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1,427 | llm | https://github.com/declare-lab/instruct-eval | [] | null | [] | [] | null | null | null | declare-lab/instruct-eval | instruct-eval | 404 | 29 | 12 | Python | https://declare-lab.net/instruct-eval/ | This repository contains code to quantitatively evaluate instruction-tuned models such as Alpaca and Flan-T5 on held-out tasks. | declare-lab | 2024-01-12 | 2023-03-28 | 44 | 9.181818 | https://avatars.githubusercontent.com/u/59164695?v=4 | This repository contains code to quantitatively evaluate instruction-tuned models such as Alpaca and Flan-T5 on held-out tasks. | ['instruct-tuning', 'llm'] | ['instruct-tuning', 'llm'] | 2023-09-26 | [('instruction-tuning-with-gpt-4/gpt-4-llm', 0.653624415397644, 'llm', 0), ('tiger-ai-lab/mammoth', 0.6364500522613525, 'llm', 0), ('yizhongw/self-instruct', 0.550851047039032, 'llm', 0), ('hiyouga/llama-factory', 0.5344966650009155, 'llm', 1), ('hiyouga/llama-efficient-tuning', 0.5344966053962708, 'llm', 1), ('zrrskywalker/llama-adapter', 0.5260562896728516, 'llm', 0), ('tatsu-lab/stanford_alpaca', 0.5018754005432129, 'llm', 0)] | 3 | 1 | null | 2.75 | 7 | 0 | 10 | 4 | 0 | 0 | 0 | 7 | 2 | 90 | 0.3 | 28 |
1,619 | testing | https://github.com/samuelcolvin/pytest-pretty | ['pytest'] | null | [] | [] | null | null | null | samuelcolvin/pytest-pretty | pytest-pretty | 388 | 6 | 7 | Python | null | pytest plugin for pretty printing the test summary. | samuelcolvin | 2024-01-04 | 2022-10-25 | 66 | 5.878788 | null | pytest plugin for pretty printing the test summary. | [] | ['pytest'] | 2023-05-04 | [('pytest-dev/pytest-cov', 0.6255528926849365, 'testing', 1), ('pytest-dev/pytest', 0.5827073454856873, 'testing', 0), ('teemu/pytest-sugar', 0.5765059590339661, 'testing', 1), ('pytest-dev/pytest-mock', 0.5683526992797852, 'testing', 1), ('pytest-dev/pytest-xdist', 0.5537928342819214, 'testing', 1), ('inducer/pudb', 0.5497497320175171, 'debug', 1), ('ionelmc/pytest-benchmark', 0.5456304550170898, 'testing', 1), ('kiwicom/pytest-recording', 0.544182538986206, 'testing', 1), ('computationalmodelling/nbval', 0.5243958234786987, 'jupyter', 1), ('samuelcolvin/dirty-equals', 0.5239970088005066, 'util', 1), ('taverntesting/tavern', 0.5094279050827026, 'testing', 1), ('hugovk/pypistats', 0.5067400932312012, 'util', 0), ('nedbat/coveragepy', 0.506611704826355, 'testing', 0)] | 5 | 4 | null | 0.29 | 0 | 0 | 15 | 8 | 5 | 4 | 5 | 0 | 0 | 90 | 0 | 28 |
708 | ml-ops | https://github.com/unionai-oss/unionml | [] | null | [] | [] | null | null | null | unionai-oss/unionml | unionml | 323 | 43 | 4 | Python | https://www.union.ai/unionml | UnionML: the easiest way to build and deploy machine learning microservices | unionai-oss | 2024-01-11 | 2021-11-17 | 114 | 2.812189 | https://avatars.githubusercontent.com/u/94206482?v=4 | UnionML: the easiest way to build and deploy machine learning microservices | ['machine-learning', 'mlops'] | ['machine-learning', 'mlops'] | 2023-09-27 | [('ml-tooling/opyrator', 0.6805552840232849, 'viz', 1), ('polyaxon/polyaxon', 0.6547122597694397, 'ml-ops', 2), ('kubeflow/pipelines', 0.6105091571807861, 'ml-ops', 2), ('fmind/mlops-python-package', 0.5882454514503479, 'template', 1), ('bodywork-ml/bodywork-core', 0.5865074396133423, 'ml-ops', 2), ('titanml/takeoff', 0.56184321641922, 'llm', 0), ('microsoft/nni', 0.5530210137367249, 'ml', 2), ('zenml-io/zenml', 0.5419542193412781, 'ml-ops', 2), ('allegroai/clearml', 0.536503255367279, 'ml-ops', 2), ('mlflow/mlflow', 0.5361176133155823, 'ml-ops', 1), ('ajndkr/lanarky', 0.5264783501625061, 'llm', 0), ('zenml-io/mlstacks', 0.5240914225578308, 'ml-ops', 1), ('janetech-inc/fast-api-admin-template', 0.5197975635528564, 'template', 0), ('kubeflow/fairing', 0.5197477340698242, 'ml-ops', 0), ('flyteorg/flyte', 0.5188993215560913, 'ml-ops', 2), ('onnx/onnx', 0.5090394020080566, 'ml', 1), ('netflix/metaflow', 0.5082710981369019, 'ml-ops', 2), ('bentoml/bentoml', 0.5069453716278076, 'ml-ops', 2), ('alpa-projects/alpa', 0.50523841381073, 'ml-dl', 1), ('automl/auto-sklearn', 0.5017037987709045, 'ml', 0)] | 16 | 6 | null | 0.08 | 1 | 0 | 26 | 4 | 0 | 8 | 8 | 1 | 0 | 90 | 0 | 28 |
84 | ml | https://github.com/stan-dev/pystan | [] | null | [] | [] | null | null | null | stan-dev/pystan | pystan | 296 | 56 | 13 | Python | null | PyStan, a Python interface to Stan, a platform for statistical modeling. Documentation: https://pystan.readthedocs.io | stan-dev | 2024-01-13 | 2017-09-17 | 332 | 0.8908 | https://avatars.githubusercontent.com/u/3374820?v=4 | PyStan, a Python interface to Stan, a platform for statistical modeling. Documentation: https://pystan.readthedocs.io | [] | [] | 2024-01-05 | [('firmai/atspy', 0.5848978757858276, 'time-series', 0), ('pandas-dev/pandas', 0.5815196633338928, 'pandas', 0), ('alkaline-ml/pmdarima', 0.5813616514205933, 'time-series', 0), ('eleutherai/pyfra', 0.5657259225845337, 'ml', 0), ('selfexplainml/piml-toolbox', 0.5616912245750427, 'ml-interpretability', 0), ('pymc-devs/pymc3', 0.5608965158462524, 'ml', 0), ('statsmodels/statsmodels', 0.5519436001777649, 'ml', 0), ('crflynn/stochastic', 0.5423630475997925, 'sim', 0), ('rjt1990/pyflux', 0.5393368601799011, 'time-series', 0), ('udst/urbansim', 0.5362882018089294, 'sim', 0), ('pysal/pysal', 0.5347137451171875, 'gis', 0), ('pytoolz/toolz', 0.5338031053543091, 'util', 0), ('pmorissette/ffn', 0.5250624418258667, 'finance', 0), ('uber/orbit', 0.5150203704833984, 'time-series', 0), ('mwaskom/seaborn', 0.5127715468406677, 'viz', 0), ('google/temporian', 0.5121117234230042, 'time-series', 0), ('brokenloop/jsontopydantic', 0.5074247717857361, 'util', 0), ('altair-viz/altair', 0.506462574005127, 'viz', 0), ('rasbt/mlxtend', 0.5054618716239929, 'ml', 0), ('scikit-learn/scikit-learn', 0.5038464665412903, 'ml', 0), ('probml/pyprobml', 0.5016106963157654, 'ml', 0)] | 14 | 4 | null | 0.17 | 9 | 7 | 77 | 1 | 0 | 3 | 3 | 9 | 12 | 90 | 1.3 | 28 |
945 | diffusion | https://github.com/lunarring/latentblending | [] | null | [] | [] | null | null | null | lunarring/latentblending | latentblending | 290 | 23 | 14 | Python | null | Create butter-smooth transitions between prompts, powered by stable diffusion | lunarring | 2024-01-08 | 2022-11-19 | 62 | 4.645309 | https://avatars.githubusercontent.com/u/78172771?v=4 | Create butter-smooth transitions between prompts, powered by stable diffusion | ['animation', 'diffusion', 'stable-diffusion'] | ['animation', 'diffusion', 'stable-diffusion'] | 2024-01-10 | [('carson-katri/dream-textures', 0.5618883967399597, 'diffusion', 1), ('nateraw/stable-diffusion-videos', 0.5257456302642822, 'diffusion', 1)] | 12 | 1 | null | 2 | 3 | 2 | 14 | 0 | 0 | 0 | 0 | 3 | 1 | 90 | 0.3 | 28 |
1,705 | util | https://github.com/mtkennerly/dunamai | [] | null | [] | [] | null | null | null | mtkennerly/dunamai | dunamai | 276 | 23 | 3 | Python | https://dunamai.readthedocs.io/en/latest | Dynamic versioning library and CLI | mtkennerly | 2024-01-13 | 2019-03-26 | 253 | 1.090909 | null | Dynamic versioning library and CLI | ['bazaar', 'cli', 'darcs', 'dynamic-version', 'fossil', 'fossil-scm', 'git', 'mercurial', 'pijul', 'semantic-versioning', 'subversion', 'versioning'] | ['bazaar', 'cli', 'darcs', 'dynamic-version', 'fossil', 'fossil-scm', 'git', 'mercurial', 'pijul', 'semantic-versioning', 'subversion', 'versioning'] | 2023-12-09 | [('mtkennerly/poetry-dynamic-versioning', 0.7415024042129517, 'util', 11), ('pypa/setuptools_scm', 0.6616849303245544, 'util', 2), ('callowayproject/bump-my-version', 0.6012407541275024, 'util', 1), ('python-versioneer/python-versioneer', 0.5904924869537354, 'util', 0), ('pypa/hatch', 0.56247878074646, 'util', 2), ('spack/spack', 0.5440518856048584, 'util', 0)] | 14 | 4 | null | 0.58 | 1 | 1 | 58 | 1 | 6 | 9 | 6 | 1 | 1 | 90 | 1 | 28 |
454 | gis | https://github.com/graal-research/deepparse | [] | null | [] | [] | null | null | null | graal-research/deepparse | deepparse | 265 | 28 | 4 | Python | https://deepparse.org/ | Deepparse is a state-of-the-art library for parsing multinational street addresses using deep learning | graal-research | 2024-01-04 | 2020-07-01 | 186 | 1.418196 | https://avatars.githubusercontent.com/u/7155143?v=4 | Deepparse is a state-of-the-art library for parsing multinational street addresses using deep learning | ['addresses-parsing', 'machine-learning'] | ['addresses-parsing', 'machine-learning'] | 2023-12-17 | [('jasonrig/address-net', 0.7487471699714661, 'gis', 1)] | 8 | 2 | null | 1.38 | 2 | 1 | 43 | 1 | 6 | 14 | 6 | 2 | 3 | 90 | 1.5 | 28 |
207 | util | https://github.com/aws/aws-lambda-python-runtime-interface-client | [] | null | [] | [] | null | null | null | aws/aws-lambda-python-runtime-interface-client | aws-lambda-python-runtime-interface-client | 237 | 65 | 17 | Python | null | null | aws | 2024-01-05 | 2020-09-02 | 177 | 1.33253 | https://avatars.githubusercontent.com/u/2232217?v=4 | aws/aws-lambda-python-runtime-interface-client | [] | [] | 2023-10-30 | [('nficano/python-lambda', 0.706771194934845, 'util', 0), ('aws/chalice', 0.672527015209198, 'web', 0), ('geeogi/async-python-lambda-template', 0.6352989673614502, 'template', 0), ('jordaneremieff/mangum', 0.6282299160957336, 'web', 0), ('boto/boto3', 0.6017659902572632, 'util', 0), ('developmentseed/geolambda', 0.5933845043182373, 'gis', 0), ('pynamodb/pynamodb', 0.5816658735275269, 'data', 0), ('rpgreen/apilogs', 0.5472556948661804, 'util', 0), ('samuelcolvin/aioaws', 0.5471480488777161, 'data', 0), ('amzn/ion-python', 0.524419903755188, 'data', 0), ('awslabs/python-deequ', 0.5195323824882507, 'ml', 0)] | 27 | 4 | null | 0.37 | 19 | 11 | 41 | 3 | 4 | 3 | 4 | 19 | 12 | 90 | 0.6 | 28 |
1,251 | study | https://github.com/stanford-crfm/ecosystem-graphs | [] | null | [] | [] | null | null | null | stanford-crfm/ecosystem-graphs | ecosystem-graphs | 214 | 25 | 14 | JavaScript | null | null | stanford-crfm | 2024-01-08 | 2022-03-10 | 98 | 2.167873 | https://avatars.githubusercontent.com/u/75054807?v=4 | stanford-crfm/ecosystem-graphs | [] | [] | 2024-01-09 | [] | 15 | 4 | null | 3.42 | 13 | 13 | 22 | 0 | 0 | 0 | 0 | 13 | 1 | 90 | 0.1 | 28 |
1,701 | llm | https://github.com/llm-tuning-safety/llms-finetuning-safety | [] | null | [] | [] | null | null | null | llm-tuning-safety/llms-finetuning-safety | LLMs-Finetuning-Safety | 110 | 8 | 3 | Python | https://llm-tuning-safety.github.io/ | We jailbreak GPT-3.5 Turbo’s safety guardrails by fine-tuning it on only 10 adversarially designed examples, at a cost of less than $0.20 via OpenAI’s APIs. | llm-tuning-safety | 2024-01-13 | 2023-10-06 | 16 | 6.637931 | null | We jailbreak GPT-3.5 Turbo’s safety guardrails by fine-tuning it on only 10 adversarially designed examples, at a cost of less than $0.20 via OpenAI’s APIs. | ['alignment', 'llm', 'llm-finetuning'] | ['alignment', 'llm', 'llm-finetuning'] | 2023-11-21 | [('guardrails-ai/guardrails', 0.6136285066604614, 'llm', 1), ('nvidia/nemo-guardrails', 0.5286512970924377, 'llm', 0)] | 4 | 2 | null | 0.27 | 4 | 4 | 3 | 2 | 0 | 0 | 0 | 4 | 4 | 90 | 1 | 28 |
1,775 | llm | https://github.com/aws-samples/serverless-pdf-chat | [] | null | [] | [] | null | null | null | aws-samples/serverless-pdf-chat | serverless-pdf-chat | 96 | 94 | 7 | TypeScript | https://aws.amazon.com/blogs/compute/building-a-serverless-document-chat-with-aws-lambda-and-amazon-bedrock/ | LLM-powered document chat using Amazon Bedrock and AWS Serverless | aws-samples | 2024-01-09 | 2023-09-30 | 17 | 5.508197 | https://avatars.githubusercontent.com/u/8931462?v=4 | LLM-powered document chat using Amazon Bedrock and AWS Serverless | ['ai', 'amazon-bedrock', 'serverless'] | ['ai', 'amazon-bedrock', 'serverless'] | 2024-01-11 | [('deep-diver/llm-as-chatbot', 0.5605927109718323, 'llm', 0), ('nomic-ai/gpt4all', 0.5228908061981201, 'llm', 0), ('aws/chalice', 0.5006961226463318, 'web', 1), ('intel/intel-extension-for-transformers', 0.5002910494804382, 'perf', 0)] | 3 | 1 | null | 0.56 | 20 | 20 | 4 | 0 | 0 | 0 | 0 | 20 | 15 | 90 | 0.8 | 28 |
762 | ml-dl | https://github.com/praw-dev/asyncpraw | [] | null | [] | [] | null | null | null | praw-dev/asyncpraw | asyncpraw | 92 | 17 | 4 | Python | https://asyncpraw.readthedocs.io | Async PRAW, an abbreviation for "Asynchronous Python Reddit API Wrapper", is a python package that allows for simple access to Reddit's API. | praw-dev | 2023-11-29 | 2019-02-05 | 260 | 0.353846 | https://avatars.githubusercontent.com/u/1696888?v=4 | Async PRAW, an abbreviation for "Asynchronous Python Reddit API Wrapper", is a python package that allows for simple access to Reddit's API. | ['api', 'async', 'asyncpraw', 'oauth', 'praw', 'reddit', 'reddit-api'] | ['api', 'async', 'asyncpraw', 'oauth', 'praw', 'reddit', 'reddit-api'] | 2024-01-10 | [('praw-dev/praw', 0.8785129189491272, 'data', 5), ('tornadoweb/tornado', 0.5063261389732361, 'web', 0)] | 233 | 4 | null | 1.04 | 19 | 19 | 60 | 0 | 2 | 2 | 2 | 19 | 6 | 90 | 0.3 | 28 |
325 | security | https://github.com/snyk-labs/pysnyk | [] | null | [] | [] | null | null | null | snyk-labs/pysnyk | pysnyk | 73 | 116 | 11 | Python | https://snyk.docs.apiary.io/ | A Python client for the Snyk API. | snyk-labs | 2023-12-26 | 2019-02-03 | 260 | 0.280461 | https://avatars.githubusercontent.com/u/47793611?v=4 | A Python client for the Snyk API. | ['api', 'snyk'] | ['api', 'snyk'] | 2024-01-13 | [('simple-salesforce/simple-salesforce', 0.6529852151870728, 'data', 1), ('cohere-ai/cohere-python', 0.6142659187316895, 'util', 0), ('googleapis/google-api-python-client', 0.5522992610931396, 'util', 0), ('encode/httpx', 0.5362645983695984, 'web', 0), ('shishirpatil/gorilla', 0.5202717185020447, 'llm', 1), ('psf/requests', 0.518162727355957, 'web', 0), ('hugapi/hug', 0.515166163444519, 'util', 0), ('falconry/falcon', 0.5076401829719543, 'web', 1), ('meilisearch/meilisearch-python', 0.5068243741989136, 'data', 1), ('python-restx/flask-restx', 0.5058279633522034, 'web', 1), ('ethereum/web3.py', 0.5023728013038635, 'crypto', 0), ('hydrosquall/tiingo-python', 0.5022001266479492, 'finance', 0)] | 41 | 4 | null | 0.81 | 19 | 14 | 60 | 0 | 14 | 7 | 14 | 19 | 10 | 90 | 0.5 | 28 |
1,024 | finance | https://github.com/borisbanushev/stockpredictionai | [] | null | [] | [] | null | null | null | borisbanushev/stockpredictionai | stockpredictionai | 3,844 | 1,627 | 266 | null | null | In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later. | borisbanushev | 2024-01-13 | 2019-01-09 | 263 | 14.568489 | null | In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later. | [] | [] | 2019-02-11 | [('ydataai/ydata-synthetic', 0.5105303525924683, 'data', 0)] | 1 | 0 | null | 0 | 6 | 0 | 61 | 60 | 0 | 0 | 0 | 6 | 4 | 90 | 0.7 | 27 |
596 | gis | https://github.com/plant99/felicette | [] | null | [] | [] | null | null | null | plant99/felicette | felicette | 1,810 | 88 | 40 | Python | null | Satellite imagery for dummies. | plant99 | 2024-01-12 | 2020-07-12 | 185 | 9.768697 | null | Satellite imagery for dummies. | ['earth-observation', 'earth-science', 'geoinformatics', 'geospatial', 'geospatial-data', 'geospatial-visualization', 'gis', 'satellite-data', 'satellite-imagery', 'satellite-images'] | ['earth-observation', 'earth-science', 'geoinformatics', 'geospatial', 'geospatial-data', 'geospatial-visualization', 'gis', 'satellite-data', 'satellite-imagery', 'satellite-images'] | 2021-09-08 | [('sentinelsat/sentinelsat', 0.6691089272499084, 'gis', 1), ('developmentseed/label-maker', 0.6269397139549255, 'gis', 1), ('microsoft/torchgeo', 0.554535448551178, 'gis', 3), ('fatiando/verde', 0.533496618270874, 'gis', 2), ('giswqs/aws-open-data-geo', 0.5306439995765686, 'gis', 2), ('developmentseed/landsat-util', 0.5221068263053894, 'gis', 0), ('azavea/raster-vision', 0.5094917416572571, 'gis', 1)] | 6 | 2 | null | 0 | 0 | 0 | 43 | 29 | 0 | 1 | 1 | 0 | 0 | 90 | 0 | 27 |
