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846 | util | https://github.com/backtick-se/cowait | [] | null | [] | [] | null | null | null | backtick-se/cowait | cowait | 53 | 5 | 9 | Python | https://cowait.io | Containerized distributed programming framework for Python | backtick-se | 2023-07-09 | 2019-09-18 | 227 | 0.232602 | https://avatars.githubusercontent.com/u/51236421?v=4 | Containerized distributed programming framework for Python | ['dask', 'data-engineering', 'data-science', 'docker', 'kubernetes', 'spark', 'task-scheduler', 'workflow-engine'] | ['dask', 'data-engineering', 'data-science', 'docker', 'kubernetes', 'spark', 'task-scheduler', 'workflow-engine'] | 2022-09-22 | [('eventual-inc/daft', 0.7438012361526489, 'pandas', 2), ('fugue-project/fugue', 0.6694428324699402, 'pandas', 2), ('orchest/orchest', 0.6478020548820496, 'ml-ops', 3), ('darribas/gds_env', 0.6343125104904175, 'gis', 1), ('flyteorg/flyte', 0.6116994023323059, 'ml-ops', 2), ('pyinfra-dev/pyinfra', 0.5932263135910034, 'util', 0), ('kestra-io/kestra', 0.5885079503059387, 'ml-ops', 2), ('dagworks-inc/hamilton', 0.5880764722824097, 'ml-ops', 2), ('merantix-momentum/squirrel-core', 0.5777786374092102, 'ml', 1), ('martinheinz/python-project-blueprint', 0.5766209363937378, 'template', 2), ('fastai/fastcore', 0.5709444284439087, 'util', 0), ('lithops-cloud/lithops', 0.5676038861274719, 'ml-ops', 1), ('aws/chalice', 0.5667223334312439, 'web', 0), ('pallets/flask', 0.5663134455680847, 'web', 0), ('willmcgugan/textual', 0.5645186305046082, 'term', 0), ('boto/boto3', 0.5623748302459717, 'util', 0), ('eleutherai/pyfra', 0.5622064471244812, 'ml', 0), ('dask/distributed', 0.56135493516922, 'perf', 1), ('spotify/luigi', 0.5577235221862793, 'ml-ops', 0), ('horovod/horovod', 0.5568536520004272, 'ml-ops', 1), ('dagster-io/dagster', 0.5563982725143433, 'ml-ops', 2), ('falconry/falcon', 0.5538949370384216, 'web', 0), ('dask/dask', 0.5500026941299438, 'perf', 1), ('pypa/pipenv', 0.5493590235710144, 'util', 0), ('bodywork-ml/bodywork-core', 0.547731876373291, 'ml-ops', 2), ('nficano/python-lambda', 0.5466134548187256, 'util', 0), ('multi-py/python-gunicorn-uvicorn', 0.5445891618728638, 'util', 1), ('kubeflow-kale/kale', 0.5415117740631104, 'ml-ops', 0), ('ianmiell/shutit', 0.5379471182823181, 'util', 1), ('airtai/faststream', 0.5363417267799377, 'perf', 0), ('masoniteframework/masonite', 0.5356951951980591, 'web', 0), ('rawheel/fastapi-boilerplate', 0.5353572964668274, 'web', 1), ('pypa/hatch', 0.5353484153747559, 'util', 0), ('fmind/mlops-python-package', 0.5339037179946899, 'template', 0), ('polyaxon/datatile', 0.533811628818512, 'pandas', 3), ('aeternalis-ingenium/fastapi-backend-template', 0.5334479212760925, 'web', 1), ('airbytehq/airbyte', 0.5309221148490906, 'data', 1), ('kubeflow/fairing', 0.5283066034317017, 'ml-ops', 0), ('polyaxon/polyaxon', 0.5269318222999573, 'ml-ops', 2), ('ploomber/ploomber', 0.5248901844024658, 'ml-ops', 2), ('klen/muffin', 0.5236403942108154, 'web', 0), ('pallets/quart', 0.5232675671577454, 'web', 0), ('gefyrahq/gefyra', 0.5228185653686523, 'util', 2), ('eventlet/eventlet', 0.519990086555481, 'perf', 0), ('apache/airflow', 0.5194593667984009, 'ml-ops', 3), ('bottlepy/bottle', 0.5192388892173767, 'web', 0), ('cython/cython', 0.5182483792304993, 'util', 0), ('multi-py/python-gunicorn', 0.5146100521087646, 'util', 1), ('py4j/py4j', 0.5144885182380676, 'util', 0), ('google/gin-config', 0.5143940448760986, 'util', 0), ('dylanhogg/awesome-python', 0.5124981999397278, 'study', 1), ('ipython/ipyparallel', 0.5108761787414551, 'perf', 0), ('ethereum/py-evm', 0.5091575384140015, 'crypto', 0), ('multi-py/python-uvicorn', 0.5087990760803223, 'util', 1), ('uber/fiber', 0.5072715282440186, 'data', 0), ('skypilot-org/skypilot', 0.5072562098503113, 'llm', 1), ('avaiga/taipy', 0.5056573152542114, 'data', 1), ('hi-primus/optimus', 0.5054327249526978, 'ml-ops', 3), ('python-trio/trio', 0.5035473704338074, 'perf', 0), ('python-restx/flask-restx', 0.5034772157669067, 'web', 0), ('netflix/metaflow', 0.5032603144645691, 'ml-ops', 2), ('agronholm/apscheduler', 0.502835750579834, 'util', 0), ('pypy/pypy', 0.5022944808006287, 'util', 0), ('ets-labs/python-dependency-injector', 0.500067412853241, 'util', 0)] | 10 | 2 | null | 0 | 0 | 0 | 53 | 16 | 0 | 10 | 10 | 0 | 0 | 90 | 0 | 14 |
1,490 | math | https://github.com/andgoldschmidt/derivative | [] | null | [] | [] | null | null | null | andgoldschmidt/derivative | derivative | 48 | 7 | 5 | Python | https://derivative.readthedocs.io/en/latest/ | Optimal numerical differentiation of noisy time series data in python. | andgoldschmidt | 2023-12-03 | 2019-02-06 | 259 | 0.184717 | null | Optimal numerical differentiation of noisy time series data in python. | ['differentiation', 'experimental-data', 'numerical-differentiation'] | ['differentiation', 'experimental-data', 'numerical-differentiation'] | 2023-08-29 | [('rjt1990/pyflux', 0.5164141654968262, 'time-series', 0), ('hips/autograd', 0.5045579075813293, 'ml', 0)] | 4 | 2 | null | 0.48 | 2 | 2 | 60 | 5 | 2 | 1 | 2 | 2 | 0 | 90 | 0 | 14 |
673 | util | https://github.com/markhershey/arxiv-dl | [] | null | [] | [] | null | null | null | markhershey/arxiv-dl | arxiv-dl | 26 | 5 | 3 | Python | https://pypi.org/project/arxiv-dl/ | Command-line ArXiv & CVF (CVPR, ICCV, WACV) Paper Downloader | markhershey | 2023-12-27 | 2021-01-21 | 157 | 0.164855 | null | Command-line ArXiv & CVF (CVPR, ICCV, WACV) Paper Downloader | ['arxiv', 'command-line-tool', 'cvpr', 'downloader', 'paper', 'paper-with-code', 'research-paper'] | ['arxiv', 'command-line-tool', 'cvpr', 'downloader', 'paper', 'paper-with-code', 'research-paper'] | 2023-11-02 | [] | 3 | 0 | null | 0.23 | 3 | 1 | 36 | 2 | 2 | 3 | 2 | 3 | 5 | 90 | 1.7 | 14 |
659 | study | https://github.com/rasbt/stat453-deep-learning-ss20 | [] | null | [] | [] | null | null | null | rasbt/stat453-deep-learning-ss20 | stat453-deep-learning-ss20 | 540 | 159 | 37 | Jupyter Notebook | http://pages.stat.wisc.edu/~sraschka/teaching/stat453-ss2020/ | STAT 453: Intro to Deep Learning @ UW-Madison (Spring 2020) | rasbt | 2024-01-04 | 2020-01-20 | 210 | 2.56968 | null | STAT 453: Intro to Deep Learning @ UW-Madison (Spring 2020) | [] | [] | 2020-05-01 | [('rasbt/stat451-machine-learning-fs20', 0.7537368535995483, 'study', 0), ('atcold/nyu-dlsp21', 0.6361130475997925, 'study', 0), ('udlbook/udlbook', 0.5738182663917542, 'study', 0), ('xl0/lovely-tensors', 0.5372262597084045, 'ml-dl', 0), ('d2l-ai/d2l-en', 0.5362268090248108, 'study', 0), ('mrdbourke/pytorch-deep-learning', 0.5198850631713867, 'study', 0), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.5113897919654846, 'study', 0), ('udacity/deep-learning-v2-pytorch', 0.5104526281356812, 'study', 0), ('tatsu-lab/stanford_alpaca', 0.5087552666664124, 'llm', 0), ('christoschristofidis/awesome-deep-learning', 0.506165087223053, 'study', 0), ('nvidia/deeplearningexamples', 0.5055111050605774, 'ml-dl', 0), ('calculatedcontent/weightwatcher', 0.505051851272583, 'ml-dl', 0)] | 1 | 1 | null | 0 | 0 | 0 | 48 | 45 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 13 |
302 | util | https://github.com/airbnb/ottr | [] | null | [] | [] | null | null | null | airbnb/ottr | ottr | 264 | 32 | 9 | Python | null | Serverless Public Key Infrastructure Framework | airbnb | 2024-01-12 | 2021-08-27 | 126 | 2.085779 | https://avatars.githubusercontent.com/u/698437?v=4 | Serverless Public Key Infrastructure Framework | [] | [] | 2022-01-04 | [] | 2 | 1 | null | 0 | 0 | 0 | 29 | 25 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 13 |
338 | perf | https://github.com/tlkh/tf-metal-experiments | [] | null | [] | [] | null | null | null | tlkh/tf-metal-experiments | tf-metal-experiments | 256 | 32 | 17 | Jupyter Notebook | null | TensorFlow Metal Backend on Apple Silicon Experiments (just for fun) | tlkh | 2024-01-04 | 2021-10-26 | 118 | 2.169492 | null | TensorFlow Metal Backend on Apple Silicon Experiments (just for fun) | ['benchmark', 'bert', 'deep-learning', 'gpu', 'm1', 'm1-max', 'tensorflow'] | ['benchmark', 'bert', 'deep-learning', 'gpu', 'm1', 'm1-max', 'tensorflow'] | 2021-11-15 | [('mrdbourke/m1-machine-learning-test', 0.7584832310676575, 'ml', 1), ('microsoft/onnxruntime', 0.6584108471870422, 'ml', 2), ('intel/intel-extension-for-pytorch', 0.6522815227508545, 'perf', 1), ('arogozhnikov/einops', 0.6099873781204224, 'ml-dl', 2), ('ml-explore/mlx', 0.6006742119789124, 'ml', 0), ('ashleve/lightning-hydra-template', 0.5975523591041565, 'util', 1), ('determined-ai/determined', 0.5877756476402283, 'ml-ops', 2), ('google/tf-quant-finance', 0.5859279036521912, 'finance', 2), ('neuralmagic/deepsparse', 0.5730476379394531, 'nlp', 0), ('tensorflow/addons', 0.5677783489227295, 'ml', 2), ('pytorch/ignite', 0.5673794746398926, 'ml-dl', 1), ('keras-team/keras', 0.565044641494751, 'ml-dl', 2), ('nvidia/deeplearningexamples', 0.5584723949432373, 'ml-dl', 2), ('huggingface/transformers', 0.5560042858123779, 'nlp', 3), ('rasbt/machine-learning-book', 0.5555431246757507, 'study', 1), ('xl0/lovely-tensors', 0.5548020005226135, 'ml-dl', 1), ('horovod/horovod', 0.5538284182548523, 'ml-ops', 2), ('tlkh/asitop', 0.540374219417572, 'perf', 2), ('ageron/handson-ml2', 0.5388534665107727, 'ml', 0), ('keras-rl/keras-rl', 0.5308326482772827, 'ml-rl', 1), ('alpa-projects/alpa', 0.5292550921440125, 'ml-dl', 1), ('tensorlayer/tensorlayer', 0.5288784503936768, 'ml-rl', 2), ('explosion/thinc', 0.5282965302467346, 'ml-dl', 2), ('microsoft/deepspeed', 0.5253063440322876, 'ml-dl', 2), ('huggingface/datasets', 0.5242385268211365, 'nlp', 2), ('pytorch/pytorch', 0.5232517123222351, 'ml-dl', 2), ('tensorflow/tensorflow', 0.5225579142570496, 'ml-dl', 2), ('aws/sagemaker-python-sdk', 0.5217053890228271, 'ml', 1), ('onnx/onnx', 0.5157482028007507, 'ml', 2), ('skorch-dev/skorch', 0.5154433250427246, 'ml-dl', 0), ('google/trax', 0.5128339529037476, 'ml-dl', 1), ('intel/scikit-learn-intelex', 0.5120803713798523, 'perf', 1), ('wandb/client', 0.5100532174110413, 'ml', 2), ('apache/incubator-mxnet', 0.5093386769294739, 'ml-dl', 0), ('nyandwi/modernconvnets', 0.5091290473937988, 'ml-dl', 1), ('pytorchlightning/pytorch-lightning', 0.5083010792732239, 'ml-dl', 1), ('d2l-ai/d2l-en', 0.508011519908905, 'study', 2), ('huggingface/optimum', 0.5074736475944519, 'ml', 0), ('aimhubio/aim', 0.501725971698761, 'ml-ops', 1)] | 2 | 1 | null | 0 | 0 | 0 | 27 | 26 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 13 |
1,187 | llm | https://github.com/anthropics/evals | [] | Model-Written Evaluation Datasets | [] | [] | null | null | null | anthropics/evals | evals | 184 | 16 | 6 | null | null | null | anthropics | 2024-01-11 | 2022-12-12 | 59 | 3.111111 | https://avatars.githubusercontent.com/u/76263028?v=4 | Model-Written Evaluation Datasets | [] | [] | 2023-01-03 | [('huggingface/evaluate', 0.7150794267654419, 'ml', 0), ('ai21labs/lm-evaluation', 0.6497257947921753, 'llm', 0), ('eleutherai/lm-evaluation-harness', 0.5547993183135986, 'llm', 0), ('openlmlab/leval', 0.5348479747772217, 'llm', 0), ('confident-ai/deepeval', 0.5211471915245056, 'testing', 0), ('openai/evals', 0.512044370174408, 'llm', 0), ('bigscience-workshop/biomedical', 0.5112786889076233, 'data', 0), ('selfexplainml/piml-toolbox', 0.5009229183197021, 'ml-interpretability', 0), ('hazyresearch/domino', 0.5007730722427368, 'ml', 0)] | 3 | 0 | null | 0 | 0 | 0 | 13 | 12 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 13 |
977 | template | https://github.com/janetech-inc/fast-api-admin-template | [] | null | [] | [] | null | null | null | janetech-inc/fast-api-admin-template | fast-api-admin-template | 111 | 12 | 4 | JavaScript | null | A test driven micro-service template to build and deploy a fast-api service with admin feature. | janetech-inc | 2023-12-22 | 2023-02-15 | 49 | 2.226361 | https://avatars.githubusercontent.com/u/52669296?v=4 | A test driven micro-service template to build and deploy a fast-api service with admin feature. | [] | [] | 2023-03-01 | [('ajndkr/lanarky', 0.6117317080497742, 'llm', 0), ('asacristani/fastapi-rocket-boilerplate', 0.5484818816184998, 'template', 0), ('unionai-oss/unionml', 0.5197975635528564, 'ml-ops', 0), ('starlite-api/starlite', 0.504593551158905, 'web', 0)] | 1 | 1 | null | 0.23 | 1 | 0 | 11 | 11 | 0 | 0 | 0 | 1 | 0 | 90 | 0 | 13 |
737 | ml-ops | https://github.com/aiqc/aiqc | [] | null | [] | [] | null | null | null | aiqc/aiqc | AIQC | 96 | 21 | 5 | Python | null | End-to-end deep learning on your desktop or server. | aiqc | 2023-10-09 | 2020-12-02 | 164 | 0.582322 | null | End-to-end deep learning on your desktop or server. | [] | [] | 2023-08-09 | [('tensorflow/tensorflow', 0.6452828049659729, 'ml-dl', 0), ('nvidia/deeplearningexamples', 0.6063768267631531, 'ml-dl', 0), ('keras-team/keras', 0.6046340465545654, 'ml-dl', 0), ('alpa-projects/alpa', 0.5878965854644775, 'ml-dl', 0), ('microsoft/onnxruntime', 0.5876568555831909, 'ml', 0), ('koaning/human-learn', 0.5812904238700867, 'data', 0), ('mosaicml/composer', 0.5669575333595276, 'ml-dl', 0), ('mlflow/mlflow', 0.5631386637687683, 'ml-ops', 0), ('onnx/onnx', 0.5626868605613708, 'ml', 0), ('apache/incubator-mxnet', 0.560641884803772, 'ml-dl', 0), ('bigscience-workshop/petals', 0.5569496154785156, 'data', 0), ('neuralmagic/deepsparse', 0.556576669216156, 'nlp', 0), ('huggingface/datasets', 0.5560346841812134, 'nlp', 0), ('microsoft/deepspeed', 0.55571448802948, 'ml-dl', 0), ('google/trax', 0.555395245552063, 'ml-dl', 0), ('tensorflow/tensor2tensor', 0.5551923513412476, 'ml', 0), ('explosion/thinc', 0.5513455867767334, 'ml-dl', 0), ('microsoft/jarvis', 0.5510006546974182, 'llm', 0), ('determined-ai/determined', 0.5507001280784607, 'ml-ops', 0), ('titanml/takeoff', 0.550189733505249, 'llm', 0), ('uber/petastorm', 0.5461896061897278, 'data', 0), ('rasbt/deeplearning-models', 0.5450917482376099, 'ml-dl', 0), ('mlc-ai/web-stable-diffusion', 0.5372481346130371, 'diffusion', 0), ('ml-tooling/opyrator', 0.532943844795227, 'viz', 0), ('horovod/horovod', 0.5296958684921265, 'ml-ops', 0), ('deepmind/dm-haiku', 0.5235515236854553, 'ml-dl', 0), ('huggingface/transformers', 0.5214504599571228, 'nlp', 0), ('karpathy/micrograd', 0.5214496850967407, 'study', 0), ('neuralmagic/sparseml', 0.5212861895561218, 'ml-dl', 0), ('paddlepaddle/paddle', 0.5197136998176575, 'ml-dl', 0), ('christoschristofidis/awesome-deep-learning', 0.5192874670028687, 'study', 0), ('rasbt/machine-learning-book', 0.519234299659729, 'study', 0), ('adap/flower', 0.5167785882949829, 'ml-ops', 0), ('kevinmusgrave/pytorch-metric-learning', 0.5146724581718445, 'ml', 0), ('ddbourgin/numpy-ml', 0.5140572190284729, 'ml', 0), ('mlc-ai/web-llm', 0.5105668306350708, 'llm', 0), ('salesforce/warp-drive', 0.509090006351471, 'ml-rl', 0), ('rom1504/img2dataset', 0.5084776282310486, 'data', 0), ('datasystemslab/geotorch', 0.5082912445068359, 'gis', 0), ('microsoft/semi-supervised-learning', 0.5059452652931213, 'ml', 0), ('unity-technologies/ml-agents', 0.5059369802474976, 'ml-rl', 0), ('dmlc/dgl', 0.5034000873565674, 'ml-dl', 0), ('microsoft/nni', 0.5022141337394714, 'ml', 0), ('pytorch/ignite', 0.5004318356513977, 'ml-dl', 0), ('deepchecks/deepchecks', 0.5002310872077942, 'data', 0)] | 8 | 2 | null | 0.12 | 0 | 0 | 38 | 5 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 13 |
