semvec/pypi-packages huggingface dataset:
nlp-dedup 0.1.2
Remove duplicates and near-duplicates from text corpora, no matter the scale.
License: MIT
Requires Python: >=3.8,<4.0

# NLPDedup

Remove duplicates and near-duplicates from text corpora, no matter the scale.

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[![Documentation](https://img.shields.io/badge/docs-passing-green)](https://saattrupdan.github.io/NLPDedup/nlp_dedup.html)
[![License](https://img.shields.io/github/license/saattrupdan/NLPDedup)](https://github.com/saattrupdan/NLPDedup/blob/main/LICENSE)
[![LastCommit](https://img.shields.io/github/last-commit/saattrupdan/NLPDedup)](https://github.com/saattrupdan/NLPDedup/commits/main)
[![Code Coverage](https://img.shields.io/badge/Coverage-0%25-red.svg)](https://github.com/saattrupdan/NLPDedup/tree/main/tests)


Developers:

- Dan Saattrup Nielsen (dan.nielsen@alexandra.dk)
- Kenneth Enevoldsen (kennethcenevoldsen@gmail.com)


# Installation

The package is available on PyPI, so you can install the package using your favourite
package manager. For instance, `pip install nlp_dedup` or `poetry add nlp_dedup`.


# Quick Start

If the corpus is stored as `corpus.txt` (both `txt` and `jsonl` files are supported),
the following deduplicates the corpus and stores the deduplicates corpus into the
folder `deduplicated`:

```
$ dedup corpus.txt deduplicated
```

This defaults to deduplicating based on blocks of 13 consecutive words, where two
documents are considered near-duplicate if they have more than 80% of these blocks in
common. This can all be changed to your specific needs, however. See `$ dedup --help`
for more information on all the settings.

Deduplication can also be done directly from Python:

```
>>> from nlp_dedup import Deduper
>>> deduper = Deduper()
>>> corpus = ["Test", "Another test", "Test"]
>>> deduper.deduplicate(corpus=corpus)
```

Here `corpus` does not have to be a list, but can also be an iterable or generator of
strings, if the corpus is too big to be stored in memory. Dictionaries are also
supported instead of strings, in which case the `text` entry in the dictionaries will
be used (change this with the `text_column` argument when calling `deduplicate`).

See more in [the documentation](https://saattrupdan.github.io/NLPDedup/nlp_dedup.html).