add datapoints in batches with field mapping
batch_size = 100
total = len(hf_dataset)

for i in range(0, total, batch_size):
    batch = hf_dataset[i:i + batch_size]
    rows = [
        {"review": review, "star": star}
        for review, star in zip(batch["review"], batch["star"])
    ]

    client.datasets.add_datapoints(
        dataset_id,
        AddDatapointsToDatasetRequest(
            data=rows,
            mapping=DatapointMapping(
                inputs=["review"],
                ground_truth=["star"],
            )
        )
    )
    print(f"Imported {min(i + batch_size, total)}/{total} datapoints")

print(f"Created dataset with {total} datapoints")

Field Mapping

Use DatapointMapping to map HuggingFace dataset columns to HoneyHive datapoint fields:

HuggingFace	DatapointMapping field	Use For
Input columns	`inputs`	Data fed to your function
Label/answer columns	`ground_truth`	Expected outputs for evaluation
Chat history columns	`history`	Conversational context

Any columns not listed in the mapping are automatically stored as metadata.

Example: Q&A Dataset

Python

# For a Q&A dataset with "question", "context", and "answers" columns
hf_dataset = load_dataset("squad", split="train[:100]")

# Step 1: Create an empty dataset
dataset = client.datasets.create(CreateDatasetRequest(
    name="SQuAD Q&A Dataset",
    description="Imported from squad",
))

# Step 2: Flatten answers and add datapoints
rows = [
    {
        "question": row["question"],
        "context": row["context"],
        "answer": row["answers"]["text"][0],
        "source": "squad",
    }
    for row in hf_dataset
]

client.datasets.add_datapoints(
    dataset.result.insertedId,
    AddDatapointsToDatasetRequest(
        data=rows,
        mapping=DatapointMapping(
            inputs=["question", "context"],
            ground_truth=["answer"],
        )
    )
)
# "source" is automatically stored as metadata

Example: Classification Dataset

Python

# For a classification dataset with "text" and "label" columns
hf_dataset = load_dataset("imdb", split="test[:100]")

# Step 1: Create an empty dataset
dataset = client.datasets.create(CreateDatasetRequest(
    name="IMDB Classification Dataset",
    description="Imported from imdb",
))

# Step 2: Add datapoints
rows = [
    {
        "text": row["text"],
        "label": "positive" if row["label"] == 1 else "negative",
    }
    for row in hf_dataset
]

client.datasets.add_datapoints(
    dataset.result.insertedId,
    AddDatapointsToDatasetRequest(
        data=rows,
        mapping=DatapointMapping(
            inputs=["text"],
            ground_truth=["label"],
        )
    )
)

Best Practices

Batch imports: For large datasets (1000+ rows), use the batching pattern from the main example above — split your data into chunks of 100 rows per add_datapoints call to avoid timeouts.

Recommendation	Reason
Start with a subset	Test your mapping with 100 rows before importing the full dataset
Add metadata	Include source information for traceability
Validate fields	Check that your field mapping produces valid datapoints

Getting Started

Observability

Evaluation

Prompt Management

Administration

Learn More

Prerequisites

Import a Dataset

Field Mapping

Example: Q&A Dataset

Example: Classification Dataset

Best Practices

Next Steps

Run Experiments

Export Datasets

​Prerequisites

​Import a Dataset

​Field Mapping

​Example: Q&A Dataset

​Example: Classification Dataset

​Best Practices

​Next Steps

Run Experiments

Export Datasets

Prerequisites

Import a Dataset

Field Mapping

Example: Q&A Dataset

Example: Classification Dataset

Best Practices

Next Steps