1,000 | finance | https://github.com/cuemacro/findatapy | [] | null | [] | [] | 1 | null | null | cuemacro/findatapy | findatapy | 1,501 | 196 | 91 | Python | null | Python library to download market data via Bloomberg, Eikon, Quandl, Yahoo etc. | cuemacro | 2024-01-13 | 2016-08-03 | 390 | 3.840278 | https://avatars.githubusercontent.com/u/20479975?v=4 | Python library to download market data via Bloomberg, Eikon, Quandl, Yahoo etc. | ['arctic', 'bloomberg', 'dukascopy', 'eikon', 'fred', 'market-data', 'python-api', 'quandl'] | ['arctic', 'bloomberg', 'dukascopy', 'eikon', 'fred', 'market-data', 'python-api', 'quandl'] | 2023-12-01 | [('hydrosquall/tiingo-python', 0.6793490052223206, 'finance', 0), ('ranaroussi/yfinance', 0.6290664672851562, 'finance', 1), ('jovianml/opendatasets', 0.6065632104873657, 'data', 0), ('gbeced/pyalgotrade', 0.5724738836288452, 'finance', 0), ('quandl/quandl-python', 0.5631470084190369, 'finance', 1), ('hugovk/pypistats', 0.562679648399353, 'util', 0), ('quantopian/zipline', 0.5615792870521545, 'finance', 0), ('nv7-github/googlesearch', 0.5598644614219666, 'util', 0), ('nasdaq/data-link-python', 0.538777232170105, 'finance', 0), ('pydata/pandas-datareader', 0.5381442308425903, 'pandas', 1), ('man-c/pycoingecko', 0.5231452584266663, 'crypto', 0), ('sentinel-hub/sentinelhub-py', 0.5211452841758728, 'gis', 0), ('goldmansachs/gs-quant', 0.5208711624145508, 'finance', 0), ('cuemacro/finmarketpy', 0.5173200368881226, 'finance', 0), ('domokane/financepy', 0.5130403637886047, 'finance', 0), ('pmorissette/ffn', 0.5108827948570251, 'finance', 0), ('matplotlib/mplfinance', 0.5023597478866577, 'finance', 1), ('openai/openai-python', 0.5010272860527039, 'util', 0)] | 7 | 1 | null | 0.08 | 1 | 1 | 91 | 1 | 3 | 4 | 3 | 1 | 0 | 90 | 0 | 27 |
896 | crypto | https://github.com/ofek/bit | [] | null | [] | [] | null | null | null | ofek/bit | bit | 1,181 | 205 | 49 | Python | https://ofek.dev/bit/ | Bitcoin made easy. | ofek | 2024-01-13 | 2016-11-12 | 376 | 3.137381 | null | Bitcoin made easy. | ['bitcoin', 'cryptocurrencies', 'libraries', 'payments'] | ['bitcoin', 'cryptocurrencies', 'libraries', 'payments'] | 2023-11-13 | [('numerai/example-scripts', 0.5733424425125122, 'finance', 0), ('1200wd/bitcoinlib', 0.5140418410301208, 'crypto', 1)] | 16 | 1 | null | 0.04 | 6 | 2 | 87 | 2 | 0 | 0 | 0 | 6 | 4 | 90 | 0.7 | 27 |
1,209 | llm | https://github.com/keirp/automatic_prompt_engineer | ['prompt-engineering', 'language-model'] | Large Language Models Are Human-Level Prompt Engineers | [] | [] | null | null | null | keirp/automatic_prompt_engineer | automatic_prompt_engineer | 860 | 109 | 16 | Python | null | null | keirp | 2024-01-13 | 2022-10-24 | 66 | 13.00216 | null | Large Language Models Are Human-Level Prompt Engineers | [] | ['language-model', 'prompt-engineering'] | 2023-05-25 | [('hazyresearch/ama_prompting', 0.7995690107345581, 'llm', 1), ('guidance-ai/guidance', 0.7347609996795654, 'llm', 2), ('ctlllll/llm-toolmaker', 0.699556291103363, 'llm', 1), ('neulab/prompt2model', 0.6848369836807251, 'llm', 1), ('microsoft/promptbase', 0.6507399082183838, 'llm', 1), ('kyegomez/tree-of-thoughts', 0.6476341485977173, 'llm', 1), ('srush/minichain', 0.6470367908477783, 'llm', 1), ('thudm/p-tuning-v2', 0.6158888339996338, 'nlp', 0), ('1rgs/jsonformer', 0.5909636616706848, 'llm', 1), ('stanfordnlp/dspy', 0.5835668444633484, 'llm', 0), ('promptslab/promptify', 0.5769301056861877, 'nlp', 1), ('agenta-ai/agenta', 0.5762568116188049, 'llm', 1), ('thudm/chatglm-6b', 0.5738417506217957, 'llm', 1), ('hannibal046/awesome-llm', 0.5738133192062378, 'study', 1), ('spcl/graph-of-thoughts', 0.5692050457000732, 'llm', 1), ('yizhongw/self-instruct', 0.5658340454101562, 'llm', 1), ('lianjiatech/belle', 0.5627188682556152, 'llm', 0), ('ai21labs/lm-evaluation', 0.5610719919204712, 'llm', 1), ('lm-sys/fastchat', 0.5581333041191101, 'llm', 1), ('bigscience-workshop/promptsource', 0.5572788715362549, 'nlp', 0), ('hazyresearch/manifest', 0.5553961396217346, 'llm', 1), ('facebookresearch/shepherd', 0.5500012636184692, 'llm', 1), ('freedomintelligence/llmzoo', 0.5354942083358765, 'llm', 1), ('promptslab/awesome-prompt-engineering', 0.5347884297370911, 'study', 1), ('conceptofmind/toolformer', 0.5263444185256958, 'llm', 1), ('next-gpt/next-gpt', 0.525351881980896, 'llm', 0), ('airi-institute/probing_framework', 0.5204843282699585, 'nlp', 0), ('juncongmoo/pyllama', 0.518515944480896, 'llm', 0), ('suno-ai/bark', 0.5167652368545532, 'ml', 0), ('likenneth/honest_llama', 0.5166599750518799, 'llm', 1), ('jina-ai/thinkgpt', 0.5156129002571106, 'llm', 1), ('openai/finetune-transformer-lm', 0.5153794884681702, 'llm', 0), ('hazyresearch/h3', 0.5113564729690552, 'llm', 0), ('tatsu-lab/stanford_alpaca', 0.511340320110321, 'llm', 1), ('jonasgeiping/cramming', 0.5112678408622742, 'nlp', 1), ('microsoft/autogen', 0.5107211470603943, 'llm', 0), ('young-geng/easylm', 0.5091218948364258, 'llm', 1), ('openbmb/toolbench', 0.5084372758865356, 'llm', 0), ('reasoning-machines/pal', 0.5067288279533386, 'llm', 1), ('baichuan-inc/baichuan-13b', 0.5003429055213928, 'llm', 0)] | 4 | 0 | null | 0.04 | 6 | 1 | 15 | 8 | 0 | 0 | 0 | 6 | 2 | 90 | 0.3 | 27 |
652 | util | https://github.com/rasbt/watermark | [] | null | [] | [] | null | null | null | rasbt/watermark | watermark | 849 | 89 | 13 | Python | null | An IPython magic extension for printing date and time stamps, version numbers, and hardware information | rasbt | 2024-01-09 | 2014-07-30 | 495 | 1.712187 | null | An IPython magic extension for printing date and time stamps, version numbers, and hardware information | ['ipython', 'jupyter', 'magic-extension'] | ['ipython', 'jupyter', 'magic-extension'] | 2023-07-02 | [('python/cpython', 0.5787110328674316, 'util', 0), ('jupyter/nbformat', 0.5627220869064331, 'jupyter', 0), ('wesm/pydata-book', 0.5561281442642212, 'study', 0), ('ipython/ipython', 0.5411689281463623, 'util', 2), ('ipython/ipykernel', 0.541002631187439, 'util', 2), ('erotemic/ubelt', 0.5379260182380676, 'util', 0), ('ipython/ipyparallel', 0.5231406688690186, 'perf', 1), ('faster-cpython/ideas', 0.5214296579360962, 'perf', 0), ('dateutil/dateutil', 0.5191126465797424, 'util', 0), ('pyston/pyston', 0.5138229131698608, 'util', 0), ('pypy/pypy', 0.5103526711463928, 'util', 0), ('faster-cpython/tools', 0.507169783115387, 'perf', 0), ('gotcha/ipdb', 0.5067563652992249, 'debug', 1), ('cohere-ai/notebooks', 0.5048350095748901, 'llm', 0)] | 19 | 5 | null | 0.38 | 1 | 0 | 115 | 7 | 1 | 2 | 1 | 1 | 1 | 90 | 1 | 27 |
695 | profiling | https://github.com/pythonspeed/filprofiler | [] | null | [] | [] | null | null | null | pythonspeed/filprofiler | filprofiler | 802 | 24 | 9 | Rust | https://pythonspeed.com/products/filmemoryprofiler/ | A Python memory profiler for data processing and scientific computing applications | pythonspeed | 2024-01-14 | 2020-06-18 | 188 | 4.249811 | null | A Python memory profiler for data processing and scientific computing applications | ['memory', 'memory-', 'memory-leak', 'memory-leak-detection', 'memory-leak-finder', 'memory-leaks', 'memory-profiler', 'memory-profiling'] | ['memory', 'memory-', 'memory-leak', 'memory-leak-detection', 'memory-leak-finder', 'memory-leaks', 'memory-profiler', 'memory-profiling'] | 2023-03-18 | [('pympler/pympler', 0.7303571105003357, 'perf', 0), ('bloomberg/memray', 0.7156088352203369, 'profiling', 4), ('benfred/py-spy', 0.7144114971160889, 'profiling', 0), ('pythonprofilers/memory_profiler', 0.664732813835144, 'profiling', 0), ('sumerc/yappi', 0.6349960565567017, 'profiling', 0), ('pyutils/line_profiler', 0.6149646043777466, 'profiling', 0), ('dgilland/cacheout', 0.6149056553840637, 'perf', 0), ('python-cachier/cachier', 0.605586588382721, 'perf', 0), ('plasma-umass/scalene', 0.6020299196243286, 'profiling', 0), ('joblib/joblib', 0.5940987467765808, 'util', 0), ('pyston/pyston', 0.5313695073127747, 'util', 0), ('pytables/pytables', 0.5271565914154053, 'data', 0), ('joerick/pyinstrument', 0.5218971967697144, 'profiling', 0), ('xrudelis/pytrait', 0.5162791013717651, 'util', 0), ('rasbt/mlxtend', 0.5145261883735657, 'ml', 0), ('micropython/micropython', 0.5140711069107056, 'util', 0), ('numpy/numpy', 0.5123228430747986, 'math', 0), ('jiffyclub/snakeviz', 0.5121504664421082, 'profiling', 0), ('cython/cython', 0.5108945369720459, 'util', 0), ('google/pytype', 0.5083953738212585, 'typing', 0), ('spotify/annoy', 0.5058916807174683, 'ml', 0), ('exaloop/codon', 0.5046871900558472, 'perf', 0), ('eleutherai/pyfra', 0.5045285820960999, 'ml', 0), ('p403n1x87/austin', 0.5042270421981812, 'profiling', 0), ('pypy/pypy', 0.5019063353538513, 'util', 0)] | 6 | 4 | null | 0.33 | 1 | 0 | 44 | 10 | 3 | 18 | 3 | 1 | 0 | 90 | 0 | 27 |
832 | finance | https://github.com/idanya/algo-trader | [] | null | [] | [] | null | null | null | idanya/algo-trader | algo-trader | 726 | 90 | 29 | Python | null | Trading bot with support for realtime trading, backtesting, custom strategies and much more. | idanya | 2024-01-13 | 2021-09-14 | 124 | 5.854839 | null | Trading bot with support for realtime trading, backtesting, custom strategies and much more. | ['algorithmic-trading', 'backtesting', 'crypto-bot', 'technical-analysis', 'trading-bot', 'trading-strategies'] | ['algorithmic-trading', 'backtesting', 'crypto-bot', 'technical-analysis', 'trading-bot', 'trading-strategies'] | 2023-11-20 | [('freqtrade/freqtrade', 0.8236120939254761, 'crypto', 2), ('polakowo/vectorbt', 0.6762666702270508, 'finance', 3), ('gbeced/basana', 0.6138817071914673, 'finance', 3), ('quantconnect/lean', 0.5937037467956543, 'finance', 2), ('ccxt/ccxt', 0.5679965019226074, 'crypto', 0), ('blankly-finance/blankly', 0.5535969138145447, 'finance', 2), ('zvtvz/zvt', 0.5369071364402771, 'finance', 5), ('kernc/backtesting.py', 0.522881269454956, 'finance', 3), ('ai4finance-foundation/finrl', 0.5114476084709167, 'finance', 1), ('openbb-finance/openbbterminal', 0.5061081051826477, 'finance', 0)] | 4 | 2 | null | 0.08 | 1 | 1 | 28 | 2 | 1 | 3 | 1 | 1 | 0 | 90 | 0 | 27 |
305 | crypto | https://github.com/palkeo/panoramix | [] | null | [] | [] | null | null | null | palkeo/panoramix | panoramix | 714 | 194 | 35 | Python | null | Ethereum decompiler | palkeo | 2024-01-12 | 2020-02-17 | 206 | 3.463617 | null | Ethereum decompiler | [] | [] | 2023-06-14 | [('ethtx/ethtx_ce', 0.6499204635620117, 'crypto', 0)] | 4 | 2 | null | 0.4 | 3 | 1 | 48 | 7 | 0 | 1 | 1 | 3 | 3 | 90 | 1 | 27 |
568 | gis | https://github.com/developmentseed/landsat-util | [] | null | [] | [] | null | null | null | developmentseed/landsat-util | landsat-util | 687 | 153 | 127 | Python | null | A utility to search, download and process Landsat 8 satellite imagery | developmentseed | 2024-01-10 | 2014-08-01 | 495 | 1.386278 | https://avatars.githubusercontent.com/u/92384?v=4 | A utility to search, download and process Landsat 8 satellite imagery | [] | [] | 2018-07-30 | [('plant99/felicette', 0.5221068263053894, 'gis', 0), ('sentinelsat/sentinelsat', 0.5123438239097595, 'gis', 0)] | 25 | 7 | null | 0 | 1 | 0 | 115 | 66 | 0 | 2 | 2 | 1 | 3 | 90 | 3 | 27 |
513 | ml-dl | https://github.com/kakaobrain/rq-vae-transformer | [] | null | [] | [] | null | null | null | kakaobrain/rq-vae-transformer | rq-vae-transformer | 647 | 74 | 16 | Jupyter Notebook | null | The official implementation of Autoregressive Image Generation using Residual Quantization (CVPR '22) | kakaobrain | 2024-01-12 | 2022-03-03 | 99 | 6.488539 | https://avatars.githubusercontent.com/u/25736994?v=4 | The official implementation of Autoregressive Image Generation using Residual Quantization (CVPR '22) | [] | [] | 2024-01-03 | [('stability-ai/stablediffusion', 0.5072489380836487, 'diffusion', 0), ('compvis/latent-diffusion', 0.5072487592697144, 'diffusion', 0)] | 2 | 2 | null | 0.02 | 1 | 0 | 23 | 0 | 0 | 0 | 0 | 1 | 0 | 90 | 0 | 27 |
1,078 | ml-ops | https://github.com/kubeflow-kale/kale | [] | null | [] | [] | null | null | null | kubeflow-kale/kale | kale | 613 | 129 | 17 | Python | http://kubeflow-kale.github.io | Kubeflow’s superfood for Data Scientists | kubeflow-kale | 2024-01-05 | 2019-01-24 | 261 | 2.342249 | https://avatars.githubusercontent.com/u/52384265?v=4 | Kubeflow’s superfood for Data Scientists | ['jupyter-notebook', 'kubeflow', 'kubeflow-pipelines', 'machine-learning'] | ['jupyter-notebook', 'kubeflow', 'kubeflow-pipelines', 'machine-learning'] | 2021-10-20 | [('kubeflow/pipelines', 0.692866325378418, 'ml-ops', 3), ('orchest/orchest', 0.6008453965187073, 'ml-ops', 1), ('getindata/kedro-kubeflow', 0.5977193117141724, 'ml-ops', 2), ('firmai/industry-machine-learning', 0.5946695804595947, 'study', 2), ('determined-ai/determined', 0.5921743512153625, 'ml-ops', 1), ('ploomber/ploomber', 0.5709792971611023, 'ml-ops', 1), ('flyteorg/flyte', 0.5685352683067322, 'ml-ops', 1), ('gradio-app/gradio', 0.5675045847892761, 'viz', 1), ('superduperdb/superduperdb', 0.5663890838623047, 'data', 0), ('polyaxon/polyaxon', 0.5640344619750977, 'ml-ops', 1), ('linealabs/lineapy', 0.5481640100479126, 'jupyter', 0), ('dagworks-inc/hamilton', 0.5480688214302063, 'ml-ops', 1), ('mito-ds/monorepo', 0.5466421842575073, 'jupyter', 0), ('ageron/handson-ml2', 0.5455378293991089, 'ml', 0), ('kedro-org/kedro-viz', 0.5434872508049011, 'ml-ops', 0), ('bodywork-ml/bodywork-core', 0.5426039695739746, 'ml-ops', 1), ('netflix/metaflow', 0.5416892170906067, 'ml-ops', 1), ('backtick-se/cowait', 0.5415117740631104, 'util', 0), ('astronomer/astro-sdk', 0.5413955450057983, 'ml-ops', 0), ('hi-primus/optimus', 0.5404044389724731, 'ml-ops', 1), ('mage-ai/mage-ai', 0.5401076674461365, 'ml-ops', 1), ('jovianml/opendatasets', 0.5395414233207703, 'data', 1), ('dylanhogg/awesome-python', 0.5359322428703308, 'study', 1), ('skops-dev/skops', 0.5353480577468872, 'ml-ops', 1), ('huggingface/datasets', 0.5326496362686157, 'nlp', 1), ('mlflow/mlflow', 0.5298233032226562, 'ml-ops', 1), ('kedro-org/kedro', 0.5225927233695984, 'ml-ops', 1), ('polyaxon/datatile', 0.5204256772994995, 'pandas', 0), ('googlecloudplatform/vertex-ai-samples', 0.5194539427757263, 'ml', 0), ('dask/dask-ml', 0.5168511867523193, 'ml', 0), ('wandb/client', 0.5164470076560974, 'ml', 1), ('kubeflow/fairing', 0.5162668824195862, 'ml-ops', 0), ('merantix-momentum/squirrel-core', 0.5156881809234619, 'ml', 1), ('eventual-inc/daft', 0.5150558352470398, 'pandas', 1), ('airbytehq/airbyte', 0.5148319005966187, 'data', 0), ('koaning/scikit-lego', 0.5133776664733887, 'ml', 1), ('vaexio/vaex', 0.5090245008468628, 'perf', 1), ('ashleve/lightning-hydra-template', 0.5069912075996399, 'util', 0), ('gefyrahq/gefyra', 0.5049176216125488, 'util', 0), ('intake/intake', 0.5041489005088806, 'data', 0), ('fastai/fastcore', 0.5032058358192444, 'util', 0)] | 10 | 4 | null | 0 | 2 | 0 | 60 | 27 | 0 | 5 | 5 | 2 | 3 | 90 | 1.5 | 27 |