1,051 | ml-dl | https://github.com/xl0/lovely-jax | ['jax'] | null | [] | [] | null | null | null | xl0/lovely-jax | lovely-jax | 85 | 3 | 3 | Jupyter Notebook | https://xl0.github.io/lovely-jax | JAX Arrays for human consumption | xl0 | 2024-01-10 | 2022-11-08 | 64 | 1.328125 | null | JAX Arrays for human consumption | [] | ['jax'] | 2023-09-18 | [] | 2 | 1 | null | 0.13 | 0 | 0 | 14 | 4 | 0 | 1 | 1 | 0 | 0 | 90 | 0 | 13 |
1,745 | template | https://github.com/fmind/mlops-python-package | [] | null | [] | [] | null | null | null | fmind/mlops-python-package | mlops-python-package | 83 | 10 | 4 | Python | null | Kickstart your MLOps initiative with a flexible, robust, and productive Python package. | fmind | 2024-01-09 | 2023-06-23 | 31 | 2.628959 | null | Kickstart your MLOps initiative with a flexible, robust, and productive Python package. | ['ai', 'ml', 'mlops', 'package'] | ['ai', 'ml', 'mlops', 'package'] | 2023-06-23 | [('polyaxon/polyaxon', 0.6891217231750488, 'ml-ops', 2), ('kubeflow/fairing', 0.6557989120483398, 'ml-ops', 0), ('skops-dev/skops', 0.6394261121749878, 'ml-ops', 1), ('zenml-io/zenml', 0.6134677529335022, 'ml-ops', 3), ('unionai-oss/unionml', 0.5882454514503479, 'ml-ops', 1), ('merantix-momentum/squirrel-core', 0.5865539312362671, 'ml', 2), ('allegroai/clearml', 0.5833768844604492, 'ml-ops', 2), ('zenml-io/mlstacks', 0.5667558312416077, 'ml-ops', 2), ('netflix/metaflow', 0.5633413195610046, 'ml-ops', 3), ('avaiga/taipy', 0.5603728890419006, 'data', 1), ('evidentlyai/evidently', 0.5556315779685974, 'ml-ops', 1), ('bentoml/bentoml', 0.5531289577484131, 'ml-ops', 2), ('backtick-se/cowait', 0.5339037179946899, 'util', 0), ('reloadware/reloadium', 0.5241882801055908, 'profiling', 1), ('pypa/pipenv', 0.5217861533164978, 'util', 0), ('mlflow/mlflow', 0.5197017788887024, 'ml-ops', 2), ('gradio-app/gradio', 0.5189513564109802, 'viz', 0), ('cheshire-cat-ai/core', 0.5158709287643433, 'llm', 1), ('willmcgugan/textual', 0.5143515467643738, 'term', 0), ('featurelabs/featuretools', 0.5135179758071899, 'ml', 0), ('orchest/orchest', 0.5126034617424011, 'ml-ops', 0), ('ploomber/ploomber', 0.5116260051727295, 'ml-ops', 1), ('microsoft/nni', 0.5072845816612244, 'ml', 1), ('ml-tooling/opyrator', 0.50709468126297, 'viz', 0), ('ray-project/ray', 0.5060107111930847, 'ml-ops', 0), ('wandb/client', 0.5048151612281799, 'ml', 1), ('salesforce/logai', 0.5032975077629089, 'util', 1), ('selfexplainml/piml-toolbox', 0.5029227137565613, 'ml-interpretability', 0), ('amaargiru/pyroad', 0.5028426051139832, 'study', 0), ('aimhubio/aim', 0.5028169751167297, 'ml-ops', 3)] | 1 | 1 | null | 0.02 | 0 | 0 | 7 | 7 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 13 |
1,360 | study | https://github.com/ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book | [] | null | [] | [] | null | null | null | ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book | Machine-Learning-for-High-Risk-Applications-Book | 82 | 20 | 6 | Jupyter Notebook | null | Official code repo for the O'Reilly Book - Machine Learning for High-Risk Applications | ml-for-high-risk-apps-book | 2023-12-23 | 2022-10-07 | 68 | 1.195833 | https://avatars.githubusercontent.com/u/92960961?v=4 | Official code repo for the O'Reilly Book - Machine Learning for High-Risk Applications | ['deep-learning', 'explainable-ai', 'interpretable-machine-learning', 'machine-learning', 'oreilly', 'oreilly-books', 'responsible-ai', 'security', 'trustworthy-ai'] | ['deep-learning', 'explainable-ai', 'interpretable-machine-learning', 'machine-learning', 'oreilly', 'oreilly-books', 'responsible-ai', 'security', 'trustworthy-ai'] | 2023-05-23 | [('csinva/imodels', 0.6214880347251892, 'ml', 2), ('rasbt/machine-learning-book', 0.5862606167793274, 'study', 2), ('patchy631/machine-learning', 0.5826952457427979, 'ml', 0), ('rasbt/stat451-machine-learning-fs20', 0.5810298323631287, 'study', 0), ('probml/pyprobml', 0.576326847076416, 'ml', 1), ('d2l-ai/d2l-en', 0.5661432147026062, 'study', 2), ('alirezadir/machine-learning-interview-enlightener', 0.5615020394325256, 'study', 2), ('tensorlayer/tensorlayer', 0.5567829608917236, 'ml-rl', 1), ('tensorflow/tensorflow', 0.5544643402099609, 'ml-dl', 2), ('nvidia/deeplearningexamples', 0.5539207458496094, 'ml-dl', 1), ('seldonio/alibi', 0.545021116733551, 'ml-interpretability', 1), ('tensorflow/lucid', 0.5438190698623657, 'ml-interpretability', 1), ('tensorflow/tensor2tensor', 0.5430309772491455, 'ml', 2), ('interpretml/interpret', 0.542955756187439, 'ml-interpretability', 3), ('maif/shapash', 0.5307745337486267, 'ml', 1), ('google-research/language', 0.5293827652931213, 'nlp', 1), ('explosion/thinc', 0.5284711122512817, 'ml-dl', 2), ('tigerlab-ai/tiger', 0.5272374749183655, 'llm', 0), ('carla-recourse/carla', 0.5270025730133057, 'ml', 2), ('christoschristofidis/awesome-deep-learning', 0.5266667008399963, 'study', 2), ('davidadsp/generative_deep_learning_2nd_edition', 0.5259917974472046, 'study', 2), ('teamhg-memex/eli5', 0.5232833623886108, 'ml', 1), ('firmai/industry-machine-learning', 0.5232796669006348, 'study', 1), ('googlecloudplatform/vertex-ai-samples', 0.5220873951911926, 'ml', 0), ('oegedijk/explainerdashboard', 0.52154141664505, 'ml-interpretability', 0), ('mlflow/mlflow', 0.5185167193412781, 'ml-ops', 1), ('rafiqhasan/auto-tensorflow', 0.5184751152992249, 'ml-dl', 1), ('udlbook/udlbook', 0.5133402347564697, 'study', 1), ('pycaret/pycaret', 0.5132032632827759, 'ml', 1), ('google/trax', 0.512615442276001, 'ml-dl', 2), ('rasbt/stat453-deep-learning-ss20', 0.5113897919654846, 'study', 0), ('google-research/google-research', 0.5109012126922607, 'ml', 1), ('fchollet/deep-learning-with-python-notebooks', 0.5104230642318726, 'study', 0), ('borealisai/advertorch', 0.5085124969482422, 'ml', 2), ('unity-technologies/ml-agents', 0.5079880356788635, 'ml-rl', 2)] | 2 | 2 | null | 1.23 | 0 | 0 | 15 | 8 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 13 |
959 | math | https://github.com/albahnsen/pycircular | [] | null | [] | [] | 1 | null | null | albahnsen/pycircular | pycircular | 81 | 4 | 5 | Python | null | pycircular is a Python module for circular data analysis | albahnsen | 2023-12-01 | 2022-09-12 | 72 | 1.122772 | null | pycircular is a Python module for circular data analysis | [] | [] | 2023-01-21 | [('pysal/pysal', 0.581803023815155, 'gis', 0), ('scikit-geometry/scikit-geometry', 0.5635026693344116, 'gis', 0), ('scitools/cartopy', 0.5550414323806763, 'gis', 0), ('altair-viz/altair', 0.5373349189758301, 'viz', 0), ('has2k1/plotnine', 0.5327418446540833, 'viz', 0), ('pandas-dev/pandas', 0.5248360633850098, 'pandas', 0), ('wesm/pydata-book', 0.5135537981987, 'study', 0), ('earthlab/earthpy', 0.5108852982521057, 'gis', 0), ('scitools/iris', 0.5095266103744507, 'gis', 0), ('gboeing/pynamical', 0.503804087638855, 'sim', 0), ('enthought/mayavi', 0.5026959776878357, 'viz', 0), ('marcomusy/vedo', 0.5011388063430786, 'viz', 0), ('kanaries/pygwalker', 0.5010564923286438, 'pandas', 0), ('eleutherai/pyfra', 0.5009065270423889, 'ml', 0)] | 5 | 0 | null | 0 | 1 | 0 | 16 | 12 | 1 | 1 | 1 | 1 | 1 | 90 | 1 | 13 |
1,589 | util | https://github.com/msaelices/py2mojo | ['mojo'] | null | [] | [] | null | null | null | msaelices/py2mojo | py2mojo | 67 | 7 | 2 | Python | null | Automated Python to Mojo code translation | msaelices | 2024-01-12 | 2023-09-08 | 20 | 3.256944 | null | Automated Python to Mojo code translation | [] | ['mojo'] | 2023-09-23 | [('stijnwoestenborghs/gradi-mojo', 0.5529176592826843, 'util', 1), ('hhatto/autopep8', 0.5481264591217041, 'util', 0), ('lsh/shims', 0.5381791591644287, 'util', 1), ('google/latexify_py', 0.5220133662223816, 'util', 0), ('lynet101/mojo_community-lib', 0.509608805179596, 'util', 1)] | 1 | 0 | null | 1.12 | 0 | 0 | 4 | 4 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 13 |
1,551 | study | https://github.com/mdmzfzl/neetcode-solutions | [] | null | [] | [] | null | null | null | mdmzfzl/neetcode-solutions | NeetCode-Solutions | 59 | 11 | 2 | C++ | null | My solutions in C++, Python and Rust for problems on NeetCode.io | mdmzfzl | 2024-01-04 | 2023-06-26 | 31 | 1.894495 | null | My solutions in C++, Python and Rust for problems on NeetCode.io | ['blind75', 'cpp', 'data-structures', 'data-structures-and-algorithms', 'interview-questions', 'leetcode', 'leetcode-solutions', 'neetcode', 'neetcode150', 'rust', 'rust-lang'] | ['blind75', 'cpp', 'data-structures', 'data-structures-and-algorithms', 'interview-questions', 'leetcode', 'leetcode-solutions', 'neetcode', 'neetcode150', 'rust', 'rust-lang'] | 2023-10-27 | [('neetcode-gh/leetcode', 0.6274089217185974, 'study', 3), ('astral-sh/ruff', 0.519241452217102, 'util', 1), ('aswinnnn/pyscan', 0.5120133757591248, 'security', 1), ('rustpython/rustpython', 0.5018560886383057, 'util', 1)] | 2 | 0 | null | 2.77 | 0 | 0 | 7 | 3 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 13 |
769 | sim | https://github.com/activitysim/populationsim | [] | null | [] | [] | null | null | null | activitysim/populationsim | populationsim | 49 | 37 | 10 | Jupyter Notebook | https://activitysim.github.io/populationsim | An Open Platform for Population Synthesis | activitysim | 2023-11-21 | 2017-02-14 | 363 | 0.134986 | https://avatars.githubusercontent.com/u/25851945?v=4 | An Open Platform for Population Synthesis | ['activitysim', 'bsd-3-clause', 'data-science', 'microsimulation', 'population-synthesis'] | ['activitysim', 'bsd-3-clause', 'data-science', 'microsimulation', 'population-synthesis'] | 2021-11-19 | [('humanoidagents/humanoidagents', 0.5014925003051758, 'sim', 0)] | 9 | 1 | null | 0 | 4 | 1 | 84 | 26 | 0 | 1 | 1 | 4 | 4 | 90 | 1 | 13 |
1,722 | sim | https://github.com/roban/cosmolopy | ['cosmology', 'astronomy'] | null | [] | [] | null | null | null | roban/cosmolopy | CosmoloPy | 44 | 29 | 7 | HTML | http://roban.github.com/CosmoloPy/ | a basic numpy/scipy-based cosmology package for python | roban | 2023-08-30 | 2009-08-09 | 755 | 0.058256 | null | a basic numpy/scipy-based cosmology package for python | [] | ['astronomy', 'cosmology'] | 2023-06-07 | [('numpy/numpy', 0.7280952334403992, 'math', 0), ('scipy/scipy', 0.6138890385627747, 'math', 0), ('scitools/iris', 0.5999904274940491, 'gis', 0), ('cosmicpython/book', 0.5579319000244141, 'study', 0), ('enthought/mayavi', 0.5520604252815247, 'viz', 0), ('cupy/cupy', 0.5431826710700989, 'math', 0), ('marcomusy/vedo', 0.537321925163269, 'viz', 0), ('cloudsen12/easystac', 0.5210418701171875, 'gis', 0), ('astropy/astropy', 0.5209611654281616, 'sim', 1), ('jakevdp/pythondatasciencehandbook', 0.5194147229194641, 'study', 0), ('blaze/blaze', 0.5100215673446655, 'pandas', 0), ('scikit-learn-contrib/metric-learn', 0.500042200088501, 'ml', 0)] | 10 | 3 | null | 0 | 0 | 0 | 176 | 7 | 0 | 1 | 1 | 0 | 0 | 90 | 0 | 13 |
1,579 | data | https://github.com/accenture/cymple | ['cypher', 'neo4j'] | null | [] | [] | null | null | null | accenture/cymple | Cymple | 37 | 5 | 7 | Python | null | Cymple - a productivity tool for creating Cypher queries in Python | accenture | 2024-01-06 | 2022-03-31 | 95 | 0.386567 | https://avatars.githubusercontent.com/u/10454368?v=4 | Cymple - a productivity tool for creating Cypher queries in Python | ['cypher', 'neo4j', 'nodes-2022', 'query-builder'] | ['cypher', 'neo4j', 'nodes-2022', 'query-builder'] | 2023-08-30 | [('neo4j/neo4j-python-driver', 0.6172598600387573, 'data', 2), ('sqlalchemy/sqlalchemy', 0.5566608309745789, 'data', 0), ('qdrant/qdrant-client', 0.5485904812812805, 'util', 0), ('graphql-python/graphene', 0.5159372091293335, 'web', 0), ('aws/graph-notebook', 0.5142837166786194, 'jupyter', 1)] | 5 | 1 | null | 0.83 | 0 | 0 | 22 | 5 | 0 | 6 | 6 | 0 | 0 | 90 | 0 | 13 |
1,007 | finance | https://github.com/mementum/bta-lib | [] | null | [] | [] | null | null | null | mementum/bta-lib | bta-lib | 426 | 102 | 26 | Python | null | Technical Analysis library in pandas for backtesting algotrading and quantitative analysis | mementum | 2024-01-13 | 2020-01-31 | 208 | 2.042466 | null | Technical Analysis library in pandas for backtesting algotrading and quantitative analysis | [] | [] | 2020-03-11 | [('twopirllc/pandas-ta', 0.6815423369407654, 'finance', 0), ('cuemacro/finmarketpy', 0.5935547947883606, 'finance', 0), ('jmcarpenter2/swifter', 0.5637902021408081, 'pandas', 0), ('eleutherai/pyfra', 0.5443049073219299, 'ml', 0), ('wesm/pydata-book', 0.5407599806785583, 'study', 0), ('mementum/backtrader', 0.5395269989967346, 'finance', 0), ('ta-lib/ta-lib-python', 0.5354213714599609, 'finance', 0), ('goldmansachs/gs-quant', 0.5344659090042114, 'finance', 0), ('nalepae/pandarallel', 0.5342207551002502, 'pandas', 0), ('lux-org/lux', 0.5323396921157837, 'viz', 0), ('alkaline-ml/pmdarima', 0.526504635810852, 'time-series', 0), ('unionai-oss/pandera', 0.5138059258460999, 'pandas', 0), ('ydataai/ydata-profiling', 0.5118176937103271, 'pandas', 0), ('rapidsai/cudf', 0.5101376175880432, 'pandas', 0), ('ranaroussi/quantstats', 0.5088109970092773, 'finance', 0), ('jakevdp/pythondatasciencehandbook', 0.5062484741210938, 'study', 0), ('tkrabel/bamboolib', 0.5043449401855469, 'pandas', 0), ('mito-ds/monorepo', 0.5002267360687256, 'jupyter', 0), ('adamerose/pandasgui', 0.5000632405281067, 'pandas', 0)] | 1 | 0 | null | 0 | 1 | 0 | 48 | 47 | 0 | 2 | 2 | 1 | 0 | 90 | 0 | 12 |
