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1,829 | jupyter | https://github.com/koaning/drawdata | [] | null | [] | [] | null | null | null | koaning/drawdata | drawdata | 579 | 72 | 8 | Python | https://calmcode.io/labs/drawdata.html | Draw datasets from within Jupyter. | koaning | 2024-01-10 | 2021-04-04 | 147 | 3.931135 | null | Draw datasets from within Jupyter. | ['data', 'drawdata', 'jupyter'] | ['data', 'drawdata', 'jupyter'] | 2022-07-24 | [('jupyterlab/jupyterlab', 0.569868266582489, 'jupyter', 1), ('vizzuhq/ipyvizzu', 0.5688337683677673, 'jupyter', 1), ('jakevdp/pythondatasciencehandbook', 0.5549478530883789, 'study', 0), ('jupyter/notebook', 0.5532419085502625, 'jupyter', 1), ('maartenbreddels/ipyvolume', 0.5521408915519714, 'jupyter', 1), ('bloomberg/ipydatagrid', 0.551867663860321, 'jupyter', 0), ('jupyter/nbformat', 0.5362401008605957, 'jupyter', 0), ('jupyter-widgets/ipywidgets', 0.5359898805618286, 'jupyter', 0), ('ipython/ipyparallel', 0.5354899168014526, 'perf', 1), ('quantopian/qgrid', 0.5323299765586853, 'jupyter', 0), ('jazzband/tablib', 0.5231077075004578, 'data', 0), ('cmudig/autoprofiler', 0.5204150080680847, 'jupyter', 1), ('ipython/ipykernel', 0.5152633190155029, 'util', 1), ('man-group/dtale', 0.5000766515731812, 'viz', 0)] | 3 | 2 | null | 0 | 0 | 0 | 34 | 18 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 19 |
328 | ml-dl | https://github.com/facebookresearch/ppuda | [] | null | [] | [] | null | null | null | facebookresearch/ppuda | ppuda | 479 | 60 | 20 | Python | null | Code for Parameter Prediction for Unseen Deep Architectures (NeurIPS 2021) | facebookresearch | 2024-01-12 | 2021-10-21 | 118 | 4.034898 | https://avatars.githubusercontent.com/u/16943930?v=4 | Code for Parameter Prediction for Unseen Deep Architectures (NeurIPS 2021) | [] | [] | 2023-07-11 | [('rasbt/deeplearning-models', 0.5405725240707397, 'ml-dl', 0), ('neuralmagic/deepsparse', 0.5350483059883118, 'nlp', 0), ('calculatedcontent/weightwatcher', 0.5242863297462463, 'ml-dl', 0)] | 3 | 1 | null | 0.08 | 0 | 0 | 27 | 6 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 19 |
1,018 | nlp | https://github.com/prithivirajdamodaran/styleformer | [] | null | [] | [] | null | null | null | prithivirajdamodaran/styleformer | Styleformer | 462 | 64 | 17 | Python | null | A Neural Language Style Transfer framework to transfer natural language text smoothly between fine-grained language styles like formal/casual, active/passive, and many more. Created by Prithiviraj Damodaran. Open to pull requests and other forms of collaboration. | prithivirajdamodaran | 2024-01-10 | 2021-06-12 | 137 | 3.361746 | null | A Neural Language Style Transfer framework to transfer natural language text smoothly between fine-grained language styles like formal/casual, active/passive, and many more. Created by Prithiviraj Damodaran. Open to pull requests and other forms of collaboration. | ['active', 'formal-languages', 'informal-sentences', 'nlp', 'passive', 'slang', 'style-transfer', 'text-style', 'text-style-transfer', 'text-style-transfer-benchmark'] | ['active', 'formal-languages', 'informal-sentences', 'nlp', 'passive', 'slang', 'style-transfer', 'text-style', 'text-style-transfer', 'text-style-transfer-benchmark'] | 2022-12-27 | [('deepset-ai/farm', 0.5097511410713196, 'nlp', 1), ('alibaba/easynlp', 0.504541277885437, 'nlp', 1), ('yueyu1030/attrprompt', 0.5027515292167664, 'llm', 0)] | 4 | 1 | null | 0 | 2 | 0 | 32 | 13 | 0 | 1 | 1 | 2 | 0 | 90 | 0 | 19 |
922 | ml | https://github.com/google-research/maxvit | [] | null | [] | [] | null | null | null | google-research/maxvit | maxvit | 403 | 26 | 9 | Jupyter Notebook | null | [ECCV 2022] Official repository for "MaxViT: Multi-Axis Vision Transformer". SOTA foundation models for classification, detection, segmentation, image quality, and generative modeling... | google-research | 2024-01-14 | 2022-07-07 | 81 | 4.931818 | https://avatars.githubusercontent.com/u/43830688?v=4 | [ECCV 2022] Official repository for "MaxViT: Multi-Axis Vision Transformer". SOTA foundation models for classification, detection, segmentation, image quality, and generative modeling... | ['architecture', 'classification', 'cnn', 'computer-vision', 'image', 'image-processing', 'mlp', 'object-detection', 'resnet', 'segmentation', 'transformer', 'transformer-architecture', 'vision-transformer'] | ['architecture', 'classification', 'cnn', 'computer-vision', 'image', 'image-processing', 'mlp', 'object-detection', 'resnet', 'segmentation', 'transformer', 'transformer-architecture', 'vision-transformer'] | 2023-06-02 | [('lucidrains/vit-pytorch', 0.6311172246932983, 'ml-dl', 1), ('deci-ai/super-gradients', 0.6301395893096924, 'ml-dl', 2), ('nvlabs/gcvit', 0.6166060566902161, 'diffusion', 2), ('roboflow/notebooks', 0.5931783318519592, 'study', 2), ('microsoft/swin-transformer', 0.5897934436798096, 'ml', 1), ('rwightman/pytorch-image-models', 0.575612485408783, 'ml-dl', 1), ('facebookresearch/vissl', 0.5404923558235168, 'ml', 0), ('roboflow/supervision', 0.5343255996704102, 'ml', 4)] | 1 | 1 | null | 0 | 1 | 0 | 18 | 8 | 0 | 0 | 0 | 1 | 0 | 90 | 0 | 19 |
1,153 | util | https://github.com/pyyoshi/cchardet | [] | null | [] | [] | null | null | null | pyyoshi/cchardet | cChardet | 374 | 51 | 12 | Python | null | universal character encoding detector | pyyoshi | 2024-01-12 | 2012-06-20 | 605 | 0.617307 | null | universal character encoding detector | [] | [] | 2021-04-28 | [] | 10 | 2 | null | 0 | 3 | 0 | 141 | 33 | 0 | 2 | 2 | 3 | 5 | 90 | 1.7 | 19 |
363 | ml-ops | https://github.com/kubeflow/fairing | [] | null | [] | [] | null | null | null | kubeflow/fairing | fairing | 335 | 145 | 40 | Jsonnet | null | Python SDK for building, training, and deploying ML models | kubeflow | 2024-01-04 | 2018-09-03 | 282 | 1.187342 | https://avatars.githubusercontent.com/u/33164907?v=4 | Python SDK for building, training, and deploying ML models | [] | [] | 2021-08-26 | [('fmind/mlops-python-package', 0.6557989120483398, 'template', 0), ('selfexplainml/piml-toolbox', 0.6489830613136292, 'ml-interpretability', 0), ('gradio-app/gradio', 0.6449424624443054, 'viz', 0), ('huggingface/datasets', 0.6306226253509521, 'nlp', 0), ('skops-dev/skops', 0.6278438568115234, 'ml-ops', 0), ('huggingface/huggingface_hub', 0.624878466129303, 'ml', 0), ('merantix-momentum/squirrel-core', 0.6223782300949097, 'ml', 0), ('featurelabs/featuretools', 0.5992475152015686, 'ml', 0), ('aws/sagemaker-python-sdk', 0.5987374186515808, 'ml', 0), ('ml-tooling/opyrator', 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0.5065484046936035, 'util', 0), ('dask/dask-ml', 0.5057757496833801, 'ml', 0), ('googlecloudplatform/vertex-ai-samples', 0.5057130455970764, 'ml', 0), ('astronomer/astro-sdk', 0.5054138898849487, 'ml-ops', 0), ('pallets/flask', 0.5050888657569885, 'web', 0), ('apple/coremltools', 0.5047186613082886, 'ml', 0), ('cohere-ai/cohere-python', 0.5045545697212219, 'util', 0), ('karpathy/micrograd', 0.5037677884101868, 'study', 0), ('pytorch/data', 0.5028988122940063, 'data', 0), ('meltano/meltano', 0.5027660131454468, 'ml-ops', 0), ('open-telemetry/opentelemetry-python', 0.5018121004104614, 'util', 0), ('skorch-dev/skorch', 0.501205325126648, 'ml-dl', 0), ('marshmallow-code/marshmallow', 0.5004829168319702, 'util', 0)] | 41 | 5 | null | 0 | 0 | 0 | 65 | 29 | 0 | 1 | 1 | 0 | 0 | 90 | 0 | 19 |
1,486 | crypto | https://github.com/primal100/pybitcointools | [] | null | [] | [] | null | null | null | primal100/pybitcointools | pybitcointools | 295 | 149 | 26 | Python | null | Simple, common-sense Bitcoin-themed Python ECC library | primal100 | 2023-12-26 | 2017-11-23 | 322 | 0.914121 | null | Simple, common-sense Bitcoin-themed Python ECC library | [] | [] | 2023-07-18 | [('1200wd/bitcoinlib', 0.7157860994338989, 'crypto', 0), ('ethereum/web3.py', 0.6811222434043884, 'crypto', 0), ('legrandin/pycryptodome', 0.6471469402313232, 'util', 0), ('pyca/cryptography', 0.6024011373519897, 'util', 0), ('pyston/pyston', 0.5950328707695007, 'util', 0), ('gbeced/pyalgotrade', 0.5933455228805542, 'finance', 0), ('gbeced/basana', 0.5842757821083069, 'finance', 0), ('pytoolz/toolz', 0.5817379951477051, 'util', 0), ('ethereum/py-evm', 0.5760893821716309, 'crypto', 0), ('amzn/ion-python', 0.5686578154563904, 'data', 0), ('pyca/pynacl', 0.56072998046875, 'util', 0), ('pypy/pypy', 0.5500690340995789, 'util', 0), ('pynamodb/pynamodb', 0.5435667634010315, 'data', 0), ('pmaji/crypto-whale-watching-app', 0.5292482972145081, 'crypto', 0), ('man-c/pycoingecko', 0.5288224220275879, 'crypto', 0), ('numerai/example-scripts', 0.5286599397659302, 'finance', 0), ('pyscf/pyscf', 0.5276904106140137, 'sim', 0), ('python/cpython', 0.5267580151557922, 'util', 0), ('eleutherai/pyfra', 0.5250184535980225, 'ml', 0), ('pmorissette/ffn', 0.5242424607276917, 'finance', 0), ('masoniteframework/masonite', 0.5237247347831726, 'web', 0), ('quantopian/zipline', 0.5228780508041382, 'finance', 0), ('adafruit/circuitpython', 0.518639862537384, 'util', 0), ('ccxt/ccxt', 0.5137020945549011, 'crypto', 0), ('libtcod/python-tcod', 0.5110588669776917, 'gamedev', 0), ('pdm-project/pdm', 0.505200207233429, 'util', 0), ('paramiko/paramiko', 0.5015236139297485, 'util', 0)] | 30 | 1 | null | 0.42 | 5 | 0 | 75 | 6 | 0 | 0 | 0 | 5 | 4 | 90 | 0.8 | 19 |
417 | pandas | https://github.com/zsailer/pandas_flavor | [] | null | [] | [] | null | null | null | zsailer/pandas_flavor | pandas_flavor | 289 | 17 | 10 | Python | https://zsailer.github.io/software/pandas-flavor/ | The easy way to write your own flavor of Pandas | zsailer | 2024-01-04 | 2018-01-25 | 313 | 0.92122 | https://avatars.githubusercontent.com/u/53411673?v=4 | The easy way to write your own flavor of Pandas | ['pandas'] | ['pandas'] | 2023-07-08 | [('lux-org/lux', 0.5670690536499023, 'viz', 1), ('adamerose/pandasgui', 0.5256001949310303, 'pandas', 1), ('tkrabel/bamboolib', 0.5217926502227783, 'pandas', 1)] | 9 | 3 | null | 0.6 | 1 | 0 | 73 | 6 | 2 | 1 | 2 | 1 | 0 | 90 | 0 | 19 |
265 | nlp | https://github.com/allenai/s2orc-doc2json | [] | null | [] | [] | null | null | null | allenai/s2orc-doc2json | s2orc-doc2json | 285 | 58 | 7 | Python | null | Parsers for scientific papers (PDF2JSON, TEX2JSON, JATS2JSON) | allenai | 2024-01-12 | 2020-12-10 | 163 | 1.740838 | https://avatars.githubusercontent.com/u/5667695?v=4 | Parsers for scientific papers (PDF2JSON, TEX2JSON, JATS2JSON) | [] | [] | 2023-08-22 | [('pdfminer/pdfminer.six', 0.5287355184555054, 'util', 0)] | 10 | 4 | null | 0.04 | 0 | 0 | 38 | 5 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 19 |
1,373 | llm | https://github.com/extreme-bert/extreme-bert | [] | null | [] | [] | null | null | null | extreme-bert/extreme-bert | extreme-bert | 283 | 15 | 11 | Python | https://extreme-bert.github.io/extreme-bert-page | ExtremeBERT is a toolkit that accelerates the pretraining of customized language models on customized datasets, described in the paper “ExtremeBERT: A Toolkit for Accelerating Pretraining of Customized BERT”. | extreme-bert | 2024-01-04 | 2022-12-01 | 60 | 4.661176 | https://avatars.githubusercontent.com/u/109327047?v=4 | ExtremeBERT is a toolkit that accelerates the pretraining of customized language models on customized datasets, described in the paper “ExtremeBERT: A Toolkit for Accelerating Pretraining of Customized BERT”. | ['bert', 'deep-learning', 'language-model', 'language-models', 'machine-learning', 'natural-language-processing', 'nlp', 'pytorch', 'transformer'] | ['bert', 'deep-learning', 'language-model', 'language-models', 'machine-learning', 'natural-language-processing', 'nlp', 'pytorch', 'transformer'] | 2023-01-02 | [('huggingface/transformers', 0.6654086709022522, 'nlp', 9), ('jonasgeiping/cramming', 0.6568642258644104, 'nlp', 2), ('paddlepaddle/paddlenlp', 0.6498593091964722, 'llm', 2), ('explosion/spacy-transformers', 0.6331859230995178, 'llm', 6), ('deepset-ai/farm', 0.6253734827041626, 'nlp', 5), ('graykode/nlp-tutorial', 0.6246617436408997, 'study', 5), ('jina-ai/finetuner', 0.6179499626159668, 'ml', 1), ('bigscience-workshop/megatron-deepspeed', 0.6164913773536682, 'llm', 0), ('microsoft/megatron-deepspeed', 0.6164913773536682, 'llm', 0), ('alibaba/easynlp', 0.614682674407959, 'nlp', 5), ('llmware-ai/llmware', 0.581847071647644, 'llm', 4), ('nvidia/deeplearningexamples', 0.5793726444244385, 'ml-dl', 3), ('maartengr/bertopic', 0.5704385042190552, 'nlp', 3), ('jina-ai/clip-as-service', 0.5639864802360535, 'nlp', 3), ('whu-zqh/chatgpt-vs.-bert', 0.5589087605476379, 'llm', 1), ('qanastek/drbert', 0.5513941645622253, 'llm', 3), ('ddangelov/top2vec', 0.5432024598121643, 'nlp', 1), ('databrickslabs/dolly', 0.5428717136383057, 'llm', 0), ('infinitylogesh/mutate', 0.5343960523605347, 'nlp', 1), ('microsoft/lora', 0.5327245593070984, 'llm', 3), ('google-research/electra', 0.5268334150314331, 'ml-dl', 2), ('lm-sys/fastchat', 0.5264178514480591, 'llm', 1), ('amansrivastava17/embedding-as-service', 0.5209690928459167, 'nlp', 4), ('huggingface/datasets', 0.5209168195724487, 'nlp', 5), ('huawei-noah/pretrained-language-model', 0.5199653506278992, 'nlp', 0), ('young-geng/easylm', 0.5198579430580139, 'llm', 4), ('explosion/thinc', 0.5198476314544678, 'ml-dl', 5), ('freedomintelligence/llmzoo', 0.5171335935592651, 'llm', 1), ('thilinarajapakse/simpletransformers', 0.5170273184776306, 'nlp', 0), ('luodian/otter', 0.5146451592445374, 'llm', 2), ('huggingface/autotrain-advanced', 0.5114012360572815, 'ml', 3), ('nvlabs/prismer', 0.5107273459434509, 'diffusion', 1), ('microsoft/unilm', 0.5103119015693665, 'nlp', 1), ('ukplab/sentence-transformers', 0.50993812084198, 'nlp', 0), ('intellabs/fastrag', 0.5088297128677368, 'nlp', 1), ('bytedance/lightseq', 0.5086089968681335, 'nlp', 2), ('plasticityai/magnitude', 0.5061821341514587, 'nlp', 3), ('explosion/spacy-models', 0.5058228969573975, 'nlp', 3), ('allenai/allennlp', 0.5051680207252502, 'nlp', 4), ('openai/finetune-transformer-lm', 0.504263699054718, 'llm', 0), ('openai/clip', 0.5033907890319824, 'ml-dl', 2), ('huggingface/text-generation-inference', 0.5023447871208191, 'llm', 4)] | 3 | 2 | null | 0 | 0 | 0 | 14 | 13 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 19 |
1,465 | llm | https://github.com/microsoft/pycodegpt | ['code-generation'] | null | [] | [] | null | null | null | microsoft/pycodegpt | PyCodeGPT | 219 | 36 | 15 | Python | null | A pre-trained GPT model for Python code completion and generation | microsoft | 2024-01-13 | 2022-03-09 | 98 | 2.215318 | https://avatars.githubusercontent.com/u/6154722?v=4 | A pre-trained GPT model for Python code completion and generation | [] | ['code-generation'] | 2023-01-08 | [('minimaxir/gpt-2-simple', 0.629753589630127, 'llm', 0), ('minimaxir/aitextgen', 0.6111297011375427, 'llm', 0), ('psf/black', 0.5714641213417053, 'util', 0), ('bigcode-project/starcoder', 0.5438132882118225, 'llm', 1), ('dosisod/refurb', 0.5347919464111328, 'util', 0), ('eleutherai/gpt-neo', 0.5271508097648621, 'llm', 0), ('promptslab/promptify', 0.5242052674293518, 'nlp', 0), ('ravenscroftj/turbopilot', 0.524189293384552, 'llm', 0), ('salesforce/codegen', 0.5231809020042419, 'nlp', 0), ('norvig/pytudes', 0.5215215682983398, 'util', 0), ('google/pyglove', 0.520497739315033, 'util', 0), ('thudm/codegeex', 0.5184867978096008, 'llm', 1), ('lianjiatech/belle', 0.5181886553764343, 'llm', 0), ('hannibal046/awesome-llm', 0.51466304063797, 'study', 0), ('python/cpython', 0.5116127133369446, 'util', 0), ('google/latexify_py', 0.5082912445068359, 'util', 0), ('instagram/libcst', 0.5071039795875549, 'util', 0), ('nedbat/coveragepy', 0.506847083568573, 'testing', 0), ('stanfordnlp/dspy', 0.5060772895812988, 'llm', 0), ('exaloop/codon', 0.5030719041824341, 'perf', 0), ('rubik/radon', 0.5025046467781067, 'util', 0), ('grantjenks/blue', 0.5024159550666809, 'util', 0), ('agronholm/sqlacodegen', 0.5004090666770935, 'data', 0)] | 6 | 2 | null | 0 | 2 | 0 | 23 | 12 | 0 | 2 | 2 | 2 | 0 | 90 | 0 | 19 |
1,421 | llm | https://github.com/whu-zqh/chatgpt-vs.-bert | [] | null | [] | [] | null | null | null | whu-zqh/chatgpt-vs.-bert | ChatGPT-vs.-BERT | 190 | 9 | 5 | Python | https://arxiv.org/abs/2302.10198 | 🎁[ChatGPT4NLU] A Comparative Study on ChatGPT and Fine-tuned BERT | whu-zqh | 2024-01-04 | 2023-02-18 | 49 | 3.843931 | null | 🎁[ChatGPT4NLU] A Comparative Study on ChatGPT and Fine-tuned BERT | ['bert', 'chain-of-thought', 'chatgpt', 'in-context-learning', 'natural-language-understanding'] | ['bert', 'chain-of-thought', 'chatgpt', 'in-context-learning', 'natural-language-understanding'] | 2023-04-17 | [('jonasgeiping/cramming', 0.6024624109268188, 'nlp', 0), ('jina-ai/finetuner', 0.577040433883667, 'ml', 1), ('openlmlab/moss', 0.5750217437744141, 'llm', 1), ('bigscience-workshop/megatron-deepspeed', 0.5593957901000977, 'llm', 0), ('microsoft/megatron-deepspeed', 0.5593957901000977, 'llm', 0), ('extreme-bert/extreme-bert', 0.5589087605476379, 'llm', 1), ('killianlucas/open-interpreter', 0.5495890974998474, 'llm', 1), ('maartengr/keybert', 0.529996395111084, 'nlp', 1), ('graykode/nlp-tutorial', 0.5263599753379822, 'study', 1), ('guidance-ai/guidance', 0.5157712697982788, 'llm', 1), ('next-gpt/next-gpt', 0.515221357345581, 'llm', 1), ('fasteval/fasteval', 0.514625608921051, 'llm', 0), ('paddlepaddle/paddlenlp', 0.5144068002700806, 'llm', 1), ('thudm/chatglm2-6b', 0.5137940645217896, 'llm', 0), ('xtekky/gpt4free', 0.5137408971786499, 'llm', 1), ('lm-sys/fastchat', 0.5135443210601807, 'llm', 0), ('run-llama/rags', 0.5091956257820129, 'llm', 1), ('microsoft/autogen', 0.5087406039237976, 'llm', 1)] | 3 | 2 | null | 1.23 | 0 | 0 | 11 | 9 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 19 |
523 | ml | https://github.com/hazyresearch/domino | [] | Discover slices of data on which your models underperform. | [] | [] | null | null | null | hazyresearch/domino | domino | 131 | 25 | 20 | Python | null | null | hazyresearch | 2024-01-04 | 2021-11-29 | 113 | 1.157828 | https://avatars.githubusercontent.com/u/2165246?v=4 | Discover slices of data on which your models underperform. | [] | [] | 2023-10-30 | [('huggingface/evaluate', 0.5064350366592407, 'ml', 0), ('anthropics/evals', 0.5007730722427368, 'llm', 0)] | 9 | 0 | null | 0.33 | 10 | 6 | 26 | 2 | 0 | 3 | 3 | 10 | 6 | 90 | 0.6 | 19 |
1,350 | perf | https://github.com/crunch-io/lazycsv | ['csv', 'parser'] | lazycsv is a C implementation of a csv parser for python | [] | [] | null | null | null | crunch-io/lazycsv | lazycsv | 118 | 1 | 3 | C | https://pypi.org/project/lazycsv/ | null | crunch-io | 2024-01-04 | 2023-03-24 | 44 | 2.647436 | https://avatars.githubusercontent.com/u/2966429?v=4 | lazycsv is a C implementation of a csv parser for python | [] | ['csv', 'parser'] | 2024-01-10 | [('wireservice/csvkit', 0.561709463596344, 'util', 0), ('dask/fastparquet', 0.5394817590713501, 'data', 0), ('pyston/pyston', 0.5371149182319641, 'util', 0), ('cython/cython', 0.5136982202529907, 'util', 0), ('python-odin/odin', 0.5088388323783875, 'util', 1), ('pytoolz/toolz', 0.5044757127761841, 'util', 0)] | 2 | 1 | null | 0.52 | 0 | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 19 |
1,653 | data | https://github.com/pachyderm/python-pachyderm | [] | null | [] | [] | null | null | null | pachyderm/python-pachyderm | python-pachyderm | 89 | 30 | 25 | Python | null | Python client for Pachyderm | pachyderm | 2024-01-05 | 2017-02-01 | 364 | 0.243931 | https://avatars.githubusercontent.com/u/10432478?v=4 | Python client for Pachyderm | ['pachyderm', 'python-bindings'] | ['pachyderm', 'python-bindings'] | 2023-08-18 | [('nvidia/cuda-python', 0.5181779861450195, 'ml', 0)] | 33 | 5 | null | 0.29 | 0 | 0 | 85 | 5 | 3 | 2 | 3 | 0 | 0 | 90 | 0 | 19 |
