similarity
haystack
| similarity | haystack | |
|---|---|---|
| 7 | 55 | |
| 996 | 13,711 | |
| 0.2% | 3.1% | |
| 6.5 | 9.9 | |
| about 1 month ago | 4 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
similarity
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New free tool that uses fine-tuned BERT model to surface answers from research papers
Tensorflow Ranking and Tensorflow similarity (maybe relevant/irrelevant contrastive learning?) look like they could be useful.
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Non-Machine Learning Image Matching with a Vector DB
There is the metric learning problem to learn a hash for similarity https://github.com/tensorflow/similarity
That said, I don't see many good models available for download on tfhub or huggingface optimized for it, but you can always programmatically modify your images (if you truly mean identical to humans) - change white balance, crop, rotate, select adjacent frames from videos, etc. and optimize a network that is small enough for you to be satisfied and see if that works, as a possible alternative.
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Face Detection for 520 People
Metric learning has great implementations inside Tensorflow Similarity library: https://github.com/tensorflow/similarity Although the documentation is quite bad, but the jupyter notebooks are great.
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[P] TensorFlow Similarity 0.16 is out
Just a quick note that TensorFlow Similarity 0.16 is out -- this release beside adding the XMB loss is mostly focus on refactoring and optimizing the core components to ensure everything works smoothly and accurately. Details are in the changelog as usual and a simple pip install -U tensorflow_similarity should just work.
- Self-supervised learning added to TensorFlow Similarity
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[P] TensorFlow Similarity now self-supervised training
Very happy to announce that as part of the 0.15 release, TensorFlow Similarity now support self-supervised learning using STOA algorithms. To help you get started we included in the release a detailed getting started notebook that you can run in Colab. This notebook shows you how to use SimSiam self-supervised pre-training to almost double the accuracy compared to a model trained from scratch on CIFAR 10.
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TensorFlow Introduces ‘TensorFlow Similarity’, An Easy And Fast Python Package To Train Similarity Models Using TensorFlow
Github: https://github.com/tensorflow/similarity
haystack
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Haystack DB – 10x faster than FAISS with binary embeddings by default
I was confused for a bit but there is no relation to https://haystack.deepset.ai/
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Release Radar • March 2024 Edition
View on GitHub
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First 15 Open Source Advent projects
4. Haystack by Deepset | Github | tutorial
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Generative AI Frameworks and Tools Every Developer Should Know!
Haystack can be classified as an end-to-end framework for building applications powered by various NLP technologies, including but not limited to generative AI. While it doesn't directly focus on building generative models from scratch, it provides a robust platform for:
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Best way to programmatically extract data from a set of .pdf files?
But if you want an API that you can use to develop your own flow, Haystack from Deepset could be worth a look.
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Which LLM framework(s) do you use in production and why?
Haystack for production. We cannot afford breaking changes in our production apps. Its stable, documentation is excellent and did I mention its' STABLE!??
- Overview: AI Assembly Architectures
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Llama2 and Haystack on Colab
I recently conducted some experiments with Llama2 and Haystack (https://github.com/deepset-ai/haystack), the NLP/LLM framework.
The notebook can be helpful for those trying to load Llama2 on Colab.
1) Installed Transformers from the main branch (and other libraries)
- Build with LLMs for production with Haystack – has 10k stars on GitHub
- Show HN: Haystack – Production-Ready LLM Framework
What are some alternatives?
pytorch-metric-learning - The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.
langchain - 🦜🔗 Build context-aware reasoning applications
pgANN - Fast Approximate Nearest Neighbor (ANN) searches with a PostgreSQL database.
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
quaterion - Blazing fast framework for fine-tuning similarity learning models
gpt-neo - An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library.
ContraD - Code for the paper "Training GANs with Stronger Augmentations via Contrastive Discriminator" (ICLR 2021)
BentoML - The most flexible way to serve AI/ML models in production - Build Model Inference Service, LLM APIs, Inference Graph/Pipelines, Compound AI systems, Multi-Modal, RAG as a Service, and more!
Real-Time-Voice-Cloning - Clone a voice in 5 seconds to generate arbitrary speech in real-time
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
sparse_dot_topn - Python package to accelerate the sparse matrix multiplication and top-n similarity selection
jina - ☁️ Build multimodal AI applications with cloud-native stack