1,085 | util | https://github.com/mnooner256/pyqrcode | [] | null | [] | [] | null | null | null | mnooner256/pyqrcode | pyqrcode | 399 | 73 | 17 | Python | null | Python 3 module to generate QR Codes | mnooner256 | 2024-01-04 | 2013-06-07 | 555 | 0.718179 | null | Python 3 module to generate QR Codes | [] | [] | 2016-06-20 | [('heuer/segno', 0.7471798658370972, 'util', 0), ('pyscf/pyscf', 0.5908879637718201, 'sim', 0), ('cqcl/lambeq', 0.5321901440620422, 'nlp', 0), ('google/latexify_py', 0.527988612651825, 'util', 0), ('hhatto/autopep8', 0.5037751793861389, 'util', 0)] | 8 | 3 | null | 0 | 0 | 0 | 129 | 92 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 12 |
339 | term | https://github.com/federicoceratto/dashing | [] | null | [] | [] | null | null | null | federicoceratto/dashing | dashing | 378 | 31 | 10 | Python | https://dashing.readthedocs.io/en/latest/ | Terminal dashboards for Python | federicoceratto | 2024-01-12 | 2017-06-03 | 347 | 1.087993 | null | Terminal dashboards for Python | ['charts', 'dashboard', 'gauges', 'terminal', 'terminal-based'] | ['charts', 'dashboard', 'gauges', 'terminal', 'terminal-based'] | 2020-09-06 | [('plotly/dash', 0.6700859069824219, 'viz', 0), ('holoviz/panel', 0.6392130851745605, 'viz', 0), ('plotly/plotly.py', 0.6343661546707153, 'viz', 1), ('rapidsai/jupyterlab-nvdashboard', 0.6266454458236694, 'jupyter', 0), ('datapane/datapane', 0.6180770993232727, 'viz', 1), ('vizzuhq/ipyvizzu', 0.5991188883781433, 'jupyter', 1), ('man-group/dtale', 0.5763976573944092, 'viz', 0), ('cuemacro/chartpy', 0.5717711448669434, 'viz', 0), ('kanaries/pygwalker', 0.569820761680603, 'pandas', 0), ('bokeh/bokeh', 0.5679104924201965, 'viz', 0), ('urwid/urwid', 0.5645572543144226, 'term', 0), ('jquast/blessed', 0.5442036986351013, 'term', 1), ('matplotlib/matplotlib', 0.539638102054596, 'viz', 0), ('holoviz/holoviz', 0.5380876064300537, 'viz', 0), ('willmcgugan/rich', 0.5353392958641052, 'term', 1), ('mwaskom/seaborn', 0.528854489326477, 'viz', 0), ('adamerose/pandasgui', 0.5236207246780396, 'pandas', 0), ('goldmansachs/gs-quant', 0.5140957832336426, 'finance', 0), ('xonsh/xonsh', 0.5140331387519836, 'util', 1), ('willmcgugan/textual', 0.5119619369506836, 'term', 1), ('artelys/geonetworkx', 0.5115811228752136, 'gis', 0), ('has2k1/plotnine', 0.5077523589134216, 'viz', 0), ('holoviz/hvplot', 0.5051881670951843, 'pandas', 0), ('lux-org/lux', 0.5034831762313843, 'viz', 0), ('holoviz/geoviews', 0.5021084547042847, 'gis', 0), ('residentmario/geoplot', 0.5019742846488953, 'gis', 0), ('wesm/pydata-book', 0.5012456178665161, 'study', 0), ('ranaroussi/quantstats', 0.5010005235671997, 'finance', 0)] | 2 | 2 | null | 0 | 0 | 0 | 81 | 41 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 12 |
658 | study | https://github.com/rasbt/stat451-machine-learning-fs20 | [] | null | [] | [] | null | null | null | rasbt/stat451-machine-learning-fs20 | stat451-machine-learning-fs20 | 360 | 188 | 19 | Jupyter Notebook | null | STAT 451: Intro to Machine Learning @ UW-Madison (Fall 2020) | rasbt | 2024-01-04 | 2020-08-06 | 181 | 1.981132 | null | STAT 451: Intro to Machine Learning @ UW-Madison (Fall 2020) | [] | [] | 2020-12-03 | [('rasbt/stat453-deep-learning-ss20', 0.7537368535995483, 'study', 0), ('patchy631/machine-learning', 0.5887393355369568, 'ml', 0), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.5810298323631287, 'study', 0), ('huggingface/evaluate', 0.5357676148414612, 'ml', 0), ('probml/pyprobml', 0.5270578861236572, 'ml', 0), ('firmai/industry-machine-learning', 0.5267770886421204, 'study', 0), ('scikit-learn/scikit-learn', 0.5173183083534241, 'ml', 0), ('ageron/handson-ml2', 0.5153390169143677, 'ml', 0)] | 2 | 1 | null | 0 | 0 | 0 | 42 | 38 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 12 |
1,397 | ml-dl | https://github.com/blackhc/toma | [] | null | [] | [] | null | null | null | blackhc/toma | toma | 342 | 9 | 10 | Python | null | Helps you write algorithms in PyTorch that adapt to the available (CUDA) memory | blackhc | 2024-01-04 | 2020-04-08 | 198 | 1.719828 | null | Helps you write algorithms in PyTorch that adapt to the available (CUDA) memory | ['data-science', 'gpu', 'machine-learning', 'pytorch'] | ['data-science', 'gpu', 'machine-learning', 'pytorch'] | 2021-04-17 | [('rentruewang/koila', 0.6982141137123108, 'ml', 2), ('intel/intel-extension-for-pytorch', 0.6170483231544495, 'perf', 2), ('cvxgrp/pymde', 0.588315486907959, 'ml', 3), ('huggingface/accelerate', 0.5751364827156067, 'ml', 0), ('tensorflow/addons', 0.5717829465866089, 'ml', 1), ('pytorch/ignite', 0.5623881220817566, 'ml-dl', 2), ('pytorch/torchrec', 0.5616428256034851, 'ml-dl', 2), ('xl0/lovely-tensors', 0.5582937002182007, 'ml-dl', 1), ('pytorch/data', 0.5554980635643005, 'data', 0), ('joblib/joblib', 0.5492387413978577, 'util', 0), ('ashleve/lightning-hydra-template', 0.545914351940155, 'util', 1), ('plasma-umass/scalene', 0.545809268951416, 'profiling', 1), ('arogozhnikov/einops', 0.5450114011764526, 'ml-dl', 1), ('mrdbourke/pytorch-deep-learning', 0.5438900589942932, 'study', 2), ('huggingface/datasets', 0.5416107177734375, 'nlp', 2), ('google/tf-quant-finance', 0.5406495928764343, 'finance', 1), ('mosaicml/composer', 0.5379732251167297, 'ml-dl', 2), ('nvidia/apex', 0.5342817902565002, 'ml-dl', 0), ('cupy/cupy', 0.5264122486114502, 'math', 1), ('nvidia/tensorrt-llm', 0.5219219923019409, 'viz', 1), ('ddbourgin/numpy-ml', 0.521062433719635, 'ml', 1), ('pytorchlightning/pytorch-lightning', 0.5127522945404053, 'ml-dl', 3), ('explosion/thinc', 0.512549638748169, 'ml-dl', 2), ('allenai/allennlp', 0.5094995498657227, 'nlp', 2), ('pytorch/captum', 0.5078623294830322, 'ml-interpretability', 0), ('neuralmagic/deepsparse', 0.5067216753959656, 'nlp', 0), ('microsoft/deepspeed', 0.5066729784011841, 'ml-dl', 3), ('denys88/rl_games', 0.5061429738998413, 'ml-rl', 1), ('isl-org/open3d', 0.5056933164596558, 'sim', 3), ('rasbt/machine-learning-book', 0.5006940960884094, 'study', 2)] | 1 | 1 | null | 0 | 0 | 0 | 46 | 33 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 12 |
33 | gis | https://github.com/jasonrig/address-net | [] | null | [] | [] | null | null | null | jasonrig/address-net | address-net | 186 | 80 | 13 | Python | null | A package to structure Australian addresses | jasonrig | 2024-01-04 | 2018-12-05 | 268 | 0.691817 | null | A package to structure Australian addresses | ['address-parser', 'deep-learning', 'machine-learning', 'rnn'] | ['address-parser', 'deep-learning', 'machine-learning', 'rnn'] | 2020-09-09 | [('graal-research/deepparse', 0.7487471699714661, 'gis', 1)] | 2 | 2 | null | 0 | 2 | 0 | 62 | 41 | 0 | 0 | 0 | 2 | 1 | 90 | 0.5 | 12 |
1,277 | sim | https://github.com/ljvmiranda921/seagull | [] | null | [] | [] | null | null | null | ljvmiranda921/seagull | seagull | 168 | 28 | 9 | Python | https://pyseagull.readthedocs.io/en/latest/index.html# | A Python Library for Conway's Game of Life | ljvmiranda921 | 2024-01-04 | 2019-05-02 | 247 | 0.678201 | null | A Python Library for Conway's Game of Life | ['artificial-life', 'artificial-life-algorithms', 'biology', 'cellular-automata', 'conways-game-of-life', 'game-of-life', 'mathematics', 'simulation-framework'] | ['artificial-life', 'artificial-life-algorithms', 'biology', 'cellular-automata', 'conways-game-of-life', 'game-of-life', 'mathematics', 'simulation-framework'] | 2020-11-08 | [('elliotwaite/rule-30-and-game-of-life', 0.7149527072906494, 'sim', 3), ('alephalpha/golly', 0.7014067769050598, 'sim', 2), ('projectmesa/mesa', 0.5834044218063354, 'sim', 1), ('pokepetter/ursina', 0.5359201431274414, 'gamedev', 0), ('lordmauve/pgzero', 0.5329146981239319, 'gamedev', 0), ('openlenia/lenia-tutorial', 0.5293267965316772, 'sim', 0), ('artemyk/dynpy', 0.5030413269996643, 'sim', 0), ('pythonarcade/arcade', 0.5029021501541138, 'gamedev', 0), ('sympy/sympy', 0.5007748603820801, 'math', 0)] | 9 | 2 | null | 0 | 0 | 0 | 57 | 39 | 0 | 1 | 1 | 0 | 0 | 90 | 0 | 12 |
231 | template | https://github.com/crmne/cookiecutter-modern-datascience | [] | null | [] | [] | null | null | null | crmne/cookiecutter-modern-datascience | cookiecutter-modern-datascience | 163 | 33 | 4 | Python | null | Start a data science project with modern tools | crmne | 2024-01-05 | 2020-07-06 | 186 | 0.875672 | null | Start a data science project with modern tools | ['cookiecutter', 'cookiecutter-data-science', 'cookiecutter-template', 'datascience'] | ['cookiecutter', 'cookiecutter-data-science', 'cookiecutter-template', 'datascience'] | 2023-08-10 | [('drivendata/cookiecutter-data-science', 0.7191076874732971, 'template', 3), ('lyz-code/cookiecutter-python-project', 0.6067662835121155, 'template', 1), ('buuntu/fastapi-react', 0.5824753046035767, 'template', 1), ('cookiecutter/cookiecutter', 0.5783246755599976, 'template', 1), ('tedivm/robs_awesome_python_template', 0.5504110455513, 'template', 1), ('ionelmc/cookiecutter-pylibrary', 0.5333271026611328, 'template', 2)] | 2 | 1 | null | 0.02 | 0 | 0 | 43 | 5 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 12 |
1,393 | nlp | https://github.com/sebischair/lbl2vec | [] | null | [] | [] | null | null | null | sebischair/lbl2vec | Lbl2Vec | 156 | 25 | 6 | Python | https://wwwmatthes.in.tum.de/pages/naimi84squl1/Lbl2Vec-An-Embedding-based-Approach-for-Unsupervised-Document-Retrieval-on-Predefined-Topics | Lbl2Vec learns jointly embedded label, document and word vectors to retrieve documents with predefined topics from an unlabeled document corpus. | sebischair | 2024-01-08 | 2021-07-18 | 132 | 1.179266 | https://avatars.githubusercontent.com/u/11438939?v=4 | Lbl2Vec learns jointly embedded label, document and word vectors to retrieve documents with predefined topics from an unlabeled document corpus. | ['document-embeddings', 'label-embeddings', 'machine-learning', 'natural-language-processing', 'nlp', 'text-classification', 'unsupervised-classification', 'unsupervised-document-retrieval', 'word-embeddings'] | ['document-embeddings', 'label-embeddings', 'machine-learning', 'natural-language-processing', 'nlp', 'text-classification', 'unsupervised-classification', 'unsupervised-document-retrieval', 'word-embeddings'] | 2023-01-18 | [('ddangelov/top2vec', 0.8003798723220825, 'nlp', 1), ('plasticityai/magnitude', 0.5967115163803101, 'nlp', 4), ('maartengr/bertopic', 0.5893922448158264, 'nlp', 2), ('koaning/whatlies', 0.5767074823379517, 'nlp', 1), ('chroma-core/chroma', 0.5661131143569946, 'data', 0), ('rare-technologies/gensim', 0.5351875424385071, 'nlp', 4), ('koaning/embetter', 0.5337308645248413, 'data', 0), ('neuml/txtai', 0.5315554738044739, 'nlp', 2), ('huggingface/text-embeddings-inference', 0.5274394154548645, 'llm', 0), ('paddlepaddle/paddlenlp', 0.514652669429779, 'llm', 1), ('ai21labs/in-context-ralm', 0.5090823173522949, 'llm', 0), ('muennighoff/sgpt', 0.5040941834449768, 'llm', 0), ('flairnlp/flair', 0.5040925145149231, 'nlp', 4), ('qdrant/fastembed', 0.5038244128227234, 'ml', 0)] | 1 | 1 | null | 0 | 0 | 0 | 30 | 12 | 0 | 1 | 1 | 0 | 0 | 90 | 0 | 12 |
1,828 | util | https://github.com/koaning/clumper | ['fluent'] | null | [] | [] | null | null | null | koaning/clumper | clumper | 144 | 14 | 3 | Python | https://koaning.github.io/clumper/ | A small python library that can clump lists of data together. | koaning | 2024-01-04 | 2020-07-25 | 183 | 0.785047 | null | A small python library that can clump lists of data together. | [] | ['fluent'] | 2021-10-11 | [('pytables/pytables', 0.5409725904464722, 'data', 0), ('saulpw/visidata', 0.5350844264030457, 'term', 0), ('linealabs/lineapy', 0.5260019302368164, 'jupyter', 0), ('fluentpython/example-code-2e', 0.5001460909843445, 'study', 0)] | 5 | 3 | null | 0 | 0 | 0 | 42 | 28 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 12 |
839 | gis | https://github.com/remotesensinglab/raster4ml | [] | null | [] | [] | null | null | null | remotesensinglab/raster4ml | raster4ml | 115 | 14 | 4 | Python | https://raster4ml.readthedocs.io | A geospatial raster processing library for machine learning | remotesensinglab | 2024-01-04 | 2022-07-11 | 81 | 1.417254 | null | A geospatial raster processing library for machine learning | ['agriculture-research', 'data-science', 'geospatial-data', 'machine-learning', 'remote-sensing', 'vegetation', 'vegetation-index'] | ['agriculture-research', 'data-science', 'geospatial-data', 'machine-learning', 'remote-sensing', 'vegetation', 'vegetation-index'] | 2022-11-01 | [('osgeo/grass', 0.6746719479560852, 'gis', 3), ('osgeo/gdal', 0.6307851076126099, 'gis', 2), ('microsoft/torchgeo', 0.6221429705619812, 'gis', 1), ('azavea/raster-vision', 0.5898652076721191, 'gis', 2), ('developmentseed/label-maker', 0.5611507296562195, 'gis', 1), ('perrygeo/python-rasterstats', 0.5588922500610352, 'gis', 0), ('fatiando/verde', 0.5404156446456909, 'gis', 1), ('earthlab/earthpy', 0.5156380534172058, 'gis', 0), ('kornia/kornia', 0.5086722373962402, 'ml-dl', 1), ('sentinel-hub/eo-learn', 0.5058966279029846, 'gis', 1), ('opengeos/segment-geospatial', 0.5023316144943237, 'gis', 1)] | 1 | 1 | null | 0 | 1 | 0 | 18 | 15 | 0 | 1 | 1 | 1 | 0 | 90 | 0 | 12 |