1,417 | data | https://github.com/mitvis/vistext | [] | null | [] | [] | null | null | null | mitvis/vistext | vistext | 66 | 3 | 6 | Jupyter Notebook | http://vis.csail.mit.edu/pubs/vistext/ | VisText is a benchmark dataset for semantically rich chart captioning. | mitvis | 2024-01-07 | 2023-04-04 | 43 | 1.534884 | https://avatars.githubusercontent.com/u/41133679?v=4 | VisText is a benchmark dataset for semantically rich chart captioning. | ['captioning', 'captioning-images', 'charts', 'dataset', 't5'] | ['captioning', 'captioning-images', 'charts', 'dataset', 't5'] | 2023-10-03 | [('mckinsey/vizro', 0.5145730972290039, 'viz', 0), ('koaning/whatlies', 0.5051085948944092, 'nlp', 0)] | 6 | 2 | null | 1.67 | 2 | 1 | 9 | 3 | 0 | 0 | 0 | 2 | 1 | 90 | 0.5 | 19 |
1,342 | util | https://github.com/prefecthq/prefect-dask | ['dask'] | null | [] | [] | null | null | null | prefecthq/prefect-dask | prefect-dask | 64 | 15 | 12 | Python | https://prefecthq.github.io/prefect-dask/ | Prefect integrations with the Dask execution framework. | prefecthq | 2024-01-13 | 2022-05-11 | 89 | 0.712242 | https://avatars.githubusercontent.com/u/39270919?v=4 | Prefect integrations with the Dask execution framework. | [] | ['dask'] | 2023-11-02 | [('dask/distributed', 0.6549785137176514, 'perf', 1), ('dask/dask-ml', 0.5907831192016602, 'ml', 0), ('fugue-project/fugue', 0.5114060044288635, 'pandas', 1), ('autoviml/auto_ts', 0.5042782425880432, 'time-series', 0)] | 14 | 2 | null | 0.4 | 7 | 3 | 20 | 2 | 4 | 7 | 4 | 7 | 0 | 90 | 0 | 19 |
1,561 | sim | https://github.com/google-research/swirl-lm | ['tpu', 'fluid-dynamics'] | Swirl-LM is a computational fluid dynamics simulation framework that is accelerated by the Tensor Processing Unit | [] | [] | null | null | null | google-research/swirl-lm | swirl-lm | 51 | 7 | 8 | Python | null | null | google-research | 2024-01-12 | 2022-01-07 | 107 | 0.474104 | https://avatars.githubusercontent.com/u/43830688?v=4 | Swirl-LM is a computational fluid dynamics simulation framework that is accelerated by the Tensor Processing Unit | [] | ['fluid-dynamics', 'tpu'] | 2024-01-03 | [] | 6 | 2 | null | 0.65 | 4 | 2 | 25 | 0 | 2 | 1 | 2 | 4 | 1 | 90 | 0.2 | 19 |
1,424 | testing | https://github.com/vedro-universe/vedro | ['testing'] | null | [] | [] | null | null | null | vedro-universe/vedro | vedro | 30 | 8 | 3 | Python | https://vedro.io | Pragmatic Testing Framework | vedro-universe | 2024-01-08 | 2015-10-19 | 432 | 0.069421 | https://avatars.githubusercontent.com/u/118679807?v=4 | Pragmatic Testing Framework | ['e2e-testing', 'testing', 'testing-tools', 'vedro'] | ['e2e-testing', 'testing', 'testing-tools', 'vedro'] | 2024-01-13 | [('pytest-dev/pytest-testinfra', 0.526130199432373, 'testing', 2), ('robotframework/robotframework', 0.5252175331115723, 'testing', 1)] | 5 | 0 | null | 0.92 | 16 | 16 | 100 | 0 | 0 | 4 | 4 | 16 | 12 | 90 | 0.8 | 19 |
1,403 | data | https://github.com/parallel-domain/pd-sdk | ['datasets'] | The Parallel Domain SDK allows the community to access Parallel Domain's synthetic data as Python objects. | [] | [] | null | null | null | parallel-domain/pd-sdk | pd-sdk | 17 | 5 | 4 | Python | null | null | parallel-domain | 2024-01-02 | 2021-05-11 | 142 | 0.119718 | https://avatars.githubusercontent.com/u/53447713?v=4 | The Parallel Domain SDK allows the community to access Parallel Domain's synthetic data as Python objects. | [] | ['datasets'] | 2023-10-31 | [('pytorch/data', 0.6059823036193848, 'data', 0), ('fastai/fastcore', 0.5881737470626831, 'util', 0), ('kubeflow/fairing', 0.5475777983665466, 'ml-ops', 0), ('huggingface/datasets', 0.5329124927520752, 'nlp', 1), ('jovianml/opendatasets', 0.5220646262168884, 'data', 1), ('dask/dask', 0.5014457702636719, 'perf', 0)] | 14 | 2 | null | 0.73 | 33 | 33 | 33 | 2 | 5 | 20 | 5 | 33 | 0 | 90 | 0 | 19 |
1,662 | data | https://github.com/mediawiki-client-tools/wikitools3 | ['wikimedia', 'wikipedia'] | null | [] | [] | null | null | null | mediawiki-client-tools/wikitools3 | wikitools3 | 4 | 2 | 2 | Python | null | Python package for working with MediaWiki wikis | mediawiki-client-tools | 2023-11-06 | 2021-08-23 | 127 | 0.031461 | https://avatars.githubusercontent.com/u/122663498?v=4 | Python package for working with MediaWiki wikis | [] | ['wikimedia', 'wikipedia'] | 2023-08-29 | [('mediawiki-client-tools/mediawiki-dump-generator', 0.8232905268669128, 'data', 2), ('goldsmith/wikipedia', 0.708003044128418, 'data', 0), ('harangju/wikinet', 0.6277413368225098, 'data', 0)] | 12 | 5 | null | 0.02 | 5 | 3 | 29 | 5 | 0 | 2 | 2 | 5 | 7 | 90 | 1.4 | 19 |
753 | study | https://github.com/jackhidary/quantumcomputingbook | [] | null | [] | [] | null | null | null | jackhidary/quantumcomputingbook | quantumcomputingbook | 729 | 201 | 57 | Jupyter Notebook | null | Companion site for the textbook Quantum Computing: An Applied Approach | jackhidary | 2024-01-04 | 2019-02-28 | 256 | 2.839733 | null | Companion site for the textbook Quantum Computing: An Applied Approach | ['cirq', 'google-quantum', 'qiskit', 'quantum', 'quantum-computing', 'quantum-information', 'quantum-information-science', 'quantum-processor', 'quantum-supremacy', 'rigetti', 'sycamore'] | ['cirq', 'google-quantum', 'qiskit', 'quantum', 'quantum-computing', 'quantum-information', 'quantum-information-science', 'quantum-processor', 'quantum-supremacy', 'rigetti', 'sycamore'] | 2021-10-14 | [('netket/netket', 0.626657247543335, 'sim', 1), ('cqcl/tket', 0.6081982851028442, 'util', 1), ('qiskit/qiskit', 0.5992211699485779, 'sim', 3), ('quantumlib/cirq', 0.5691633224487305, 'sim', 2), ('cqcl/lambeq', 0.5265811681747437, 'nlp', 0)] | 8 | 1 | null | 0 | 1 | 1 | 59 | 27 | 0 | 0 | 0 | 1 | 0 | 90 | 0 | 18 |
1,181 | math | https://github.com/sj001/ai-feynman | ['regression', 'physics'] | Implementation of AI Feynman: a Physics-Inspired Method for Symbolic Regression | [] | [] | null | null | null | sj001/ai-feynman | AI-Feynman | 567 | 171 | 26 | Python | null | null | sj001 | 2024-01-14 | 2020-03-08 | 203 | 2.789178 | null | Implementation of AI Feynman: a Physics-Inspired Method for Symbolic Regression | [] | ['physics', 'regression'] | 2021-05-16 | [] | 2 | 1 | null | 0 | 1 | 0 | 47 | 32 | 0 | 0 | 0 | 1 | 1 | 90 | 1 | 18 |
374 | viz | https://github.com/vhranger/nodevectors | [] | null | [] | [] | null | null | null | vhranger/nodevectors | nodevectors | 485 | 58 | 11 | Python | null | Fastest network node embeddings in the west | vhranger | 2024-01-04 | 2019-07-25 | 235 | 2.057576 | null | Fastest network node embeddings in the west | [] | [] | 2021-11-06 | [('rom1504/embedding-reader', 0.53115314245224, 'ml', 0), ('facebookresearch/pytorch-biggraph', 0.5276350975036621, 'ml-dl', 0)] | 6 | 2 | null | 0 | 0 | 0 | 54 | 27 | 0 | 3 | 3 | 0 | 0 | 90 | 0 | 18 |
