semantic-search-through-wikipedia-with-weaviate
CLIP
semantic-search-through-wikipedia-with-weaviate | CLIP | |
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9 | 103 | |
223 | 22,209 | |
- | 2.5% | |
3.2 | 1.2 | |
11 months ago | 22 days ago | |
Python | Jupyter Notebook | |
MIT License | MIT License |
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semantic-search-through-wikipedia-with-weaviate
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Named entity recognition extraction from website
Although the Wikipedia demo dataset does not have NER enabled, you can play around with the interface. You can create a custom setup for NER using this configurator. Good luck!
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Find anything fast with Google's vector search technology
* Wikipedia demo dataset: https://github.com/semi-technologies/semantic-search-through...
- Semantic search through Wikipedia with the Weaviate vector search engine
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[D] Are you seeing any compelling use cases of semantic search being leveraged at scale?
Semantic search through Wikipedia with the Weaviate vector search engine
- [P] Semantic search through a vectorized Wikipedia
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Semantic search through complete EN-language Wikipedia with the Weaviate vector search engine
The source code to run the dataset yourself is completely open on Github
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Semantic search using GraphQL through the complete EN-Wikipedia
Github
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[P] Semantic search through Wikipedia with Weaviate and Sentence-BERT transformers
Github: https://github.com/semi-technologies/semantic-search-through-Wikipedia-with-Weaviate
CLIP
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How to Cluster Images
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:
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Show HN: Memories, FOSS Google Photos alternative built for high performance
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.
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Zero-Shot Prediction Plugin for FiftyOne
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:
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A History of CLIP Model Training Data Advances
(Github Repo | Most Popular Model | Paper | Project Page)
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NLP Algorithms for Clustering AI Content Search Keywords
the first thing that comes to mind is CLIP: https://github.com/openai/CLIP
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How to Build a Semantic Search Engine for Emojis
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.
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COMFYUI SDXL WORKFLOW INBOUND! Q&A NOW OPEN! (WIP EARLY ACCESS WORKFLOW INCLUDED!)
in the modal card it says: pretrained text encoders (OpenCLIP-ViT/G and CLIP-ViT/L).
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Stability Matrix v1.1.0 - Portable mode, Automatic updates, Revamped console, and more
Command: "C:\StabilityMatrix\Packages\stable-diffusion-webui\venv\Scripts\python.exe" -m pip install https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip --prefer-binary
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[D] LLM or model that does image -> prompt?
CLIP might work for your needs.
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Where can this be used? I have seen some tutorials to run deepfloyd on Google colab. Any way it can be done on local?
pip install deepfloyd_if==1.0.2rc0 pip install xformers==0.0.16 pip install git+https://github.com/openai/CLIP.git --no-deps pip install huggingface_hub --upgrade
What are some alternatives?
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
open_clip - An open source implementation of CLIP.
hnswlib - Header-only C++/python library for fast approximate nearest neighbors
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
Weaviate - Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database.
latent-diffusion - High-Resolution Image Synthesis with Latent Diffusion Models
biggraph-wikidata-search-with-weaviate - Search through Facebook Research's PyTorch BigGraph Wikidata-dataset with the Weaviate vector search engine
disco-diffusion
awesome-vector-search - Collections of vector search related libraries, service and research papers
DALLE2-pytorch - Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
google-research - Google Research
BLIP - PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation