Google Colab · Hugging Face · Datasets

How to load Hugging Face datasets in Google Colab (without errors)

Hugging Face datasets is the easiest way to get popular NLP / CV datasets, but in Google Colab it often leads to weird errors, slow downloads, or out-of-memory crashes. Let’s fix that with a clean, reproducible pattern.

Quick answer

In most cases you only need to:

!pip install -q datasets

from datasets import load_dataset

ds = load_dataset("imdb")     # or your dataset name
print(ds)
print(ds["train"][0])

But on Colab, you should also:

• Pin versions of datasets and transformers.
• Store datasets in Google Drive so they survive runtime resets.
• Use streaming or subsets for very large datasets.

Step 1 – Install the right libraries in Colab

Use a dedicated cell at the top of your notebook:

!pip install -q "datasets>=2.18.0" "transformers>=4.40.0"

Run this, then restart the runtime if Colab asks. Avoid mixing very old and very new versions in the same notebook.

Step 2 – Load a small dataset first (sanity check)

Test with something small like IMDB:

from datasets import load_dataset

ds = load_dataset("imdb")
print(ds)
print("Train size:", len(ds["train"]))
print("Example:", ds["train"][0])

Step 3 – Cache datasets in Google Drive (so they persist)

By default, Hugging Face caches data under /root/.cache/huggingface which is wiped when Colab resets.

1. Mount Drive:

from google.colab import drive
drive.mount("/content/drive")

2. Tell datasets to use a cache folder inside Drive:

import os
os.environ["HF_DATASETS_CACHE"] = "/content/drive/MyDrive/hf_datasets_cache"

Now future runs will reuse the downloaded dataset, even if Colab restarts.

Step 4 – Use subsets & streaming for large datasets

Loading the entire dataset into RAM can crash Colab. Use:

Load only a split

ds = load_dataset("ag_news", split="train[:10%]")  # first 10% of train

Use streaming (don’t load all into RAM)

ds_stream = load_dataset("c4", "en", split="train", streaming=True)

for i, example in enumerate(ds_stream):
    print(example)
    if i == 3:
        break

Streaming is great when you just need to iterate, not hold everything in memory.

Common errors & how to fix them

1. ModuleNotFoundError: No module named 'datasets'

• You didn’t run the pip install cell, or you ran it after importing.
• Solution: run the install cell, then restart runtime and import again.

2. SSL / network errors when downloading

• Retry the cell after a minute – sometimes it's just Colab’s network.
• If it keeps failing, you can download=True locally and upload to Drive, but that’s rare.

3. Out-of-memory when converting to pandas / NumPy

The dataset object is lazy; converting everything to a huge DataFrame can kill RAM:

# Risky: loads everything
df = ds["train"].to_pandas()

Instead, sample or iterate in batches:

sample = ds["train"].shuffle(seed=42).select(range(5000))  # 5k rows

df = sample.to_pandas()

Make your Hugging Face pipeline reproducible with NoteCapsule

After you finally get the dataset + preprocessing + training working in Colab, you should capture that working state so it doesn’t break right before submission.

!pip install "git+https://github.com/YOUR_GITHUB_USERNAME/notecapsule.git@main"

from notecapsule import create_capsule

create_capsule(
    name="huggingface-experiment-v1",
    notebook_path="your_notebook.ipynb",
    data_dirs=["./"],
    base_dir="."
)