479 | sim | https://github.com/udst/urbansim | [] | null | [] | [] | null | null | null | udst/urbansim | urbansim | 457 | 128 | 80 | Python | https://udst.github.io/urbansim/ | Platform for building statistical models of cities and regions | udst | 2024-01-10 | 2013-08-15 | 545 | 0.837435 | https://avatars.githubusercontent.com/u/5187765?v=4 | Platform for building statistical models of cities and regions | [] | [] | 2020-05-11 | [('mcordts/cityscapesscripts', 0.6591488718986511, 'gis', 0), ('spatialucr/geosnap', 0.5915238857269287, 'gis', 0), ('pysal/momepy', 0.5606027245521545, 'gis', 0), ('gregorhd/mapcompare', 0.5511513352394104, 'gis', 0), ('stan-dev/pystan', 0.5362882018089294, 'ml', 0), ('gboeing/street-network-models', 0.5066875219345093, 'sim', 0)] | 20 | 6 | null | 0 | 0 | 0 | 127 | 45 | 0 | 1 | 1 | 0 | 0 | 90 | 0 | 18 |
812 | time-series | https://github.com/salesforce/deeptime | [] | null | [] | [] | null | null | null | salesforce/deeptime | DeepTime | 317 | 60 | 9 | Python | null | PyTorch code for Learning Deep Time-index Models for Time Series Forecasting (ICML 2023) | salesforce | 2024-01-13 | 2022-06-27 | 83 | 3.812715 | https://avatars.githubusercontent.com/u/453694?v=4 | PyTorch code for Learning Deep Time-index Models for Time Series Forecasting (ICML 2023) | ['deep-learning', 'forecasting', 'implicit-neural-representation', 'meta-learning', 'time-series', 'time-series-forecasting', 'time-series-regression'] | ['deep-learning', 'forecasting', 'implicit-neural-representation', 'meta-learning', 'time-series', 'time-series-forecasting', 'time-series-regression'] | 2023-08-01 | [('aistream-peelout/flow-forecast', 0.7200793027877808, 'time-series', 5), ('ourownstory/neural_prophet', 0.5901058316230774, 'ml', 3), ('salesforce/merlion', 0.5836126804351807, 'time-series', 2), ('awslabs/gluonts', 0.5721127390861511, 'time-series', 4), ('unit8co/darts', 0.5719814300537109, 'time-series', 3), ('winedarksea/autots', 0.5623159408569336, 'time-series', 3), ('rasbt/machine-learning-book', 0.559136688709259, 'study', 1), ('sktime/sktime', 0.5567091107368469, 'time-series', 3), ('huggingface/transformers', 0.5546357035636902, 'nlp', 1), ('nvidia/deeplearningexamples', 0.5429381728172302, 'ml-dl', 2), ('opengeos/earthformer', 0.5404602289199829, 'gis', 2), ('tensorflow/tensor2tensor', 0.5346342325210571, 'ml', 1), ('alkaline-ml/pmdarima', 0.533848762512207, 'time-series', 2), ('nixtla/statsforecast', 0.5292312502861023, 'time-series', 2), ('rafiqhasan/auto-tensorflow', 0.5278478264808655, 'ml-dl', 0), ('pytorch/ignite', 0.5224276185035706, 'ml-dl', 1), ('awslabs/autogluon', 0.5168805718421936, 'ml', 3), ('firmai/atspy', 0.5146031975746155, 'time-series', 2), ('linkedin/greykite', 0.5127387642860413, 'ml', 0), ('keras-team/autokeras', 0.5080786347389221, 'ml-dl', 1), ('skorch-dev/skorch', 0.506636381149292, 'ml-dl', 0), ('karpathy/micrograd', 0.5016177892684937, 'study', 0), ('arogozhnikov/einops', 0.5012999773025513, 'ml-dl', 1)] | 2 | 0 | null | 0.06 | 2 | 1 | 19 | 6 | 0 | 0 | 0 | 2 | 1 | 90 | 0.5 | 18 |
428 | jupyter | https://github.com/chaoleili/jupyterlab_tensorboard | [] | null | [] | [] | null | null | null | chaoleili/jupyterlab_tensorboard | jupyterlab_tensorboard | 310 | 36 | 12 | TypeScript | null | Tensorboard extension for jupyterlab. | chaoleili | 2024-01-12 | 2018-08-14 | 285 | 1.087719 | null | Tensorboard extension for jupyterlab. | ['jupyterlab', 'jupyterlab-extension', 'tensorboard'] | ['jupyterlab', 'jupyterlab-extension', 'tensorboard'] | 2022-07-18 | [('jupyter-widgets/ipywidgets', 0.6089569330215454, 'jupyter', 1), ('mamba-org/gator', 0.5936612486839294, 'jupyter', 1), ('jupyterlab/jupyter-ai', 0.5836908221244812, 'jupyter', 2), ('jupyterlab/jupyterlab', 0.5774864554405212, 'jupyter', 1), ('ipython/ipykernel', 0.5732330679893494, 'util', 0), ('jupyter/notebook', 0.5390121340751648, 'jupyter', 0), ('jupyter/nbformat', 0.5145013928413391, 'jupyter', 0)] | 7 | 3 | null | 0 | 1 | 0 | 66 | 18 | 0 | 0 | 0 | 1 | 1 | 90 | 1 | 18 |
880 | study | https://github.com/amaargiru/pyroad | [] | null | [] | [] | null | null | null | amaargiru/pyroad | pyroad | 299 | 37 | 8 | Jupyter Notebook | null | Detailed Python developer roadmap | amaargiru | 2024-01-12 | 2022-11-03 | 64 | 4.620309 | null | Detailed Python developer roadmap | ['roadmap', 'tutorial'] | ['roadmap', 'tutorial'] | 2023-02-27 | [('eleutherai/pyfra', 0.6402732729911804, 'ml', 0), ('realpython/python-guide', 0.6154986023902893, 'study', 0), ('willmcgugan/textual', 0.6114891171455383, 'term', 0), ('python/cpython', 0.5899056792259216, 'util', 0), ('brandon-rhodes/python-patterns', 0.5854448676109314, 'util', 0), ('samuelcolvin/python-devtools', 0.5811712741851807, 'debug', 0), ('kubeflow/fairing', 0.5755603313446045, 'ml-ops', 0), ('pypa/hatch', 0.5697453618049622, 'util', 0), ('sourcery-ai/sourcery', 0.5690412521362305, 'util', 0), ('norvig/pytudes', 0.5647484064102173, 'util', 0), ('pypa/pipenv', 0.5548535585403442, 'util', 0), ('pytoolz/toolz', 0.5507724285125732, 'util', 0), ('mitmproxy/pdoc', 0.5501058101654053, 'util', 0), ('featurelabs/featuretools', 0.548759937286377, 'ml', 0), ('landscapeio/prospector', 0.5477283596992493, 'util', 0), ('mkdocstrings/griffe', 0.5457209944725037, 'util', 0), ('dosisod/refurb', 0.5455231070518494, 'util', 0), ('fchollet/deep-learning-with-python-notebooks', 0.5438991785049438, 'study', 0), ('eugeneyan/python-collab-template', 0.5436649918556213, 'template', 0), ('goldmansachs/gs-quant', 0.5344632863998413, 'finance', 0), ('masoniteframework/masonite', 0.5333690643310547, 'web', 0), ('pypy/pypy', 0.5301415324211121, 'util', 0), ('pyston/pyston', 0.5288054347038269, 'util', 0), ('python-rope/rope', 0.528800904750824, 'util', 0), ('rubik/radon', 0.5273672342300415, 'util', 0), ('python-odin/odin', 0.5243930220603943, 'util', 0), ('google/pyglove', 0.5239276885986328, 'util', 0), ('wesm/pydata-book', 0.5237447023391724, 'study', 0), ('mynameisfiber/high_performance_python_2e', 0.5223120450973511, 'study', 0), ('microsoft/playwright-python', 0.5205329060554504, 'testing', 0), ('pypa/build', 0.5195523500442505, 'util', 0), ('malloydata/malloy-py', 0.5178565979003906, 'data', 0), ('gradio-app/gradio', 0.5175772905349731, 'viz', 0), ('nedbat/coveragepy', 0.5170186161994934, 'testing', 0), ('requests/toolbelt', 0.5161598324775696, 'util', 0), ('webpy/webpy', 0.5149017572402954, 'web', 0), ('fastai/fastcore', 0.5148802995681763, 'util', 0), ('lordmauve/pgzero', 0.5130576491355896, 'gamedev', 0), ('googleapis/google-api-python-client', 0.5120947957038879, 'util', 0), ('jakevdp/pythondatasciencehandbook', 0.5113055109977722, 'study', 0), ('python-poetry/poetry', 0.5109917521476746, 'util', 0), ('r0x0r/pywebview', 0.5109246969223022, 'gui', 0), ('pygamelib/pygamelib', 0.5083182454109192, 'gamedev', 0), ('falconry/falcon', 0.5072835087776184, 'web', 0), ('urwid/urwid', 0.5068714618682861, 'term', 0), ('holoviz/holoviz', 0.5063855648040771, 'viz', 0), ('selfexplainml/piml-toolbox', 0.5047166347503662, 'ml-interpretability', 0), ('opengeos/leafmap', 0.503073513507843, 'gis', 0), ('fmind/mlops-python-package', 0.5028426051139832, 'template', 0), ('pallets/flask', 0.5025720596313477, 'web', 0), ('alexmojaki/snoop', 0.5022026896476746, 'debug', 0), ('plotly/dash', 0.5013555884361267, 'viz', 0), ('scikit-mobility/scikit-mobility', 0.5013498067855835, 'gis', 0), ('renpy/renpy', 0.500852644443512, 'viz', 0), ('reloadware/reloadium', 0.5002206563949585, 'profiling', 0)] | 1 | 1 | null | 0.08 | 0 | 0 | 15 | 11 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 18 |
275 | crypto | https://github.com/ethtx/ethtx_ce | [] | null | [] | [] | null | null | null | ethtx/ethtx_ce | ethtx_ce | 264 | 64 | 12 | Python | https://ethtx.info | Ethereum transaction decoder (community version). | ethtx | 2024-01-12 | 2021-07-26 | 131 | 2.013072 | https://avatars.githubusercontent.com/u/70520035?v=4 | Ethereum transaction decoder (community version). | [] | [] | 2023-08-08 | [('palkeo/panoramix', 0.6499204635620117, 'crypto', 0), ('ethtx/ethtx', 0.5777018666267395, 'crypto', 0)] | 7 | 2 | null | 0.04 | 0 | 0 | 30 | 5 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 18 |
517 | gis | https://github.com/lydorn/polygonization-by-frame-field-learning | [] | null | [] | [] | null | null | null | lydorn/polygonization-by-frame-field-learning | Polygonization-by-Frame-Field-Learning | 257 | 56 | 13 | Python | null | This repository contains the code for our fast polygonal building extraction from overhead images pipeline. | lydorn | 2024-01-04 | 2020-05-26 | 192 | 1.338542 | null | This repository contains the code for our fast polygonal building extraction from overhead images pipeline. | ['field', 'frame', 'polygonization', 'remote', 'segmentation', 'sensing'] | ['field', 'frame', 'polygonization', 'remote', 'segmentation', 'sensing'] | 2023-10-02 | [('zorzi-s/polyworldpretrainednetwork', 0.6778478026390076, 'gis', 0), ('microsoft/globalmlbuildingfootprints', 0.5449085831642151, 'gis', 0)] | 2 | 1 | null | 0.02 | 2 | 0 | 44 | 3 | 0 | 0 | 0 | 2 | 2 | 90 | 1 | 18 |
1,401 | nlp | https://github.com/facebookresearch/dpr-scale | ['retrieval'] | null | [] | [] | null | null | null | facebookresearch/dpr-scale | dpr-scale | 225 | 22 | 18 | Python | null | Scalable training for dense retrieval models. | facebookresearch | 2024-01-11 | 2021-10-20 | 118 | 1.893029 | https://avatars.githubusercontent.com/u/16943930?v=4 | Scalable training for dense retrieval models. | [] | ['retrieval'] | 2023-05-27 | [('paddlepaddle/rocketqa', 0.6294921636581421, 'nlp', 0), ('castorini/pyserini', 0.5861307978630066, 'ml', 0), ('ai21labs/in-context-ralm', 0.5703505873680115, 'llm', 0), ('intellabs/fastrag', 0.5437898635864258, 'nlp', 0)] | 5 | 3 | null | 0.13 | 0 | 0 | 27 | 8 | 1 | 1 | 1 | 0 | 0 | 90 | 0 | 18 |
1,566 | llm | https://github.com/deep-diver/pingpong | ['lanuage-model', 'contexts'] | PingPong is a simple library to manage pings(prompt) and pongs(response). The main purpose of this library is to manage histories and contexts in LLM applied applications. | [] | [] | null | null | null | deep-diver/pingpong | PingPong | 83 | 8 | 4 | Python | https://pypi.org/project/bingbong/ | manage histories of LLM applied applications | deep-diver | 2024-01-13 | 2023-04-11 | 42 | 1.97619 | null | manage histories of LLM applied applications | [] | ['contexts', 'lanuage-model'] | 2023-11-17 | [('citadel-ai/langcheck', 0.6283354759216309, 'llm', 0), ('hwchase17/langchain', 0.589103639125824, 'llm', 0), ('agenta-ai/agenta', 0.557935893535614, 'llm', 0), ('eugeneyan/open-llms', 0.5374715328216553, 'study', 0), ('confident-ai/deepeval', 0.5305920243263245, 'testing', 0), ('langchain-ai/langgraph', 0.5210304260253906, 'llm', 0), ('ray-project/ray-llm', 0.5193211436271667, 'llm', 0), ('alpha-vllm/llama2-accessory', 0.5120090842247009, 'llm', 0), ('ibm/dromedary', 0.510129988193512, 'llm', 0), ('mooler0410/llmspracticalguide', 0.5093730688095093, 'study', 0), ('nat/openplayground', 0.5059070587158203, 'llm', 0), ('pathwaycom/llm-app', 0.5041395425796509, 'llm', 0), ('jina-ai/thinkgpt', 0.5024981498718262, 'llm', 0), ('berriai/litellm', 0.5002941489219666, 'llm', 0)] | 2 | 1 | null | 1.21 | 0 | 0 | 9 | 2 | 5 | 7 | 5 | 0 | 0 | 90 | 0 | 18 |
1,468 | gis | https://github.com/opengeos/earthformer | [] | null | [] | [] | null | null | null | opengeos/earthformer | earthformer | 73 | 5 | 7 | Python | https://open.gishub.org/earthformer | A Python package for Earth forecasting transformer | opengeos | 2024-01-05 | 2023-07-31 | 26 | 2.79235 | https://avatars.githubusercontent.com/u/129896036?v=4 | A Python package for Earth forecasting transformer | ['deep-learning', 'earthformer', 'forecasting', 'geospatial', 'transformer'] | ['deep-learning', 'earthformer', 'forecasting', 'geospatial', 'transformer'] | 2023-08-09 | [('alignmentresearch/tuned-lens', 0.6345276832580566, 'ml-interpretability', 0), ('sentinel-hub/eo-learn', 0.5971592664718628, 'gis', 0), ('aistream-peelout/flow-forecast', 0.5866661071777344, 'time-series', 3), ('microsoft/torchgeo', 0.5563389658927917, 'gis', 2), ('amazon-science/earth-forecasting-transformer', 0.5462614893913269, 'gis', 0), ('salesforce/deeptime', 0.5404602289199829, 'time-series', 2), ('unit8co/darts', 0.5380537509918213, 'time-series', 2), ('marella/ctransformers', 0.5367976427078247, 'nlp', 0), ('pytroll/satpy', 0.5178881287574768, 'gis', 0), ('huggingface/transformers', 0.5169755816459656, 'nlp', 2), ('nielsrogge/transformers-tutorials', 0.5140464901924133, 'study', 0), ('ourownstory/neural_prophet', 0.5128765106201172, 'ml', 2), ('nvidia/megatron-lm', 0.507581353187561, 'llm', 0), ('awslabs/gluonts', 0.502416729927063, 'time-series', 2)] | 1 | 1 | null | 0.52 | 0 | 0 | 6 | 5 | 7 | 14 | 7 | 0 | 0 | 90 | 0 | 18 |
1,512 | template | https://github.com/martinheinz/python-project-blueprint | [] | null | [] | [] | null | null | null | martinheinz/python-project-blueprint | python-project-blueprint | 932 | 266 | 41 | Makefile | null | Blueprint/Boilerplate For Python Projects | martinheinz | 2024-01-07 | 2019-12-26 | 213 | 4.360963 | null | Blueprint/Boilerplate For Python Projects | ['blueprint', 'boilerplate', 'docker', 'kubernetes', 'template'] | ['blueprint', 'boilerplate', 'docker', 'kubernetes', 'template'] | 2023-01-06 | [('pypa/hatch', 0.5873016119003296, 'util', 0), ('pyscaffold/pyscaffold', 0.5791087746620178, 'template', 0), ('backtick-se/cowait', 0.5766209363937378, 'util', 2), ('eugeneyan/python-collab-template', 0.5737303495407104, 'template', 0), ('sqlalchemy/mako', 0.5644561648368835, 'template', 0), ('rawheel/fastapi-boilerplate', 0.5532102584838867, 'web', 2), ('ianmiell/shutit', 0.5357551574707031, 'util', 1), ('tedivm/robs_awesome_python_template', 0.5347585082054138, 'template', 0), ('pypa/pipenv', 0.5226168036460876, 'util', 0), ('python-attrs/attrs', 0.521111011505127, 'typing', 1), ('mitmproxy/pdoc', 0.514594554901123, 'util', 0), ('orchest/orchest', 0.5042280554771423, 'ml-ops', 2), ('asacristani/fastapi-rocket-boilerplate', 0.5014850497245789, 'template', 1)] | 3 | 0 | null | 0 | 0 | 0 | 49 | 12 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 17 |
628 | profiling | https://github.com/csurfer/pyheat | [] | null | [] | [] | null | null | null | csurfer/pyheat | pyheat | 775 | 41 | 12 | Python | null | pprofile + matplotlib = Python program profiled as an awesome heatmap! | csurfer | 2024-01-04 | 2017-02-04 | 364 | 2.126617 | null | pprofile + matplotlib = Python program profiled as an awesome heatmap! | ['heatmap', 'matplotlib', 'profiling'] | ['heatmap', 'matplotlib', 'profiling'] | 2021-09-18 | [('mwaskom/seaborn', 0.5626926422119141, 'viz', 1), ('matplotlib/basemap', 0.5544516444206238, 'gis', 0), ('matplotlib/matplotlib', 0.547273576259613, 'viz', 1), ('scitools/cartopy', 0.542796790599823, 'gis', 1), ('altair-viz/altair', 0.5379376411437988, 'viz', 0), ('pyutils/line_profiler', 0.5279625654220581, 'profiling', 0), ('benfred/py-spy', 0.5209768414497375, 'profiling', 1), ('pysal/pysal', 0.5142292380332947, 'gis', 0)] | 5 | 2 | null | 0 | 0 | 0 | 84 | 28 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 17 |
118 | nlp | https://github.com/iclrandd/blackstone | [] | null | [] | [] | null | null | null | iclrandd/blackstone | Blackstone | 630 | 100 | 39 | Python | https://research.iclr.co.uk | :black_circle: A spaCy pipeline and model for NLP on unstructured legal text. | iclrandd | 2024-01-04 | 2019-03-25 | 253 | 2.488713 | null | ⚫ A spaCy pipeline and model for NLP on unstructured legal text. | ['caselaw', 'law', 'legaltech', 'nlp', 'spacy-models'] | ['caselaw', 'law', 'legaltech', 'nlp', 'spacy-models'] | 2021-01-31 | [('coastalcph/lex-glue', 0.6491376757621765, 'nlp', 2), ('explosion/spacy-models', 0.612856388092041, 'nlp', 2), ('explosion/spacy-stanza', 0.5856739282608032, 'nlp', 1), ('thoppe/the-pile-freelaw', 0.585483193397522, 'data', 0), ('lexpredict/lexpredict-lexnlp', 0.582179069519043, 'nlp', 3), ('explosion/spacy-llm', 0.5293763279914856, 'llm', 1)] | 8 | 2 | null | 0 | 0 | 0 | 59 | 36 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 17 |
240 | ml | https://github.com/microsoft/focal-transformer | [] | null | [] | [] | null | null | null | microsoft/focal-transformer | Focal-Transformer | 534 | 58 | 17 | Python | null | [NeurIPS 2021 Spotlight] Official code for "Focal Self-attention for Local-Global Interactions in Vision Transformers" | microsoft | 2024-01-12 | 2021-07-10 | 133 | 4.002141 | https://avatars.githubusercontent.com/u/6154722?v=4 | [NeurIPS 2021 Spotlight] Official code for "Focal Self-attention for Local-Global Interactions in Vision Transformers" | [] | [] | 2022-03-27 | [('nvlabs/gcvit', 0.5749183893203735, 'diffusion', 0), ('abertsch72/unlimiformer', 0.5153154134750366, 'nlp', 0)] | 2 | 1 | null | 0 | 1 | 0 | 31 | 22 | 0 | 0 | 0 | 1 | 0 | 90 | 0 | 17 |
1,001 | viz | https://github.com/cuemacro/chartpy | [] | null | [] | [] | null | null | null | cuemacro/chartpy | chartpy | 534 | 99 | 48 | Python | null | Easy to use Python API wrapper to plot charts with matplotlib, plotly, bokeh and more | cuemacro | 2024-01-04 | 2016-08-03 | 390 | 1.366228 | https://avatars.githubusercontent.com/u/20479975?v=4 | Easy to use Python API wrapper to plot charts with matplotlib, plotly, bokeh and more | ['bokeh', 'chart', 'matplotlib', 'plotly', 'plotting'] | ['bokeh', 'chart', 'matplotlib', 'plotly', 'plotting'] | 2023-10-12 | [('matplotlib/matplotlib', 0.6975510120391846, 'viz', 2), ('holoviz/hvplot', 0.6870236992835999, 'pandas', 1), ('plotly/plotly.py', 0.6865983605384827, 'viz', 1), ('bokeh/bokeh', 0.6531518697738647, 'viz', 2), ('vizzuhq/ipyvizzu', 0.6341903209686279, 'jupyter', 2), ('kanaries/pygwalker', 0.6271337270736694, 'pandas', 2), ('mwaskom/seaborn', 0.6233909130096436, 'viz', 1), ('holoviz/panel', 0.6215455532073975, 'viz', 3), ('nschloe/tikzplotlib', 0.5781739354133606, 'util', 1), ('matplotlib/mplfinance', 0.574644148349762, 'finance', 1), ('federicoceratto/dashing', 0.5717711448669434, 'term', 0), ('scitools/cartopy', 0.5699009299278259, 'gis', 1), ('residentmario/geoplot', 0.5584993958473206, 'gis', 1), ('maartenbreddels/ipyvolume', 0.5550907254219055, 'jupyter', 1), ('holoviz/holoviz', 0.5522693395614624, 'viz', 0), ('man-group/dtale', 0.5520331263542175, 'viz', 0), ('altair-viz/altair', 0.5500401258468628, 'viz', 0), ('plotly/dash', 0.5469133853912354, 'viz', 1), ('holoviz/geoviews', 0.5426660180091858, 'gis', 1), ('has2k1/plotnine', 0.5336882472038269, 'viz', 1), ('lux-org/lux', 0.5205578804016113, 'viz', 0), ('westhealth/pyvis', 0.5167423486709595, 'graph', 0), ('pygraphviz/pygraphviz', 0.5139192342758179, 'viz', 0), ('jakevdp/pythondatasciencehandbook', 0.5052139759063721, 'study', 1), ('graphistry/pygraphistry', 0.5035216212272644, 'data', 0), ('enthought/mayavi', 0.5027343034744263, 'viz', 0), ('jmcnamara/xlsxwriter', 0.500167191028595, 'data', 0)] | 1 | 1 | null | 0.02 | 0 | 0 | 91 | 3 | 1 | 2 | 1 | 0 | 0 | 90 | 0 | 17 |
334 | ml | https://github.com/mrdbourke/m1-machine-learning-test | [] | null | [] | [] | null | null | null | mrdbourke/m1-machine-learning-test | m1-machine-learning-test | 477 | 147 | 16 | Jupyter Notebook | null | Code for testing various M1 Chip benchmarks with TensorFlow. | mrdbourke | 2024-01-14 | 2021-11-14 | 115 | 4.137546 | null | Code for testing various M1 Chip benchmarks with TensorFlow. | ['machine-learning', 'metal', 'tensorflow', 'tensorflow-macos'] | ['machine-learning', 'metal', 'tensorflow', 'tensorflow-macos'] | 2022-07-16 | [('tlkh/tf-metal-experiments', 0.7584832310676575, 'perf', 1), ('intel/intel-extension-for-pytorch', 0.5805241465568542, 'perf', 1), ('klen/py-frameworks-bench', 0.5594583749771118, 'perf', 0), ('arogozhnikov/einops', 0.556743323802948, 'ml-dl', 1), ('microsoft/onnxruntime', 0.5451663136482239, 'ml', 2), ('ionelmc/pytest-benchmark', 0.5336135625839233, 'testing', 0), ('google/tf-quant-finance', 0.5282031893730164, 'finance', 1), ('determined-ai/determined', 0.5143718719482422, 'ml-ops', 2), ('tlkh/asitop', 0.5042293071746826, 'perf', 0), ('ml-explore/mlx', 0.5017737150192261, 'ml', 0), ('pytorch/pytorch', 0.5010038018226624, 'ml-dl', 1), ('plasma-umass/scalene', 0.5002366900444031, 'profiling', 0)] | 2 | 1 | null | 0 | 0 | 0 | 26 | 18 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 17 |
