# Plagiarism Checker
This is a command-line tool for checking the similarity between a given text and a set of reference documents. The tool uses the Jaccard similarity algorithm to compare the input text with the reference documents.
## Installation
Install in an isolated environment using pipx (or normal pip):
```
pipx install sentence-plagiarism
```
## CLI Usage
To run the plagiarism checker, use the following command:
```sh
sentence-plagiarism <path-to-input-file> <path-to-reference-file-1> <path-to-reference-file-2> ... [--threshold <threshold-value>] [--output_file <path-to-output-file>] [--quiet]
```
- `<path-to-input-file>`: Path to the input file to be checked for plagiarism.
- `<path-to-reference-file-1> ...`: Paths to the reference files to compare against.
- `--threshold`: (optional) The minimum similarity score required to consider a sentence as plagiarized. The value should be between 0 and 1.
- `--output-file` (optional): Path to the output file to save the results in JSON format.
- `--quiet` (optional): Flag to suppress the display of similar sentences in the console.
## Example
The following command:
```sh
sentence-plagiarism input.txt --reference-files ref1.txt ref2.txt --similarity-threshold 0.8 --output-file results.json
```
can produce the following output on stdout:
```
Input Sentence: The retriever and seq2seq modules commence their operations as pretrained models, and through a joint fine-tuning process, they adapt collaboratively, thus enhancing both retrieval and generation for specific downstream tasks.
Reference Sentence: foobar The retriever and seq2seq modules commence their operations as pretrained models, and through a joint fine-tuning process, they adapt collaboratively, thus enhancing both retrieval and generation for specific downstream tasks.
Reference Document: ref1.txt
Similarity Score: 0.9667
Input Sentence: Closing thoughts For a comprehensive understanding of the RAG technique, we offer an in-depth exploration, commencing with a simplified overview and progressively delving into more intricate technical facets.
Reference Sentence: barfoo For a comprehensive understanding of the RAG technique, we offer an in-depth exploration, commencing with a simplified overview and progressively delving into more intricate technical facets.
Reference Document: ref2.txt
Similarity Score: 0.8966
Results saved to results.json
```
and save results to `results.json`.
## Programmatic Usage
```python
from sentence_plagiarism import check
check(
examined_file="txt/txt1.txt",
reference_files=["txt/txt2.txt", "txt/txt3.txt"],
similarity_threshold=0.8,
output_file=None,
quiet=False,
)
```
## License
Distributed under the MIT License. See `LICENSE` for more information.
## Contact
Krystian Safjan - ksafjan@gmail.com
Project Link: [https://github.com/izikeros/sentence-plagiarism](https://github.com/izikeros/sentence-plagiarism)
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