1,283 | llm | https://github.com/larsbaunwall/bricky | ['haystack'] | null | [] | [] | null | null | null | larsbaunwall/bricky | bricky | 94 | 18 | 6 | Python | null | Haystack/OpenAI based chatbot curating a custom knowledgebase | larsbaunwall | 2024-01-08 | 2023-01-29 | 52 | 1.797814 | null | Haystack/OpenAI based chatbot curating a custom knowledgebase | ['ai', 'haystack', 'nextjs', 'openai'] | ['ai', 'haystack', 'nextjs', 'openai'] | 2023-03-30 | [('rcgai/simplyretrieve', 0.6707216501235962, 'llm', 0), ('embedchain/embedchain', 0.634949803352356, 'llm', 1), ('togethercomputer/openchatkit', 0.6274131536483765, 'nlp', 0), ('run-llama/rags', 0.5981244444847107, 'llm', 1), ('cheshire-cat-ai/core', 0.598059892654419, 'llm', 1), ('lm-sys/fastchat', 0.5944631099700928, 'llm', 0), ('rasahq/rasa', 0.5906454920768738, 'llm', 0), ('deeppavlov/deeppavlov', 0.5805360078811646, 'nlp', 1), ('prefecthq/marvin', 0.577139675617218, 'nlp', 2), ('krohling/bondai', 0.5706357359886169, 'llm', 0), ('minimaxir/simpleaichat', 0.5494028925895691, 'llm', 1), ('nomic-ai/gpt4all', 0.5481459498405457, 'llm', 0), ('laion-ai/open-assistant', 0.5381844639778137, 'llm', 2), ('langchain-ai/chat-langchain', 0.5356094837188721, 'llm', 0), ('chatarena/chatarena', 0.5276904106140137, 'llm', 1), ('openai/openai-cookbook', 0.527281641960144, 'ml', 1), ('openai/openai-python', 0.5239132642745972, 'util', 1), ('mindsdb/mindsdb', 0.5211383700370789, 'data', 1), ('salesforce/logai', 0.5161862969398499, 'util', 1), ('pathwaycom/llm-app', 0.5143710374832153, 'llm', 0), ('mayooear/gpt4-pdf-chatbot-langchain', 0.5141124725341797, 'llm', 2), ('deepset-ai/haystack', 0.5118600130081177, 'llm', 2), ('shishirpatil/gorilla', 0.5044655799865723, 'llm', 0), ('deep-diver/llm-as-chatbot', 0.5015654563903809, 'llm', 0)] | 2 | 1 | null | 0.31 | 0 | 0 | 12 | 10 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 12 |
1,236 | llm | https://github.com/zrrskywalker/llama-adapter | ['instruction-tuning', 'llama', 'language-model'] | null | [] | [] | null | null | null | zrrskywalker/llama-adapter | LLaMA-Adapter | 66 | 5 | 3 | null | null | Fine-tuning LLaMA to follow Instructions within 1 Hour and 1.2M Parameters | zrrskywalker | 2024-01-05 | 2023-06-14 | 32 | 2.008696 | null | Fine-tuning LLaMA to follow Instructions within 1 Hour and 1.2M Parameters | [] | ['instruction-tuning', 'language-model', 'llama'] | 2023-06-14 | [('tloen/alpaca-lora', 0.7604994773864746, 'llm', 2), ('mshumer/gpt-llm-trainer', 0.684531569480896, 'llm', 0), ('microsoft/llama-2-onnx', 0.6588950157165527, 'llm', 2), ('facebookresearch/llama-recipes', 0.6457222700119019, 'llm', 2), ('jzhang38/tinyllama', 0.6362742185592651, 'llm', 2), ('hiyouga/llama-factory', 0.6037132740020752, 'llm', 3), ('hiyouga/llama-efficient-tuning', 0.6037131547927856, 'llm', 3), ('run-llama/llama-lab', 0.5806187391281128, 'llm', 2), ('lightning-ai/lit-llama', 0.5725643038749695, 'llm', 2), ('instruction-tuning-with-gpt-4/gpt-4-llm', 0.5689181685447693, 'llm', 2), ('facebookresearch/llama', 0.5667337775230408, 'llm', 2), ('h2oai/h2o-llmstudio', 0.5344659686088562, 'llm', 1), ('declare-lab/instruct-eval', 0.5260562896728516, 'llm', 0), ('bentoml/openllm', 0.5195848345756531, 'ml-ops', 1), ('bigscience-workshop/petals', 0.5049505829811096, 'data', 1), ('karpathy/llama2.c', 0.5048466920852661, 'llm', 2)] | 1 | 1 | null | 0.08 | 0 | 0 | 7 | 7 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 12 |
1,587 | util | https://github.com/lynet101/mojo_community-lib | ['mojo'] | null | [] | [] | null | null | null | lynet101/mojo_community-lib | Mojo_community-lib | 45 | 3 | 5 | Python | null | A community driven mojo lib | lynet101 | 2023-12-08 | 2023-09-09 | 20 | 2.202797 | null | A community driven mojo lib | [] | ['mojo'] | 2023-10-02 | [('lsh/shims', 0.7806389927864075, 'util', 1), ('msaelices/py2mojo', 0.509608805179596, 'util', 1)] | 3 | 0 | null | 1.63 | 0 | 0 | 4 | 3 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 12 |
1,588 | util | https://github.com/lsh/shims | ['mojo'] | null | [] | [] | null | null | null | lsh/shims | shims | 38 | 2 | 2 | null | null | Utils for mojo projects | lsh | 2023-12-10 | 2023-09-12 | 20 | 1.9 | null | Utils for mojo projects | [] | ['mojo'] | 2023-10-02 | [('lynet101/mojo_community-lib', 0.7806389927864075, 'util', 1), ('msaelices/py2mojo', 0.5381791591644287, 'util', 1)] | 1 | 1 | null | 0.13 | 0 | 0 | 4 | 3 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 12 |
574 | term | https://github.com/matthewdeanmartin/terminaltables | [] | null | [] | [] | null | null | null | matthewdeanmartin/terminaltables | terminaltables | 35 | 5 | 1 | Python | https://robpol86.github.io/terminaltables | Generate simple tables in terminals from a nested list of strings. | matthewdeanmartin | 2023-12-10 | 2021-12-04 | 112 | 0.311309 | null | Generate simple tables in terminals from a nested list of strings. | [] | [] | 2022-01-30 | [] | 10 | 2 | null | 0 | 1 | 0 | 26 | 24 | 0 | 7 | 7 | 1 | 0 | 90 | 0 | 12 |
1,507 | sim | https://github.com/cyrus2d/pyrus2d | [] | null | [] | [] | null | null | null | cyrus2d/pyrus2d | Pyrus2D | 33 | 4 | 1 | Python | https://cyrus2d.com/ | PYRUS Soccer Simulation 2D base code is the first Python base code (sample team) for RoboCup Soccer 2D Simulator. This project is implemented by members of CYRUS soccer simulation 2D team. | cyrus2d | 2024-01-12 | 2021-05-31 | 139 | 0.237166 | https://avatars.githubusercontent.com/u/44771435?v=4 | PYRUS Soccer Simulation 2D base code is the first Python base code (sample team) for RoboCup Soccer 2D Simulator. This project is implemented by members of CYRUS soccer simulation 2D team. | ['robocup', 'simulation', 'soccer', 'soccer-simulation'] | ['robocup', 'simulation', 'soccer', 'soccer-simulation'] | 2023-07-18 | [('viblo/pymunk', 0.5240238904953003, 'sim', 0)] | 3 | 2 | null | 3.85 | 3 | 0 | 32 | 6 | 0 | 0 | 0 | 3 | 0 | 90 | 0 | 12 |
1,141 | security | https://github.com/snyk/faker-security | [] | null | [] | [] | null | null | null | snyk/faker-security | faker-security | 30 | 6 | 13 | Python | null | Python Faker provider for security related data | snyk | 2024-01-12 | 2022-03-18 | 97 | 0.307467 | https://avatars.githubusercontent.com/u/12959162?v=4 | Python Faker provider for security related data | [] | [] | 2023-09-21 | [('joke2k/faker', 0.7436192631721497, 'data', 0), ('legrandin/pycryptodome', 0.5843793153762817, 'util', 0), ('lk-geimfari/mimesis', 0.5424483418464661, 'data', 0), ('nedbat/coveragepy', 0.5369202494621277, 'testing', 0), ('pyeve/cerberus', 0.5294828414916992, 'data', 0), ('pallets/itsdangerous', 0.5056630969047546, 'data', 0), ('getsentry/responses', 0.5025610327720642, 'testing', 0)] | 5 | 1 | null | 0.37 | 0 | 0 | 22 | 4 | 3 | 2 | 3 | 0 | 0 | 90 | 0 | 12 |
958 | ml-dl | https://github.com/suanrong/sdne | [] | null | [] | [] | null | null | null | suanrong/sdne | SDNE | 319 | 123 | 10 | Python | http://www.kdd.org/kdd2016/subtopic/view/structural-deep-network-embedding | This is a implementation of SDNE (Structural Deep Network embedding) | suanrong | 2024-01-04 | 2016-11-30 | 373 | 0.853267 | null | This is a implementation of SDNE (Structural Deep Network embedding) | [] | [] | 2021-09-10 | [] | 6 | 1 | null | 0 | 0 | 0 | 87 | 29 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 11 |
1,550 | testing | https://github.com/eugeneyan/testing-ml | [] | null | [] | [] | null | null | null | eugeneyan/testing-ml | testing-ml | 218 | 46 | 7 | Python | https://eugeneyan.com/writing/testing-ml/ | 🔍 Minimal examples of machine learning tests for implementation, behaviour, and performance. | eugeneyan | 2024-01-11 | 2020-08-30 | 178 | 1.222756 | null | 🔍 Minimal examples of machine learning tests for implementation, behaviour, and performance. | ['machine-learning', 'model-evaluation', 'testing'] | ['machine-learning', 'model-evaluation', 'testing'] | 2022-09-21 | [('huggingface/evaluate', 0.6167011260986328, 'ml', 1), ('patchy631/machine-learning', 0.5529058575630188, 'ml', 0), ('tensorflow/data-validation', 0.5382910966873169, 'ml-ops', 0), ('teamhg-memex/eli5', 0.5324820280075073, 'ml', 1), ('xplainable/xplainable', 0.5317504405975342, 'ml-interpretability', 1), ('automl/auto-sklearn', 0.528367280960083, 'ml', 0), ('ml-tooling/opyrator', 0.5247636437416077, 'viz', 1), ('seldonio/alibi', 0.5242266654968262, 'ml-interpretability', 1), ('districtdatalabs/yellowbrick', 0.5194308757781982, 'ml', 1), ('shankarpandala/lazypredict', 0.5170032382011414, 'ml', 1), ('nccr-itmo/fedot', 0.5155410766601562, 'ml-ops', 1), ('microsoft/nni', 0.5123538374900818, 'ml', 1), ('giskard-ai/giskard', 0.5097730755805969, 'data', 1)] | 2 | 1 | null | 0 | 0 | 0 | 41 | 16 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 11 |
882 | graph | https://github.com/h4kor/graph-force | [] | null | [] | [] | null | null | null | h4kor/graph-force | graph-force | 161 | 1 | 10 | Rust | https://pypi.org/project/graph-force/ | Python library for embedding large graphs in 2D space, using force-directed layouts. | h4kor | 2024-01-09 | 2022-11-28 | 61 | 2.633178 | null | Python library for embedding large graphs in 2D space, using force-directed layouts. | ['force-directed-graphs', 'graph-algorithms'] | ['force-directed-graphs', 'graph-algorithms'] | 2022-11-28 | [('graphistry/pygraphistry', 0.6990000605583191, 'data', 0), ('facebookresearch/pytorch-biggraph', 0.6328504085540771, 'ml-dl', 0), ('westhealth/pyvis', 0.6132168173789978, 'graph', 0), ('artelys/geonetworkx', 0.5840879678726196, 'gis', 0), ('dmlc/dgl', 0.573490560054779, 'ml-dl', 0), ('pygraphviz/pygraphviz', 0.571456253528595, 'viz', 0), ('a-r-j/graphein', 0.5339735150337219, 'sim', 0), ('networkx/networkx', 0.5262413620948792, 'graph', 1), ('kuanb/peartree', 0.5150353908538818, 'gis', 0), ('pyg-team/pytorch_geometric', 0.5138907432556152, 'ml-dl', 0), ('plotly/plotly.py', 0.5063482522964478, 'viz', 0), ('benedekrozemberczki/tigerlily', 0.503591001033783, 'ml-dl', 0)] | 2 | 0 | null | 0 | 0 | 0 | 14 | 14 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 11 |
1,097 | ml | https://github.com/eleutherai/pyfra | [] | null | [] | [] | null | null | null | eleutherai/pyfra | pyfra | 108 | 12 | 4 | Python | null | Python Research Framework | eleutherai | 2024-01-04 | 2021-04-16 | 145 | 0.741904 | https://avatars.githubusercontent.com/u/68924597?v=4 | Python Research Framework | [] | [] | 2022-11-03 | [('pytoolz/toolz', 0.6730476021766663, 'util', 0), ('willmcgugan/textual', 0.6594876646995544, 'term', 0), ('python/cpython', 0.6574167013168335, 'util', 0), ('masoniteframework/masonite', 0.6563796401023865, 'web', 0), ('pyston/pyston', 0.650770366191864, 'util', 0), ('holoviz/panel', 0.6469884514808655, 'viz', 0), ('pypy/pypy', 0.6457244753837585, 'util', 0), ('amaargiru/pyroad', 0.6402732729911804, 'study', 0), ('wesm/pydata-book', 0.631885826587677, 'study', 0), ('bottlepy/bottle', 0.6280529499053955, 'web', 0), ('buildbot/buildbot', 0.6259655952453613, 'util', 0), ('pandas-dev/pandas', 0.6219991445541382, 'pandas', 0), ('goldmansachs/gs-quant', 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1,687 | perf | https://github.com/faster-cpython/tools | ['cpython'] | Tools fo Faster CPython project. | [] | [] | null | null | null | faster-cpython/tools | tools | 88 | 14 | 22 | Python | null | null | faster-cpython | 2024-01-05 | 2021-04-09 | 146 | 0.60039 | https://avatars.githubusercontent.com/u/81193161?v=4 | Tools fo Faster CPython project. | [] | ['cpython'] | 2023-01-19 | [('markshannon/faster-cpython', 0.8188387751579285, 'perf', 0), ('faster-cpython/ideas', 0.8179801106452942, 'perf', 1), ('python/cpython', 0.7096896171569824, 'util', 1), ('pypy/pypy', 0.6747263669967651, 'util', 1), ('brandtbucher/specialist', 0.6422561407089233, 'perf', 1), ('cython/cython', 0.6359994411468506, 'util', 1), ('p403n1x87/austin', 0.6291638612747192, 'profiling', 0), ('ipython/ipyparallel', 0.6260746717453003, 'perf', 0), ('pyston/pyston', 0.5962938070297241, 'util', 0), ('fastai/fastcore', 0.5940293669700623, 'util', 0), ('intel/intel-extension-for-pytorch', 0.5863217115402222, 'perf', 0), ('pytorch/data', 0.5645531415939331, 'data', 0), ('facebookincubator/cinder', 0.5622638463973999, 'perf', 1), ('scikit-build/scikit-build', 0.5580853819847107, 'ml', 1), ('wesm/pydata-book', 0.5520884990692139, 'study', 0), ('gotcha/ipdb', 0.549821138381958, 'debug', 0), ('hoffstadt/dearpygui', 0.5421075820922852, 'gui', 0), ('klen/py-frameworks-bench', 0.5376661419868469, 'perf', 0), ('fchollet/deep-learning-with-python-notebooks', 0.533972442150116, 'study', 0), ('lcompilers/lpython', 0.5253562927246094, 'util', 0), ('nvidia/apex', 0.5224118828773499, 'ml-dl', 0), ('mynameisfiber/high_performance_python_2e', 0.5218332409858704, 'study', 0), ('exaloop/codon', 0.518226146697998, 'perf', 0), ('wxwidgets/phoenix', 0.5171552300453186, 'gui', 0), ('erotemic/ubelt', 0.5155495405197144, 'util', 0), ('ipython/ipython', 0.5140804052352905, 'util', 0), ('ipython/ipykernel', 0.5114536285400391, 'util', 0), ('tqdm/tqdm', 0.5104278922080994, 'term', 0), ('cohere-ai/notebooks', 0.5094420313835144, 'llm', 0), ('pytorch-labs/gpt-fast', 0.5083655118942261, 'llm', 0), ('rasbt/watermark', 0.507169783115387, 'util', 0), ('pyqtgraph/pyqtgraph', 0.5059671998023987, 'viz', 0), ('adafruit/circuitpython', 0.5050845742225647, 'util', 1), ('agronholm/apscheduler', 0.5040667057037354, 'util', 0), ('pytoolz/toolz', 0.5024548172950745, 'util', 0)] | 5 | 2 | null | 0 | 0 | 0 | 34 | 12 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 11 |
1,089 | graph | https://github.com/hamed1375/exphormer | [] | null | [] | [] | null | null | null | hamed1375/exphormer | Exphormer | 86 | 13 | 0 | Python | null | Exphormer: Sparse Transformer for Graphs | hamed1375 | 2024-01-12 | 2023-03-05 | 47 | 1.818731 | null | Exphormer: Sparse Transformer for Graphs | [] | [] | 2023-07-20 | [('rampasek/graphgps', 0.6791350841522217, 'graph', 0), ('hazyresearch/hgcn', 0.5110289454460144, 'ml', 0)] | 3 | 0 | null | 0.29 | 0 | 0 | 10 | 6 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 11 |
531 | gis | https://github.com/cloudsen12/easystac | [] | null | [] | [] | null | null | null | cloudsen12/easystac | easystac | 63 | 2 | 3 | Python | https://easystac.readthedocs.io/ | A Python package for simple STAC queries | cloudsen12 | 2023-09-04 | 2022-01-20 | 105 | 0.595946 | https://avatars.githubusercontent.com/u/76630702?v=4 | A Python package for simple STAC queries | ['earth-observation', 'gis', 'planetary-computer', 'radiant', 'remote-sensing', 'spatio-temporal', 'spatio-temporal-data', 'stac'] | ['earth-observation', 'gis', 'planetary-computer', 'radiant', 'remote-sensing', 'spatio-temporal', 'spatio-temporal-data', 'stac'] | 2022-08-07 | [('radiantearth/radiant-mlhub', 0.6044291853904724, 'gis', 1), ('scitools/iris', 0.6042935252189636, 'gis', 0), ('sentinel-hub/eo-learn', 0.5572369694709778, 'gis', 0), ('pytroll/satpy', 0.5563086271286011, 'gis', 0), ('geopandas/geopandas', 0.534330427646637, 'gis', 1), ('earthlab/earthpy', 0.5297847390174866, 'gis', 0), ('roban/cosmolopy', 0.5210418701171875, 'sim', 0), ('opengeos/leafmap', 0.5067197680473328, 'gis', 1), ('artelys/geonetworkx', 0.5005823373794556, 'gis', 0)] | 3 | 3 | null | 0 | 0 | 0 | 24 | 18 | 0 | 1 | 1 | 0 | 0 | 90 | 0 | 11 |
