Stable-Diffusion-Pickle-Scanner-GUI VS spaCy

Pickle scanner: https://github.com/diStyApps/Stable-Diffusion-Pickle-Scanner-GUI

Did you do a pickle scan on it already? You can find Distys pickle scanner here. If it's clean, then you could load it into Auto1111 and just do a checkpoint merge of that model at "0" (Meaning 100%) and save it as safetensor.

Safetensor is a new file format for models. Checkpoints create a security risk, since they are Python code encapsulated in binary form, so a malicious actor could put whatever code they wanted in one of those, and which is why it's recommended that you only use .ckpt models from reputable sources or use a checker like this https://github.com/diStyApps/Stable-Diffusion-Pickle-Scanner-GUI to make sure they are "clean."

If you are concerned about malware being embedded in .ckpt files, you can use an online tool to scan the models, or my Stable-Diffusion-Pickle-Scanner-GUI tool to scan your models before converting them.

I also recently updated my Stable Diffusion Pickle Scanner GUI, which you can find here: https://github.com/diStyApps/Stable-Diffusion-Pickle-Scanner-GUI Let me know what you think and if you have any suggestions for improvement!

There is also an update for Stable-Diffusion-Pickle-Scanner-GUI https://github.com/diStyApps/Stable-Diffusion-Pickle-Scanner-GUI

Hi Community, In this article, I will demonstrate below steps to create your own chatbot by using spaCy (spaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython):

SpaCy: An open-source library providing tools for advanced NLP tasks like tokenization, entity recognition, and part-of-speech tagging.

spaCy https://spacy.io/

In this article, I'm going to walk through a sentiment analysis project from start to finish, using open-source Amazon product reviews. However, using the same approach, you can easily implement mass sentiment analysis on your own products. We'll explore an approach to sentiment analysis with one of the most popular Python NLP packages: spaCy.

Spacy [0] is a state-of-art / easy-to-use NLP library from the pre-LLM era. This post is the Spacy founder's thoughts on how to integrate LLMs with the kind of problems that "traditional" NLP is used for right now. It's an advertisement for Prodigy [1], their paid tool for using LLMs to assist data labeling. That said, I think I largely agree with the premise, and it's worth reading the entire post.

The steps described in "LLM pragmatism" are basically what I see my data science friends doing — it's hard to justify the cost (money and latency) in using LLMs directly for all tasks, and even if you want to you'll need a baseline model to compare against, so why not use LLMs for dataset creation or augmentation in order to train a classic supervised model?

[0] https://spacy.io/

[1] https://prodi.gy/

spaCy

I chose spacy. Although it's not state of the art, it's very well established and stable.