1,349 | ml | https://github.com/ray-project/tune-sklearn | [] | null | [] | [] | null | null | null | ray-project/tune-sklearn | tune-sklearn | 462 | 51 | 18 | Python | https://docs.ray.io/en/master/tune/api_docs/sklearn.html | A drop-in replacement for Scikit-Learn’s GridSearchCV / RandomizedSearchCV -- but with cutting edge hyperparameter tuning techniques. | ray-project | 2024-01-06 | 2019-11-28 | 217 | 2.122047 | https://avatars.githubusercontent.com/u/22125274?v=4 | A drop-in replacement for Scikit-Learn’s GridSearchCV / RandomizedSearchCV -- but with cutting edge hyperparameter tuning techniques. | ['automl', 'bayesian-optimization', 'gridsearchcv', 'hyperparameter-tuning', 'scikit-learn'] | ['automl', 'bayesian-optimization', 'gridsearchcv', 'hyperparameter-tuning', 'scikit-learn'] | 2023-11-04 | [('automl/auto-sklearn', 0.6818026304244995, 'ml', 4), ('microsoft/flaml', 0.6497718095779419, 'ml', 2), ('kubeflow/katib', 0.626979410648346, 'ml', 0), ('microsoft/nni', 0.580021321773529, 'ml', 3), ('mljar/mljar-supervised', 0.5580164194107056, 'ml', 2), ('scikit-optimize/scikit-optimize', 0.550593912601471, 'ml', 3), ('google/vizier', 0.5394313931465149, 'ml', 2), ('determined-ai/determined', 0.5187373161315918, 'ml-ops', 1), ('optuna/optuna', 0.5114966034889221, 'ml', 0), ('rasbt/machine-learning-book', 0.5028691291809082, 'study', 1)] | 14 | 3 | null | 0.15 | 12 | 8 | 50 | 2 | 2 | 3 | 2 | 12 | 3 | 90 | 0.2 | 27 |
1,115 | web | https://github.com/pylons/webob | ['wsgi'] | null | [] | [] | null | null | null | pylons/webob | webob | 428 | 189 | 20 | Python | https://webob.org/ | WSGI request and response objects | pylons | 2024-01-12 | 2011-09-17 | 645 | 0.663125 | https://avatars.githubusercontent.com/u/452227?v=4 | WSGI request and response objects | [] | ['wsgi'] | 2023-09-05 | [('pallets/werkzeug', 0.6051927804946899, 'web', 1), ('pylons/waitress', 0.5591251254081726, 'web', 0), ('requests/toolbelt', 0.5443535447120667, 'util', 0), ('benoitc/gunicorn', 0.5041623711585999, 'web', 1)] | 110 | 3 | null | 0.13 | 3 | 0 | 150 | 4 | 0 | 6 | 6 | 3 | 3 | 90 | 1 | 27 |
516 | gis | https://github.com/scikit-geometry/scikit-geometry | [] | null | [] | [] | null | null | null | scikit-geometry/scikit-geometry | scikit-geometry | 398 | 53 | 14 | Jupyter Notebook | https://scikit-geometry.github.io/scikit-geometry | Scientific Python Geometric Algorithms Library | scikit-geometry | 2024-01-09 | 2016-03-28 | 409 | 0.972765 | https://avatars.githubusercontent.com/u/59055868?v=4 | Scientific Python Geometric Algorithms Library | ['cgal', 'geometric-algorithms', 'geometry', 'wrapper'] | ['cgal', 'geometric-algorithms', 'geometry', 'wrapper'] | 2023-12-04 | [('scipy/scipy', 0.6300176382064819, 'math', 0), ('shapely/shapely', 0.5902884602546692, 'gis', 2), ('pysal/pysal', 0.5866171717643738, 'gis', 0), ('fredrik-johansson/mpmath', 0.5644670724868774, 'math', 0), ('albahnsen/pycircular', 0.5635026693344116, 'math', 0), ('marcomusy/vedo', 0.5506011843681335, 'viz', 0), ('numpy/numpy', 0.5440155267715454, 'math', 0), ('sympy/sympy', 0.5399286150932312, 'math', 0), ('kornia/kornia', 0.5317684412002563, 'ml-dl', 0), ('artelys/geonetworkx', 0.5264812707901001, 'gis', 0), ('benbovy/spherely', 0.5209958553314209, 'gis', 2), ('dfki-ric/pytransform3d', 0.5100734233856201, 'math', 0)] | 18 | 6 | null | 0.02 | 9 | 4 | 95 | 1 | 0 | 0 | 0 | 9 | 11 | 90 | 1.2 | 27 |
840 | gis | https://github.com/mapbox/mercantile | [] | null | [] | [] | null | null | null | mapbox/mercantile | mercantile | 384 | 62 | 124 | Python | null | Spherical mercator tile and coordinate utilities | mapbox | 2024-01-14 | 2014-02-12 | 519 | 0.738664 | https://avatars.githubusercontent.com/u/600935?v=4 | Spherical mercator tile and coordinate utilities | ['imagery', 'pxm', 'satellite'] | ['imagery', 'pxm', 'satellite'] | 2023-11-02 | [] | 23 | 5 | null | 0 | 1 | 1 | 121 | 2 | 0 | 4 | 4 | 1 | 1 | 90 | 1 | 27 |
1,157 | gamedev | https://github.com/libtcod/python-tcod | [] | null | [] | [] | null | null | null | libtcod/python-tcod | python-tcod | 382 | 37 | 19 | Python | null | A high-performance Python port of libtcod. Includes the libtcodpy module for backwards compatibility with older projects. | libtcod | 2024-01-13 | 2015-03-14 | 463 | 0.824291 | https://avatars.githubusercontent.com/u/40313210?v=4 | A high-performance Python port of libtcod. Includes the libtcodpy module for backwards compatibility with older projects. | ['field-of-view', 'libtcod', 'libtcodpy', 'pathfinding', 'pypy', 'pypy3', 'python-tcod'] | ['field-of-view', 'libtcod', 'libtcodpy', 'pathfinding', 'pypy', 'pypy3', 'python-tcod'] | 2024-01-08 | [('pypy/pypy', 0.6247069835662842, 'util', 0), ('pyodide/micropip', 0.5887089967727661, 'util', 0), ('pyodide/pyodide', 0.5775824785232544, 'util', 0), ('cython/cython', 0.5713533759117126, 'util', 0), ('1200wd/bitcoinlib', 0.5591480731964111, 'crypto', 0), ('pyston/pyston', 0.5553798079490662, 'util', 0), ('pyo3/maturin', 0.5518137812614441, 'util', 1), ('erotemic/ubelt', 0.5495151877403259, 'util', 0), ('hoffstadt/dearpygui', 0.5448720455169678, 'gui', 0), ('pdm-project/pdm', 0.5411107540130615, 'util', 0), ('pytoolz/toolz', 0.5379080176353455, 'util', 0), ('fastai/fastcore', 0.5340726375579834, 'util', 0), ('pypa/hatch', 0.5316311120986938, 'util', 0), ('pytest-dev/pytest-bdd', 0.5265241265296936, 'testing', 0), ('pympler/pympler', 0.52317214012146, 'perf', 0), ('klen/py-frameworks-bench', 0.5221824049949646, 'perf', 0), ('dgilland/cacheout', 0.5180550813674927, 'perf', 0), ('primal100/pybitcointools', 0.5110588669776917, 'crypto', 0), ('dosisod/refurb', 0.5105434060096741, 'util', 0), ('beeware/toga', 0.5042990446090698, 'gui', 0), ('paramiko/paramiko', 0.502030611038208, 'util', 0)] | 23 | 1 | null | 2.67 | 10 | 10 | 108 | 0 | 10 | 20 | 10 | 10 | 1 | 90 | 0.1 | 27 |
1,874 | ml | https://github.com/oneil512/insight | [] | null | [] | [] | null | null | null | oneil512/insight | INSIGHT | 373 | 54 | 13 | Python | null | INSIGHT is an autonomous AI that can do medical research! | oneil512 | 2024-01-12 | 2023-04-08 | 42 | 8.791246 | null | INSIGHT is an autonomous AI that can do medical research! | ['agent', 'ai', 'chatgpt', 'gpt', 'llm', 'medical', 'ml'] | ['agent', 'ai', 'chatgpt', 'gpt', 'llm', 'medical', 'ml'] | 2023-10-21 | [('lucidrains/medical-chatgpt', 0.6099081635475159, 'llm', 0), ('torantulino/auto-gpt', 0.5754587054252625, 'llm', 1), ('mindsdb/mindsdb', 0.5716148614883423, 'data', 4), ('microsoft/lmops', 0.5636782050132751, 'llm', 2), ('assafelovic/gpt-researcher', 0.5495372414588928, 'llm', 0), ('gventuri/pandas-ai', 0.5358114242553711, 'pandas', 2), ('google-research/google-research', 0.5356377363204956, 'ml', 1), ('prefecthq/marvin', 0.5314114093780518, 'nlp', 3), ('project-monai/monai', 0.5232803225517273, 'ml', 0), ('oegedijk/explainerdashboard', 0.5162001848220825, 'ml-interpretability', 0), ('antonosika/gpt-engineer', 0.5132570862770081, 'llm', 1), ('alirezadir/machine-learning-interview-enlightener', 0.5009233355522156, 'study', 1)] | 3 | 0 | null | 1.42 | 3 | 3 | 9 | 3 | 0 | 0 | 0 | 3 | 2 | 90 | 0.7 | 27 |
1,351 | util | https://github.com/nv7-github/googlesearch | ['google-crawler'] | null | [] | [] | null | null | null | nv7-github/googlesearch | googlesearch | 345 | 91 | 6 | Python | https://pypi.org/project/googlesearch-python/ | A Python library for scraping the Google search engine. | nv7-github | 2024-01-12 | 2020-07-05 | 186 | 1.851994 | null | A Python library for scraping the Google search engine. | [] | ['google-crawler'] | 2023-05-30 | [('scrapy/scrapy', 0.7149690985679626, 'data', 0), ('alirezamika/autoscraper', 0.7142531275749207, 'data', 0), ('googleapis/google-api-python-client', 0.6702570915222168, 'util', 0), ('serpapi/google-search-results-python', 0.6652640700340271, 'util', 1), ('roniemartinez/dude', 0.6479972004890442, 'util', 0), ('binux/pyspider', 0.6098272204399109, 'data', 0), ('scholarly-python-package/scholarly', 0.6054853796958923, 'data', 0), ('jovianml/opendatasets', 0.5820246338844299, 'data', 0), ('clips/pattern', 0.5700188279151917, 'nlp', 0), ('cuemacro/findatapy', 0.5598644614219666, 'finance', 0), ('goldsmith/wikipedia', 0.5323612093925476, 'data', 0), ('meilisearch/meilisearch-python', 0.5313740968704224, 'data', 0), ('requests/toolbelt', 0.5127140879631042, 'util', 0), ('psf/requests', 0.5044116973876953, 'web', 0), ('qdrant/qdrant-client', 0.5012821555137634, 'util', 0)] | 8 | 4 | null | 0.15 | 9 | 1 | 43 | 8 | 0 | 1 | 1 | 9 | 18 | 90 | 2 | 27 |
341 | term | https://github.com/rockhopper-technologies/enlighten | [] | null | [] | [] | null | null | null | rockhopper-technologies/enlighten | enlighten | 335 | 23 | 5 | Python | https://python-enlighten.readthedocs.io | Enlighten Progress Bar for Python Console Apps | rockhopper-technologies | 2024-01-11 | 2017-09-22 | 331 | 1.01034 | https://avatars.githubusercontent.com/u/20388549?v=4 | Enlighten Progress Bar for Python Console Apps | [] | [] | 2023-12-25 | [('wolph/python-progressbar', 0.7673426270484924, 'util', 0), ('tqdm/tqdm', 0.7503482699394226, 'term', 0), ('urwid/urwid', 0.5648614764213562, 'term', 0), ('rsalmei/alive-progress', 0.5524495244026184, 'util', 0), ('inducer/pudb', 0.5411313772201538, 'debug', 0), ('jquast/blessed', 0.5384596586227417, 'term', 0), ('willmcgugan/rich', 0.5262578725814819, 'term', 0), ('alexmojaki/heartrate', 0.5111202597618103, 'debug', 0), ('teemu/pytest-sugar', 0.5103371739387512, 'testing', 0)] | 6 | 2 | null | 0.92 | 2 | 2 | 77 | 1 | 6 | 5 | 6 | 2 | 3 | 90 | 1.5 | 27 |
1,649 | llm | https://github.com/lchen001/llmdrift | ['drift', 'language-model'] | LLM Drifts: How Is ChatGPT’s Behavior Changing over Time? | [] | [] | null | null | null | lchen001/llmdrift | LLMDrift | 320 | 28 | 15 | Jupyter Notebook | null | null | lchen001 | 2024-01-12 | 2023-07-18 | 28 | 11.428571 | null | LLM Drifts: How Is ChatGPT’s Behavior Changing over Time? | [] | ['drift', 'language-model'] | 2024-01-03 | [('thudm/chatglm2-6b', 0.5880559086799622, 'llm', 0), ('hwchase17/langchain', 0.5510751008987427, 'llm', 1), ('microsoft/autogen', 0.5390075445175171, 'llm', 0), ('nomic-ai/gpt4all', 0.5283440351486206, 'llm', 1), ('fasteval/fasteval', 0.5002023577690125, 'llm', 0)] | 5 | 0 | null | 0.48 | 1 | 1 | 6 | 0 | 0 | 0 | 0 | 1 | 0 | 90 | 0 | 27 |
986 | time-series | https://github.com/microprediction/microprediction | [] | null | [] | [] | null | null | null | microprediction/microprediction | microprediction | 311 | 55 | 15 | Jupyter Notebook | http://www.microprediction.org | If you can measure it, consider it predicted | microprediction | 2024-01-09 | 2020-02-20 | 205 | 1.511806 | null | If you can measure it, consider it predicted | ['fbprophet', 'filterpy', 'hmmlearn', 'kalman-filter', 'keras', 'nowcasting', 'online-algorithms', 'pmdarima', 'time-series', 'timeseries', 'timeseries-analysis', 'timeseries-clustering', 'timeseries-data', 'timeseries-database', 'timeseries-forecasting', 'timeseries-prediction', 'tsfresh', 'tslearn'] | ['fbprophet', 'filterpy', 'hmmlearn', 'kalman-filter', 'keras', 'nowcasting', 'online-algorithms', 'pmdarima', 'time-series', 'timeseries', 'timeseries-analysis', 'timeseries-clustering', 'timeseries-data', 'timeseries-database', 'timeseries-forecasting', 'timeseries-prediction', 'tsfresh', 'tslearn'] | 2024-01-05 | [('ourownstory/neural_prophet', 0.5824498534202576, 'ml', 3), ('alkaline-ml/pmdarima', 0.5424190759658813, 'time-series', 2), ('awslabs/gluonts', 0.5331199765205383, 'time-series', 2), ('unit8co/darts', 0.5138264298439026, 'time-series', 1), ('salesforce/merlion', 0.5122169852256775, 'time-series', 1), ('firmai/atspy', 0.5109694004058838, 'time-series', 1), ('sktime/sktime', 0.5038242340087891, 'time-series', 1)] | 14 | 3 | null | 4.37 | 0 | 0 | 47 | 0 | 5 | 36 | 5 | 0 | 0 | 90 | 0 | 27 |
1,448 | util | https://github.com/salesforce/logai | [] | null | [] | [] | null | null | null | salesforce/logai | logai | 298 | 39 | 15 | Python | null | LogAI - An open-source library for log analytics and intelligence | salesforce | 2024-01-10 | 2022-10-27 | 65 | 4.534783 | https://avatars.githubusercontent.com/u/453694?v=4 | LogAI - An open-source library for log analytics and intelligence | ['ai', 'aiops', 'anomaly-detection', 'benchmarking', 'log-analysis', 'log-intelligence', 'machine-learning'] | ['ai', 'aiops', 'anomaly-detection', 'benchmarking', 'log-analysis', 'log-intelligence', 'machine-learning'] | 2023-03-02 | [('whylabs/whylogs', 0.7454851269721985, 'util', 1), ('aimhubio/aim', 0.5716543197631836, 'ml-ops', 2), ('polyaxon/datatile', 0.5616445541381836, 'pandas', 0), ('pycaret/pycaret', 0.5456848740577698, 'ml', 2), ('ray-project/ray', 0.5410916805267334, 'ml-ops', 1), ('mindsdb/mindsdb', 0.5326856970787048, 'data', 2), ('metachris/logzero', 0.5321155786514282, 'util', 0), ('yzhao062/pyod', 0.5268727540969849, 'data', 2), ('mlflow/mlflow', 0.5248235464096069, 'ml-ops', 2), ('larsbaunwall/bricky', 0.5161862969398499, 'llm', 1), ('oegedijk/explainerdashboard', 0.5160885453224182, 'ml-interpretability', 0), ('unit8co/darts', 0.5133403539657593, 'time-series', 2), ('nebuly-ai/nebullvm', 0.5123262405395508, 'perf', 1), ('netflix/metaflow', 0.5108799934387207, 'ml-ops', 2), ('alirezadir/machine-learning-interview-enlightener', 0.5083866119384766, 'study', 2), ('wandb/client', 0.5036634206771851, 'ml', 1), ('fmind/mlops-python-package', 0.5032975077629089, 'template', 1)] | 5 | 2 | null | 0.92 | 2 | 0 | 15 | 11 | 4 | 4 | 4 | 2 | 2 | 90 | 1 | 27 |
1,620 | data | https://github.com/samuelcolvin/rtoml | ['toml', 'rust'] | null | [] | [] | null | null | null | samuelcolvin/rtoml | rtoml | 285 | 29 | 8 | Python | https://pypi.org/project/rtoml/ | A fast TOML library for python implemented in rust. | samuelcolvin | 2024-01-12 | 2020-01-07 | 212 | 1.34434 | null | A fast TOML library for python implemented in rust. | ['deserialization', 'parser', 'rust', 'toml'] | ['deserialization', 'parser', 'rust', 'toml'] | 2023-12-21 | [('astral-sh/ruff', 0.5641447305679321, 'util', 1), ('rustpython/rustpython', 0.5515300035476685, 'util', 1), ('yukinarit/pyserde', 0.5448687076568604, 'util', 1), ('pyo3/pyo3', 0.5289204716682434, 'util', 1), ('marshmallow-code/marshmallow', 0.5252963900566101, 'util', 1), ('deepmind/chex', 0.5042668581008911, 'ml-dl', 0), ('sfu-db/connector-x', 0.5036177039146423, 'data', 1)] | 14 | 3 | null | 0.13 | 8 | 6 | 49 | 1 | 1 | 3 | 1 | 8 | 8 | 90 | 1 | 27 |
521 | util | https://github.com/venth/aws-adfs | [] | null | [] | [] | null | null | null | venth/aws-adfs | aws-adfs | 283 | 96 | 11 | Python | null | Command line tool to ease aws cli authentication against ADFS (multi factor authentication with active directory) | venth | 2024-01-11 | 2016-06-25 | 396 | 0.713874 | null | Command line tool to ease aws cli authentication against ADFS (multi factor authentication with active directory) | ['adfs', 'aws', 'command-line', 'duo-security', 'multi-factor-authentication', 'tools'] | ['adfs', 'aws', 'command-line', 'duo-security', 'multi-factor-authentication', 'tools'] | 2023-12-16 | [] | 50 | 2 | null | 0.87 | 15 | 12 | 92 | 1 | 6 | 16 | 6 | 15 | 7 | 90 | 0.5 | 27 |
726 | ml | https://github.com/autonlab/auton-survival | [] | null | [] | [] | null | null | null | autonlab/auton-survival | auton-survival | 275 | 70 | 8 | Python | http://autonlab.github.io/auton-survival | Auton Survival - an open source package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Events | autonlab | 2024-01-12 | 2020-04-01 | 199 | 1.375983 | https://avatars.githubusercontent.com/u/11739208?v=4 | Auton Survival - an open source package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Events | ['causal-inference', 'counterfactual-inference', 'data-science', 'deep-learning', 'graphical-models', 'machine-learning', 'regression', 'reliability-analysis', 'survival-analysis', 'time-to-event'] | ['causal-inference', 'counterfactual-inference', 'data-science', 'deep-learning', 'graphical-models', 'machine-learning', 'regression', 'reliability-analysis', 'survival-analysis', 'time-to-event'] | 2023-10-16 | [] | 12 | 4 | null | 0.31 | 26 | 2 | 46 | 3 | 2 | 1 | 2 | 24 | 26 | 90 | 1.1 | 27 |
1,786 | util | https://github.com/cohere-ai/cohere-python | [] | null | [] | [] | null | null | null | cohere-ai/cohere-python | cohere-python | 157 | 33 | 23 | Python | https://docs.cohere.ai | Python Library for Accessing the Cohere API | cohere-ai | 2024-01-09 | 2021-01-20 | 157 | 0.99457 | https://avatars.githubusercontent.com/u/54850923?v=4 | Python Library for Accessing the Cohere API | ['sdk'] | ['sdk'] | 2024-01-10 | [('snyk-labs/pysnyk', 0.6142659187316895, 'security', 0), ('cohere-ai/notebooks', 0.5595293641090393, 'llm', 0), ('simple-salesforce/simple-salesforce', 0.5442338585853577, 'data', 0), ('openai/openai-python', 0.5279492735862732, 'util', 0), ('open-telemetry/opentelemetry-python', 0.5118368864059448, 'util', 1), ('anthropics/anthropic-sdk-python', 0.5114951133728027, 'util', 1), ('googleapis/google-api-python-client', 0.5053079128265381, 'util', 0), ('kubeflow/fairing', 0.5045545697212219, 'ml-ops', 0)] | 37 | 2 | null | 2.9 | 48 | 38 | 36 | 0 | 0 | 0 | 0 | 48 | 30 | 90 | 0.6 | 27 |
1,546 | llm | https://github.com/luohongyin/sail | ['search-augmentation', 'search', 'language-model'] | null | [] | [] | null | null | null | luohongyin/sail | SAIL | 147 | 14 | 3 | Python | null | SAIL: Search Augmented Instruction Learning | luohongyin | 2024-01-04 | 2023-05-24 | 35 | 4.099602 | null | SAIL: Search Augmented Instruction Learning | [] | ['language-model', 'search', 'search-augmentation'] | 2023-06-06 | [('ai21labs/in-context-ralm', 0.5805896520614624, 'llm', 1), ('openbmb/toolbench', 0.5190768241882324, 'llm', 0), ('intellabs/fastrag', 0.5123240947723389, 'nlp', 0), ('yizhongw/self-instruct', 0.5114395022392273, 'llm', 1), ('srush/minichain', 0.5050071477890015, 'llm', 0)] | 2 | 1 | null | 0.19 | 3 | 2 | 8 | 7 | 0 | 0 | 0 | 3 | 19 | 90 | 6.3 | 27 |
904 | security | https://github.com/abnamro/repository-scanner | [] | null | [] | [] | null | null | null | abnamro/repository-scanner | repository-scanner | 141 | 13 | 7 | Python | null | Tool to detect secrets in source code management systems. | abnamro | 2024-01-09 | 2022-09-08 | 72 | 1.939096 | https://avatars.githubusercontent.com/u/42280701?v=4 | Tool to detect secrets in source code management systems. | [] | [] | 2023-12-20 | [('ionelmc/python-hunter', 0.5003989338874817, 'debug', 0)] | 10 | 1 | null | 4.21 | 23 | 22 | 16 | 1 | 9 | 8 | 9 | 23 | 5 | 90 | 0.2 | 27 |
1,600 | llm | https://github.com/krohling/bondai | ['autonomous-agents', 'agents'] | Open-source framework tailored for integrating and customizing Conversational AI Agents | [] | [] | null | null | null | krohling/bondai | bondai | 128 | 20 | 11 | Python | null | null | krohling | 2024-01-12 | 2023-07-16 | 28 | 4.525253 | null | Open-source framework tailored for integrating and customizing Conversational AI Agents | [] | ['agents', 'autonomous-agents'] | 2024-01-14 | [('rasahq/rasa', 0.7222825288772583, 'llm', 0), ('nvidia/nemo', 0.7121801972389221, 'nlp', 0), ('facebookresearch/parlai', 0.680033266544342, 'nlp', 0), ('aiwaves-cn/agents', 0.6704409718513489, 'nlp', 1), ('deeppavlov/deeppavlov', 0.6658996939659119, 'nlp', 0), ('prefecthq/marvin', 0.6641014814376831, 'nlp', 1), ('rcgai/simplyretrieve', 0.6295303702354431, 'llm', 0), ('embedchain/embedchain', 0.6167078018188477, 'llm', 0), ('openlmlab/moss', 0.6103507876396179, 'llm', 0), ('togethercomputer/openchatkit', 0.596705973148346, 'nlp', 0), ('minimaxir/simpleaichat', 0.5760908722877502, 'llm', 0), ('larsbaunwall/bricky', 0.5706357359886169, 'llm', 0), ('cheshire-cat-ai/core', 0.5621045827865601, 'llm', 0), ('chatarena/chatarena', 0.5611550807952881, 'llm', 0), ('google-research/language', 0.5564771294593811, 'nlp', 0), ('antonosika/gpt-engineer', 0.5529053211212158, 'llm', 0), ('transformeroptimus/superagi', 0.5482898354530334, 'llm', 2), ('lm-sys/fastchat', 0.5441566109657288, 'llm', 0), ('thilinarajapakse/simpletransformers', 0.534867525100708, 'nlp', 0), ('operand/agency', 0.5317063331604004, 'llm', 2), ('nomic-ai/gpt4all', 0.5283734798431396, 'llm', 0), ('smol-ai/developer', 0.5269186496734619, 'llm', 0), ('run-llama/rags', 0.5235958099365234, 'llm', 0), ('humanoidagents/humanoidagents', 0.5202670693397522, 'sim', 1), ('laion-ai/open-assistant', 0.5133852362632751, 'llm', 0), ('unity-technologies/ml-agents', 0.5124642252922058, 'ml-rl', 0), ('gunthercox/chatterbot', 0.5118715763092041, 'nlp', 0), ('microsoft/chatgpt-robot-manipulation-prompts', 0.5104005932807922, 'llm', 0), ('deepset-ai/haystack', 0.5101301074028015, 'llm', 0), ('minimaxir/aitextgen', 0.5016161799430847, 'llm', 0)] | 2 | 0 | null | 2.62 | 7 | 6 | 6 | 0 | 0 | 71 | 71 | 7 | 1 | 90 | 0.1 | 27 |
558 | gis | https://github.com/geopandas/xyzservices | [] | null | [] | [] | null | null | null | geopandas/xyzservices | xyzservices | 125 | 23 | 14 | Python | https://xyzservices.readthedocs.io/ | Source of XYZ tiles providers | geopandas | 2024-01-09 | 2021-05-21 | 140 | 0.889228 | https://avatars.githubusercontent.com/u/8130715?v=4 | Source of XYZ tiles providers | [] | [] | 2023-12-15 | [] | 18 | 5 | null | 0.77 | 5 | 3 | 32 | 1 | 5 | 8 | 5 | 5 | 3 | 90 | 0.6 | 27 |
1,445 | util | https://github.com/pypa/installer | ['wheel'] | null | [] | [] | null | null | null | pypa/installer | installer | 105 | 51 | 15 | Python | https://installer.readthedocs.io/ | A low-level library for installing from a Python wheel distribution. | pypa | 2024-01-08 | 2020-04-11 | 198 | 0.529158 | https://avatars.githubusercontent.com/u/647025?v=4 | A low-level library for installing from a Python wheel distribution. | ['wheel'] | ['wheel'] | 2024-01-05 | [('pyo3/maturin', 0.6027328372001648, 'util', 0), ('pyodide/micropip', 0.5991305708885193, 'util', 0), ('pytoolz/toolz', 0.5778533220291138, 'util', 0), ('getsentry/milksnake', 0.5740995407104492, 'util', 0), ('indygreg/pyoxidizer', 0.5681904554367065, 'util', 0), ('erotemic/ubelt', 0.5646870136260986, 'util', 0), ('pdm-project/pdm', 0.5561202168464661, 'util', 0), ('pypy/pypy', 0.5516636371612549, 'util', 0), ('ofek/pyapp', 0.5315470695495605, 'util', 0), ('pyston/pyston', 0.5297871828079224, 'util', 0), ('mitsuhiko/rye', 0.5275384187698364, 'util', 0), ('pypi/warehouse', 0.5235101580619812, 'util', 0), ('legrandin/pycryptodome', 0.5224993228912354, 'util', 0), ('python-poetry/poetry', 0.522213339805603, 'util', 0), ('scitools/cartopy', 0.5148464441299438, 'gis', 0), ('python/cpython', 0.505916953086853, 'util', 0), ('urwid/urwid', 0.5019884705543518, 'term', 0), ('jquast/blessed', 0.5001348853111267, 'term', 0)] | 24 | 3 | null | 0.63 | 16 | 11 | 46 | 0 | 0 | 3 | 3 | 16 | 16 | 90 | 1 | 27 |
994 | finance | https://github.com/quantopian/research_public | [] | null | [] | [] | null | null | null | quantopian/research_public | research_public | 2,262 | 1,544 | 201 | Jupyter Notebook | https://www.quantopian.com/lectures | Quantitative research and educational materials | quantopian | 2024-01-13 | 2015-02-26 | 465 | 4.857055 | https://avatars.githubusercontent.com/u/1393215?v=4 | Quantitative research and educational materials | [] | [] | 2020-10-30 | [] | 52 | 4 | null | 0 | 0 | 0 | 108 | 39 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 26 |
266 | nlp | https://github.com/arxiv-vanity/arxiv-vanity | [] | null | [] | [] | null | null | null | arxiv-vanity/arxiv-vanity | arxiv-vanity | 1,575 | 102 | 23 | Python | https://www.arxiv-vanity.com | Renders papers from arXiv as responsive web pages so you don't have to squint at a PDF. | arxiv-vanity | 2024-01-12 | 2017-08-12 | 337 | 4.667655 | https://avatars.githubusercontent.com/u/31142715?v=4 | Renders papers from arXiv as responsive web pages so you don't have to squint at a PDF. | ['academic-publishing', 'arxiv', 'latex', 'science'] | ['academic-publishing', 'arxiv', 'latex', 'science'] | 2022-01-18 | [('lukasschwab/arxiv.py', 0.5270992517471313, 'util', 1)] | 9 | 3 | null | 0 | 2 | 0 | 78 | 24 | 0 | 0 | 0 | 2 | 1 | 90 | 0.5 | 26 |
1,124 | nlp | https://github.com/gunthercox/chatterbot-corpus | [] | null | [] | [] | null | null | null | gunthercox/chatterbot-corpus | chatterbot-corpus | 1,333 | 1,151 | 69 | Python | http://chatterbot-corpus.readthedocs.io | A multilingual dialog corpus | gunthercox | 2024-01-12 | 2017-01-11 | 367 | 3.623689 | null | A multilingual dialog corpus | ['chatterbot', 'corpus', 'dialog', 'language', 'yaml'] | ['chatterbot', 'corpus', 'dialog', 'language', 'yaml'] | 2020-08-24 | [('lm-sys/fastchat', 0.6321564316749573, 'llm', 0), ('deeppavlov/deeppavlov', 0.6254500150680542, 'nlp', 0), ('thudm/chatglm-6b', 0.6088327765464783, 'llm', 0), ('rasahq/rasa', 0.6020722985267639, 'llm', 0), ('gunthercox/chatterbot', 0.5911657214164734, 'nlp', 2), ('langchain-ai/chat-langchain', 0.5771211981773376, 'llm', 0), ('fasteval/fasteval', 0.5639832019805908, 'llm', 0), ('nvidia/nemo', 0.5635098218917847, 'nlp', 0), ('bigscience-workshop/promptsource', 0.5633988976478577, 'nlp', 0), ('nomic-ai/gpt4all', 0.5547274947166443, 'llm', 0), ('thudm/chatglm2-6b', 0.5543924570083618, 'llm', 0), ('openlmlab/moss', 0.5456989407539368, 'llm', 0), ('killianlucas/open-interpreter', 0.5440534949302673, 'llm', 0), ('databrickslabs/dolly', 0.541381299495697, 'llm', 0), ('pemistahl/lingua-py', 0.5412878394126892, 'nlp', 0), ('run-llama/rags', 0.5412566065788269, 'llm', 0), ('srush/minichain', 0.5370650291442871, 'llm', 0), ('facebookresearch/parlai', 0.5316453576087952, 'nlp', 0), ('nltk/nltk', 0.5303866267204285, 'nlp', 0), ('togethercomputer/openchatkit', 0.5263169407844543, 'nlp', 0), ('pndurette/gtts', 0.5201086401939392, 'util', 0), ('blinkdl/chatrwkv', 0.5196929574012756, 'llm', 0), ('suno-ai/bark', 0.5169947743415833, 'ml', 0), ('facebookresearch/seamless_communication', 0.5149349570274353, 'nlp', 0), ('guidance-ai/guidance', 0.5125293135643005, 'llm', 0), ('embedchain/embedchain', 0.5088998079299927, 'llm', 0), ('aiwaves-cn/agents', 0.5078116059303284, 'nlp', 0), ('minimaxir/simpleaichat', 0.5073516964912415, 'llm', 0), ('rcgai/simplyretrieve', 0.5069754123687744, 'llm', 0), ('lingjzhu/charsiug2p', 0.5040284395217896, 'nlp', 0), ('explosion/spacy-models', 0.5032789707183838, 'nlp', 0)] | 72 | 5 | null | 0 | 0 | 0 | 85 | 41 | 0 | 1 | 1 | 0 | 0 | 90 | 0 | 26 |
1,426 | util | https://github.com/py4j/py4j | [] | null | [] | [] | null | null | null | py4j/py4j | py4j | 1,123 | 206 | 41 | Java | https://www.py4j.org | Py4J enables Python programs to dynamically access arbitrary Java objects | py4j | 2024-01-13 | 2010-11-02 | 691 | 1.625181 | https://avatars.githubusercontent.com/u/99001623?v=4 | Py4J enables Python programs to dynamically access arbitrary Java objects | ['distributed-systems', 'java', 'programming-languages'] | ['distributed-systems', 'java', 'programming-languages'] | 2023-02-12 | [('pympler/pympler', 0.5723164677619934, 'perf', 0), ('pyston/pyston', 0.5638821721076965, 'util', 0), ('oracle/graalpython', 0.5628305077552795, 'util', 1), ('pypy/pypy', 0.5454596877098083, 'util', 0), ('micropython/micropython', 0.5158247947692871, 'util', 0), ('backtick-se/cowait', 0.5144885182380676, 'util', 0), ('numba/llvmlite', 0.5133116245269775, 'util', 0), ('pyglet/pyglet', 0.5067098140716553, 'gamedev', 0), ('secdev/scapy', 0.5051878690719604, 'util', 0), ('hoffstadt/dearpygui', 0.5022397041320801, 'gui', 0)] | 38 | 5 | null | 0.02 | 6 | 1 | 161 | 11 | 0 | 2 | 2 | 6 | 2 | 90 | 0.3 | 26 |
1,681 | util | https://github.com/klen/pylama | ['linter'] | null | [] | [] | null | null | null | klen/pylama | pylama | 1,034 | 102 | 20 | Python | null | Code audit tool for python. | klen | 2024-01-09 | 2012-08-17 | 597 | 1.730337 | null | Code audit tool for python. | [] | ['linter'] | 2022-08-08 | [('pycqa/pyflakes', 0.6928360462188721, 'util', 1), ('landscapeio/prospector', 0.5915239453315735, 'util', 0), ('nedbat/coveragepy', 0.5741574764251709, 'testing', 0), ('google/pytype', 0.5735052824020386, 'typing', 1), ('instagram/fixit', 0.5459169745445251, 'util', 1), ('rubik/radon', 0.5425077676773071, 'util', 0), ('trailofbits/pip-audit', 0.5406633019447327, 'security', 0), ('gaogaotiantian/viztracer', 0.5285407304763794, 'profiling', 0), ('pycqa/pylint-django', 0.5170671939849854, 'util', 1), ('astral-sh/ruff', 0.5164267420768738, 'util', 1), ('pycqa/pylint', 0.5042141675949097, 'util', 1), ('alexmojaki/snoop', 0.500612735748291, 'debug', 0)] | 46 | 5 | null | 0 | 3 | 0 | 139 | 17 | 0 | 12 | 12 | 3 | 0 | 90 | 0 | 26 |
1,020 | finance | https://github.com/enthought/pyql | [] | null | [] | [] | null | null | null | enthought/pyql | pyql | 889 | 192 | 108 | Cython | null | Cython QuantLib wrappers | enthought | 2024-01-12 | 2012-03-08 | 620 | 1.432221 | https://avatars.githubusercontent.com/u/539651?v=4 | Cython QuantLib wrappers | ['cython', 'quantlib'] | ['cython', 'quantlib'] | 2023-11-22 | [('lballabio/quantlib-swig', 0.5862199664115906, 'finance', 0)] | 24 | 4 | null | 1.37 | 12 | 12 | 144 | 2 | 0 | 0 | 0 | 12 | 0 | 90 | 0 | 26 |
503 | gis | https://github.com/scikit-mobility/scikit-mobility | [] | null | [] | [] | null | null | null | scikit-mobility/scikit-mobility | scikit-mobility | 672 | 151 | 29 | Python | https://scikit-mobility.github.io/scikit-mobility/ | scikit-mobility: mobility analysis in Python | scikit-mobility | 2024-01-09 | 2019-04-30 | 248 | 2.709677 | https://avatars.githubusercontent.com/u/45601440?v=4 | scikit-mobility: mobility analysis in Python | ['complex-systems', 'data-analysis', 'data-science', 'human-mobility', 'mobility-analysis', 'mobility-flows', 'network-science', 'risk-assessment', 'scikit-mobility', 'statistics', 'synthetic-flows'] | ['complex-systems', 'data-analysis', 'data-science', 'human-mobility', 'mobility-analysis', 'mobility-flows', 'network-science', 'risk-assessment', 'scikit-mobility', 'statistics', 'synthetic-flows'] | 2023-01-20 | [('ranaroussi/quantstats', 0.6154873967170715, 'finance', 0), ('networkx/networkx', 0.614628255367279, 'graph', 0), ('statsmodels/statsmodels', 0.5975031852722168, 'ml', 3), ('scikit-learn/scikit-learn', 0.5857199430465698, 'ml', 3), ('pandas-dev/pandas', 0.5808542966842651, 'pandas', 2), ('goldmansachs/gs-quant', 0.5793623328208923, 'finance', 0), ('wesm/pydata-book', 0.5627614259719849, 'study', 0), ('plotly/dash', 0.5490847826004028, 'viz', 1), ('firmai/atspy', 0.5477433800697327, 'time-series', 0), ('eleutherai/pyfra', 0.5473421216011047, 'ml', 0), ('thealgorithms/python', 0.5455461740493774, 'study', 0), ('atsushisakai/pythonrobotics', 0.5399863719940186, 'sim', 0), ('anitagraser/movingpandas', 0.5389496684074402, 'gis', 0), ('geopandas/geopandas', 0.5326095819473267, 'gis', 0), ('krzjoa/awesome-python-data-science', 0.5322821736335754, 'study', 3), ('fatiando/verde', 0.531174898147583, 'gis', 0), ('pysal/pysal', 0.5261431336402893, 'gis', 0), ('rasbt/mlxtend', 0.5235726833343506, 'ml', 1), ('python-odin/odin', 0.5235087275505066, 'util', 0), ('pycaret/pycaret', 0.5205168128013611, 'ml', 1), ('online-ml/river', 0.5196621417999268, 'ml', 1), ('dagworks-inc/hamilton', 0.5164218544960022, 'ml-ops', 2), ('ta-lib/ta-lib-python', 0.5160692930221558, 'finance', 0), ('makepath/xarray-spatial', 0.5155532956123352, 'gis', 0), ('sympy/sympy', 0.5121610164642334, 'math', 0), ('facebook/pyre-check', 0.5081936120986938, 'typing', 0), ('projectmesa/mesa', 0.5076199769973755, 'sim', 1), ('opengeos/leafmap', 0.5058870911598206, 'gis', 1), ('cuemacro/finmarketpy', 0.5044008493423462, 'finance', 0), ('keon/algorithms', 0.5026717782020569, 'util', 0), ('alkaline-ml/pmdarima', 0.5019405484199524, 'time-series', 0), ('amaargiru/pyroad', 0.5013498067855835, 'study', 0), ('quantecon/quantecon.py', 0.5013092756271362, 'sim', 0), ('artelys/geonetworkx', 0.5012951493263245, 'gis', 0), ('pytoolz/toolz', 0.5007225871086121, 'util', 0)] | 23 | 3 | null | 0 | 9 | 1 | 57 | 12 | 0 | 2 | 2 | 9 | 2 | 90 | 0.2 | 26 |
1,204 | util | https://github.com/serpapi/google-search-results-python | [] | null | [] | [] | null | null | null | serpapi/google-search-results-python | google-search-results-python | 474 | 89 | 14 | Python | null | Google Search Results via SERP API pip Python Package | serpapi | 2024-01-12 | 2018-01-10 | 315 | 1.500678 | https://avatars.githubusercontent.com/u/34724717?v=4 | Google Search Results via SERP API pip Python Package | ['bing-image', 'google-crawler', 'google-images', 'scraping', 'serp-api', 'serpapi', 'web-scraping'] | ['bing-image', 'google-crawler', 'google-images', 'scraping', 'serp-api', 'serpapi', 'web-scraping'] | 2023-09-01 | [('nv7-github/googlesearch', 0.6652640700340271, 'util', 1), ('alirezamika/autoscraper', 0.5220003724098206, 'data', 2), ('googleapis/google-api-python-client', 0.5091744065284729, 'util', 0)] | 17 | 2 | null | 0.17 | 12 | 7 | 73 | 4 | 0 | 1 | 1 | 12 | 12 | 90 | 1 | 26 |
1,114 | util | https://github.com/pylons/colander | [] | null | [] | [] | null | null | null | pylons/colander | colander | 438 | 146 | 28 | Python | https://docs.pylonsproject.org/projects/colander/en/latest/ | A serialization/deserialization/validation library for strings, mappings and lists. | pylons | 2024-01-03 | 2011-02-16 | 675 | 0.648066 | https://avatars.githubusercontent.com/u/452227?v=4 | A serialization/deserialization/validation library for strings, mappings and lists. | ['deserialization', 'forms', 'serialization', 'validation'] | ['deserialization', 'forms', 'serialization', 'validation'] | 2023-09-09 | [('yukinarit/pyserde', 0.6523554921150208, 'util', 1), ('marshmallow-code/marshmallow', 0.6454058885574341, 'util', 3), ('python-odin/odin', 0.63074791431427, 'util', 1), ('pyeve/cerberus', 0.5786774754524231, 'data', 0), ('pydantic/pydantic', 0.531548798084259, 'util', 2), ('lidatong/dataclasses-json', 0.5289405584335327, 'util', 0), ('google/flatbuffers', 0.5039762258529663, 'perf', 1)] | 111 | 4 | null | 0.02 | 3 | 2 | 157 | 4 | 0 | 4 | 4 | 3 | 2 | 90 | 0.7 | 26 |
820 | gis | https://github.com/datasystemslab/geotorch | [] | null | [] | [] | null | null | null | datasystemslab/geotorch | GeoTorchAI | 433 | 31 | 13 | Jupyter Notebook | https://kanchanchy.github.io/geotorchai/ | GeoTorchAI: A Framework for Training and Using Spatiotemporal Deep Learning Models at Scale | datasystemslab | 2024-01-10 | 2022-05-23 | 88 | 4.91248 | https://avatars.githubusercontent.com/u/92130061?v=4 | GeoTorchAI: A Framework for Training and Using Spatiotemporal Deep Learning Models at Scale | ['classification-model', 'convlstm-pytorch', 'deep-learning', 'deep-neural-networks', 'deepsat', 'prediction-model', 'raster-data', 'satellite-classification', 'satellite-images', 'segmentation-models', 'sequence-models', 'spatial-data-analysis', 'spatio-temporal-analysis', 'spatio-temporal-models', 'spatio-temporal-prediction', 'st-resnet'] | ['classification-model', 'convlstm-pytorch', 'deep-learning', 'deep-neural-networks', 'deepsat', 'prediction-model', 'raster-data', 'satellite-classification', 'satellite-images', 'segmentation-models', 'sequence-models', 'spatial-data-analysis', 'spatio-temporal-analysis', 'spatio-temporal-models', 'spatio-temporal-prediction', 'st-resnet'] | 2023-10-22 | [('azavea/raster-vision', 0.6860873103141785, 'gis', 1), ('microsoft/torchgeo', 0.6509654521942139, 'gis', 1), ('developmentseed/label-maker', 0.5983750224113464, 'gis', 1), ('tensorflow/tensorflow', 0.5481935739517212, 'ml-dl', 2), ('nyandwi/modernconvnets', 0.5321657061576843, 'ml-dl', 0), ('rwightman/pytorch-image-models', 0.5261633992195129, 'ml-dl', 0), ('rasbt/deeplearning-models', 0.5235190391540527, 'ml-dl', 0), ('kevinmusgrave/pytorch-metric-learning', 0.5219647884368896, 'ml', 1), ('roboflow/notebooks', 0.5124086141586304, 'study', 2), ('aiqc/aiqc', 0.5082912445068359, 'ml-ops', 0), ('aistream-peelout/flow-forecast', 0.5054202675819397, 'time-series', 2)] | 5 | 2 | null | 1.21 | 1 | 0 | 20 | 3 | 0 | 1 | 1 | 1 | 0 | 90 | 0 | 26 |
749 | ml-dl | https://github.com/samuela/git-re-basin | [] | null | [] | [] | null | null | null | samuela/git-re-basin | git-re-basin | 429 | 36 | 8 | Python | https://arxiv.org/abs/2209.04836 | Code release for "Git Re-Basin: Merging Models modulo Permutation Symmetries" | samuela | 2024-01-12 | 2022-09-13 | 72 | 5.958333 | null | Code release for "Git Re-Basin: Merging Models modulo Permutation Symmetries" | ['deep-learning', 'deeplearning', 'jax', 'machine-learning', 'neural-networks'] | ['deep-learning', 'deeplearning', 'jax', 'machine-learning', 'neural-networks'] | 2023-03-07 | [('rasbt/machine-learning-book', 0.564606785774231, 'study', 3), ('huggingface/transformers', 0.5258132219314575, 'nlp', 3), ('alpa-projects/alpa', 0.507290780544281, 'ml-dl', 3)] | 2 | 0 | null | 0.12 | 1 | 1 | 16 | 10 | 0 | 0 | 0 | 1 | 4 | 90 | 4 | 26 |
559 | gis | https://github.com/geopandas/dask-geopandas | [] | null | [] | [] | null | null | null | geopandas/dask-geopandas | dask-geopandas | 424 | 45 | 23 | Python | https://dask-geopandas.readthedocs.io/ | Parallel GeoPandas with Dask | geopandas | 2024-01-05 | 2020-02-13 | 206 | 2.05114 | https://avatars.githubusercontent.com/u/8130715?v=4 | Parallel GeoPandas with Dask | [] | [] | 2023-05-19 | [('dask/dask', 0.5673131942749023, 'perf', 0), ('nalepae/pandarallel', 0.5014110207557678, 'pandas', 0)] | 20 | 7 | null | 0.19 | 3 | 0 | 48 | 8 | 2 | 4 | 2 | 3 | 1 | 90 | 0.3 | 26 |
1,668 | testing | https://github.com/jamielennox/requests-mock | ['mocking'] | null | [] | [] | null | null | null | jamielennox/requests-mock | requests-mock | 400 | 65 | 5 | Python | https://requests-mock.readthedocs.io | Mocked responses for the requests library | jamielennox | 2024-01-11 | 2014-12-16 | 476 | 0.840336 | null | Mocked responses for the requests library | [] | ['mocking'] | 2023-11-04 | [('getsentry/responses', 0.7649958729743958, 'testing', 1), ('kevin1024/vcrpy', 0.6570014953613281, 'testing', 1), ('lundberg/respx', 0.6144221425056458, 'testing', 1)] | 51 | 5 | null | 0.17 | 13 | 4 | 110 | 3 | 1 | 3 | 1 | 13 | 4 | 90 | 0.3 | 26 |
12 | nlp | https://github.com/dialogflow/dialogflow-python-client-v2 | [] | null | [] | [] | null | null | null | dialogflow/dialogflow-python-client-v2 | python-dialogflow | 398 | 187 | 56 | null | https://dialogflow.com/ | This library has moved to https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-dialogflow | dialogflow | 2024-01-10 | 2017-10-24 | 327 | 1.217125 | https://avatars.githubusercontent.com/u/16785467?v=4 | This library has moved to https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-dialogflow | ['dialogflow', 'machine-learning'] | ['dialogflow', 'machine-learning'] | 2023-09-21 | [('googleapis/python-speech', 0.743192732334137, 'ml', 0), ('googleapis/google-api-python-client', 0.5611771941184998, 'util', 0), ('deeppavlov/deeppavlov', 0.5524868369102478, 'nlp', 1), ('pndurette/gtts', 0.5459315776824951, 'util', 0), ('googlecloudplatform/vertex-ai-samples', 0.5129502415657043, 'ml', 0), ('google/vizier', 0.5073549151420593, 'ml', 1)] | 37 | 5 | null | 0.96 | 0 | 0 | 76 | 4 | 10 | 9 | 10 | 0 | 0 | 90 | 0 | 26 |
1,190 | llm | https://github.com/microsoft/chatgpt-robot-manipulation-prompts | [] | null | [] | [] | null | null | null | microsoft/chatgpt-robot-manipulation-prompts | ChatGPT-Robot-Manipulation-Prompts | 304 | 30 | 8 | null | null | null | microsoft | 2024-01-10 | 2023-04-06 | 42 | 7.117057 | https://avatars.githubusercontent.com/u/6154722?v=4 | microsoft/ChatGPT-Robot-Manipulation-Prompts | [] | [] | 2023-11-28 | [('embedchain/embedchain', 0.5969848036766052, 'llm', 0), ('microsoft/promptcraft-robotics', 0.580747663974762, 'sim', 0), ('togethercomputer/openchatkit', 0.5749940872192383, 'nlp', 0), ('minimaxir/simpleaichat', 0.5525240302085876, 'llm', 0), ('weaviate/verba', 0.5466259121894836, 'llm', 0), ('run-llama/rags', 0.5412412881851196, 'llm', 0), ('cheshire-cat-ai/core', 0.5411517024040222, 'llm', 0), ('promptslab/promptify', 0.541034460067749, 'nlp', 0), ('prefecthq/marvin', 0.5360046029090881, 'nlp', 0), ('nomic-ai/gpt4all', 0.5312781929969788, 'llm', 0), ('promptslab/awesome-prompt-engineering', 0.5258796811103821, 'study', 0), ('rcgai/simplyretrieve', 0.5226764678955078, 'llm', 0), ('killianlucas/open-interpreter', 0.5223796367645264, 'llm', 0), ('humanoidagents/humanoidagents', 0.5163865685462952, 'sim', 0), ('krohling/bondai', 0.5104005932807922, 'llm', 0), ('gunthercox/chatterbot', 0.5103239417076111, 'nlp', 0), ('chatarena/chatarena', 0.5086156725883484, 'llm', 0), ('guidance-ai/guidance', 0.5060887932777405, 'llm', 0), ('microsoft/autogen', 0.5033249258995056, 'llm', 0), ('xtekky/gpt4free', 0.50308758020401, 'llm', 0)] | 3 | 1 | null | 0.42 | 2 | 2 | 9 | 2 | 0 | 0 | 0 | 2 | 1 | 90 | 0.5 | 26 |
834 | jupyter | https://github.com/cmudig/autoprofiler | [] | null | [] | [] | null | null | null | cmudig/autoprofiler | AutoProfiler | 294 | 8 | 6 | Svelte | null | Automatically profile dataframes in the Jupyter sidebar | cmudig | 2024-01-12 | 2022-03-24 | 96 | 3.039882 | https://avatars.githubusercontent.com/u/56060038?v=4 | Automatically profile dataframes in the Jupyter sidebar | ['jupyter', 'pandas'] | ['jupyter', 'pandas'] | 2023-09-26 | [('tkrabel/bamboolib', 0.6205199956893921, 'pandas', 1), ('quantopian/qgrid', 0.5721290111541748, 'jupyter', 0), ('lux-org/lux', 0.5497778058052063, 'viz', 2), ('koaning/drawdata', 0.5204150080680847, 'jupyter', 1), ('adamerose/pandasgui', 0.5127207040786743, 'pandas', 1), ('jakevdp/pythondatasciencehandbook', 0.5069370865821838, 'study', 1)] | 4 | 2 | null | 1.17 | 1 | 0 | 22 | 4 | 0 | 0 | 0 | 1 | 2 | 90 | 2 | 26 |
915 | gis | https://github.com/giswqs/aws-open-data-geo | [] | null | [] | [] | null | null | null | giswqs/aws-open-data-geo | aws-open-data-geo | 263 | 7 | 11 | Python | null | A list of open geospatial datasets on AWS | giswqs | 2024-01-05 | 2022-12-18 | 58 | 4.512255 | https://avatars.githubusercontent.com/u/129896036?v=4 | A list of open geospatial datasets on AWS | ['aws', 'environment', 'geospatial', 'mapping', 'open-data', 'satellite-imagery', 'sustainability'] | ['aws', 'environment', 'geospatial', 'mapping', 'open-data', 'satellite-imagery', 'sustainability'] | 2024-01-13 | [('sentinelsat/sentinelsat', 0.6044768691062927, 'gis', 2), ('plant99/felicette', 0.5306439995765686, 'gis', 2), ('developmentseed/geolambda', 0.507233738899231, 'gis', 0)] | 2 | 2 | null | 2.15 | 1 | 1 | 13 | 0 | 0 | 0 | 0 | 1 | 0 | 90 | 0 | 26 |
1,590 | util | https://github.com/soft-matter/pims | ['formats', 'video'] | null | [] | [] | null | null | null | soft-matter/pims | pims | 256 | 66 | 14 | Python | http://soft-matter.github.io/pims/ | Python Image Sequence: Load video and sequential images in many formats with a simple, consistent interface. | soft-matter | 2024-01-04 | 2013-11-12 | 533 | 0.4803 | https://avatars.githubusercontent.com/u/5857177?v=4 | Python Image Sequence: Load video and sequential images in many formats with a simple, consistent interface. | [] | ['formats', 'video'] | 2023-11-26 | [('zulko/moviepy', 0.6140278577804565, 'util', 1), ('imageio/imageio', 0.5806184411048889, 'util', 1)] | 38 | 5 | null | 0.1 | 3 | 1 | 124 | 2 | 0 | 2 | 2 | 3 | 3 | 90 | 1 | 26 |
1,648 | nlp | https://github.com/microsoft/vert-papers | [] | null | [] | [] | null | null | null | microsoft/vert-papers | vert-papers | 256 | 90 | 12 | Python | null | This repository contains code and datasets related to entity/knowledge papers from the VERT (Versatile Entity Recognition & disambiguation Toolkit) project, by the Knowledge Computing group at Microsoft Research Asia (MSRA). | microsoft | 2024-01-08 | 2019-07-25 | 235 | 1.086061 | https://avatars.githubusercontent.com/u/6154722?v=4 | This repository contains code and datasets related to entity/knowledge papers from the VERT (Versatile Entity Recognition & disambiguation Toolkit) project, by the Knowledge Computing group at Microsoft Research Asia (MSRA). | ['bertel', 'can-ner', 'cross-lingual-ner', 'entity-disambiguation', 'entity-extraction', 'entity-linking', 'entity-resolution', 'grn', 'language-understanding', 'linkingpark', 'ml', 'named-entity-recognition', 'ner', 'nlp', 'nlp-resources', 'unitrans', 'xl-ner'] | ['bertel', 'can-ner', 'cross-lingual-ner', 'entity-disambiguation', 'entity-extraction', 'entity-linking', 'entity-resolution', 'grn', 'language-understanding', 'linkingpark', 'ml', 'named-entity-recognition', 'ner', 'nlp', 'nlp-resources', 'unitrans', 'xl-ner'] | 2023-10-07 | [('zjunlp/deepke', 0.5728095769882202, 'ml', 3), ('babelscape/rebel', 0.5618175864219666, 'nlp', 2), ('franck-dernoncourt/neuroner', 0.5278816223144531, 'nlp', 2), ('neuml/txtai', 0.5036661028862, 'nlp', 1), ('dylanhogg/llmgraph', 0.5032587647438049, 'ml', 0)] | 13 | 5 | null | 0.13 | 2 | 1 | 54 | 3 | 0 | 0 | 0 | 2 | 5 | 90 | 2.5 | 26 |
580 | gis | https://github.com/spatialucr/geosnap | [] | null | [] | [] | null | null | null | spatialucr/geosnap | geosnap | 218 | 31 | 17 | Python | https://oturns.github.io/geosnap/ | The Geospatial Neighborhood Analysis Package | spatialucr | 2024-01-04 | 2018-09-19 | 279 | 0.778969 | https://avatars.githubusercontent.com/u/122131626?v=4 | The Geospatial Neighborhood Analysis Package | ['geodemographics', 'neighborhood-dynamics', 'spatial-analysis', 'urban-modeling'] | ['geodemographics', 'neighborhood-dynamics', 'spatial-analysis', 'urban-modeling'] | 2023-12-11 | [('udst/urbansim', 0.5915238857269287, 'sim', 0), ('pysal/momepy', 0.5846999287605286, 'gis', 0), ('gregorhd/mapcompare', 0.54576176404953, 'gis', 0), ('mcordts/cityscapesscripts', 0.5296457409858704, 'gis', 0)] | 9 | 6 | null | 1.15 | 7 | 6 | 65 | 1 | 5 | 6 | 5 | 7 | 1 | 90 | 0.1 | 26 |