400 | crypto | https://github.com/dylanhogg/crazy-awesome-crypto | ['awesome'] | null | [] | ['<hide>'] | null | null | null | dylanhogg/crazy-awesome-crypto | crazy-awesome-crypto | 60 | 16 | 5 | Python | https://www.awesomecrypto.xyz/ | A list of awesome crypto and blockchain projects | dylanhogg | 2024-01-08 | 2021-09-27 | 122 | 0.491228 | null | A list of awesome crypto and blockchain projects | ['awesome', 'awesome-list', 'bitcoin', 'blockchain', 'crypto', 'cryptocurrency', 'data', 'data-analysis', 'ethereum', 'github'] | ['awesome', 'awesome-list', 'bitcoin', 'blockchain', 'crypto', 'cryptocurrency', 'data', 'data-analysis', 'ethereum', 'github'] | 2023-10-22 | [('dylanhogg/awesome-python', 0.5653820037841797, 'study', 3), ('numerai/example-scripts', 0.5541864633560181, 'finance', 1), ('christoschristofidis/awesome-deep-learning', 0.5464283227920532, 'study', 2), ('1200wd/bitcoinlib', 0.5096782445907593, 'crypto', 1)] | 1 | 1 | null | 0.17 | 0 | 0 | 28 | 3 | 0 | 1 | 1 | 0 | 0 | 90 | 0 | 11 |
1,565 | llm | https://github.com/deep-diver/gradio-chat | ['gradio'] | null | [] | [] | null | null | null | deep-diver/gradio-chat | gradio-chat | 56 | 7 | 2 | Python | null | HuggingChat like UI in Gradio | deep-diver | 2024-01-04 | 2023-05-19 | 36 | 1.53125 | null | HuggingChat like UI in Gradio | [] | ['gradio'] | 2023-05-23 | [] | 1 | 1 | null | 0.23 | 0 | 0 | 8 | 8 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 11 |
1,630 | template | https://github.com/tedivm/robs_awesome_python_template | [] | null | [] | [] | null | null | null | tedivm/robs_awesome_python_template | robs_awesome_python_template | 20 | 4 | 3 | Python | https://blog.tedivm.com/open-source/2023/02/robs-awesome-python-template/ | A Highly Configurable Python Project Template for Modern Python Projects | tedivm | 2024-01-12 | 2022-12-11 | 59 | 0.337349 | null | A Highly Configurable Python Project Template for Modern Python Projects | ['cookiecutter-python', 'cookiecutter-template'] | ['cookiecutter-python', 'cookiecutter-template'] | 2023-12-31 | [('lyz-code/cookiecutter-python-project', 0.9047797918319702, 'template', 0), ('cookiecutter/cookiecutter', 0.8344708681106567, 'template', 0), ('ionelmc/cookiecutter-pylibrary', 0.8226386308670044, 'template', 1), ('giswqs/pypackage', 0.7693808078765869, 'template', 1), ('buuntu/fastapi-react', 0.6574198603630066, 'template', 0), ('tezromach/python-package-template', 0.647948682308197, 'template', 0), ('pypa/hatch', 0.582638144493103, 'util', 0), ('cjolowicz/cookiecutter-hypermodern-python', 0.5808916687965393, 'template', 0), ('crmne/cookiecutter-modern-datascience', 0.5504110455513, 'template', 1), ('martinheinz/python-project-blueprint', 0.5347585082054138, 'template', 0), ('psf/requests', 0.5218177437782288, 'web', 0), ('pallets/flask', 0.5124099254608154, 'web', 0), ('s3rius/fastapi-template', 0.5110516548156738, 'web', 1), ('pypa/build', 0.5029564499855042, 'util', 0), ('pdm-project/pdm', 0.5013588070869446, 'util', 0)] | 1 | 1 | null | 0.75 | 1 | 1 | 13 | 0 | 0 | 0 | 0 | 1 | 0 | 90 | 0 | 11 |
934 | sim | https://github.com/crflynn/stochastic | [] | null | [] | [] | null | null | null | crflynn/stochastic | stochastic | 388 | 71 | 15 | Python | http://stochastic.readthedocs.io/en/stable/ | Generate realizations of stochastic processes in python. | crflynn | 2024-01-04 | 2017-02-17 | 362 | 1.070134 | null | Generate realizations of stochastic processes in python. | ['probability', 'stochastic', 'stochastic-differential-equations', 'stochastic-processes', 'stochastic-simulation-algorithm', 'stochastic-volatility-models'] | ['probability', 'stochastic', 'stochastic-differential-equations', 'stochastic-processes', 'stochastic-simulation-algorithm', 'stochastic-volatility-models'] | 2022-07-12 | [('pymc-devs/pymc3', 0.6376107335090637, 'ml', 0), ('awslabs/gluonts', 0.5971387624740601, 'time-series', 0), ('probml/pyprobml', 0.5735928416252136, 'ml', 0), ('firmai/atspy', 0.5687724351882935, 'time-series', 0), ('stan-dev/pystan', 0.5423630475997925, 'ml', 0), ('artemyk/dynpy', 0.5386329889297485, 'sim', 0), ('statsmodels/statsmodels', 0.5304479002952576, 'ml', 0), ('scikit-learn/scikit-learn', 0.5197334885597229, 'ml', 0), ('gboeing/pynamical', 0.509087860584259, 'sim', 0), ('online-ml/river', 0.507440447807312, 'ml', 0), ('scipy/scipy', 0.5048527121543884, 'math', 0), ('google/temporian', 0.5019282698631287, 'time-series', 0), ('goldmansachs/gs-quant', 0.5013076663017273, 'finance', 0), ('bashtage/arch', 0.5002689957618713, 'time-series', 0), ('uber/orbit', 0.5002565979957581, 'time-series', 0)] | 7 | 0 | null | 0 | 0 | 0 | 84 | 18 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 10 |
778 | util | https://github.com/brokenloop/jsontopydantic | [] | null | [] | [] | null | null | null | brokenloop/jsontopydantic | jsontopydantic | 275 | 9 | 4 | TypeScript | https://jsontopydantic.com | Web tool for generating Pydantic models from JSON objects | brokenloop | 2024-01-12 | 2020-11-28 | 165 | 1.662349 | null | Web tool for generating Pydantic models from JSON objects | [] | [] | 2022-05-13 | [('developmentseed/geojson-pydantic', 0.7540448904037476, 'gis', 0), ('jsonpickle/jsonpickle', 0.5869114398956299, 'data', 0), ('1rgs/jsonformer', 0.5837192535400391, 'llm', 0), ('marshmallow-code/marshmallow', 0.5406548380851746, 'util', 0), ('kellyjonbrazil/jello', 0.5349708795547485, 'util', 0), ('agronholm/sqlacodegen', 0.5330995321273804, 'data', 0), ('scikit-hep/awkward-1.0', 0.5177565813064575, 'data', 0), ('selfexplainml/piml-toolbox', 0.515333354473114, 'ml-interpretability', 0), ('lk-geimfari/mimesis', 0.5148674845695496, 'data', 0), ('python-odin/odin', 0.5140889286994934, 'util', 0), ('stan-dev/pystan', 0.5074247717857361, 'ml', 0)] | 2 | 0 | null | 0 | 0 | 0 | 38 | 20 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 10 |
736 | study | https://github.com/koaning/calm-notebooks | ['annoy', 'sklearn', 'jax'] | null | [] | [] | null | null | null | koaning/calm-notebooks | calm-notebooks | 214 | 175 | 9 | Jupyter Notebook | https://calmcode.io | notebooks that are used at calmcode.io | koaning | 2024-01-01 | 2020-03-01 | 204 | 1.047552 | null | notebooks that are used at calmcode.io | [] | ['annoy', 'jax', 'sklearn'] | 2021-10-21 | [('cohere-ai/notebooks', 0.5802757143974304, 'llm', 0), ('huggingface/notebooks', 0.5623078942298889, 'ml', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5319899320602417, 'study', 0), ('alphasecio/langchain-examples', 0.5073686242103577, 'llm', 0), ('jupyter/nbgrader', 0.5042494535446167, 'jupyter', 0)] | 1 | 1 | null | 0 | 0 | 0 | 47 | 27 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 10 |
525 | nlp | https://github.com/hazyresearch/fonduer-tutorials | [] | null | [] | [] | null | null | null | hazyresearch/fonduer-tutorials | fonduer-tutorials | 98 | 26 | 18 | Jupyter Notebook | https://github.com/HazyResearch/fonduer | A collection of simple tutorials for using Fonduer | hazyresearch | 2024-01-04 | 2018-03-23 | 305 | 0.320711 | https://avatars.githubusercontent.com/u/2165246?v=4 | A collection of simple tutorials for using Fonduer | [] | [] | 2020-05-27 | [] | 6 | 2 | null | 0 | 0 | 0 | 71 | 44 | 0 | 2 | 2 | 0 | 0 | 90 | 0 | 10 |
530 | ml-dl | https://github.com/benedekrozemberczki/tigerlily | [] | null | [] | [] | null | null | null | benedekrozemberczki/tigerlily | tigerlily | 96 | 9 | 2 | Jupyter Notebook | null | TigerLily: Finding drug interactions in silico with the Graph. | benedekrozemberczki | 2024-01-04 | 2022-02-28 | 100 | 0.958631 | null | TigerLily: Finding drug interactions in silico with the Graph. | ['biology', 'ddi', 'deep-learning', 'drug-drug-interaction', 'embedding', 'gradient-boosting', 'graph', 'graph-database', 'graph-embedding', 'graph-machine-learning', 'heterogeneous-graph', 'knowledge-graph', 'machine-learning', 'network-science', 'node', 'node-embedding', 'pharmaceuticals', 'tigergraph', 'unsupervised-learning'] | ['biology', 'ddi', 'deep-learning', 'drug-drug-interaction', 'embedding', 'gradient-boosting', 'graph', 'graph-database', 'graph-embedding', 'graph-machine-learning', 'heterogeneous-graph', 'knowledge-graph', 'machine-learning', 'network-science', 'node', 'node-embedding', 'pharmaceuticals', 'tigergraph', 'unsupervised-learning'] | 2022-12-17 | [('a-r-j/graphein', 0.6609140634536743, 'sim', 1), ('stellargraph/stellargraph', 0.6446079015731812, 'graph', 3), ('google-deepmind/materials_discovery', 0.5921242237091064, 'sim', 0), ('pyg-team/pytorch_geometric', 0.561957597732544, 'ml-dl', 1), ('danielegrattarola/spektral', 0.5574583411216736, 'ml-dl', 1), ('dmlc/dgl', 0.5503193140029907, 'ml-dl', 1), ('graphistry/pygraphistry', 0.5483189225196838, 'data', 2), ('chandlerbang/awesome-self-supervised-gnn', 0.5460976362228394, 'study', 2), ('accenture/ampligraph', 0.5214887857437134, 'data', 2), ('awslabs/dgl-ke', 0.50465327501297, 'ml', 2), ('h4kor/graph-force', 0.503591001033783, 'graph', 0)] | 1 | 1 | null | 0 | 0 | 0 | 23 | 13 | 0 | 1 | 1 | 0 | 0 | 90 | 0 | 10 |
843 | profiling | https://github.com/kshitij12345/torchnnprofiler | [] | null | [] | [] | null | null | null | kshitij12345/torchnnprofiler | torchnnprofiler | 81 | 4 | 5 | Python | null | Context Manager to profile the forward and backward times of PyTorch's nn.Module | kshitij12345 | 2024-01-10 | 2022-10-22 | 66 | 1.219355 | null | Context Manager to profile the forward and backward times of PyTorch's nn.Module | [] | [] | 2022-11-02 | [('pytorch/ignite', 0.6059595346450806, 'ml-dl', 0), ('pytorch/data', 0.5611929297447205, 'data', 0), ('nvidia/apex', 0.5381832718849182, 'ml-dl', 0), ('intel/intel-extension-for-pytorch', 0.5366755723953247, 'perf', 0), ('skorch-dev/skorch', 0.5358409881591797, 'ml-dl', 0), ('mrdbourke/pytorch-deep-learning', 0.5249190926551819, 'study', 0), ('nvlabs/gcvit', 0.5133840441703796, 'diffusion', 0), ('pytorch/botorch', 0.5020992159843445, 'ml-dl', 0)] | 1 | 0 | null | 0 | 1 | 0 | 15 | 15 | 0 | 0 | 0 | 1 | 1 | 90 | 1 | 10 |
526 | ml | https://github.com/brohrer/cottonwood | [] | null | [] | [] | null | null | null | brohrer/cottonwood | cottonwood | 76 | 13 | 15 | Python | https://end-to-end-machine-learning.teachable.com/p/write-a-neural-network-framework/ | A flexible neural network framework for running experiments and trying ideas. | brohrer | 2024-01-04 | 2019-09-29 | 226 | 0.335859 | null | A flexible neural network framework for running experiments and trying ideas. | [] | [] | 2020-02-02 | [] | 3 | 2 | null | 0 | 0 | 0 | 52 | 48 | 0 | 3 | 3 | 0 | 0 | 90 | 0 | 10 |
910 | ml-ops | https://github.com/anyscale/airflow-provider-ray | [] | null | [] | [] | null | null | null | anyscale/airflow-provider-ray | airflow-provider-ray | 41 | 9 | 13 | Python | null | Ray provider for Apache Airflow | anyscale | 2024-01-05 | 2021-03-05 | 151 | 0.2705 | https://avatars.githubusercontent.com/u/51251046?v=4 | Ray provider for Apache Airflow | [] | [] | 2021-10-03 | [('astronomer/astronomer', 0.5748955011367798, 'ml-ops', 0), ('apache/airflow', 0.521160364151001, 'ml-ops', 0)] | 8 | 4 | null | 0 | 0 | 0 | 35 | 28 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 10 |
332 | util | https://github.com/gondolav/pyfuncol | [] | null | [] | [] | null | null | null | gondolav/pyfuncol | pyfuncol | 32 | 2 | 3 | Python | https://pyfuncol.readthedocs.io/ | Functional collections extension functions for Python | gondolav | 2022-11-16 | 2021-12-16 | 110 | 0.289032 | https://avatars.githubusercontent.com/u/98323830?v=4 | Functional collections extension functions for Python | ['collections', 'extension-functions', 'functional', 'parallel'] | ['collections', 'extension-functions', 'functional', 'parallel'] | 2023-03-26 | [('pytoolz/toolz', 0.6170833706855774, 'util', 0), ('suor/funcy', 0.5316035747528076, 'util', 0), ('evhub/coconut', 0.5076735019683838, 'util', 1)] | 4 | 1 | null | 0.15 | 0 | 0 | 25 | 10 | 0 | 3 | 3 | 0 | 0 | 90 | 0 | 10 |
373 | data | https://github.com/ndrplz/google-drive-downloader | [] | null | [] | [] | null | null | null | ndrplz/google-drive-downloader | google-drive-downloader | 261 | 63 | 13 | Python | null | Minimal class to download shared files from Google Drive. | ndrplz | 2024-01-04 | 2017-12-08 | 320 | 0.814171 | null | Minimal class to download shared files from Google Drive. | [] | [] | 2019-02-09 | [] | 5 | 1 | null | 0 | 0 | 0 | 74 | 60 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 9 |
289 | template | https://github.com/eugeneyan/python-collab-template | [] | null | [] | [] | null | null | null | eugeneyan/python-collab-template | python-collab-template | 136 | 39 | 5 | Python | https://eugeneyan.com/writing/setting-up-python-project-for-automation-and-collaboration/ | 🛠 Python project template with unit tests, code coverage, linting, type checking, Makefile wrapper, and GitHub Actions. | eugeneyan | 2024-01-14 | 2020-06-21 | 188 | 0.722307 | null | 🛠 Python project template with unit tests, code coverage, linting, type checking, Makefile wrapper, and GitHub Actions. | ['coverage', 'github-actions', 'linting', 'makefile', 'type-checking', 'unit-testing'] | ['coverage', 'github-actions', 'linting', 'makefile', 'type-checking', 'unit-testing'] | 2022-07-02 | [('nedbat/coveragepy', 0.6858402490615845, 'testing', 0), ('landscapeio/prospector', 0.6496816873550415, 'util', 1), ('pypa/hatch', 0.613045871257782, 'util', 0), ('pyscaffold/pyscaffold', 0.6021620631217957, 'template', 0), ('psf/black', 0.5849995613098145, 'util', 0), ('martinheinz/python-project-blueprint', 0.5737303495407104, 'template', 0), ('mkdocstrings/griffe', 0.5616798400878906, 'util', 0), ('mitmproxy/pdoc', 0.5599040389060974, 'util', 0), ('sqlalchemy/mako', 0.5552263855934143, 'template', 0), ('python-rope/rope', 0.5535590648651123, 'util', 0), ('facebook/pyre-check', 0.5504177212715149, 'typing', 0), ('wolever/parameterized', 0.5470243692398071, 'testing', 0), ('amaargiru/pyroad', 0.5436649918556213, 'study', 0), ('google/pytype', 0.5415253043174744, 'typing', 0), ('eleutherai/pyfra', 0.5403453707695007, 'ml', 0), ('sourcery-ai/sourcery', 0.5378775596618652, 'util', 0), ('omry/omegaconf', 0.5363547205924988, 'util', 0), ('hhatto/autopep8', 0.5339161157608032, 'util', 0), ('pytest-dev/pytest-testinfra', 0.5332326292991638, 'testing', 0), ('grantjenks/blue', 0.5308860540390015, 'util', 0), ('rubik/radon', 0.5297834873199463, 'util', 0), ('pypa/build', 0.523838460445404, 'util', 0), ('pytoolz/toolz', 0.5230408906936646, 'util', 0), ('tezromach/python-package-template', 0.518621563911438, 'template', 1), ('samuelcolvin/dirty-equals', 0.514961302280426, 'util', 1), ('instagram/fixit', 0.5144226551055908, 'util', 0), ('pdoc3/pdoc', 0.5139718651771545, 'util', 0), ('ionelmc/pytest-benchmark', 0.5136798024177551, 'testing', 0), ('pypa/pipenv', 0.5131263732910156, 'util', 0), ('grahamdumpleton/wrapt', 0.5126464366912842, 'util', 0), ('dosisod/refurb', 0.5078116059303284, 'util', 0), ('pdm-project/pdm', 0.5047013163566589, 'util', 0)] | 2 | 1 | null | 0 | 0 | 0 | 43 | 19 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 9 |
1,379 | llm | https://github.com/ai21labs/lm-evaluation | [] | null | [] | [] | null | null | null | ai21labs/lm-evaluation | lm-evaluation | 122 | 13 | 5 | Python | null | Evaluation suite for large-scale language models. | ai21labs | 2024-01-04 | 2021-08-05 | 129 | 0.940529 | https://avatars.githubusercontent.com/u/33798954?v=4 | Evaluation suite for large-scale language models. | ['evaluation-framework', 'language-model'] | ['evaluation-framework', 'language-model'] | 2021-08-15 | [('eleutherai/lm-evaluation-harness', 0.7471644282341003, 'llm', 2), ('freedomintelligence/llmzoo', 0.7427138090133667, 'llm', 1), ('lm-sys/fastchat', 0.7379257678985596, 'llm', 1), ('openlmlab/leval', 0.7304577231407166, 'llm', 1), ('hannibal046/awesome-llm', 0.725335955619812, 'study', 1), ('juncongmoo/pyllama', 0.699636697769165, 'llm', 0), ('ctlllll/llm-toolmaker', 0.6955636143684387, 'llm', 1), ('lianjiatech/belle', 0.6819735169410706, 'llm', 0), ('openbmb/toolbench', 0.6778345704078674, 'llm', 0), ('cg123/mergekit', 0.6523741483688354, 'llm', 0), ('anthropics/evals', 0.6497257947921753, 'llm', 0), ('bigscience-workshop/biomedical', 0.6412531137466431, 'data', 0), ('confident-ai/deepeval', 0.622775137424469, 'testing', 2), ('jonasgeiping/cramming', 0.6214629411697388, 'nlp', 1), ('togethercomputer/redpajama-data', 0.60612553358078, 'llm', 0), ('guidance-ai/guidance', 0.6006115674972534, 'llm', 1), ('young-geng/easylm', 0.5989466309547424, 'llm', 1), ('fasteval/fasteval', 0.5941077470779419, 'llm', 0), ('huggingface/evaluate', 0.5928609371185303, 'ml', 0), ('next-gpt/next-gpt', 0.5911809802055359, 'llm', 0), ('salesforce/xgen', 0.5776482224464417, 'llm', 1), ('microsoft/autogen', 0.5751771926879883, 'llm', 0), ('srush/minichain', 0.5739299654960632, 'llm', 0), ('oobabooga/text-generation-webui', 0.5664257407188416, 'llm', 1), ('prefecthq/langchain-prefect', 0.5636144280433655, 'llm', 0), ('keirp/automatic_prompt_engineer', 0.5610719919204712, 'llm', 1), ('huggingface/text-generation-inference', 0.5604375004768372, 'llm', 0), ('microsoft/lora', 0.55837482213974, 'llm', 1), ('conceptofmind/toolformer', 0.5530425310134888, 'llm', 1), ('infinitylogesh/mutate', 0.5527203679084778, 'nlp', 1), ('openlmlab/moss', 0.5499148368835449, 'llm', 1), ('eleutherai/the-pile', 0.5482072830200195, 'data', 0), ('bigscience-workshop/megatron-deepspeed', 0.5460460186004639, 'llm', 0), ('microsoft/megatron-deepspeed', 0.5460460186004639, 'llm', 0), ('baichuan-inc/baichuan-13b', 0.5453725457191467, 'llm', 0), ('neulab/prompt2model', 0.5453250408172607, 'llm', 1), ('openai/evals', 0.5421918034553528, 'llm', 1), ('princeton-nlp/alce', 0.5408934950828552, 'llm', 0), ('hiyouga/llama-factory', 0.5385951995849609, 'llm', 1), ('hiyouga/llama-efficient-tuning', 0.5385950803756714, 'llm', 1), ('reasoning-machines/pal', 0.5380016565322876, 'llm', 1), ('facebookresearch/shepherd', 0.5377524495124817, 'llm', 1), ('databrickslabs/dolly', 0.5367728471755981, 'llm', 0), ('yizhongw/self-instruct', 0.5341971516609192, 'llm', 1), ('llmware-ai/llmware', 0.5320602655410767, 'llm', 0), ('sjtu-ipads/powerinfer', 0.5313429236412048, 'llm', 0), ('thudm/chatglm2-6b', 0.5304546356201172, 'llm', 0), ('ai21labs/in-context-ralm', 0.5293609499931335, 'llm', 1), ('jalammar/ecco', 0.5280724167823792, 'ml-interpretability', 0), ('mit-han-lab/streaming-llm', 0.5273094773292542, 'llm', 0), ('optimalscale/lmflow', 0.5266632437705994, 'llm', 1), ('agenta-ai/agenta', 0.5219810009002686, 'llm', 0), ('citadel-ai/langcheck', 0.518677294254303, 'llm', 1), ('tatsu-lab/stanford_alpaca', 0.5184262990951538, 'llm', 1), ('yueyu1030/attrprompt', 0.5179035067558289, 'llm', 0), ('hazyresearch/h3', 0.5161812901496887, 'llm', 0), ('nvlabs/prismer', 0.5158585906028748, 'diffusion', 1), ('thudm/chatglm-6b', 0.5155556201934814, 'llm', 1), ('lupantech/chameleon-llm', 0.5124858617782593, 'llm', 1), ('hpcaitech/energonai', 0.5110083818435669, 'ml', 0), ('cstankonrad/long_llama', 0.5108537077903748, 'llm', 1), ('explosion/spacy-models', 0.5086509585380554, 'nlp', 0), ('night-chen/toolqa', 0.5064146518707275, 'llm', 0), ('lvwerra/trl', 0.5060834288597107, 'llm', 0), ('facebookresearch/seamless_communication', 0.505820631980896, 'nlp', 0), ('1rgs/jsonformer', 0.5037403702735901, 'llm', 0), ('guardrails-ai/guardrails', 0.5022191405296326, 'llm', 0), ('bytedance/lightseq', 0.5000201463699341, 'nlp', 0)] | 3 | 1 | null | 0 | 0 | 0 | 30 | 29 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 9 |
1,398 | diffusion | https://github.com/pollinations/dance-diffusion | ['audio'] | null | [] | [] | null | null | null | pollinations/dance-diffusion | dance-diffusion | 55 | 11 | 2 | Jupyter Notebook | null | Tools to train a generative model on arbitrary audio samples | pollinations | 2023-12-31 | 2022-09-28 | 69 | 0.787321 | https://avatars.githubusercontent.com/u/86964862?v=4 | Tools to train a generative model on arbitrary audio samples | [] | ['audio'] | 2022-09-29 | [('suno-ai/bark', 0.6367303729057312, 'ml', 1), ('facebookresearch/audiocraft', 0.5851640105247498, 'util', 1), ('openai/image-gpt', 0.5518842935562134, 'llm', 0), ('huggingface/diffusers', 0.5509036779403687, 'diffusion', 0)] | 4 | 1 | null | 0 | 0 | 0 | 16 | 16 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 9 |
1,377 | nlp | https://github.com/amazon-science/dq-bart | [] | null | [] | [] | null | null | null | amazon-science/dq-bart | dq-bart | 48 | 11 | 3 | Python | null | DQ-BART: Efficient Sequence-to-Sequence Model via Joint Distillation and Quantization (ACL 2022) | amazon-science | 2023-12-05 | 2022-03-15 | 98 | 0.489796 | https://avatars.githubusercontent.com/u/70298811?v=4 | DQ-BART: Efficient Sequence-to-Sequence Model via Joint Distillation and Quantization (ACL 2022) | [] | [] | 2022-12-27 | [('hazyresearch/safari', 0.5790391564369202, 'ml', 0), ('bytedance/lightseq', 0.564974844455719, 'nlp', 0), ('artidoro/qlora', 0.5504317283630371, 'llm', 0), ('predibase/llm_distillation_playbook', 0.5063695311546326, 'llm', 0)] | 4 | 2 | null | 0 | 0 | 0 | 22 | 13 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 9 |
970 | sim | https://github.com/glpcc/pokerpy | [] | null | [] | [] | null | null | null | glpcc/pokerpy | PokerPy | 43 | 4 | 1 | C++ | null | Texas Hold'em Poker Probability Calculator in Python | glpcc | 2023-07-08 | 2022-12-11 | 59 | 0.725301 | null | Texas Hold'em Poker Probability Calculator in Python | ['cpp', 'fast', 'performance', 'poker', 'pybind11', 'texas-holdem'] | ['cpp', 'fast', 'performance', 'poker', 'pybind11', 'texas-holdem'] | 2023-02-10 | [] | 2 | 1 | null | 0.83 | 0 | 0 | 13 | 11 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 9 |
1,065 | nlp | https://github.com/airi-institute/probing_framework | [] | null | [] | [] | null | null | null | airi-institute/probing_framework | Probing_framework | 22 | 10 | 1 | Python | null | Framework for probing tasks | airi-institute | 2023-12-22 | 2022-01-21 | 105 | 0.20839 | https://avatars.githubusercontent.com/u/92741417?v=4 | Framework for probing tasks | ['multilinguality', 'natural-language-processing', 'probing', 'transformers', 'universal-dependencies'] | ['multilinguality', 'natural-language-processing', 'probing', 'transformers', 'universal-dependencies'] | 2023-08-28 | [('keirp/automatic_prompt_engineer', 0.5204843282699585, 'llm', 0), ('openbmb/toolbench', 0.5175856351852417, 'llm', 0)] | 7 | 0 | null | 1.77 | 8 | 4 | 24 | 5 | 0 | 0 | 0 | 8 | 1 | 90 | 0.1 | 9 |
1,633 | util | https://github.com/multi-py/python-gunicorn-uvicorn | [] | null | [] | [] | null | null | null | multi-py/python-gunicorn-uvicorn | python-gunicorn-uvicorn | 15 | 1 | 1 | Shell | null | Multiarchitecture Docker Containers for Python using Gunicorn and Uvicorn | multi-py | 2023-10-24 | 2021-10-31 | 117 | 0.127893 | https://avatars.githubusercontent.com/u/92491059?v=4 | Multiarchitecture Docker Containers for Python using Gunicorn and Uvicorn | ['alpine', 'amd64', 'arm64', 'armv7', 'docker', 'ghcr', 'gunicorn', 'uvicorn'] | ['alpine', 'amd64', 'arm64', 'armv7', 'docker', 'ghcr', 'gunicorn', 'uvicorn'] | 2023-12-20 | [('multi-py/python-gunicorn', 0.9625324010848999, 'util', 7), ('multi-py/python-uvicorn', 0.9309157729148865, 'util', 6), ('multi-py/python-celery', 0.7121555209159851, 'util', 6), ('backtick-se/cowait', 0.5445891618728638, 'util', 1), ('rawheel/fastapi-boilerplate', 0.5090985894203186, 'web', 1)] | 2 | 1 | null | 0.48 | 0 | 0 | 27 | 1 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 9 |
1,634 | util | https://github.com/multi-py/python-uvicorn | [] | null | [] | [] | null | null | null | multi-py/python-uvicorn | python-uvicorn | 14 | 0 | 1 | Shell | https://blog.tedivm.com | Multiarchitecture Docker Containers for Python and Uvicorn | multi-py | 2023-08-21 | 2021-10-05 | 121 | 0.115702 | https://avatars.githubusercontent.com/u/92491059?v=4 | Multiarchitecture Docker Containers for Python and Uvicorn | ['amd64', 'arm64', 'armv7', 'docker', 'ghcr', 'uvicorn'] | ['amd64', 'arm64', 'armv7', 'docker', 'ghcr', 'uvicorn'] | 2023-12-20 | [('multi-py/python-gunicorn-uvicorn', 0.9309157729148865, 'util', 6), ('multi-py/python-gunicorn', 0.8768712878227234, 'util', 5), ('multi-py/python-celery', 0.7029248476028442, 'util', 5), ('backtick-se/cowait', 0.5087990760803223, 'util', 1)] | 2 | 1 | null | 0.48 | 0 | 0 | 28 | 1 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 9 |
1,660 | data | https://github.com/unstructured-io/pipeline-paddleocr | ['unstructured', 'pdf', 'pipeline', 'ocr'] | null | [] | [] | null | null | null | unstructured-io/pipeline-paddleocr | pipeline-paddleocr | 14 | 5 | 13 | Jupyter Notebook | null | Pipeline for converting PDFs to raw text with PaddleOCR | unstructured-io | 2023-12-25 | 2022-12-08 | 59 | 0.23445 | https://avatars.githubusercontent.com/u/108372208?v=4 | Pipeline for converting PDFs to raw text with PaddleOCR | [] | ['ocr', 'pdf', 'pipeline', 'unstructured'] | 2023-06-29 | [('camelot-dev/camelot', 0.5457990169525146, 'util', 0), ('py-pdf/pypdf2', 0.5316691398620605, 'util', 1), ('pyfpdf/fpdf2', 0.531453549861908, 'util', 1), ('pdfminer/pdfminer.six', 0.5148302316665649, 'util', 1), ('rapidai/rapidocr', 0.5100756287574768, 'data', 1)] | 10 | 2 | null | 0.25 | 0 | 0 | 13 | 7 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 9 |
1,623 | template | https://github.com/lyz-code/cookiecutter-python-project | ['cookiecutter'] | null | [] | [] | null | null | null | lyz-code/cookiecutter-python-project | cookiecutter-python-project | 13 | 1 | 3 | CSS | https://lyz-code.github.io/cookiecutter-python-project | Cookiecutter template to generate python projects following the best practices gathered over the time. | lyz-code | 2024-01-12 | 2020-10-16 | 171 | 0.07577 | null | Cookiecutter template to generate python projects following the best practices gathered over the time. | [] | ['cookiecutter'] | 2023-03-15 | [('tedivm/robs_awesome_python_template', 0.9047797918319702, 'template', 0), ('ionelmc/cookiecutter-pylibrary', 0.8550078272819519, 'template', 1), ('cookiecutter/cookiecutter', 0.8542928099632263, 'template', 1), ('giswqs/pypackage', 0.815499484539032, 'template', 1), ('buuntu/fastapi-react', 0.6677143573760986, 'template', 1), ('cjolowicz/cookiecutter-hypermodern-python', 0.6242104768753052, 'template', 0), ('crmne/cookiecutter-modern-datascience', 0.6067662835121155, 'template', 1), ('tezromach/python-package-template', 0.5960633754730225, 'template', 1), ('psf/requests', 0.5095129013061523, 'web', 0)] | 2 | 1 | null | 0.08 | 0 | 0 | 39 | 10 | 0 | 4 | 4 | 0 | 0 | 90 | 0 | 9 |
912 | ml-ops | https://github.com/anyscale/prefect-anyscale | [] | null | [] | [] | null | null | null | anyscale/prefect-anyscale | prefect-anyscale | 8 | 2 | 3 | Python | null | Prefect integration with Anyscale | anyscale | 2023-10-02 | 2022-11-07 | 64 | 0.124722 | https://avatars.githubusercontent.com/u/51251046?v=4 | Prefect integration with Anyscale | [] | [] | 2023-10-11 | [] | 2 | 1 | null | 0.21 | 0 | 0 | 14 | 3 | 2 | 8 | 2 | 0 | 0 | 90 | 0 | 9 |
1,347 | ml-rl | https://github.com/zacwellmer/worldmodels | ['agent-based-modeling'] | null | [] | [] | null | null | null | zacwellmer/worldmodels | WorldModels | 259 | 29 | 5 | Jupyter Notebook | null | World Models with TensorFlow 2 | zacwellmer | 2024-01-11 | 2020-04-09 | 198 | 1.303379 | null | World Models with TensorFlow 2 | [] | ['agent-based-modeling'] | 2021-06-09 | [('projectmesa/mesa', 0.5980151891708374, 'sim', 1), ('operand/agency', 0.5862823128700256, 'llm', 0), ('tensorflow/mesh', 0.5606265068054199, 'ml-dl', 0), ('rafiqhasan/auto-tensorflow', 0.5352241396903992, 'ml-dl', 0), ('geekan/metagpt', 0.5185655355453491, 'llm', 0), ('aiwaves-cn/agents', 0.5116593241691589, 'nlp', 0), ('unity-technologies/ml-agents', 0.5110092163085938, 'ml-rl', 0)] | 1 | 0 | null | 0 | 0 | 0 | 46 | 32 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 8 |
239 | sim | https://github.com/nv-tlabs/gamegan_code | [] | null | [] | [] | null | null | null | nv-tlabs/gamegan_code | GameGAN_code | 212 | 36 | 11 | Python | null | Learning to Simulate Dynamic Environments with GameGAN (CVPR 2020) | nv-tlabs | 2024-01-09 | 2020-12-11 | 163 | 1.29607 | https://avatars.githubusercontent.com/u/49653101?v=4 | Learning to Simulate Dynamic Environments with GameGAN (CVPR 2020) | [] | [] | 2021-11-11 | [] | 2 | 0 | null | 0 | 0 | 0 | 38 | 26 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 8 |
724 | gis | https://github.com/bowenc0221/boundary-iou-api | [] | null | [] | [] | null | null | null | bowenc0221/boundary-iou-api | boundary-iou-api | 198 | 22 | 8 | Python | null | Boundary IoU API (Beta version) | bowenc0221 | 2024-01-13 | 2021-03-29 | 148 | 1.336548 | null | Boundary IoU API (Beta version) | [] | [] | 2021-04-05 | [] | 2 | 0 | null | 0 | 0 | 0 | 34 | 34 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 8 |
455 | nlp | https://github.com/coastalcph/lex-glue | [] | null | [] | [] | null | null | null | coastalcph/lex-glue | lex-glue | 149 | 29 | 6 | Python | null | LexGLUE: A Benchmark Dataset for Legal Language Understanding in English | coastalcph | 2024-01-07 | 2021-09-27 | 122 | 1.219883 | https://avatars.githubusercontent.com/u/6862219?v=4 | LexGLUE: A Benchmark Dataset for Legal Language Understanding in English | ['benchmark', 'lawtech', 'legal', 'legaltech', 'nlp'] | ['benchmark', 'lawtech', 'legal', 'legaltech', 'nlp'] | 2022-11-04 | [('lexpredict/lexpredict-lexnlp', 0.6552823185920715, 'nlp', 3), ('iclrandd/blackstone', 0.6491376757621765, 'nlp', 2), ('thoppe/the-pile-freelaw', 0.6147865653038025, 'data', 0), ('hazyresearch/legalbench', 0.5063652992248535, 'llm', 2)] | 2 | 0 | null | 0 | 0 | 0 | 28 | 15 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 8 |
461 | data | https://github.com/psycoguana/subredditmediadownloader | [] | null | [] | [] | null | null | null | psycoguana/subredditmediadownloader | SubredditMediaDownloader | 122 | 9 | 3 | Python | null | Simple Python script to download images and videos from public subreddits without using Reddit's API 😎 | psycoguana | 2024-01-10 | 2022-02-18 | 101 | 1.201125 | null | Simple Python script to download images and videos from public subreddits without using Reddit's API 😎 | [] | [] | 2023-01-17 | [('pytube/pytube', 0.5245307087898254, 'util', 0)] | 2 | 0 | null | 0 | 0 | 0 | 23 | 12 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 8 |
885 | sim | https://github.com/crowdbotp/socialways | [] | null | [] | [] | null | null | null | crowdbotp/socialways | socialways | 116 | 45 | 9 | Python | null | Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories with GANs (CVPR 2019) | crowdbotp | 2024-01-04 | 2019-04-23 | 249 | 0.465863 | https://avatars.githubusercontent.com/u/70031889?v=4 | Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories with GANs (CVPR 2019) | ['crowd-simulation', 'gan', 'generative-adversarial-network', 'human-trajectory-prediction', 'info-gan', 'pedestrian', 'pedestrian-trajectories', 'prediction-model', 'self-driving-car', 'social-gan', 'social-navigation', 'social-robots', 'social-ways', 'trajectory-forecasting', 'trajectory-prediction'] | ['crowd-simulation', 'gan', 'generative-adversarial-network', 'human-trajectory-prediction', 'info-gan', 'pedestrian', 'pedestrian-trajectories', 'prediction-model', 'self-driving-car', 'social-gan', 'social-navigation', 'social-robots', 'social-ways', 'trajectory-forecasting', 'trajectory-prediction'] | 2020-03-20 | [] | 3 | 2 | null | 0 | 0 | 0 | 58 | 46 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 8 |
662 | gis | https://github.com/zorzi-s/projectregularization | [] | null | [] | [] | null | null | null | zorzi-s/projectregularization | projectRegularization | 101 | 12 | 2 | Python | null | Regularization of Building Boundaries using Adversarial and Regularized losses | zorzi-s | 2024-01-04 | 2021-05-18 | 141 | 0.716312 | null | Regularization of Building Boundaries using Adversarial and Regularized losses | [] | [] | 2023-09-13 | [('cleverhans-lab/cleverhans', 0.5036975741386414, 'ml', 0)] | 1 | 0 | null | 0.02 | 0 | 0 | 32 | 4 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 8 |
705 | gis | https://github.com/artelys/geonetworkx | [] | null | [] | [] | null | null | null | artelys/geonetworkx | geonetworkx | 31 | 1 | 7 | Python | null | Python tools for geographic graphs | artelys | 2023-07-20 | 2019-10-24 | 222 | 0.139192 | https://avatars.githubusercontent.com/u/17453408?v=4 | Python tools for geographic graphs | [] | [] | 2021-06-28 | [('geopandas/geopandas', 0.7633013725280762, 'gis', 0), ('holoviz/geoviews', 0.663130521774292, 'gis', 0), ('residentmario/geoplot', 0.6383180022239685, 'gis', 0), ('graphistry/pygraphistry', 0.6271733045578003, 'data', 0), ('pysal/pysal', 0.625034749507904, 'gis', 0), ('opengeos/leafmap', 0.6138547658920288, 'gis', 0), ('toblerity/rtree', 0.597081184387207, 'gis', 0), ('westhealth/pyvis', 0.5923200249671936, 'graph', 0), ('pygraphviz/pygraphviz', 0.5921590328216553, 'viz', 0), ('raphaelquast/eomaps', 0.585459291934967, 'gis', 0), ('h4kor/graph-force', 0.5840879678726196, 'graph', 0), ('plotly/plotly.py', 0.5817664861679077, 'viz', 0), ('earthlab/earthpy', 0.5808680057525635, 'gis', 0), ('networkx/networkx', 0.5718642473220825, 'graph', 0), ('pyproj4/pyproj', 0.5713775753974915, 'gis', 0), ('has2k1/plotnine', 0.569690465927124, 'viz', 0), ('gregorhd/mapcompare', 0.5692752599716187, 'gis', 0), ('altair-viz/altair', 0.5582142472267151, 'viz', 0), ('scitools/iris', 0.5512532591819763, 'gis', 0), ('scitools/cartopy', 0.5421754121780396, 'gis', 0), ('holoviz/holoviz', 0.5396808385848999, 'viz', 0), ('makepath/xarray-spatial', 0.5338029265403748, 'gis', 0), ('dfki-ric/pytransform3d', 0.5281858444213867, 'math', 0), ('mwaskom/seaborn', 0.5267704129219055, 'viz', 0), ('scikit-geometry/scikit-geometry', 0.5264812707901001, 'gis', 0), ('openeventdata/mordecai', 0.5222339630126953, 'gis', 0), ('holoviz/hvplot', 0.5222033858299255, 'pandas', 0), ('contextlab/hypertools', 0.5169349908828735, 'ml', 0), ('federicoceratto/dashing', 0.5115811228752136, 'term', 0), ('enthought/mayavi', 0.5101572275161743, 'viz', 0), ('kuanb/peartree', 0.5083203911781311, 'gis', 0), ('matplotlib/matplotlib', 0.5047987103462219, 'viz', 0), ('scikit-mobility/scikit-mobility', 0.5012951493263245, 'gis', 0), ('cloudsen12/easystac', 0.5005823373794556, 'gis', 0)] | 6 | 1 | null | 0 | 0 | 0 | 51 | 31 | 0 | 2 | 2 | 0 | 0 | 90 | 0 | 8 |
682 | ml-dl | https://github.com/jerryyli/valhalla-nmt | [] | null | [] | [] | null | null | null | jerryyli/valhalla-nmt | valhalla-nmt | 25 | 4 | 1 | Python | null | Code repository for CVPR 2022 paper "VALHALLA: Visual Hallucination for Machine Translation" | jerryyli | 2024-01-04 | 2022-03-22 | 97 | 0.257732 | null | Code repository for CVPR 2022 paper "VALHALLA: Visual Hallucination for Machine Translation" | ['computer-vision', 'machine-translation', 'multimodal-learning', 'natural-language-processing'] | ['computer-vision', 'machine-translation', 'multimodal-learning', 'natural-language-processing'] | 2023-02-19 | [('salesforce/blip', 0.5753607749938965, 'diffusion', 0), ('nvlabs/prismer', 0.5498813390731812, 'diffusion', 0), ('openai/image-gpt', 0.5397126078605652, 'llm', 0)] | 3 | 1 | null | 0.02 | 0 | 0 | 22 | 11 | 0 | 1 | 1 | 0 | 0 | 90 | 0 | 8 |
37 | math | https://github.com/jszymon/pacal | [] | null | [] | [] | null | null | null | jszymon/pacal | pacal | 22 | 8 | 7 | Python | null | PaCAL - ProbAbilistic CALculator | jszymon | 2023-07-14 | 2014-08-04 | 495 | 0.044432 | null | PaCAL - ProbAbilistic CALculator | [] | [] | 2022-11-02 | [] | 8 | 1 | null | 0 | 0 | 0 | 115 | 15 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 8 |
1,163 | llm | https://github.com/qanastek/drbert | [] | null | [] | [] | null | null | null | qanastek/drbert | DrBERT | 13 | 1 | 1 | Python | https://drbert.univ-avignon.fr/ | DrBERT: A Robust Pre-trained Model in French for Biomedical and Clinical domains | qanastek | 2023-11-09 | 2023-01-05 | 55 | 0.233333 | null | DrBERT: A Robust Pre-trained Model in French for Biomedical and Clinical domains | ['bert', 'biomedical', 'french', 'learning', 'machine', 'machine-learning', 'medical', 'ml', 'nlp', 'nlp-machine-learning', 'taln', 'text'] | ['bert', 'biomedical', 'french', 'learning', 'machine', 'machine-learning', 'medical', 'ml', 'nlp', 'nlp-machine-learning', 'taln', 'text'] | 2023-11-13 | [('bigscience-workshop/biomedical', 0.6520806550979614, 'data', 0), ('jonasgeiping/cramming', 0.5827434659004211, 'nlp', 1), ('microsoft/unilm', 0.5781573057174683, 'nlp', 1), ('epfllm/meditron', 0.5632989406585693, 'llm', 1), ('deepset-ai/farm', 0.5561661720275879, 'nlp', 2), ('extreme-bert/extreme-bert', 0.5513941645622253, 'llm', 3), ('explosion/spacy-models', 0.5431317090988159, 'nlp', 2), ('openai/finetune-transformer-lm', 0.5415375828742981, 'llm', 0), ('alibaba/easynlp', 0.5349388718605042, 'nlp', 3), ('thudm/glm-130b', 0.5260924696922302, 'llm', 0), ('google-research/electra', 0.5233448147773743, 'ml-dl', 1), ('maartengr/bertopic', 0.51610267162323, 'nlp', 3), ('explosion/spacy-transformers', 0.5139819979667664, 'llm', 3), ('jina-ai/finetuner', 0.5117612481117249, 'ml', 1), ('llmware-ai/llmware', 0.5081828832626343, 'llm', 3), ('flairnlp/flair', 0.5062006711959839, 'nlp', 2), ('explosion/spacy-llm', 0.5015144348144531, 'llm', 2)] | 2 | 1 | null | 0.37 | 0 | 0 | 12 | 2 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 8 |
1,504 | util | https://github.com/evdcush/fart | [] | Fart is focused on making text banners for use in code documentation. | [] | [] | null | null | null | evdcush/fart | fart | 10 | 0 | 2 | Python | null | fart on your code | evdcush | 2024-01-03 | 2020-03-23 | 201 | 0.049716 | null | fart on your code | ['art', 'ascii-art', 'figlet', 'figlet-fonts', 'smells-good'] | ['art', 'ascii-art', 'figlet', 'figlet-fonts', 'smells-good'] | 2024-01-03 | [] | 1 | 0 | null | 0.12 | 0 | 0 | 46 | 0 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 8 |
181 | sim | https://github.com/artemyk/dynpy | [] | null | [] | [] | null | null | null | artemyk/dynpy | dynpy | 7 | 5 | 3 | Python | https://dynpy.readthedocs.io/ | Dynamical systems for Python | artemyk | 2023-08-29 | 2014-09-12 | 489 | 0.014298 | null | Dynamical systems for Python | [] | [] | 2023-11-29 | [('pytoolz/toolz', 0.6178411841392517, 'util', 0), ('sympy/sympy', 0.6068984270095825, 'math', 0), ('shangtongzhang/reinforcement-learning-an-introduction', 0.583624005317688, 'study', 0), ('gboeing/pynamical', 0.5693354606628418, 'sim', 0), ('pyston/pyston', 0.5571711659431458, 'util', 0), ('pytransitions/transitions', 0.55495285987854, 'util', 0), ('wilsonrljr/sysidentpy', 0.5530008673667908, 'time-series', 0), ('projectmesa/mesa', 0.5512898564338684, 'sim', 0), ('micropython/micropython', 0.5470327734947205, 'util', 0), ('python/cpython', 0.5442706346511841, 'util', 0), ('pytorch/pytorch', 0.5410839319229126, 'ml-dl', 0), ('eleutherai/pyfra', 0.538719654083252, 'ml', 0), ('crflynn/stochastic', 0.5386329889297485, 'sim', 0), ('google/jax', 0.5380663871765137, 'ml', 0), ('pypy/pypy', 0.5257759690284729, 'util', 0), ('pymc-devs/pymc3', 0.5243752002716064, 'ml', 0), ('infer-actively/pymdp', 0.5208140015602112, 'ml', 0), ('google/pyglove', 0.5151891708374023, 'util', 0), ('hgrecco/pint', 0.5099499225616455, 'util', 0), ('allrod5/injectable', 0.5095717906951904, 'util', 0), ('firmai/atspy', 0.5067782998085022, 'time-series', 0), ('pyomo/pyomo', 0.5054152011871338, 'math', 0), ('agronholm/apscheduler', 0.5037860870361328, 'util', 0), ('ljvmiranda921/seagull', 0.5030413269996643, 'sim', 0)] | 5 | 0 | null | 0.21 | 0 | 0 | 114 | 2 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 8 |
1,637 | util | https://github.com/multi-py/python-gunicorn | [] | null | [] | [] | null | null | null | multi-py/python-gunicorn | python-gunicorn | 4 | 0 | 1 | Shell | https://blog.tedivm.com | Multiarchitecture Docker Containers for Python and Gunicorn | multi-py | 2022-07-08 | 2021-10-30 | 117 | 0.034063 | https://avatars.githubusercontent.com/u/92491059?v=4 | Multiarchitecture Docker Containers for Python and Gunicorn | ['alpine', 'amd64', 'arm64', 'armv7', 'docker', 'ghcr', 'gunicorn'] | ['alpine', 'amd64', 'arm64', 'armv7', 'docker', 'ghcr', 'gunicorn'] | 2023-11-18 | [('multi-py/python-gunicorn-uvicorn', 0.9625324010848999, 'util', 7), ('multi-py/python-uvicorn', 0.8768712878227234, 'util', 5), ('multi-py/python-celery', 0.7504483461380005, 'util', 6), ('backtick-se/cowait', 0.5146100521087646, 'util', 1), ('rawheel/fastapi-boilerplate', 0.5125967264175415, 'web', 1), ('darribas/gds_env', 0.5109146237373352, 'gis', 1)] | 2 | 1 | null | 0.27 | 0 | 0 | 27 | 2 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 8 |
304 | util | https://github.com/xrudelis/pytrait | [] | null | [] | [] | null | null | null | xrudelis/pytrait | pytrait | 133 | 3 | 5 | Python | null | Traits for Python3 | xrudelis | 2024-01-08 | 2021-11-21 | 114 | 1.16375 | null | Traits for Python3 | [] | [] | 2021-11-27 | [('pytoolz/toolz', 0.5742577314376831, 'util', 0), ('facebook/pyre-check', 0.5590521693229675, 'typing', 0), ('landscapeio/prospector', 0.5579221248626709, 'util', 0), ('instagram/monkeytype', 0.5478743314743042, 'typing', 0), ('google/pytype', 0.5462278127670288, 'typing', 0), ('eleutherai/pyfra', 0.5457850098609924, 'ml', 0), ('python/cpython', 0.5432665944099426, 'util', 0), ('pyston/pyston', 0.5349142551422119, 'util', 0), ('pyutils/line_profiler', 0.5251006484031677, 'profiling', 0), ('pypy/pypy', 0.517668604850769, 'util', 0), ('python-rope/rope', 0.5170307159423828, 'util', 0), ('faif/python-patterns', 0.516368567943573, 'util', 0), ('pythonspeed/filprofiler', 0.5162791013717651, 'profiling', 0), ('rasbt/mlxtend', 0.5159803628921509, 'ml', 0), ('sumerc/yappi', 0.5159697532653809, 'profiling', 0), ('marshmallow-code/marshmallow', 0.5146151185035706, 'util', 0), ('sqlalchemy/mako', 0.505092203617096, 'template', 0), ('brandon-rhodes/python-patterns', 0.5037622451782227, 'util', 0), ('norvig/pytudes', 0.5027519464492798, 'util', 0), ('python-attrs/attrs', 0.5021693110466003, 'typing', 0), ('pympler/pympler', 0.5008642077445984, 'perf', 0)] | 2 | 0 | null | 0 | 0 | 0 | 26 | 26 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 7 |
