LSH VS Neural-Scam-Artist

Compare LSH vs Neural-Scam-Artist and see what are their differences.

LSH

Locality Sensitive Hashing using MinHash in Python/Cython to detect near duplicate text documents (by mattilyra)

Neural-Scam-Artist

Web Scraping, Document Deduplication & GPT-2 Fine-tuning with a newly created scam dataset. (by davidsvy)
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LSH Neural-Scam-Artist
1 2
273 22
- -
2.8 0.0
11 months ago over 2 years ago
Python Python
MIT License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

LSH

Posts with mentions or reviews of LSH. We have used some of these posts to build our list of alternatives and similar projects.

Neural-Scam-Artist

Posts with mentions or reviews of Neural-Scam-Artist. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing LSH and Neural-Scam-Artist you can also consider the following projects:

datasketch - MinHash, LSH, LSH Forest, Weighted MinHash, HyperLogLog, HyperLogLog++, LSH Ensemble and HNSW

image-ndd-lsh - Near-duplicate image detection using Locality Sensitive Hashing

bertviz - BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)

Cython - The most widely used Python to C compiler

transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

dedup - Find duplicate text files.

Transformers4Rec - Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation and works with PyTorch.

intertext - Detect and visualize text reuse

Extracting-Training-Data-from-Large-Langauge-Models - A re-implementation of the "Extracting Training Data from Large Language Models" paper by Carlini et al., 2020

tasksource - Datasets collection and standardization preprocessings for NLP extreme multitask learning