SemHash can be used to efficiently deduplicate datasets based on semantic similarity. This allows you to not only remove exact duplicates and near-duplicates, but also samples that are semantically similar, unlike methods like MinHash, which only consider matches that share exact n-grams.

This is particularly useful for cleaning up datasets where you want to ensure that similar entries are not counted multiple times, such as in training datasets for machine learning models, or a RAG application where you want to avoid redundancy in your knowledge base.

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Initialize a SemHash Instance

To use SemHash for deduplication, you first need to initialize a SemHash instance with your dataset. This will build an index, which can then be used for fast deduplication. You can set several parameters here, such as the model to use. The default model is minishlab/potion-base-8M, which is a lightweight model that works well for most English text datasets. For multilingual datasets, you can use minishlab/potion-multilingual-128M, which is optimized for multilingual data. Note that you can also use your own custom model, or any SentenceTransformer model.

from datasets import load_dataset
from semhash import SemHash

# Initialize a SemHash instance
semhash = SemHash.from_records(records=texts)

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Deduplicate a Single Dataset

To deduplicate a single dataset, you can use the self_deduplicate method. This will remove semantic duplicates from the dataset.

from datasets import load_dataset
from semhash import SemHash

# Load a dataset to deduplicate
texts = load_dataset("ag_news", split="train")["text"]

# Initialize a SemHash instance
semhash = SemHash.from_records(records=texts)

# Deduplicate the texts
deduplicated_texts = semhash.self_deduplicate().selected

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Deduplicate Across Multiple Datasets

To deduplicate across multiple datasets, you can use the deduplicate method. This allows you to remove duplicates from one dataset against another dataset, which is useful for ensuring that your test set does not overlap with your training set.

from datasets import load_dataset
from semhash import SemHash

# Load two datasets to deduplicate
train_texts = load_dataset("ag_news", split="train")["text"]
test_texts = load_dataset("ag_news", split="test")["text"]

# Initialize a SemHash instance with the training data
semhash = SemHash.from_records(records=train_texts)

# Deduplicate the test data against the training data, optionally with a specific threshold
deduplicated_test_texts = semhash.deduplicate(records=test_texts).selected

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Deduplicate a Multi-Column Dataset

If you have a multi-column dataset, you can deduplicate it by specifying the columns to use for deduplication. For example, if you have a question-answering dataset with question and context columns, you can deduplicate based on both columns. This will filter out records that have similar questions and contexts, ensuring that you do not have redundant entries in your dataset. This is useful for datasets like SQuAD, where you can have the same question asked with different contexts, and you want to ensure that each question-context pair is unique.

from datasets import load_dataset
from semhash import SemHash

# Load the dataset
dataset = load_dataset("squad_v2", split="train")

# Convert the dataset to a list of dictionaries
records = [dict(row) for row in dataset]

# Initialize SemHash with the columns to deduplicate
semhash = SemHash.from_records(records=records, columns=["question", "context"])

# Deduplicate the records
deduplicated_records = semhash.self_deduplicate().selected

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DeduplicationResult Functionality

The DeduplicationResult object returned by the deduplication methods provides several useful attributes that help you understand the deduplication process:

  • selected: The deduplicated records.
  • filtered: The records that were removed as duplicates. This is returned as a list of DeduplicationResult, each consisting of:
    • record: The original record that was removed.
    • exact: Whether the record was an exact duplicate.
    • duplicates: A list of records that were considered duplicates of the removed record, along with their similarity score.
  • duplicate_ratio: The ratio of duplicates found in the dataset.
  • exact_duplicate_ratio: The ratio of exact duplicates found in the dataset.

It also provides several useful methods:

  • get_least_similar_from_duplicates(n): Returns the least similar record from the duplicates. This allows you to easily find the right deduplcation threhsold.
  • rethreshold(threshold): Re-applies the deduplication with a new threshold, allowing you to adjust the sensitivity of the deduplication process without rebuilding the SemHas index.