Neural-Scam-Artist VS Extracting-Training-Data-from-Large-Langauge-Models

Compare Neural-Scam-Artist vs Extracting-Training-Data-from-Large-Langauge-Models and see what are their differences.

Neural-Scam-Artist

Web Scraping, Document Deduplication & GPT-2 Fine-tuning with a newly created scam dataset. (by davidsvy)

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 (by shreyansh26)
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Neural-Scam-Artist Extracting-Training-Data-from-Large-Langauge-Models
2 1
23 26
- -
0.0 5.1
over 2 years ago almost 2 years ago
Python Python
MIT License MIT License
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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.

Extracting-Training-Data-from-Large-Langauge-Models

Posts with mentions or reviews of Extracting-Training-Data-from-Large-Langauge-Models. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing Neural-Scam-Artist and Extracting-Training-Data-from-Large-Langauge-Models you can also consider the following projects:

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

funcbygpt - tell the function what to do and gpt will write the code

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

finetune-gpt2xl - Guide: Finetune GPT2-XL (1.5 Billion Parameters) and finetune GPT-NEO (2.7 B) on a single GPU with Huggingface Transformers using DeepSpeed

LSH - Locality Sensitive Hashing using MinHash in Python/Cython to detect near duplicate text documents

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

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

intertext - Detect and visualize text reuse

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