202 | crypto | https://github.com/nerolation/ethereum-datafarm | [] | null | [] | [] | null | null | null | nerolation/ethereum-datafarm | ethereum-datafarm | 58 | 11 | 2 | Python | null | Scrap blockchain data from the public API of Etherscan.io | nerolation | 2024-01-01 | 2021-03-13 | 150 | 0.385565 | null | Scrap blockchain data from the public API of Etherscan.io | [] | [] | 2023-02-25 | [] | 1 | 1 | null | 0.02 | 0 | 0 | 35 | 11 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 7 |
385 | nlp | https://github.com/ferdinandzhong/punctuator | [] | null | [] | [] | null | null | null | ferdinandzhong/punctuator | punctuator | 47 | 7 | 3 | Python | null | A small seq2seq punctuator tool based on DistilBERT | ferdinandzhong | 2024-01-12 | 2020-11-19 | 166 | 0.281919 | null | A small seq2seq punctuator tool based on DistilBERT | ['bert', 'bert-ner', 'chinese-nlp', 'deep-learning', 'nlp', 'punctuation', 'pytorch', 'seq2seq'] | ['bert', 'bert-ner', 'chinese-nlp', 'deep-learning', 'nlp', 'punctuation', 'pytorch', 'seq2seq'] | 2022-09-28 | [('bytedance/lightseq', 0.5860391855239868, 'nlp', 1), ('allenai/allennlp', 0.5085445046424866, 'nlp', 3), ('cqcl/lambeq', 0.5024779438972473, 'nlp', 0)] | 4 | 0 | null | 0 | 0 | 0 | 38 | 16 | 0 | 2 | 2 | 0 | 0 | 90 | 0 | 7 |
886 | sim | https://github.com/crowddynamics/crowddynamics | [] | null | [] | [] | null | null | null | crowddynamics/crowddynamics | crowddynamics | 32 | 9 | 9 | Python | https://jaantollander.com/post/how-to-implement-continuous-time-multi-agent-crowd-simulation/ | Continuous-time multi-agent crowd simulation engine implemented in Python using Numba and Numpy for performance. | crowddynamics | 2023-12-07 | 2016-03-22 | 410 | 0.078049 | https://avatars.githubusercontent.com/u/80580011?v=4 | Continuous-time multi-agent crowd simulation engine implemented in Python using Numba and Numpy for performance. | ['continuous-time', 'crowd-dynamics', 'crowd-simulation', 'multi-agent'] | ['continuous-time', 'crowd-dynamics', 'crowd-simulation', 'multi-agent'] | 2020-01-02 | [('google-deepmind/concordia', 0.5502240657806396, 'sim', 1), ('projectmesa/mesa', 0.5487060546875, 'sim', 0), ('bilhim/trafficsimulator', 0.5411049723625183, 'sim', 0)] | 7 | 2 | null | 0 | 0 | 0 | 95 | 49 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 7 |
500 | gis | https://github.com/gregorhd/mapcompare | [] | null | [] | [] | null | null | null | gregorhd/mapcompare | mapcompare | 30 | 0 | 2 | Python | null | Comparison of Python packages and libraries for visualising geospatial vector data: applications for Smarter Cities. | gregorhd | 2023-11-13 | 2021-05-21 | 140 | 0.213415 | null | Comparison of Python packages and libraries for visualising geospatial vector data: applications for Smarter Cities. | ['comparison', 'data-visualisation', 'data-viz', 'interactive-visualisations', 'sample-visualisation', 'urban-data-science', 'visualisation-libraries'] | ['comparison', 'data-visualisation', 'data-viz', 'interactive-visualisations', 'sample-visualisation', 'urban-data-science', 'visualisation-libraries'] | 2022-12-03 | [('residentmario/geoplot', 0.7236021161079407, 'gis', 0), ('earthlab/earthpy', 0.6392421722412109, 'gis', 0), ('raphaelquast/eomaps', 0.625028669834137, 'gis', 0), ('holoviz/geoviews', 0.624692440032959, 'gis', 0), ('giswqs/geemap', 0.6100561022758484, 'gis', 0), ('opengeos/leafmap', 0.6006041169166565, 'gis', 0), ('scitools/iris', 0.6005646586418152, 'gis', 0), ('altair-viz/altair', 0.5779780149459839, 'viz', 0), ('mwaskom/seaborn', 0.573523998260498, 'viz', 0), ('artelys/geonetworkx', 0.5692752599716187, 'gis', 0), ('contextlab/hypertools', 0.565951406955719, 'ml', 0), ('holoviz/holoviz', 0.5640184879302979, 'viz', 0), ('geopandas/geopandas', 0.5594103932380676, 'gis', 0), ('marceloprates/prettymaps', 0.557414710521698, 'viz', 0), ('mcordts/cityscapesscripts', 0.5555253624916077, 'gis', 0), ('udst/urbansim', 0.5511513352394104, 'sim', 0), ('man-group/dtale', 0.5462871789932251, 'viz', 0), ('spatialucr/geosnap', 0.54576176404953, 'gis', 0), ('pyqtgraph/pyqtgraph', 0.5434110760688782, 'viz', 0), ('enthought/mayavi', 0.5386293530464172, 'viz', 0), ('scitools/cartopy', 0.5211345553398132, 'gis', 0), ('makepath/xarray-spatial', 0.5138509273529053, 'gis', 0), ('toblerity/rtree', 0.5135495662689209, 'gis', 0), ('bokeh/bokeh', 0.5074924826622009, 'viz', 1), ('mckinsey/vizro', 0.506889283657074, 'viz', 0), ('matplotlib/matplotlib', 0.5066385269165039, 'viz', 0)] | 1 | 1 | null | 0 | 0 | 0 | 32 | 14 | 0 | 1 | 1 | 0 | 0 | 90 | 0 | 7 |
1,264 | util | https://github.com/weaviate/demo-text2vec-openai | [] | null | [] | [] | null | null | null | weaviate/demo-text2vec-openai | DEMO-text2vec-openai | 29 | 5 | 4 | Python | null | This repository contains an example of how to use the Weaviate vector search engine's text2vec-openai module | weaviate | 2023-09-07 | 2022-01-26 | 104 | 0.276567 | https://avatars.githubusercontent.com/u/37794290?v=4 | This repository contains an example of how to use the Weaviate vector search engine's text2vec-openai module | ['gpt-3', 'openai', 'vector-search', 'weaviate'] | ['gpt-3', 'openai', 'vector-search', 'weaviate'] | 2022-03-17 | [('minimaxir/gpt-2-simple', 0.555767834186554, 'llm', 1), ('weaviate/semantic-search-through-wikipedia-with-weaviate', 0.55460125207901, 'data', 1), ('marqo-ai/marqo', 0.5455718040466309, 'ml', 1), ('muennighoff/sgpt', 0.5382280945777893, 'llm', 0), ('weaviate/weaviate-python-client', 0.5271196365356445, 'util', 2), ('kagisearch/vectordb', 0.5153085589408875, 'data', 0), ('qdrant/vector-db-benchmark', 0.5073652267456055, 'perf', 1), ('chroma-core/chroma', 0.5036654472351074, 'data', 0)] | 3 | 2 | null | 0 | 0 | 0 | 24 | 22 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 7 |
1,636 | util | https://github.com/multi-py/python-celery | [] | null | [] | [] | null | null | null | multi-py/python-celery | python-celery | 5 | 1 | 1 | Shell | https://blog.tedivm.com/ | Multiarchitecture Docker Containers for Celery | multi-py | 2023-07-30 | 2022-03-24 | 96 | 0.051699 | https://avatars.githubusercontent.com/u/92491059?v=4 | Multiarchitecture Docker Containers for Celery | ['alpine', 'alpine-linux', 'amd64', 'arm64', 'armv7', 'celery', 'celerybeat', 'docker', 'ghcr'] | ['alpine', 'alpine-linux', 'amd64', 'arm64', 'armv7', 'celery', 'celerybeat', 'docker', 'ghcr'] | 2023-11-22 | [('multi-py/python-gunicorn', 0.7504483461380005, 'util', 6), ('multi-py/python-gunicorn-uvicorn', 0.7121555209159851, 'util', 6), ('multi-py/python-uvicorn', 0.7029248476028442, 'util', 5)] | 1 | 1 | null | 0.35 | 0 | 0 | 22 | 2 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 7 |
1,098 | ml-interpretability | https://github.com/eleutherai/knowledge-neurons | [] | null | [] | [] | null | null | null | eleutherai/knowledge-neurons | knowledge-neurons | 126 | 18 | 4 | Python | null | A library for finding knowledge neurons in pretrained transformer models. | eleutherai | 2024-01-04 | 2021-07-28 | 130 | 0.962882 | https://avatars.githubusercontent.com/u/68924597?v=4 | A library for finding knowledge neurons in pretrained transformer models. | ['interpretability', 'transformers'] | ['interpretability', 'transformers'] | 2021-08-11 | [('alignmentresearch/tuned-lens', 0.6871256828308105, 'ml-interpretability', 1), ('cdpierse/transformers-interpret', 0.6161298155784607, 'ml-interpretability', 2), ('huggingface/transformers', 0.6015819907188416, 'nlp', 0), ('nvidia/megatron-lm', 0.5722100138664246, 'llm', 0), ('lvwerra/trl', 0.5679098963737488, 'llm', 0), ('bigscience-workshop/megatron-deepspeed', 0.548306405544281, 'llm', 0), ('microsoft/megatron-deepspeed', 0.548306405544281, 'llm', 0), ('karpathy/mingpt', 0.5461640954017639, 'llm', 0), ('ist-daslab/gptq', 0.53592449426651, 'llm', 0), ('huggingface/optimum', 0.5159322619438171, 'ml', 1), ('apple/ml-ane-transformers', 0.5135902762413025, 'ml', 0), ('thilinarajapakse/simpletransformers', 0.5122392177581787, 'nlp', 1)] | 1 | 0 | null | 0 | 0 | 0 | 30 | 30 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 6 |
564 | sim | https://github.com/gboeing/street-network-models | [] | null | [] | [] | null | null | null | gboeing/street-network-models | street-network-models | 72 | 9 | 4 | Python | https://osf.io/f2dqc | Street network models and indicators for every urban area in the world | gboeing | 2023-12-10 | 2020-04-13 | 198 | 0.363374 | null | Street network models and indicators for every urban area in the world | [] | [] | 2021-03-05 | [('pysal/momepy', 0.5618022680282593, 'gis', 0), ('gboeing/osmnx', 0.5225948691368103, 'gis', 0), ('udst/urbansim', 0.5066875219345093, 'sim', 0)] | 1 | 1 | null | 0 | 0 | 0 | 46 | 35 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 6 |
925 | ml | https://github.com/pgniewko/forward_forward_vhts | [] | null | [] | [] | null | null | null | pgniewko/forward_forward_vhts | forward_forward_vhts | 33 | 5 | 1 | Jupyter Notebook | null | The Forward-Forward Algorithm for Drug Discovery | pgniewko | 2024-01-12 | 2022-12-29 | 56 | 0.581864 | null | The Forward-Forward Algorithm for Drug Discovery | [] | [] | 2022-12-30 | [] | 1 | 1 | null | 0 | 0 | 0 | 13 | 13 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 6 |
1,033 | math | https://github.com/mimecorg/fraqtive | [] | null | [] | [] | null | null | null | mimecorg/fraqtive | fraqtive | 30 | 7 | 4 | C++ | https://fraqtive.mimec.org/ | Generator of the Mandelbrot family fractals. | mimecorg | 2023-10-22 | 2018-03-09 | 307 | 0.097538 | null | Generator of the Mandelbrot family fractals. | ['fractal', 'mandelbrot', 'qt'] | ['fractal', 'mandelbrot', 'qt'] | 2023-03-06 | [] | 2 | 0 | null | 0.02 | 0 | 0 | 71 | 10 | 1 | 2 | 1 | 0 | 0 | 90 | 0 | 6 |
1,095 | data | https://github.com/thoppe/the-pile-freelaw | [] | null | [] | [] | null | null | null | thoppe/the-pile-freelaw | The-Pile-FreeLaw | 5 | 3 | 3 | Python | null | Download, parse, and filter data from Court Listener, part of the FreeLaw projects. Data-ready for The-Pile. | thoppe | 2024-01-13 | 2020-09-11 | 176 | 0.028317 | null | Download, parse, and filter data from Court Listener, part of the FreeLaw projects. Data-ready for The-Pile. | ['datasets'] | ['datasets'] | 2023-06-03 | [('coastalcph/lex-glue', 0.6147865653038025, 'nlp', 0), ('iclrandd/blackstone', 0.585483193397522, 'nlp', 0)] | 2 | 1 | null | 0 | 0 | 0 | 41 | 8 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 6 |
148 | graph | https://github.com/guyallard/markov_clustering | [] | null | [] | [] | null | null | null | guyallard/markov_clustering | markov_clustering | 156 | 36 | 9 | Python | null | markov clustering in python | guyallard | 2024-01-05 | 2017-09-27 | 330 | 0.471503 | null | markov clustering in python | ['clustering', 'markov-clustering', 'networks'] | ['clustering', 'markov-clustering', 'networks'] | 2018-12-11 | [('pymc-devs/pymc3', 0.5610766410827637, 'ml', 0), ('infer-actively/pymdp', 0.5361882448196411, 'ml', 0), ('scikit-learn/scikit-learn', 0.5356784462928772, 'ml', 0), ('scikit-learn-contrib/metric-learn', 0.5289827585220337, 'ml', 0), ('rasbt/mlxtend', 0.5017951130867004, 'ml', 0)] | 3 | 0 | null | 0 | 0 | 0 | 77 | 62 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 5 |
1,833 | data | https://github.com/koaning/scikit-partial | ['sklearn', 'online-learning'] | Offers a pipeline that can run partial_fit. This allows of online learning on an entire pipeline. | [] | [] | null | null | null | koaning/scikit-partial | scikit-partial | 35 | 1 | 2 | Python | null | Pipeline components that support partial_fit. | koaning | 2024-01-04 | 2022-05-16 | 89 | 0.392628 | null | Pipeline components that support partial_fit. | [] | ['online-learning', 'sklearn'] | 2022-05-16 | [('koaning/scikit-lego', 0.6257511377334595, 'ml', 0), ('orchest/orchest', 0.5157948732376099, 'ml-ops', 0), ('linealabs/lineapy', 0.5030436515808105, 'jupyter', 0)] | 1 | 1 | null | 0 | 0 | 0 | 20 | 20 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 5 |
1,252 | sim | https://github.com/causalsim/unbiased-trace-driven-simulation | [] | null | [] | [] | null | null | null | causalsim/unbiased-trace-driven-simulation | Unbiased-Trace-Driven-Simulation | 31 | 6 | 0 | Python | null | null | causalsim | 2023-11-28 | 2022-09-19 | 71 | 0.435743 | https://avatars.githubusercontent.com/u/113940717?v=4 | causalsim/Unbiased-Trace-Driven-Simulation | [] | [] | 2023-04-15 | [] | 2 | 0 | null | 0.12 | 0 | 0 | 16 | 9 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 5 |
1,463 | finance | https://github.com/mega-barrel/yfin-etl | [] | null | [] | [] | null | null | null | mega-barrel/yfin-etl | yfin-etl | 6 | 2 | 1 | Python | null | Yahoo Finance ETL script | mega-barrel | 2023-12-10 | 2023-06-27 | 31 | 0.193548 | null | Yahoo Finance ETL script | ['pytest', 'sqlite3', 'yahoo-finance'] | ['pytest', 'sqlite3', 'yahoo-finance'] | 2023-07-21 | [('ranaroussi/yfinance', 0.535876452922821, 'finance', 1)] | 1 | 1 | null | 0.96 | 0 | 0 | 7 | 6 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 5 |
744 | gis | https://github.com/edomel/boundaryvt | [] | null | [] | [] | null | null | null | edomel/boundaryvt | BoundaryVT | 1 | 0 | 2 | Python | null | null | edomel | 2022-12-19 | 2022-07-29 | 78 | 0.012727 | null | edomel/BoundaryVT | [] | [] | 2023-02-24 | [] | 4 | 1 | null | 0.08 | 0 | 0 | 18 | 11 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 5 |
1,547 | perf | https://github.com/baruchel/tco | ['optimization', 'perf'] | null | [] | [] | null | null | null | baruchel/tco | tco | 223 | 3 | 19 | Python | null | Tail Call Optimization for Python | baruchel | 2023-12-01 | 2015-07-12 | 446 | 0.49968 | null | Tail Call Optimization for Python | [] | ['optimization', 'perf'] | 2016-10-12 | [('hyperopt/hyperopt', 0.5423157215118408, 'ml', 0), ('joblib/joblib', 0.5076671838760376, 'util', 0)] | 1 | 0 | null | 0 | 0 | 0 | 104 | 88 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 4 |