huggingface_hub VS spaCy

Something to think about:

https://github.com/huggingface/huggingface_hub

Yes! We have a well documented Python library (https://github.com/huggingface/huggingface_hub) and public endpoints (https://huggingface.co/docs/hub/api#endpoints-table) you can use to retrieve information about the models and potentially build UIs with specific use cases in mind

i think if you search for pixel art here there are some models worth checking out: https://huggingface.co/

I received another error saying OSError: We couldn't connect to 'https://huggingface.co' to load this model, couldn't find it in the cached files and it looks like ./vae is not the path to a directory containing a file named diffusion_pytorch_model.bin

I plan to release it on https://huggingface.co/, where all this cool AI stuff is available for free for everyone that wishes to try it.

For this I used mainly the chainner application with models from here but I also used the google colab automatic1111 stable diffusion webui (for example for Lanczos) and also spaces fromhuggingface like this one or then from the replicate.com website super resolution collection.

you will now need to create a huggingface account ( https://huggingface.co/) if you haven't already. When you have, go here and accept the terms, https://huggingface.co/runwayml/stable-diffusion-v1-5. When you have done both, click on your profile icon and go to settings. Click access tokens and then create token, name it whatever you want, select "write". When you are finished with all this, then you can run the next cell which is the hugging face cell. It will ask for a token, you copy and paste what you just created.

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.