MetaSpore
CLIP
MetaSpore | CLIP | |
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11 | 103 | |
628 | 22,209 | |
0.2% | 2.5% | |
3.7 | 1.2 | |
28 days ago | 19 days ago | |
Python | Jupyter Notebook | |
Apache License 2.0 | MIT License |
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MetaSpore
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Quickly develop risk control algorithms in business scenarios based on MetaSpore
The evaluation problems related to financial loans are mainly based on tabular data, so the importance of feature engineering is self-evident. The common features in the dataset include ID type, Categorical type, and continuous number type, which require common data handling such as EDA, missing value completion, outlier processing, normalization, feature binning, and importance assessment. The process can reference the GitHub codebase: https://github.com/meta-soul/MetaSpore/blob/main/demo/dataset, which part about tianchi_loan instructions.
DMetaSoul uses MetaSpore on AlphaIDE to quickly implement a loan default rate prediction model on an open-source dataset and build a scorecard based on this model. Based on the Demo system of this version, the methods of feature derivation, binning, and screening can be more delicate, which often determines the upper limit of the performance of the risk control system. Finally, give the address of the code base and the AlphaIDE trial link (AlphaIDE tutorial): Default rate forecast: https://github.com/meta-soul/MetaSpore/tree/main/demo/riskmodels/loan_default MetaSpore's one-stop machine learning development platform: https://github.com/meta-soul/MetaSpore AlphaIDE trial link: https://registry-alphaide.dmetasoul.com
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A New One-stop AI development and production platform, AlphaIDE
I’ve posted about LakeSoul, an open-source framework for unified streaming and batch table storage, and MetaSpore, an open-source platform for machine learning.
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Usage Guide:Quickly deploy an intelligent data platform with the One-stop AI development and production platform, AlphaIDE
AlphaIDE is already integrated with MetaSpore. You can test MetaSpore’s introductory tutorial Notebook: https://github.com/meta-soul/MetaSpore/blob/main/tutorials/metaspore-getting-started.ipynb.
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[P]MMML | Deploy HuggingFace training model rapidly based on MetaSpore
Presented here on the lot code: https://github.com/meta-soul/MetaSpore/compare/add_python_preprocessor
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MMML | Deploy HuggingFace training model rapidly based on MetaSpore
DMetaSoul aims at the above technical pain points, abstracting and uniting many links such as model training optimization, online reasoning, and algorithm experiment, forming a set of solutions that can quickly apply offline pre-training model to online. This paper will introduce how to use the HuggingFace community pre-training model to conduct online reasoning and algorithm experiments based on MetaSpore technology ecology so that the benefits of the pre-training model can be fully released to the specific business or industry and small and medium-sized enterprises. And we will give the text search text and text search graph two multimodal retrieval demonstration examples for your reference.
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MMML | Deployment HuggingFace training model rapidly based on MetaSpore
A few days ago, HuggingFace announced a $100 million Series C funding round, which was big news in open source machine learning and could be a sign of where the industry is headed. Two days before the HuggingFace funding announcement, open-source machine learning platform MetaSpore released a demo based on the HuggingFace Rapid deployment pre-training model.
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The design concept of an almighty Opensource project about machine learning platform
2.5 [MetaSpore](https://github.com/meta-soul/MetaSpore**) online algorithm application framework** Offline training frameworks and online Serving services are now available. Then, an algorithm in the business scene landing is still a final step: an online algorithm experiment. In a service scenario, to verify the validity of an algorithm model, a baseline needs to be established and compared with the new algorithm model. Therefore, an online experimental framework is needed which can easily define algorithm experiments, read online features, and call model prediction services. In addition, multiple experiments can be traffic segmented to achieve ABTest effect comparison. A configuration center is also needed to quickly carry out multiple experimental iterations, which can dynamically load refresh experiments and cut flow configurations, support hot loading of experimental parameters, and various debugging and trace functions. This link also directly determines whether the AI model can be finally implemented into practical business applications.
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Almighty Opensource project about machine learning you should try out
MetaSpore, it has to be said, is a new machine learning platform with transcendent qualities that can solve problems that other products cannot. However, as a new open source project, it still has a lot to go, and I'll be keeping an eye on MetaSpore and sharing and reposting more information.
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A new machine learning platform that helps you quickly build industrial-grade recommendation systems
MetaSpore is an open-source one-stop machine learning development platform produced by DMetaSoul, providing the whole process framework and development interface from data preprocessing, model training, offline experiment, and online prediction to online experiment bucket ABTest. It is hoped that users can quickly build industrial-grade AI systems with distributed machine learning training, high-performance model reasoning, high availability AB experimental framework, and other capabilities in a low-code way based on MetaSpore.
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?
LakeSoul - LakeSoul is an end-to-end, realtime and cloud native Lakehouse framework with fast data ingestion, concurrent update and incremental data analytics on cloud storages for both BI and AI applications.
open_clip - An open source implementation of CLIP.
Best_AI_paper_2020 - A curated list of the latest breakthroughs in AI by release date with a clear video explanation, link to a more in-depth article, and code
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
onepanel - The open source, end-to-end computer vision platform. Label, build, train, tune, deploy and automate in a unified platform that runs on any cloud and on-premises.
latent-diffusion - High-Resolution Image Synthesis with Latent Diffusion Models
Deep-Learning-In-Production - Build, train, deploy, scale and maintain deep learning models. Understand ML infrastructure and MLOps using hands-on examples.
disco-diffusion
AirSim - Open source simulator for autonomous vehicles built on Unreal Engine / Unity, from Microsoft AI & Research
DALLE2-pytorch - Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
BLIP - PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows