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Showing 131 open source projects for "can"
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Satori
Enlightened library to convert HTML and CSS to SVG
...Under the hood, it handles layout calculation, font, typography and more, to generate a SVG that matches the exact same HTML and CSS in a browser. Satori only accepts JSX elements that are pure and stateless. You can use a subset of HTML elements (see section below), or custom React components, but React APIs such as useState, useEffect, dangerouslySetInnerHTML are not supported. Satori supports a limited subset of HTML and CSS features, due to its special use cases. In general, only these static and visible elements and properties that are implemented. ... -
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Diffusers
State-of-the-art diffusion models for image and audio generation
...Our library is designed with a focus on usability over performance, simple over easy, and customizability over abstractions. State-of-the-art diffusion pipelines that can be run in inference with just a few lines of code. Interchangeable noise schedulers for different diffusion speeds and output quality. Pretrained models that can be used as building blocks, and combined with schedulers, for creating your own end-to-end diffusion systems. We recommend installing Diffusers in a virtual environment from PyPi or Conda. ... -
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InvokeAI
InvokeAI is a leading creative engine for Stable Diffusion models
...InvokeAI offers an industry leading Web Interface, interactive Command Line Interface, and also serves as the foundation for multiple commercial products. This fork is supported across Linux, Windows and Macintosh. Linux users can use either an Nvidia-based card (with CUDA support) or an AMD card (using the ROCm driver). We do not recommend the GTX 1650 or 1660 series video cards. They are unable to run in half-precision mode and do not have sufficient VRAM to render 512x512 images. -
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SentenceTransformers
Multilingual sentence & image embeddings with BERT
SentenceTransformers is a Python framework for state-of-the-art sentence, text and image embeddings. The initial work is described in our paper Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. You can use this framework to compute sentence / text embeddings for more than 100 languages. These embeddings can then be compared e.g. with cosine-similarity to find sentences with a similar meaning. This can be useful for semantic textual similar, semantic search, or paraphrase mining. The framework is based on PyTorch and Transformers and offers a large collection of pre-trained models tuned for various tasks. ... -
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Synthetic Data Vault (SDV)
Synthetic Data Generation for tabular, relational and time series data
The Synthetic Data Vault (SDV) is a Synthetic Data Generation ecosystem of libraries that allows users to easily learn single-table, multi-table and timeseries datasets to later on generate new Synthetic Data that has the same format and statistical properties as the original dataset. Synthetic data can then be used to supplement, augment and in some cases replace real data when training Machine Learning models. Additionally, it enables the testing of Machine Learning or other data dependent software systems without the risk of exposure that comes with data disclosure. Underneath the hood it uses several probabilistic graphical modeling and deep learning based techniques. ... -
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Simple StyleGan2 for Pytorch
Simplest working implementation of Stylegan2
Simple Pytorch implementation of Stylegan2 that can be completely trained from the command-line, no coding needed. You will need a machine with a GPU and CUDA installed. You can also specify the location where intermediate results and model checkpoints should be stored. You can increase the network capacity (which defaults to 16) to improve generation results, at the cost of more memory. -
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CodiumAI PR-Agent
AI-Powered tool for automated pull request analysis
CodiumAI PR-Agent is an open-source tool aiming to help developers review pull requests faster and more efficiently. It automatically analyzes the pull request and can provide several types of commands. See the Usage Guide for instructions how to run the different tools from CLI, online usage, Or by automatically triggering them when a new PR is opened. You can try GPT-4 powered PR-Agent, on your public GitHub repository, instantly. Just mention @CodiumAI-Agent and add the desired command in any PR comment. ... -
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BERTopic
Leveraging BERT and c-TF-IDF to create easily interpretable topics
...BERTopic supports guided, supervised, semi-supervised, manual, long-document, hierarchical, class-based, dynamic, and online topic modeling. It even supports visualizations similar to LDAvis! Corresponding medium posts can be found here, here and here. For a more detailed overview, you can read the paper or see a brief overview. After having trained our BERTopic model, we can iteratively go through hundreds of topics to get a good understanding of the topics that were extracted. However, that takes quite some time and lacks a global representation. ... -
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PHP Client For NLP Cloud
NLP Cloud serves high performance pre-trained or custom models for NER
NLP Cloud serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, dialogue summarization, paraphrasing, intent classification, product description and ad generation, chatbot, grammar and spelling correction, keywords and keyphrases extraction, text generation, image generation, blog post generation, code generation, question answering, automatic speech recognition, machine translation, language detection, semantic search, semantic similarity, tokenization, POS tagging, embeddings, and dependency parsing. It is ready for production, served through a REST API. You can either use the NLP Cloud pre-trained models, fine-tune your own models, or deploy your own models. Pass the model you want to use and the NLP Cloud token to the client during initialization. If you are making asynchronous requests, you will always receive a quick response containing a URL. -
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pytorch-cpp
C++ Implementation of PyTorch Tutorials for Everyone
...Libtorch only supports 64bit Windows and an x64 generator needs to be specified. Create all required script module files for pre-learned models/weights during the build. Requires installed python3 with PyTorch and torch-vision. You can choose to only build tutorials in one of the categories basics, intermediate, advanced or popular. You can build and run the tutorials (on CPU) in a Docker container using the provided Dockerfile and docker-compose.yml files. -
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SDGym
Benchmarking synthetic data generation methods
...Measure performance and memory usage across different synthetic data modeling techniques – classical statistics, deep learning and more! The SDGym library integrates with the Synthetic Data Vault ecosystem. You can use any of its synthesizers, datasets or metrics for benchmarking. You also customize the process to include your own work. Select any of the publicly available datasets from the SDV project, or input your own data. Choose from any of the SDV synthesizers and baselines. Or write your own custom machine learning model. In addition to performance and memory usage, you can also measure synthetic data quality and privacy through a variety of metrics. ... -
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Lightweight' GAN
Implementation of 'lightweight' GAN, proposed in ICLR 2021
...Quoting the one-line summary "converge on single gpu with few hours' training, on 1024 resolution sub-hundred images". Augmentation is essential for Lightweight GAN to work effectively in a low data setting. You can test and see how your images will be augmented before they pass into a neural network (if you use augmentation). The general recommendation is to use suitable augs for your data and as many as possible, then after some time of training disable the most destructive (for image) augs. You can turn on automatic mixed precision with one flag --amp. ... -
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AudioLM - Pytorch
Implementation of AudioLM audio generation model in Pytorch
Implementation of AudioLM, a Language Modeling Approach to Audio Generation out of Google Research, in Pytorch It also extends the work for conditioning with classifier free guidance with T5. This allows for one to do text-to-audio or TTS, not offered in the paper. Yes, this means VALL-E can be trained from this repository. It is essentially the same. This repository now also contains a MIT licensed version of SoundStream. It is also compatible with EnCodec, however, be aware that it has a more restrictive non-commercial license, if you choose to use it. -
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gptcommit
A git prepare-commit-msg hook for authoring commit messages with GPT-3
A git prepare-commit-msg hook for authoring commit messages with GPT-3. With this tool, you can easily generate clear, comprehensive and descriptive commit messages letting you focus on writing code. To use gptcommit, simply run git commit as you normally would. The hook will automatically generate a commit message for you using a large language model like GPT. If you're not satisfied with the generated message, you can always edit it before committing. -
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LangChain
⚡ Building applications with LLMs through composability ⚡
Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. But using these LLMs in isolation is often not enough to create a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge. This library is aimed at assisting in the development of those types of applications. -
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Haystack
Haystack is an open source NLP framework to interact with your data
Apply the latest NLP technology to your own data with the use of Haystack's pipeline architecture. Implement production-ready semantic search, question answering, summarization and document ranking for a wide range of NLP applications. Evaluate components and fine-tune models. Ask questions in natural language and find granular answers in your documents using the latest QA models with the help of Haystack pipelines. Perform semantic search and retrieve ranked documents according to meaning,... -
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ML for Trading
Code for machine learning for algorithmic trading, 2nd edition
On over 800 pages, this revised and expanded 2nd edition demonstrates how ML can add value to algorithmic trading through a broad range of applications. Organized in four parts and 24 chapters, it covers the end-to-end workflow from data sourcing and model development to strategy backtesting and evaluation. Covers key aspects of data sourcing, financial feature engineering, and portfolio management. The design and evaluation of long-short strategies based on a broad range of ML algorithms, how to extract tradeable signals from financial text data like SEC filings, earnings call transcripts or financial news. ... -
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GPTel
A no-frills ChatGPT client for Emacs
...Interact with ChatGPT from any buffer in Emacs. ChatGPT’s responses are in Markdown or Org markup (configurable). Supports conversations (not just one-off queries) and multiple independent sessions. You can go back and edit your previous prompts, or even ChatGPT’s previous responses when continuing a conversation. These will be fed back to ChatGPT. Run M-x gptel to start or switch to the ChatGPT buffer. It will ask you for the key if you skipped the previous step. Run it with a prefix-arg to start a new session. In the gptel buffer, send your prompt with M-x gptel-send, bound to C-c RET. ... -
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PaddleNLP
Easy-to-use and powerful NLP library with Awesome model zoo
PaddleNLP It is a natural language processing development library for flying paddles, with Easy-to-use text area API, Examples of applications for multiple scenarios, and High-performance distributed training Three major features, aimed at improving the modeling efficiency of the flying oar developer's text field, aiming to improve the developer's development efficiency in the text field, and provide rich examples of NLP applications. Provide rich industry-level pre-task capabilities Taskflow And process-wide text area API: Support for the loading of rich Chinese data sets Dataset API, can flexibly and efficiently complete data pretreatment Data API, Preset 60 + pre-training word vector Embedding API, Providing 100 + pre-training model Transformer API Wait, the efficiency of NLP task modeling can be greatly improved. -
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Albumentations
Fast image augmentation library and an easy-to-use wrapper
...Albumentations works well with data from different domains: photos, medical images, satellite imagery, manufacturing and industrial applications, Generative Adversarial Networks. Albumentations can work with various deep learning frameworks such as PyTorch and Keras. -
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marqo
Tensor search for humans
A tensor-based search and analytics engine that seamlessly integrates with your applications, websites, and workflows. Marqo is a versatile and robust search and analytics engine that can be integrated into any website or application. Due to horizontal scalability, Marqo provides lightning-fast query times, even with millions of documents. Marqo helps you configure deep-learning models like CLIP to pull semantic meaning from images. It can seamlessly handle image-to-image, image-to-text and text-to-image search and analytics. ... -
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Phenaki - Pytorch
Implementation of Phenaki Video, which uses Mask GIT
...It will also combine another technique involving a token critic for potentially even better generations. A new paper suggests that instead of relying on the predicted probabilities of each token as a measure of confidence, one can train an extra critic to decide what to iteratively mask during sampling. This repository will also endeavor to allow the researcher to train on text-to-image and then text-to-video. Similarly, for unconditional training, the researcher should be able to first train on images and then fine tune on video. -
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BertViz
BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)
BertViz is an interactive tool for visualizing attention in Transformer language models such as BERT, GPT2, or T5. It can be run inside a Jupyter or Colab notebook through a simple Python API that supports most Huggingface models. BertViz extends the Tensor2Tensor visualization tool by Llion Jones, providing multiple views that each offer a unique lens into the attention mechanism. The head view visualizes attention for one or more attention heads in the same layer. -
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Python Client For NLP Cloud
NLP Cloud serves high performance pre-trained or custom models for NER
NLP Cloud serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, dialogue summarization, paraphrasing, intent classification, product description and ad generation, chatbot, grammar and spelling correction, keywords and keyphrases extraction, text generation, image generation, blog post generation, source code generation, question answering, automatic speech recognition, machine translation, language detection, semantic search, semantic similarity, tokenization, POS tagging, embeddings, and dependency parsing. It is ready for production, served through a REST API. You can either use the NLP Cloud pre-trained models, fine-tune your own models, or deploy your own models. -
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Regex
Generate matching and non matching strings based on regex patterns
Generate matching and non-matching strings. This is a java library that, given a regex pattern, allows to generation of matching strings. Iterate through unique matching strings. Generate not matching strings. Follow the link to Online IDE with created project: JDoodle. Enter your pattern and see the results. By design a+, a* and a{n,} patterns in regex imply an infinite number of characters should be matched. When generating data, that would mean values of infinite length might be...