1,785 | llm | https://github.com/cohere-ai/notebooks | ['notebooks', 'cohere'] | null | [] | [] | null | null | null | cohere-ai/notebooks | notebooks | 204 | 54 | 12 | Jupyter Notebook | null | Code examples and jupyter notebooks for the Cohere Platform | cohere-ai | 2024-01-12 | 2021-10-06 | 120 | 1.687943 | https://avatars.githubusercontent.com/u/54850923?v=4 | Code examples and jupyter notebooks for the Cohere Platform | [] | ['cohere', 'notebooks'] | 2024-01-14 | [('fchollet/deep-learning-with-python-notebooks', 0.7323339581489563, 'study', 0), ('jupyter/nbformat', 0.7138713598251343, 'jupyter', 0), ('aws/graph-notebook', 0.6492716073989868, 'jupyter', 0), ('jupyter/notebook', 0.6436967253684998, 'jupyter', 0), ('jupyter-widgets/ipywidgets', 0.6400914192199707, 'jupyter', 0), ('mwouts/jupytext', 0.6391822695732117, 'jupyter', 1), ('jakevdp/pythondatasciencehandbook', 0.6190292835235596, 'study', 0), ('jupyter/nbconvert', 0.6110220551490784, 'jupyter', 0), ('python/cpython', 0.603980302810669, 'util', 0), ('voila-dashboards/voila', 0.60094153881073, 'jupyter', 0), ('wesm/pydata-book', 0.5929689407348633, 'study', 0), ('jupyter-lsp/jupyterlab-lsp', 0.5917092561721802, 'jupyter', 0), ('jupyter/nbgrader', 0.5908788442611694, 'jupyter', 0), ('ageron/handson-ml2', 0.5897052884101868, 'ml', 0), ('jupyterlab/jupyterlab-desktop', 0.5881903767585754, 'jupyter', 0), ('faster-cpython/ideas', 0.5839570760726929, 'perf', 0), ('koaning/calm-notebooks', 0.5802757143974304, 'study', 0), ('nteract/papermill', 0.5747708082199097, 'jupyter', 1), ('mynameisfiber/high_performance_python_2e', 0.5679949522018433, 'study', 0), ('alphasecio/langchain-examples', 0.5620157718658447, 'llm', 0), ('vizzuhq/ipyvizzu', 0.5615155100822449, 'jupyter', 0), ('jupyter/nbdime', 0.559609591960907, 'jupyter', 0), ('cohere-ai/cohere-python', 0.5595293641090393, 'util', 0), ('ipython/ipyparallel', 0.5586426258087158, 'perf', 0), ('quantopian/qgrid', 0.5572461485862732, 'jupyter', 0), ('ipython/ipykernel', 0.5502215623855591, 'util', 0), ('nbqa-dev/nbqa', 0.550182044506073, 'jupyter', 0), ('opengeos/leafmap', 0.546214759349823, 'gis', 0), ('jupyterlab/jupyterlab', 0.5374837517738342, 'jupyter', 0), ('brandtbucher/specialist', 0.5358579158782959, 'perf', 0), ('adafruit/circuitpython', 0.5329226851463318, 'util', 0), ('huggingface/notebooks', 0.529309868812561, 'ml', 0), ('masoniteframework/masonite', 0.5226303935050964, 'web', 0), ('maartenbreddels/ipyvolume', 0.5213759541511536, 'jupyter', 0), ('eleutherai/pyfra', 0.5172991752624512, 'ml', 0), ('giswqs/mapwidget', 0.51350337266922, 'gis', 0), ('holoviz/panel', 0.5109737515449524, 'viz', 0), ('pypy/pypy', 0.5104973316192627, 'util', 0), ('faster-cpython/tools', 0.5094420313835144, 'perf', 0), ('mito-ds/monorepo', 0.5074410438537598, 'jupyter', 0), ('rasbt/watermark', 0.5048350095748901, 'util', 0), ('willmcgugan/textual', 0.5048252940177917, 'term', 0), ('wxwidgets/phoenix', 0.5029077529907227, 'gui', 0), ('timofurrer/awesome-asyncio', 0.5014423131942749, 'study', 0), ('pytoolz/toolz', 0.5008567571640015, 'util', 0)] | 9 | 3 | null | 1.56 | 33 | 25 | 28 | 0 | 0 | 0 | 0 | 33 | 7 | 90 | 0.2 | 26 |
1,099 | gis | https://github.com/giswqs/mapwidget | [] | null | [] | [] | null | null | null | giswqs/mapwidget | mapwidget | 201 | 12 | 9 | Python | http://mapwidget.gishub.org | Custom Jupyter widgets for creating interactive 2D/3D maps using popular JavaScript libraries with bidirectional communication, such as Cesium, Mapbox, MapLibre, Leaflet, and OpenLayers | giswqs | 2024-01-04 | 2023-01-21 | 53 | 3.762032 | https://avatars.githubusercontent.com/u/129896036?v=4 | Custom Jupyter widgets for creating interactive 2D/3D maps using popular JavaScript libraries with bidirectional communication, such as Cesium, Mapbox, MapLibre, Leaflet, and OpenLayers | ['anywidget', 'cesium', 'geopython', 'geospatial', 'ipywidgets', 'jupyter', 'leaflet', 'mapbox', 'maplibre', 'mapping', 'openlayers'] | ['anywidget', 'cesium', 'geopython', 'geospatial', 'ipywidgets', 'jupyter', 'leaflet', 'mapbox', 'maplibre', 'mapping', 'openlayers'] | 2023-03-24 | [('jupyter-widgets/ipyleaflet', 0.6565911769866943, 'gis', 2), ('maartenbreddels/ipyvolume', 0.6517302989959717, 'jupyter', 1), ('jupyter-widgets/ipywidgets', 0.6259199976921082, 'jupyter', 0), ('opengeos/leafmap', 0.5904461741447449, 'gis', 4), ('python-visualization/folium', 0.5697619915008545, 'gis', 0), ('vizzuhq/ipyvizzu', 0.5461418032646179, 'jupyter', 1), ('bokeh/bokeh', 0.5409641861915588, 'viz', 1), ('voila-dashboards/voila', 0.5279869437217712, 'jupyter', 1), ('wxwidgets/phoenix', 0.5239470601081848, 'gui', 0), ('aws/graph-notebook', 0.5196599364280701, 'jupyter', 1), ('jupyterlab/jupyterlab-desktop', 0.5143319964408875, 'jupyter', 1), ('cohere-ai/notebooks', 0.51350337266922, 'llm', 0), ('plotly/plotly.py', 0.504154622554779, 'viz', 0)] | 1 | 1 | null | 0.9 | 1 | 0 | 12 | 10 | 8 | 8 | 8 | 1 | 2 | 90 | 2 | 26 |
739 | data | https://github.com/ktrueda/parquet-tools | [] | null | [] | [] | null | null | null | ktrueda/parquet-tools | parquet-tools | 136 | 18 | 4 | Python | null | easy install parquet-tools | ktrueda | 2024-01-04 | 2020-05-02 | 195 | 0.695906 | null | easy install parquet-tools | ['cli', 'parquet', 'parquet-tools'] | ['cli', 'parquet', 'parquet-tools'] | 2024-01-02 | [('dask/fastparquet', 0.5958738327026367, 'data', 0)] | 14 | 2 | null | 0.13 | 7 | 6 | 45 | 0 | 4 | 5 | 4 | 7 | 10 | 90 | 1.4 | 26 |
1,903 | data | https://github.com/typesense/typesense-python | ['search-engine', 'sdk', 'api'] | Open Source alternative to to ElasticSearch. Fast, typo tolerant, in-memory fuzzy Search Engine. | [] | [] | null | null | null | typesense/typesense-python | typesense-python | 125 | 28 | 5 | Python | null | Python client for Typesense: https://github.com/typesense/typesense | typesense | 2024-01-16 | 2018-01-30 | 313 | 0.399361 | https://avatars.githubusercontent.com/u/19822348?v=4 | Python client for Typesense: https://github.com/typesense/typesense | [] | ['api', 'sdk', 'search-engine'] | 2024-01-03 | [('meilisearch/meilisearch-python', 0.5989935994148254, 'data', 3), ('googleapis/google-api-python-client', 0.5588173866271973, 'util', 0), ('qdrant/qdrant-client', 0.5577874779701233, 'util', 0), ('tiangolo/typer', 0.5246941447257996, 'term', 0), ('strawberry-graphql/strawberry', 0.5120099782943726, 'web', 0), ('simple-salesforce/simple-salesforce', 0.503971517086029, 'data', 1)] | 14 | 5 | null | 0.4 | 8 | 5 | 73 | 0 | 0 | 0 | 0 | 8 | 17 | 90 | 2.1 | 26 |
1,430 | sim | https://github.com/srivatsankrishnan/oss-arch-gym | ['architecture', 'simulator'] | null | [] | [] | null | null | null | srivatsankrishnan/oss-arch-gym | oss-arch-gym | 91 | 15 | 6 | Jupyter Notebook | null | Open source version of ArchGym project. | srivatsankrishnan | 2024-01-04 | 2023-04-11 | 42 | 2.166667 | null | Open source version of ArchGym project. | [] | ['architecture', 'simulator'] | 2023-12-28 | [] | 14 | 2 | null | 5.33 | 28 | 22 | 9 | 1 | 0 | 0 | 0 | 28 | 14 | 90 | 0.5 | 26 |
884 | util | https://github.com/pyodide/micropip | [] | null | [] | [] | null | null | null | pyodide/micropip | micropip | 42 | 12 | 6 | Python | https://micropip.pyodide.org | A lightweight Python package installer for Pyodide | pyodide | 2024-01-03 | 2022-09-15 | 71 | 0.585657 | https://avatars.githubusercontent.com/u/77002075?v=4 | A lightweight Python package installer for Pyodide | ['package-installer', 'pyodide', 'webassembly'] | ['package-installer', 'pyodide', 'webassembly'] | 2024-01-03 | [('pyodide/pyodide', 0.7593028545379639, 'util', 1), ('indygreg/pyoxidizer', 0.6752342581748962, 'util', 0), ('ofek/pyapp', 0.6668835282325745, 'util', 0), ('mitsuhiko/rye', 0.6547753810882568, 'util', 0), ('pypi/warehouse', 0.6480095386505127, 'util', 0), ('pypa/flit', 0.6164460778236389, 'util', 0), ('python-poetry/poetry', 0.609188437461853, 'util', 0), ('pdm-project/pdm', 0.6069954037666321, 'util', 0), ('pypa/installer', 0.5991305708885193, 'util', 0), ('pypa/hatch', 0.5937002897262573, 'util', 0), ('pypy/pypy', 0.5889347195625305, 'util', 0), ('libtcod/python-tcod', 0.5887089967727661, 'gamedev', 0), ('bottlepy/bottle', 0.5800544023513794, 'web', 0), ('webpy/webpy', 0.5706849694252014, 'web', 0), ('regebro/pyroma', 0.5652766227722168, 'util', 0), ('beeware/briefcase', 0.561262845993042, 'util', 0), ('mamba-org/mamba', 0.5484160780906677, 'util', 0), ('pallets/flask', 0.5379033088684082, 'web', 0), ('pyo3/maturin', 0.5349652171134949, 'util', 0), ('conda/constructor', 0.5295533537864685, 'util', 0), ('hoffstadt/dearpygui', 0.5266197919845581, 'gui', 0), ('pyinstaller/pyinstaller', 0.5264869332313538, 'util', 0), ('pytables/pytables', 0.5223343968391418, 'data', 0), ('malloydata/malloy-py', 0.5204178690910339, 'data', 0), ('pypa/build', 0.5155296325683594, 'util', 0), ('tezromach/python-package-template', 0.5130401253700256, 'template', 0), ('tox-dev/pipdeptree', 0.5127100944519043, 'util', 0), ('linkedin/shiv', 0.5121049284934998, 'util', 0), ('pyinfra-dev/pyinfra', 0.5114571452140808, 'util', 0), ('pomponchik/instld', 0.5101442337036133, 'util', 0), ('hugovk/pypistats', 0.5070849657058716, 'util', 0), ('erotemic/ubelt', 0.5013461709022522, 'util', 0), ('pypa/virtualenv', 0.5000215172767639, 'util', 0)] | 8 | 2 | null | 0.48 | 5 | 2 | 16 | 0 | 0 | 5 | 5 | 5 | 14 | 90 | 2.8 | 26 |
903 | data | https://github.com/malloydata/malloy-py | [] | null | [] | [] | null | null | null | malloydata/malloy-py | malloy-py | 15 | 6 | 8 | JavaScript | null | Python package for executing Malloy | malloydata | 2024-01-12 | 2022-11-02 | 64 | 0.231278 | https://avatars.githubusercontent.com/u/115666028?v=4 | Python package for executing Malloy | ['business-analytics', 'business-intelligence', 'data', 'data-modeling', 'semantic-modeling', 'sql'] | ['business-analytics', 'business-intelligence', 'data', 'data-modeling', 'semantic-modeling', 'sql'] | 2024-01-12 | [('tiangolo/sqlmodel', 0.6022137403488159, 'data', 1), ('ibis-project/ibis', 0.5992289185523987, 'data', 1), ('plotly/dash', 0.5759779810905457, 'viz', 0), ('sqlalchemy/sqlalchemy', 0.5755621790885925, 'data', 1), ('tobymao/sqlglot', 0.5748046040534973, 'data', 1), ('krzjoa/awesome-python-data-science', 0.5664848685264587, 'study', 0), ('eleutherai/pyfra', 0.5658804178237915, 'ml', 0), ('willmcgugan/textual', 0.5623626708984375, 'term', 0), ('pympler/pympler', 0.5531739592552185, 'perf', 0), ('pypa/hatch', 0.547518789768219, 'util', 0), ('kubeflow/fairing', 0.5466133952140808, 'ml-ops', 0), ('goldmansachs/gs-quant', 0.5456441044807434, 'finance', 0), ('fastai/fastcore', 0.5410973429679871, 'util', 0), ('pdm-project/pdm', 0.5352970957756042, 'util', 0), ('gradio-app/gradio', 0.5345299243927002, 'viz', 0), ('dagworks-inc/hamilton', 0.532253086566925, 'ml-ops', 0), ('python-odin/odin', 0.5316958427429199, 'util', 0), ('pandas-dev/pandas', 0.5308995246887207, 'pandas', 0), ('pytables/pytables', 0.5297611951828003, 'data', 0), ('indygreg/pyoxidizer', 0.5247126221656799, 'util', 0), ('omry/omegaconf', 0.5221198201179504, 'util', 0), ('ploomber/ploomber', 0.5213688015937805, 'ml-ops', 0), ('pyodide/micropip', 0.5204178690910339, 'util', 0), ('holoviz/panel', 0.5195130109786987, 'viz', 0), ('wesm/pydata-book', 0.5186687707901001, 'study', 0), ('ranaroussi/quantstats', 0.5185449123382568, 'finance', 0), ('amaargiru/pyroad', 0.5178565979003906, 'study', 0), ('ta-lib/ta-lib-python', 0.5163371562957764, 'finance', 0), ('machow/siuba', 0.513921856880188, 'pandas', 1), ('nteract/papermill', 0.5121511220932007, 'jupyter', 0), ('dylanhogg/awesome-python', 0.5110769867897034, 'study', 1), ('spotify/luigi', 0.5109559893608093, 'ml-ops', 0), ('macbre/sql-metadata', 0.5103316307067871, 'data', 1), ('pypy/pypy', 0.5085586309432983, 'util', 0), ('python/cpython', 0.5069926381111145, 'util', 0), ('pytoolz/toolz', 0.5053905844688416, 'util', 0), ('saulpw/visidata', 0.5046626925468445, 'term', 0)] | 9 | 4 | null | 4.63 | 16 | 15 | 15 | 0 | 46 | 92 | 46 | 16 | 10 | 90 | 0.6 | 26 |
160 | data | https://github.com/goldsmith/wikipedia | [] | null | [] | [] | null | null | null | goldsmith/wikipedia | Wikipedia | 2,774 | 569 | 83 | Python | https://wikipedia.readthedocs.org/ | A Pythonic wrapper for the Wikipedia API | goldsmith | 2024-01-12 | 2013-08-20 | 545 | 5.089908 | null | A Pythonic wrapper for the Wikipedia API | [] | [] | 2020-10-09 | [('harangju/wikinet', 0.7525506615638733, 'data', 0), ('mediawiki-client-tools/mediawiki-dump-generator', 0.709117591381073, 'data', 0), ('mediawiki-client-tools/wikitools3', 0.708003044128418, 'data', 0), ('weaviate/semantic-search-through-wikipedia-with-weaviate', 0.5848309397697449, 'data', 0), ('urschrei/pyzotero', 0.5380213856697083, 'util', 0), ('facebookresearch/drqa', 0.5370073318481445, 'nlp', 0), ('nv7-github/googlesearch', 0.5323612093925476, 'util', 0), ('meilisearch/meilisearch-python', 0.5276321768760681, 'data', 0), ('dit/dit', 0.525985062122345, 'math', 0), ('googleapis/google-api-python-client', 0.5227052569389343, 'util', 0), ('pytoolz/toolz', 0.5172760486602783, 'util', 0), ('scholarly-python-package/scholarly', 0.5016988515853882, 'data', 0)] | 23 | 3 | null | 0 | 0 | 0 | 127 | 40 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 25 |
1,256 | llm | https://github.com/openai/finetune-transformer-lm | [] | null | [] | [] | null | null | null | openai/finetune-transformer-lm | finetune-transformer-lm | 1,996 | 485 | 73 | Python | https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf | Code and model for the paper "Improving Language Understanding by Generative Pre-Training" | openai | 2024-01-12 | 2018-06-11 | 294 | 6.785818 | https://avatars.githubusercontent.com/u/14957082?v=4 | Code and model for the paper "Improving Language Understanding by Generative Pre-Training" | ['paper'] | ['paper'] | 2018-11-22 | [('openai/gpt-2', 0.6553829312324524, 'llm', 1), ('srush/minichain', 0.6227275133132935, 'llm', 0), ('openai/image-gpt', 0.6050621867179871, 'llm', 0), ('huawei-noah/pretrained-language-model', 0.5976941585540771, 'nlp', 0), ('thudm/glm-130b', 0.590366780757904, 'llm', 0), ('yizhongw/self-instruct', 0.5861937999725342, 'llm', 0), ('jonasgeiping/cramming', 0.5850217342376709, 'nlp', 0), ('salesforce/blip', 0.5705159902572632, 'diffusion', 0), ('microsoft/unilm', 0.5644842982292175, 'nlp', 0), ('facebookresearch/shepherd', 0.5617777705192566, 'llm', 0), ('yueyu1030/attrprompt', 0.5512840747833252, 'llm', 0), ('togethercomputer/redpajama-data', 0.5497167706489563, 'llm', 0), ('qanastek/drbert', 0.5415375828742981, 'llm', 0), ('suno-ai/bark', 0.5396863222122192, 'ml', 0), ('google-research/electra', 0.5301187634468079, 'ml-dl', 0), ('tatsu-lab/stanford_alpaca', 0.5281931757926941, 'llm', 0), ('hannibal046/awesome-llm', 0.527682363986969, 'study', 0), ('cg123/mergekit', 0.5270984768867493, 'llm', 0), ('openai/clip', 0.5225598812103271, 'ml-dl', 0), ('microsoft/lora', 0.520248532295227, 'llm', 0), ('kyegomez/tree-of-thoughts', 0.5154716968536377, 'llm', 0), ('keirp/automatic_prompt_engineer', 0.5153794884681702, 'llm', 0), ('huggingface/text-generation-inference', 0.5149126648902893, 'llm', 0), ('lupantech/chameleon-llm', 0.5111390352249146, 'llm', 0), ('graykode/nlp-tutorial', 0.5100629329681396, 'study', 1), ('bigscience-workshop/megatron-deepspeed', 0.5082074999809265, 'llm', 0), ('microsoft/megatron-deepspeed', 0.5082074999809265, 'llm', 0), ('guidance-ai/guidance', 0.5050269961357117, 'llm', 0), ('bigscience-workshop/biomedical', 0.5047228336334229, 'data', 0), ('extreme-bert/extreme-bert', 0.504263699054718, 'llm', 0), ('thudm/codegeex', 0.5009891390800476, 'llm', 0)] | 5 | 1 | null | 0 | 1 | 0 | 68 | 63 | 0 | 0 | 0 | 1 | 1 | 90 | 1 | 25 |
