tiktoken VS CLIP

Their tokenizer is open source: https://github.com/openai/tiktoken

Data files that contain vocabulary are listed here: https://github.com/openai/tiktoken/blob/9e79899bc248d5313c7d...

OpenAI's refference tokeniser - https://github.com/openai/tiktoken

ChatGPT presumably adds them as special tokens to the cl100k_base tokenizer, as they demo in the tiktoken documentation: https://github.com/openai/tiktoken#extending-tiktoken

I want to measure the approximate number of tokens in a piece of text to understand if I will need to modify it before passing it into the context of an OpenAI API call. Tiktoken can do this, but I'm not sure if it's overkill to use that library just for this simple task. I don't need to actually tokenize the text, I just need an approximate count (e.g. within like 1% of the text's actual token length for text that represents the visible text on a webpage).

https://platform.openai.com/tokenizer or the official python library tiktoken https://github.com/openai/tiktoken or this JS port of tiktoken https://github.com/dqbd/tiktoken

It's built on top of the tiktoken library and is basically just a lambda function in the backend.

"All" is a single token in OpenAI's tiktoken Tokenizer, unrelated to the token for capital "A". Even lowercase "all" is a distinct token from "All" or "ALL."

check tiktoken

pip install -U huggingface_hub umap-learn git+https://github.com/openai/CLIP.git

We will also need two more libraries: OpenAI’s CLIP GitHub repo, enabling us to generate image features with the CLIP model, and the umap-learn library, which will let us apply a dimensionality reduction technique called Uniform Manifold Approximation and Projection (UMAP) to those features to visualize them in 2D:

Biggest missing feature for all these self hosted photo hosting is the lack of a real search. Being able to search for things like "beach at night" is a time saver instead of browsing through hundreds or thousands of photos. There are trained neural networks out there like https://github.com/openai/CLIP which are quite good.

In computer vision, this is known as zero-shot learning, or zero-shot prediction, because the goal is to generate predictions without explicitly being given any example predictions to learn from. With the advent of high quality multimodal models like CLIP and foundation models like Segment Anything, it is now possible to generate remarkably good zero-shot predictions for a variety of computer vision tasks, including:

(Github Repo | Most Popular Model | Paper | Project Page)

the first thing that comes to mind is CLIP: https://github.com/openai/CLIP

Whenever I’m working on semantic search applications that connect images and text, I start with a family of models known as contrastive language image pre-training (CLIP). These models are trained on image-text pairs to generate similar vector representations or embeddings for images and their captions, and dissimilar vectors when images are paired with other text strings. There are multiple CLIP-style models, including OpenCLIP and MetaCLIP, but for simplicity we’ll focus on the original CLIP model from OpenAI. No model is perfect, and at a fundamental level there is no right way to compare images and text, but CLIP certainly provides a good starting point.

in the modal card it says: pretrained text encoders (OpenCLIP-ViT/G and CLIP-ViT/L).

Command: "C:\StabilityMatrix\Packages\stable-diffusion-webui\venv\Scripts\python.exe" -m pip install https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip --prefer-binary

CLIP might work for your needs.