AutoDedup at Scale: Cleaning Your Datasets with Google-Powered Precision

Deduplication has always been a painful, messy, often overlooked process.
And yet… in the age of foundation models and large-scale training data, one silent killer keeps haunting your models: duplicated data.

I ran into this issue again recently while preparing a bank default prediction pipeline.
Results looked suspiciously good. Feature importances felt too stable.

And sure enough—it was duplicated rows hiding in plain sight. That why I decided to write this article before sleeping to share some ideas and tips :

Why It Matters

In credit risk modeling, duplicated data can bias loss functions, overinflate the importance of features, and lead to overfitting with confidence. Just like poor risk assumptions contributed to Silicon Valley Bank's downfall (beautifully analyzed here by Prof. Ashwin Rao), duplicated assumptions in data can ruin a predictive system.

At first glance, duplicates seem harmless.
After all, they’re the same rows—just more of them, right?

Wrong.

In reality, duplicated entries:

• Skew your loss function

• Create artificial patterns (false feature importance)

• Bias your evaluation metrics

• Hurt generalization — especially on noisy, real-world data

If you're building risk scoring models, language models, or even just fine-tuning a public dataset:
You need a dedup strategy — and preferably one that goes beyond simple drop_duplicates().

The Problem: Duplication Is Not Just an Error — It’s a Risk

In modeling credit risk or banking default systems, especially in real-time environments, duplication can come from:

• Log replay in Kafka pipelines

• Sensor noise or submission retries in frontend systems

• Even poorly joined tables in BigQuery or more …

The Solution: Semantic + Syntactic Deduplication on Colab

With Google Colab, you can plug into Hugging Face datasets, visualize duplicates, and prototype pipelines that scale.

🔗 Try my full notebook here

Step 1: Load the Dataset

pythonCopierModifierfrom datasets import load_dataset
dataset = load_dataset("dair-ai/emotion")["train"]
texts = dataset["text"]

Step 2: Deduplicate with MinHash (via Rensa)

pythonCopierModifierfrom rensa import MinHashDeduplicator

deduper = MinHashDeduplicator(threshold=0.85)
unique_texts = deduper.deduplicate(texts)

print(f"Before: {len(texts)} | After: {len(unique_texts)}")

Step 3: Semantic Deduplication with SemHash + Google’s MiniLM

pythonCopierModifierfrom semhash.semantic_dedup import SemanticDeduplicator

deduper = SemanticDeduplicator(model_name="sentence-transformers/all-MiniLM-L6-v2")
deduper.build_index(texts)
deduped_idxs = deduper.get_deduplicated_indices(threshold=0.9)

deduped_texts = [texts[i] for i in deduped_idxs]

Uses Google-trained MiniLM-L6 — fast, accurate, and TinyML-ready.

Go Further with Google Cloud

Once you’ve got deduplication right locally, here’s how to scale:

Final Thoughts

Bad data decisions are like bad bond investments — they don’t seem wrong until it’s too late.
AutoDedup is not just a utility — it’s defensive AI hygiene.

With the right mix of:

• Google Colab for fast experimentation

• GCP services for real-time processing

• Open-source tools like Rensa & SemHash
  you can clean up your pipelines before they lead to model rot.

Before going, I just want to remind you this

Feedback is always welcome.

And as always, use AI responsibly.

If you like this content please like it ten times, share the best you can and let a comment or feedback.

@#PeaceAndLove

@Copyright_by_Kaz’Art

@ArthurStarks