205 | debug | https://github.com/alexmojaki/heartrate | [] | null | [] | [] | null | null | null | alexmojaki/heartrate | heartrate | 1,685 | 124 | 33 | Python | null | Simple real time visualisation of the execution of a Python program. | alexmojaki | 2024-01-13 | 2019-04-24 | 248 | 6.770953 | null | Simple real time visualisation of the execution of a Python program. | ['debugger', 'visualization'] | ['debugger', 'visualization'] | 2021-11-13 | [('gaogaotiantian/viztracer', 0.6707364320755005, 'profiling', 1), ('altair-viz/altair', 0.6655700206756592, 'viz', 1), ('alexmojaki/snoop', 0.6388193964958191, 'debug', 1), ('pympler/pympler', 0.57415771484375, 'perf', 0), ('holoviz/holoviz', 0.573762834072113, 'viz', 0), ('bokeh/bokeh', 0.571494460105896, 'viz', 1), ('inducer/pudb', 0.5674254894256592, 'debug', 1), ('brandtbucher/specialist', 0.56365966796875, 'perf', 0), ('mwaskom/seaborn', 0.5591291785240173, 'viz', 0), ('alexmojaki/birdseye', 0.5575025081634521, 'debug', 1), ('p403n1x87/austin', 0.5422681570053101, 'profiling', 0), ('pythonprofilers/memory_profiler', 0.5310880541801453, 'profiling', 0), ('kanaries/pygwalker', 0.5303294658660889, 'pandas', 1), ('holoviz/geoviews', 0.5235070586204529, 'gis', 0), ('pyutils/line_profiler', 0.522416353225708, 'profiling', 0), ('samuelcolvin/python-devtools', 0.5222712755203247, 'debug', 0), ('pyqtgraph/pyqtgraph', 0.5196880102157593, 'viz', 1), ('has2k1/plotnine', 0.5188327431678772, 'viz', 0), ('ionelmc/python-hunter', 0.5184060335159302, 'debug', 1), ('pyglet/pyglet', 0.5163092613220215, 'gamedev', 0), ('rockhopper-technologies/enlighten', 0.5111202597618103, 'term', 0), ('plotly/plotly.py', 0.5061368942260742, 'viz', 1), ('nschloe/perfplot', 0.5038774013519287, 'perf', 0), ('residentmario/geoplot', 0.5028942823410034, 'gis', 0), ('holoviz/panel', 0.5002499222755432, 'viz', 0)] | 3 | 0 | null | 0 | 1 | 1 | 58 | 26 | 0 | 0 | 0 | 1 | 2 | 90 | 2 | 25 |
1,503 | util | https://github.com/asweigart/pyperclip | ['clipboard'] | null | [] | [] | null | null | null | asweigart/pyperclip | pyperclip | 1,497 | 184 | 35 | Python | https://pypi.python.org/pypi/pyperclip | Python module for cross-platform clipboard functions. | asweigart | 2024-01-14 | 2011-06-15 | 658 | 2.272116 | null | Python module for cross-platform clipboard functions. | [] | ['clipboard'] | 2021-10-12 | [('taylorsmarks/playsound', 0.5268693566322327, 'util', 0), ('pytoolz/toolz', 0.5117189288139343, 'util', 0), ('hoffstadt/dearpygui', 0.5023604035377502, 'gui', 0), ('p403n1x87/austin', 0.5013977885246277, 'profiling', 0)] | 34 | 3 | null | 0 | 4 | 1 | 153 | 27 | 0 | 0 | 0 | 4 | 4 | 90 | 1 | 25 |
995 | finance | https://github.com/quantopian/empyrical | [] | null | [] | [] | null | null | null | quantopian/empyrical | empyrical | 1,189 | 365 | 71 | Python | https://quantopian.github.io/empyrical | Common financial risk and performance metrics. Used by zipline and pyfolio. | quantopian | 2024-01-13 | 2016-03-18 | 410 | 2.895964 | https://avatars.githubusercontent.com/u/1393215?v=4 | Common financial risk and performance metrics. Used by zipline and pyfolio. | [] | [] | 2020-10-14 | [('quantopian/pyfolio', 0.6045350432395935, 'finance', 0)] | 22 | 4 | null | 0 | 3 | 0 | 95 | 40 | 0 | 4 | 4 | 3 | 0 | 90 | 0 | 25 |
615 | testing | https://github.com/wolever/parameterized | [] | null | [] | [] | null | null | null | wolever/parameterized | parameterized | 797 | 104 | 18 | Python | null | Parameterized testing with any Python test framework | wolever | 2024-01-13 | 2012-03-10 | 620 | 1.284596 | null | Parameterized testing with any Python test framework | [] | [] | 2023-03-27 | [('nedbat/coveragepy', 0.6841293573379517, 'testing', 0), ('pmorissette/bt', 0.620602011680603, 'finance', 0), ('getsentry/responses', 0.6193545460700989, 'testing', 0), ('ionelmc/pytest-benchmark', 0.6030191779136658, 'testing', 0), ('klen/py-frameworks-bench', 0.5862182974815369, 'perf', 0), ('spulec/freezegun', 0.5846211314201355, 'testing', 0), ('locustio/locust', 0.5832534432411194, 'testing', 0), ('buildbot/buildbot', 0.5751279592514038, 'util', 0), ('pytest-dev/pytest', 0.5712449550628662, 'testing', 0), ('pytest-dev/pytest-bdd', 0.5707379579544067, 'testing', 0), ('eleutherai/pyfra', 0.5701524615287781, 'ml', 0), ('taverntesting/tavern', 0.5629963874816895, 'testing', 0), ('pytest-dev/pytest-xdist', 0.5527563095092773, 'testing', 0), ('eugeneyan/python-collab-template', 0.5470243692398071, 'template', 0), ('cobrateam/splinter', 0.5428141355514526, 'testing', 0), ('computationalmodelling/nbval', 0.5427061319351196, 'jupyter', 0), ('seleniumbase/seleniumbase', 0.5371958613395691, 'testing', 0), ('pyeve/cerberus', 0.5334105491638184, 'data', 0), ('pytoolz/toolz', 0.5330343842506409, 'util', 0), ('mementum/backtrader', 0.5309708714485168, 'finance', 0), ('samuelcolvin/dirty-equals', 0.524022102355957, 'util', 0), ('requests/toolbelt', 0.5131222009658813, 'util', 0), ('pytest-dev/pytest-mock', 0.5089722275733948, 'testing', 0), ('unionai-oss/pandera', 0.5074113607406616, 'pandas', 0), ('cuemacro/finmarketpy', 0.5073219537734985, 'finance', 0), ('pympler/pympler', 0.5015774965286255, 'perf', 0)] | 31 | 5 | null | 0.27 | 6 | 0 | 144 | 10 | 0 | 1 | 1 | 6 | 4 | 90 | 0.7 | 25 |
1,388 | nlp | https://github.com/keredson/wordninja | ['tokeniser'] | null | [] | [] | null | null | null | keredson/wordninja | wordninja | 743 | 107 | 10 | Python | null | Probabilistically split concatenated words using NLP based on English Wikipedia unigram frequencies. | keredson | 2024-01-11 | 2017-04-20 | 353 | 2.100565 | null | Probabilistically split concatenated words using NLP based on English Wikipedia unigram frequencies. | [] | ['tokeniser'] | 2023-02-14 | [] | 6 | 3 | null | 0 | 1 | 0 | 82 | 11 | 0 | 0 | 0 | 1 | 2 | 90 | 2 | 25 |
169 | gis | https://github.com/openeventdata/mordecai | [] | null | [] | [] | null | null | null | openeventdata/mordecai | mordecai | 722 | 98 | 34 | Python | null | Full text geoparsing as a Python library | openeventdata | 2024-01-04 | 2016-06-23 | 396 | 1.81995 | https://avatars.githubusercontent.com/u/1460393?v=4 | Full text geoparsing as a Python library | ['geocoding', 'geonames', 'geoparsing', 'nlp', 'spacy', 'toponym-resolution'] | ['geocoding', 'geonames', 'geoparsing', 'nlp', 'spacy', 'toponym-resolution'] | 2021-02-01 | [('geopandas/geopandas', 0.6333655714988708, 'gis', 0), ('kagisearch/vectordb', 0.5354642868041992, 'data', 0), ('opengeos/leafmap', 0.5265222191810608, 'gis', 0), ('artelys/geonetworkx', 0.5222339630126953, 'gis', 0), ('pemistahl/lingua-py', 0.5192466974258423, 'nlp', 1)] | 6 | 3 | null | 0 | 2 | 0 | 92 | 36 | 0 | 1 | 1 | 2 | 7 | 90 | 3.5 | 25 |
165 | nlp | https://github.com/explosion/spacy-stanza | [] | null | [] | [] | null | null | null | explosion/spacy-stanza | spacy-stanza | 705 | 57 | 26 | Python | null | 💥 Use the latest Stanza (StanfordNLP) research models directly in spaCy | explosion | 2024-01-04 | 2019-01-31 | 260 | 2.70411 | https://avatars.githubusercontent.com/u/20011530?v=4 | 💥 Use the latest Stanza (StanfordNLP) research models directly in spaCy | ['corenlp', 'data-science', 'machine-learning', 'natural-language-processing', 'nlp', 'spacy', 'spacy-pipeline', 'stanford-corenlp', 'stanford-machine-learning', 'stanford-nlp', 'stanza'] | ['corenlp', 'data-science', 'machine-learning', 'natural-language-processing', 'nlp', 'spacy', 'spacy-pipeline', 'stanford-corenlp', 'stanford-machine-learning', 'stanford-nlp', 'stanza'] | 2023-10-09 | [('explosion/spacy-models', 0.7454922199249268, 'nlp', 4), ('huggingface/neuralcoref', 0.6504446864128113, 'nlp', 4), ('explosion/spacy-transformers', 0.6275186538696289, 'llm', 5), ('iclrandd/blackstone', 0.5856739282608032, 'nlp', 1), ('explosion/spacy-llm', 0.5577221512794495, 'llm', 4), ('explosion/spacy-streamlit', 0.5425686240196228, 'nlp', 4), ('norskregnesentral/skweak', 0.5375661849975586, 'nlp', 3), ('explosion/spacy', 0.5188982486724854, 'nlp', 5)] | 8 | 4 | null | 0.17 | 0 | 0 | 60 | 3 | 2 | 3 | 2 | 0 | 0 | 90 | 0 | 25 |
1,413 | llm | https://github.com/hazyresearch/ama_prompting | ['prompt-engineering'] | null | [] | [] | null | null | null | hazyresearch/ama_prompting | ama_prompting | 522 | 45 | 24 | Python | null | Ask Me Anything language model prompting | hazyresearch | 2024-01-09 | 2022-10-01 | 69 | 7.518519 | https://avatars.githubusercontent.com/u/2165246?v=4 | Ask Me Anything language model prompting | [] | ['prompt-engineering'] | 2023-07-05 | [('keirp/automatic_prompt_engineer', 0.7995690107345581, 'llm', 1), ('microsoft/promptbase', 0.7119161486625671, 'llm', 1), ('neulab/prompt2model', 0.687862753868103, 'llm', 0), ('guidance-ai/guidance', 0.6410424709320068, 'llm', 1), ('hazyresearch/manifest', 0.5880966782569885, 'llm', 1), ('srush/minichain', 0.5800570845603943, 'llm', 1), ('1rgs/jsonformer', 0.5634579062461853, 'llm', 1), ('promptslab/promptify', 0.5550077557563782, 'nlp', 1), ('stanfordnlp/dspy', 0.540431022644043, 'llm', 0), ('suno-ai/bark', 0.5254032015800476, 'ml', 0), ('bigscience-workshop/promptsource', 0.512933611869812, 'nlp', 0), ('ctlllll/llm-toolmaker', 0.5080617666244507, 'llm', 0), ('kyegomez/tree-of-thoughts', 0.5045536160469055, 'llm', 1), ('agenta-ai/agenta', 0.5027536153793335, 'llm', 1)] | 6 | 2 | null | 0.02 | 0 | 0 | 16 | 6 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 25 |
1,088 | graph | https://github.com/rampasek/graphgps | [] | null | [] | [] | null | null | null | rampasek/graphgps | GraphGPS | 520 | 95 | 9 | Python | null | Recipe for a General, Powerful, Scalable Graph Transformer | rampasek | 2024-01-12 | 2022-05-24 | 88 | 5.909091 | null | Recipe for a General, Powerful, Scalable Graph Transformer | ['graph-neural-network', 'graph-representation-learning', 'graph-transformer', 'long-range-dependence'] | ['graph-neural-network', 'graph-representation-learning', 'graph-transformer', 'long-range-dependence'] | 2023-02-17 | [('hamed1375/exphormer', 0.6791350841522217, 'graph', 0), ('pyg-team/pytorch_geometric', 0.6243663430213928, 'ml-dl', 0), ('danielegrattarola/spektral', 0.5940183997154236, 'ml-dl', 0), ('stellargraph/stellargraph', 0.5865841507911682, 'graph', 0), ('dmlc/dgl', 0.5740870833396912, 'ml-dl', 0), ('chandlerbang/awesome-self-supervised-gnn', 0.5653940439224243, 'study', 0), ('graphistry/pygraphistry', 0.5173921585083008, 'data', 0)] | 2 | 0 | null | 0.13 | 12 | 6 | 20 | 11 | 1 | 1 | 1 | 12 | 13 | 90 | 1.1 | 25 |
1,830 | data | https://github.com/koaning/doubtlab | ['data-quality'] | null | [] | [] | null | null | null | koaning/doubtlab | doubtlab | 485 | 19 | 7 | Python | https://koaning.github.io/doubtlab/ | Doubt your data, find bad labels. | koaning | 2024-01-09 | 2021-11-05 | 116 | 4.160539 | null | Doubt your data, find bad labels. | [] | ['data-quality'] | 2022-11-25 | [('koaning/bulk', 0.5618610382080078, 'data', 1), ('ydataai/ydata-quality', 0.5491899251937866, 'data', 0)] | 6 | 3 | null | 0 | 2 | 2 | 27 | 14 | 0 | 4 | 4 | 2 | 0 | 90 | 0 | 25 |
735 | nlp | https://github.com/koaning/whatlies | [] | null | [] | [] | null | null | null | koaning/whatlies | whatlies | 463 | 53 | 15 | Python | https://koaning.github.io/whatlies/ | Toolkit to help understand "what lies" in word embeddings. Also benchmarking! | koaning | 2024-01-04 | 2020-02-22 | 205 | 2.253825 | null | Toolkit to help understand "what lies" in word embeddings. Also benchmarking! | ['embeddings', 'nlp', 'visualisations'] | ['embeddings', 'nlp', 'visualisations'] | 2023-02-06 | [('plasticityai/magnitude', 0.6194629669189453, 'nlp', 2), ('koaning/embetter', 0.6023291945457458, 'data', 0), ('qdrant/fastembed', 0.5915380120277405, 'ml', 1), ('ddangelov/top2vec', 0.5833550095558167, 'nlp', 0), ('sebischair/lbl2vec', 0.5767074823379517, 'nlp', 1), ('allenai/allennlp', 0.570354700088501, 'nlp', 1), ('alibaba/easynlp', 0.5676613450050354, 'nlp', 1), ('jina-ai/clip-as-service', 0.553788423538208, 'nlp', 0), ('chroma-core/chroma', 0.5464308261871338, 'data', 1), ('jalammar/ecco', 0.5461040735244751, 'ml-interpretability', 1), ('huggingface/text-embeddings-inference', 0.5444438457489014, 'llm', 1), ('flairnlp/flair', 0.5374947190284729, 'nlp', 1), ('paddlepaddle/paddlenlp', 0.5240222215652466, 'llm', 1), ('maartengr/bertopic', 0.5192699432373047, 'nlp', 1), ('neuml/txtai', 0.5187655687332153, 'nlp', 2), ('milvus-io/bootcamp', 0.5146546363830566, 'data', 2), ('explosion/spacy-models', 0.5144206881523132, 'nlp', 1), ('jbesomi/texthero', 0.5086743831634521, 'nlp', 1), ('muennighoff/sgpt', 0.5074957013130188, 'llm', 0), ('amansrivastava17/embedding-as-service', 0.5058193802833557, 'nlp', 2), ('ukplab/sentence-transformers', 0.5054284334182739, 'nlp', 0), ('mitvis/vistext', 0.5051085948944092, 'data', 0), ('jina-ai/vectordb', 0.5039339661598206, 'data', 0), ('cvxgrp/pymde', 0.5038027167320251, 'ml', 0), ('llmware-ai/llmware', 0.501112163066864, 'llm', 2)] | 13 | 6 | null | 0.06 | 0 | 0 | 47 | 11 | 0 | 7 | 7 | 0 | 0 | 90 | 0 | 25 |
699 | ml-ops | https://github.com/bodywork-ml/bodywork-core | [] | null | [] | [] | null | null | null | bodywork-ml/bodywork-core | bodywork-core | 431 | 22 | 11 | Python | https://bodywork.readthedocs.io/en/latest/ | ML pipeline orchestration and model deployments on Kubernetes. | bodywork-ml | 2024-01-04 | 2020-11-17 | 167 | 2.580838 | https://avatars.githubusercontent.com/u/74599515?v=4 | ML pipeline orchestration and model deployments on Kubernetes. | ['batch', 'cicd', 'continuous-deployment', 'data-science', 'devops', 'framework', 'kubernetes', 'machine-learning', 'mlops', 'orchestration', 'pipeline', 'serving'] | ['batch', 'cicd', 'continuous-deployment', 'data-science', 'devops', 'framework', 'kubernetes', 'machine-learning', 'mlops', 'orchestration', 'pipeline', 'serving'] | 2022-07-04 | [('kubeflow/pipelines', 0.8104010820388794, 'ml-ops', 5), ('polyaxon/polyaxon', 0.6861110925674438, 'ml-ops', 4), ('flyteorg/flyte', 0.6820202469825745, 'ml-ops', 4), ('getindata/kedro-kubeflow', 0.6706922650337219, 'ml-ops', 1), ('orchest/orchest', 0.6645437479019165, 'ml-ops', 3), ('allegroai/clearml', 0.6049355268478394, 'ml-ops', 3), ('bentoml/bentoml', 0.5959450602531433, 'ml-ops', 3), ('zenml-io/zenml', 0.5911761522293091, 'ml-ops', 3), ('unionai-oss/unionml', 0.5865074396133423, 'ml-ops', 2), ('netflix/metaflow', 0.5834348201751709, 'ml-ops', 4), ('dagster-io/dagster', 0.5800526142120361, 'ml-ops', 3), ('mage-ai/mage-ai', 0.5791720151901245, 'ml-ops', 4), ('ploomber/ploomber', 0.5688678026199341, 'ml-ops', 3), ('gefyrahq/gefyra', 0.5484521389007568, 'util', 1), ('backtick-se/cowait', 0.547731876373291, 'util', 2), ('kubeflow-kale/kale', 0.5426039695739746, 'ml-ops', 1), ('skypilot-org/skypilot', 0.5361641049385071, 'llm', 2), ('kestra-io/kestra', 0.5349816083908081, 'ml-ops', 2), ('feast-dev/feast', 0.5267590284347534, 'ml-ops', 3), ('zenml-io/mlstacks', 0.5243047475814819, 'ml-ops', 1), ('jina-ai/jina', 0.523324728012085, 'ml', 6), ('tox-dev/tox', 0.5215947031974792, 'testing', 0), ('avaiga/taipy', 0.5173805952072144, 'data', 3), ('apache/airflow', 0.5161139965057373, 'ml-ops', 4), ('onnx/onnx', 0.5088913440704346, 'ml', 1)] | 4 | 2 | null | 0 | 1 | 1 | 38 | 19 | 0 | 19 | 19 | 1 | 1 | 90 | 1 | 25 |
1,227 | time-series | https://github.com/microsoft/robustlearn | [] | null | [] | [] | null | null | null | microsoft/robustlearn | robustlearn | 384 | 45 | 7 | Python | http://aka.ms/roblearn | Robust machine learning for responsible AI | microsoft | 2024-01-13 | 2022-10-20 | 66 | 5.755889 | https://avatars.githubusercontent.com/u/6154722?v=4 | Robust machine learning for responsible AI | [] | [] | 2023-10-08 | [('seldonio/alibi', 0.5054094791412354, 'ml-interpretability', 0), ('maif/shapash', 0.5023934841156006, 'ml', 0)] | 8 | 1 | null | 1.54 | 1 | 1 | 15 | 3 | 0 | 0 | 0 | 1 | 0 | 90 | 0 | 25 |
1,364 | gamedev | https://github.com/renpy/pygame_sdl2 | ['pygame', 'sdl2'] | null | [] | [] | null | null | null | renpy/pygame_sdl2 | pygame_sdl2 | 311 | 63 | 29 | Python | null | Reimplementation of portions of the pygame API using SDL2. | renpy | 2023-12-27 | 2014-10-23 | 483 | 0.642942 | https://avatars.githubusercontent.com/u/1900740?v=4 | Reimplementation of portions of the pygame API using SDL2. | [] | ['pygame', 'sdl2'] | 2023-12-20 | [('pygame/pygame', 0.7130681872367859, 'gamedev', 2), ('lordmauve/pgzero', 0.5067479610443115, 'gamedev', 1)] | 26 | 1 | null | 0.44 | 3 | 3 | 112 | 1 | 0 | 33 | 33 | 3 | 2 | 90 | 0.7 | 25 |
990 | util | https://github.com/stub42/pytz | [] | null | [] | [] | null | null | null | stub42/pytz | pytz | 294 | 80 | 15 | C | null | pytz Python historical timezone library and database | stub42 | 2024-01-13 | 2016-07-12 | 394 | 0.746193 | null | pytz Python historical timezone library and database | [] | [] | 2023-09-05 | [('sdispater/pendulum', 0.646413266658783, 'util', 0), ('dateutil/dateutil', 0.621961236000061, 'util', 0), ('arrow-py/arrow', 0.5504962205886841, 'util', 0), ('rjt1990/pyflux', 0.5127301812171936, 'time-series', 0)] | 21 | 3 | null | 0.23 | 3 | 1 | 91 | 4 | 0 | 10 | 10 | 3 | 2 | 90 | 0.7 | 25 |
1,096 | ml | https://github.com/eleutherai/oslo | [] | null | [] | [] | null | null | null | eleutherai/oslo | oslo | 169 | 29 | 5 | Python | https://oslo.eleuther.ai | OSLO: Open Source for Large-scale Optimization | eleutherai | 2024-01-10 | 2022-08-25 | 74 | 2.26195 | https://avatars.githubusercontent.com/u/68924597?v=4 | OSLO: Open Source for Large-scale Optimization | [] | [] | 2023-09-09 | [('determined-ai/determined', 0.56780606508255, 'ml-ops', 0), ('optuna/optuna', 0.5354965925216675, 'ml', 0), ('tensorflow/tensorflow', 0.5216479301452637, 'ml-dl', 0), ('microsoft/olive', 0.5006281733512878, 'ml', 0)] | 50 | 2 | null | 1.33 | 1 | 0 | 17 | 4 | 0 | 7 | 7 | 1 | 0 | 90 | 0 | 25 |
1,011 | finance | https://github.com/daxm/fmpsdk | [] | null | [] | [] | null | null | null | daxm/fmpsdk | fmpsdk | 125 | 48 | 8 | Python | null | SDK for Financial Modeling Prep's (FMP) API | daxm | 2024-01-12 | 2020-12-06 | 164 | 0.76087 | null | SDK for Financial Modeling Prep's (FMP) API | [] | [] | 2024-01-13 | [('pmorissette/ffn', 0.5664402842521667, 'finance', 0)] | 15 | 3 | null | 0.4 | 6 | 6 | 38 | 0 | 0 | 0 | 0 | 6 | 8 | 90 | 1.3 | 25 |
1,658 | data | https://github.com/unstructured-io/pipeline-sec-filings | ['unstructured', 'sec', 'pipeline'] | null | [] | [] | null | null | null | unstructured-io/pipeline-sec-filings | pipeline-sec-filings | 119 | 21 | 12 | Jupyter Notebook | null | Preprocessing pipeline notebooks and API supporting text extraction from SEC documents | unstructured-io | 2024-01-04 | 2022-09-27 | 70 | 1.7 | https://avatars.githubusercontent.com/u/108372208?v=4 | Preprocessing pipeline notebooks and API supporting text extraction from SEC documents | [] | ['pipeline', 'sec', 'unstructured'] | 2023-10-02 | [('linealabs/lineapy', 0.6072432994842529, 'jupyter', 0), ('unstructured-io/unstructured-api', 0.5717188715934753, 'data', 1), ('paperswithcode/sota-extractor', 0.537769615650177, 'data', 0)] | 15 | 5 | null | 0.4 | 14 | 9 | 16 | 3 | 0 | 0 | 0 | 14 | 9 | 90 | 0.6 | 25 |
1,756 | ml | https://github.com/rom1504/embedding-reader | ['filesystem', 'embeddings'] | null | [] | [] | null | null | null | rom1504/embedding-reader | embedding-reader | 77 | 16 | 4 | Python | null | Efficiently read embedding in streaming from any filesystem | rom1504 | 2024-01-09 | 2022-02-27 | 100 | 0.767806 | null | Efficiently read embedding in streaming from any filesystem | [] | ['embeddings', 'filesystem'] | 2024-01-11 | [('vhranger/nodevectors', 0.53115314245224, 'viz', 0)] | 8 | 3 | null | 0.19 | 8 | 8 | 23 | 0 | 3 | 12 | 3 | 8 | 10 | 90 | 1.2 | 25 |
1,723 | study | https://github.com/giswqs/geog-414 | [] | null | [] | [] | null | null | null | giswqs/geog-414 | geog-414 | 66 | 16 | 7 | HTML | https://geog-414.gishub.org | A repo for GEOG-414 (Spatial Data Management) at the University of Tennessee | giswqs | 2023-12-31 | 2023-08-16 | 23 | 2.766467 | null | A repo for GEOG-414 (Spatial Data Management) at the University of Tennessee | ['database', 'earthengine', 'geospatial', 'postgis'] | ['database', 'earthengine', 'geospatial', 'postgis'] | 2023-12-04 | [('apache/incubator-sedona', 0.560769259929657, 'gis', 1)] | 1 | 1 | null | 1.08 | 1 | 1 | 5 | 1 | 0 | 0 | 0 | 1 | 4 | 90 | 4 | 25 |
1,226 | util | https://github.com/joowani/binarytree | [] | null | [] | [] | null | null | null | joowani/binarytree | binarytree | 1,796 | 173 | 46 | Python | http://binarytree.readthedocs.io | Python Library for Studying Binary Trees | joowani | 2024-01-12 | 2016-09-20 | 384 | 4.677083 | null | Python Library for Studying Binary Trees | ['algorithm', 'binary-search-tree', 'binary-tree', 'binary-trees', 'bst', 'data-structure', 'data-structures', 'heap', 'heaps', 'interview', 'interview-practice', 'learning', 'practise'] | ['algorithm', 'binary-search-tree', 'binary-tree', 'binary-trees', 'bst', 'data-structure', 'data-structures', 'heap', 'heaps', 'interview', 'interview-practice', 'learning', 'practise'] | 2022-06-28 | [('keon/algorithms', 0.6219033002853394, 'util', 2), ('krzjoa/awesome-python-data-science', 0.5454331636428833, 'study', 0), ('pandas-dev/pandas', 0.5415753722190857, 'pandas', 0), ('thealgorithms/python', 0.5343596935272217, 'study', 2), ('pyparsing/pyparsing', 0.5169668197631836, 'util', 0)] | 9 | 1 | null | 0 | 1 | 0 | 89 | 19 | 0 | 2 | 2 | 1 | 0 | 90 | 0 | 24 |
1,131 | ml | https://github.com/scikit-learn-contrib/lightning | [] | null | [] | [] | null | null | null | scikit-learn-contrib/lightning | lightning | 1,695 | 215 | 38 | Python | https://contrib.scikit-learn.org/lightning/ | Large-scale linear classification, regression and ranking in Python | scikit-learn-contrib | 2024-01-11 | 2012-01-11 | 628 | 2.695366 | https://avatars.githubusercontent.com/u/17349883?v=4 | Large-scale linear classification, regression and ranking in Python | ['machine-learning'] | ['machine-learning'] | 2022-01-30 | [('scikit-learn/scikit-learn', 0.6304967403411865, 'ml', 1), ('scikit-learn-contrib/metric-learn', 0.6158003807067871, 'ml', 1), ('dask/dask-ml', 0.5908797979354858, 'ml', 0), ('scikit-learn-contrib/imbalanced-learn', 0.5669341087341309, 'ml', 1), ('rasbt/mlxtend', 0.5572295188903809, 'ml', 1), ('pycaret/pycaret', 0.5490888953208923, 'ml', 1), ('amzn/pecos', 0.5417201519012451, 'ml', 0), ('lmcinnes/pynndescent', 0.5341041684150696, 'ml', 0), ('huggingface/evaluate', 0.5336165428161621, 'ml', 1), ('ggerganov/ggml', 0.5173808336257935, 'ml', 1), ('ageron/handson-ml2', 0.5109522938728333, 'ml', 0), ('gradio-app/gradio', 0.5077913403511047, 'viz', 1), ('catboost/catboost', 0.5035675764083862, 'ml', 1)] | 17 | 6 | null | 0 | 0 | 0 | 146 | 24 | 0 | 1 | 1 | 0 | 0 | 90 | 0 | 24 |
144 | nlp | https://github.com/plasticityai/magnitude | [] | null | [] | [] | null | null | null | plasticityai/magnitude | magnitude | 1,608 | 117 | 38 | Python | null | A fast, efficient universal vector embedding utility package. | plasticityai | 2024-01-12 | 2018-02-24 | 309 | 5.196676 | https://avatars.githubusercontent.com/u/36324344?v=4 | A fast, efficient universal vector embedding utility package. | ['embeddings', 'fast', 'fasttext', 'gensim', 'glove', 'machine-learning', 'machine-learning-library', 'memory-efficient', 'natural-language-processing', 'nlp', 'vectors', 'word-embeddings', 'word2vec'] | ['embeddings', 'fast', 'fasttext', 'gensim', 'glove', 'machine-learning', 'machine-learning-library', 'memory-efficient', 'natural-language-processing', 'nlp', 'vectors', 'word-embeddings', 'word2vec'] | 2020-07-17 | [('qdrant/fastembed', 0.6370154619216919, 'ml', 1), ('amansrivastava17/embedding-as-service', 0.6195082068443298, 'nlp', 5), ('koaning/whatlies', 0.6194629669189453, 'nlp', 2), ('sebischair/lbl2vec', 0.5967115163803101, 'nlp', 4), ('ddangelov/top2vec', 0.584179162979126, 'nlp', 1), ('jina-ai/vectordb', 0.5780693888664246, 'data', 0), ('chroma-core/chroma', 0.5727373957633972, 'data', 1), ('jina-ai/clip-as-service', 0.5636166334152222, 'nlp', 0), ('huggingface/text-embeddings-inference', 0.5618109703063965, 'llm', 1), ('llmware-ai/llmware', 0.5475419759750366, 'llm', 3), ('jina-ai/finetuner', 0.5438824892044067, 'ml', 0), ('facebookresearch/faiss', 0.5381598472595215, 'ml', 1), ('kagisearch/vectordb', 0.5367014408111572, 'data', 1), ('koaning/embetter', 0.5330700278282166, 'data', 0), ('flairnlp/flair', 0.5328223705291748, 'nlp', 4), ('allenai/allennlp', 0.5297778248786926, 'nlp', 2), ('neuml/txtai', 0.526243269443512, 'nlp', 3), ('muennighoff/sgpt', 0.5156716704368591, 'llm', 0), ('awslabs/dgl-ke', 0.5072051286697388, 'ml', 1), ('extreme-bert/extreme-bert', 0.5061821341514587, 'llm', 3), ('paddlepaddle/paddlenlp', 0.5057362914085388, 'llm', 1), ('google-research/electra', 0.500442385673523, 'ml-dl', 1)] | 4 | 1 | null | 0 | 1 | 0 | 72 | 42 | 0 | 23 | 23 | 1 | 0 | 90 | 0 | 24 |
541 | ml | https://github.com/borealisai/advertorch | [] | null | [] | [] | null | null | null | borealisai/advertorch | advertorch | 1,243 | 192 | 27 | Jupyter Notebook | null | A Toolbox for Adversarial Robustness Research | borealisai | 2024-01-09 | 2018-11-29 | 269 | 4.608581 | https://avatars.githubusercontent.com/u/38730800?v=4 | A Toolbox for Adversarial Robustness Research | ['adversarial-attacks', 'adversarial-example', 'adversarial-examples', 'adversarial-learning', 'adversarial-machine-learning', 'adversarial-perturbations', 'benchmarking', 'machine-learning', 'pytorch', 'robustness', 'security', 'toolbox'] | ['adversarial-attacks', 'adversarial-example', 'adversarial-examples', 'adversarial-learning', 'adversarial-machine-learning', 'adversarial-perturbations', 'benchmarking', 'machine-learning', 'pytorch', 'robustness', 'security', 'toolbox'] | 2022-05-29 | [('cleverhans-lab/cleverhans', 0.7116900086402893, 'ml', 3), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.5085124969482422, 'study', 2)] | 21 | 3 | null | 0 | 0 | 0 | 62 | 20 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 24 |
447 | gis | https://github.com/residentmario/geoplot | [] | null | [] | [] | 1 | null | null | residentmario/geoplot | geoplot | 1,101 | 97 | 35 | Python | https://residentmario.github.io/geoplot/index.html | High-level geospatial data visualization library for Python. | residentmario | 2024-01-13 | 2016-06-29 | 395 | 2.781306 | null | High-level geospatial data visualization library for Python. | ['geopandas', 'geospatial-data', 'geospatial-visualization', 'matplotlib', 'spatial-analysis'] | ['geopandas', 'geospatial-data', 'geospatial-visualization', 'matplotlib', 'spatial-analysis'] | 2023-07-05 | [('geopandas/geopandas', 0.7451832890510559, 'gis', 1), ('mwaskom/seaborn', 0.7261803150177002, 'viz', 1), ('gregorhd/mapcompare', 0.7236021161079407, 'gis', 0), ('raphaelquast/eomaps', 0.707546055316925, 'gis', 1), ('holoviz/holoviz', 0.6968002319335938, 'viz', 0), ('holoviz/geoviews', 0.6962321400642395, 'gis', 0), ('opengeos/leafmap', 0.6929628252983093, 'gis', 0), ('contextlab/hypertools', 0.686218798160553, 'ml', 0), ('altair-viz/altair', 0.6802260875701904, 'viz', 0), ('giswqs/geemap', 0.6778126358985901, 'gis', 0), ('man-group/dtale', 0.6714649796485901, 'viz', 0), ('scitools/iris', 0.6692157983779907, 'gis', 0), ('scitools/cartopy', 0.6602010726928711, 'gis', 1), ('holoviz/panel', 0.6484428644180298, 'viz', 1), ('earthlab/earthpy', 0.63930344581604, 'gis', 0), ('artelys/geonetworkx', 0.6383180022239685, 'gis', 0), ('bokeh/bokeh', 0.6318153738975525, 'viz', 0), ('enthought/mayavi', 0.6169453859329224, 'viz', 0), ('holoviz/hvplot', 0.6143922805786133, 'pandas', 0), ('holoviz/spatialpandas', 0.6135755777359009, 'pandas', 1), ('kanaries/pygwalker', 0.6120375394821167, 'pandas', 1), ('matplotlib/matplotlib', 0.6070235967636108, 'viz', 1), ('makepath/xarray-spatial', 0.59319007396698, 'gis', 1), ('has2k1/plotnine', 0.5918059349060059, 'viz', 0), ('pyqtgraph/pyqtgraph', 0.5889087915420532, 'viz', 0), ('plotly/plotly.py', 0.5845940709114075, 'viz', 0), ('pysal/pysal', 0.579075276851654, 'gis', 0), ('pyproj4/pyproj', 0.5774697065353394, 'gis', 0), ('visgl/deck.gl', 0.5753275156021118, 'viz', 0), ('dfki-ric/pytransform3d', 0.5743772983551025, 'math', 1), ('anitagraser/movingpandas', 0.5739251375198364, 'gis', 1), ('pandas-dev/pandas', 0.5717509984970093, 'pandas', 0), ('graphistry/pygraphistry', 0.56211256980896, 'data', 0), ('jakevdp/pythondatasciencehandbook', 0.5592259168624878, 'study', 1), ('cuemacro/chartpy', 0.5584993958473206, 'viz', 1), ('wesm/pydata-book', 0.5581743121147156, 'study', 0), ('lux-org/lux', 0.5569419860839844, 'viz', 0), ('plotly/dash', 0.5490462779998779, 'viz', 0), ('vispy/vispy', 0.5477085709571838, 'viz', 0), ('marcomusy/vedo', 0.5391072034835815, 'viz', 0), ('vaexio/vaex', 0.5356773734092712, 'perf', 0), ('marceloprates/prettymaps', 0.5294914245605469, 'viz', 1), ('maartenbreddels/ipyvolume', 0.5270797610282898, 'jupyter', 0), ('imageio/imageio', 0.525743305683136, 'util', 0), ('pyvista/pyvista', 0.5253320336341858, 'viz', 0), ('nomic-ai/deepscatter', 0.5221992135047913, 'viz', 0), ('opengeos/segment-geospatial', 0.5210736989974976, 'gis', 0), ('osgeo/gdal', 0.5153622627258301, 'gis', 1), ('krzjoa/awesome-python-data-science', 0.5134770274162292, 'study', 0), ('eleutherai/pyfra', 0.506131649017334, 'ml', 0), ('vizzuhq/ipyvizzu', 0.5058284997940063, 'jupyter', 0), ('matplotlib/mplfinance', 0.5038678050041199, 'finance', 1), ('alexmojaki/heartrate', 0.5028942823410034, 'debug', 0), ('mckinsey/vizro', 0.5022686719894409, 'viz', 0), ('federicoceratto/dashing', 0.5019742846488953, 'term', 0), ('uber/h3-py', 0.500649094581604, 'gis', 0)] | 6 | 2 | null | 0.04 | 1 | 0 | 92 | 6 | 0 | 3 | 3 | 1 | 0 | 90 | 0 | 24 |
1,580 | data | https://github.com/brettkromkamp/contextualise | ['knowledge-graph'] | null | [] | [] | null | null | null | brettkromkamp/contextualise | contextualise | 1,023 | 43 | 26 | Python | https://contextualise.dev/ | Contextualise is an effective tool particularly suited for organising information-heavy projects and activities consisting of unstructured and widely diverse data and information resources | brettkromkamp | 2024-01-04 | 2019-04-22 | 249 | 4.106078 | null | Contextualise is an effective tool particularly suited for organising information-heavy projects and activities consisting of unstructured and widely diverse data and information resources | ['cms-backend', 'commonplace-book', 'content-management-system', 'flask-application', 'knowledge-graph', 'knowledge-management-graph', 'metamodel', 'research-tool', 'semantic-web', 'sqlite-database', 'vuejs'] | ['cms-backend', 'commonplace-book', 'content-management-system', 'flask-application', 'knowledge-graph', 'knowledge-management-graph', 'metamodel', 'research-tool', 'semantic-web', 'sqlite-database', 'vuejs'] | 2023-09-30 | [('wagtail/wagtail', 0.5667254328727722, 'web', 0), ('indico/indico', 0.5198062658309937, 'web', 0), ('zenodo/zenodo', 0.5124539136886597, 'util', 0), ('airbnb/knowledge-repo', 0.5030013918876648, 'data', 0)] | 5 | 2 | null | 0.37 | 0 | 0 | 58 | 4 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 24 |
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