498 | study | https://github.com/googlecloudplatform/practical-ml-vision-book | [] | null | [] | [] | null | null | null | googlecloudplatform/practical-ml-vision-book | practical-ml-vision-book | 425 | 197 | 23 | Jupyter Notebook | null | null | googlecloudplatform | 2024-01-12 | 2020-11-18 | 166 | 2.547089 | https://avatars.githubusercontent.com/u/2810941?v=4 | googlecloudplatform/practical-ml-vision-book | [] | [] | 2023-05-16 | [('googlecloudplatform/vertex-ai-samples', 0.5228831768035889, 'ml', 0), ('developmentseed/label-maker', 0.505915641784668, 'gis', 0), ('googlecloudplatform/dataflow-geobeam', 0.504852831363678, 'gis', 0), ('google/automl', 0.5014958381652832, 'ml', 0)] | 6 | 1 | null | 0.02 | 0 | 0 | 38 | 8 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 17 |
1,156 | gamedev | https://github.com/bitcraft/pytmx | [] | null | [] | [] | null | null | null | bitcraft/pytmx | pytmx | 365 | 76 | 23 | Python | null | Python library to read Tiled Map Editor's TMX maps. | bitcraft | 2024-01-04 | 2012-02-22 | 622 | 0.586009 | null | Python library to read Tiled Map Editor's TMX maps. | [] | [] | 2023-08-18 | [('geopandas/contextily', 0.5879520177841187, 'gis', 0), ('opengeos/leafmap', 0.5061635375022888, 'gis', 0)] | 42 | 2 | null | 0.15 | 3 | 0 | 145 | 5 | 0 | 0 | 0 | 3 | 0 | 90 | 0 | 17 |
512 | data | https://github.com/jovianml/opendatasets | [] | null | [] | [] | null | null | null | jovianml/opendatasets | opendatasets | 298 | 137 | 14 | Python | null | A Python library for downloading datasets from Kaggle, Google Drive, and other online sources. | jovianml | 2024-01-09 | 2020-09-17 | 175 | 1.695935 | https://avatars.githubusercontent.com/u/46194244?v=4 | A Python library for downloading datasets from Kaggle, Google Drive, and other online sources. | ['data-science', 'datasets', 'machine-learning'] | ['data-science', 'datasets', 'machine-learning'] | 2022-11-01 | [('rasbt/mlxtend', 0.6177619695663452, 'ml', 2), ('cuemacro/findatapy', 0.6065632104873657, 'finance', 0), ('nv7-github/googlesearch', 0.5820246338844299, 'util', 0), ('scikit-learn-contrib/imbalanced-learn', 0.5723727941513062, 'ml', 2), ('tensorflow/data-validation', 0.5710594654083252, 'ml-ops', 0), ('krzjoa/awesome-python-data-science', 0.5699965357780457, 'study', 2), ('scikit-learn/scikit-learn', 0.5696676969528198, 'ml', 2), ('pycaret/pycaret', 0.5661379098892212, 'ml', 2), ('firmai/industry-machine-learning', 0.5650241374969482, 'study', 2), ('rasbt/machine-learning-book', 0.5631752610206604, 'study', 1), ('gradio-app/gradio', 0.5612888932228088, 'viz', 2), ('huggingface/evaluate', 0.5602533221244812, 'ml', 1), ('online-ml/river', 0.5532159209251404, 'ml', 2), ('googleapis/google-api-python-client', 0.551531970500946, 'util', 0), ('dylanhogg/awesome-python', 0.5494063496589661, 'study', 2), ('kubeflow-kale/kale', 0.5395414233207703, 'ml-ops', 1), ('ta-lib/ta-lib-python', 0.5373624563217163, 'finance', 0), ('wesm/pydata-book', 0.5370580554008484, 'study', 0), ('erotemic/ubelt', 0.5337167978286743, 'util', 0), ('mattbierbaum/arxiv-public-datasets', 0.5266126990318298, 'data', 0), ('featurelabs/featuretools', 0.5258838534355164, 'ml', 2), ('scrapy/scrapy', 0.5252864360809326, 'data', 0), ('parallel-domain/pd-sdk', 0.5220646262168884, 'data', 1), ('skops-dev/skops', 0.5186297297477722, 'ml-ops', 1), ('polyaxon/datatile', 0.5163763165473938, 'pandas', 1), ('alirezamika/autoscraper', 0.5161617398262024, 'data', 1), ('kubeflow/fairing', 0.5135595202445984, 'ml-ops', 0), ('google/temporian', 0.512967050075531, 'time-series', 0), ('pytorch/data', 0.5112810730934143, 'data', 0), ('imageio/imageio', 0.5112266540527344, 'util', 0), ('dlt-hub/dlt', 0.5093324184417725, 'data', 0), ('pandas-dev/pandas', 0.5063053965568542, 'pandas', 1), ('mito-ds/monorepo', 0.5031294822692871, 'jupyter', 1), ('roniemartinez/dude', 0.5030663013458252, 'util', 0), ('hazyresearch/meerkat', 0.5027737021446228, 'viz', 2), ('merantix-momentum/squirrel-core', 0.5021243095397949, 'ml', 3), ('intake/intake', 0.5019022226333618, 'data', 0), ('probml/pyprobml', 0.5017038583755493, 'ml', 1), ('patchy631/machine-learning', 0.5012773275375366, 'ml', 0), ('radiantearth/radiant-mlhub', 0.5003646016120911, 'gis', 1)] | 3 | 2 | null | 0 | 3 | 0 | 40 | 15 | 0 | 0 | 0 | 3 | 1 | 90 | 0.3 | 17 |
384 | nlp | https://github.com/kootenpv/contractions | [] | null | [] | [] | null | null | null | kootenpv/contractions | contractions | 293 | 38 | 9 | Python | null | Fixes contractions such as `you're` to `you are` | kootenpv | 2024-01-12 | 2016-12-25 | 370 | 0.791281 | null | Fixes contractions such as `you're` to `you are` | [] | [] | 2022-11-15 | [] | 14 | 6 | null | 0 | 0 | 0 | 86 | 14 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 17 |
687 | data | https://github.com/paperswithcode/sota-extractor | [] | null | [] | [] | null | null | null | paperswithcode/sota-extractor | sota-extractor | 279 | 30 | 14 | Python | null | The SOTA extractor pipeline | paperswithcode | 2024-01-07 | 2018-12-07 | 268 | 1.03883 | https://avatars.githubusercontent.com/u/40305508?v=4 | The SOTA extractor pipeline | [] | [] | 2022-03-09 | [('linealabs/lineapy', 0.5607438087463379, 'jupyter', 0), ('unstructured-io/pipeline-sec-filings', 0.537769615650177, 'data', 0), ('facebookresearch/vissl', 0.5020662546157837, 'ml', 0)] | 8 | 3 | null | 0 | 0 | 0 | 62 | 23 | 0 | 3 | 3 | 0 | 0 | 90 | 0 | 17 |
1,299 | llm | https://github.com/salesforce/jaxformer | [] | null | [] | [] | null | null | null | salesforce/jaxformer | jaxformer | 254 | 37 | 7 | Python | null | Minimal library to train LLMs on TPU in JAX with pjit(). | salesforce | 2024-01-12 | 2022-08-29 | 74 | 3.425819 | https://avatars.githubusercontent.com/u/453694?v=4 | Minimal library to train LLMs on TPU in JAX with pjit(). | [] | [] | 2023-07-25 | [('young-geng/easylm', 0.5536481142044067, 'llm', 0), ('alpha-vllm/llama2-accessory', 0.5040596723556519, 'llm', 0)] | 2 | 0 | null | 0.02 | 2 | 1 | 17 | 6 | 0 | 0 | 0 | 2 | 1 | 90 | 0.5 | 17 |
459 | nlp | https://github.com/yoadtew/zero-shot-image-to-text | [] | null | [] | [] | null | null | null | yoadtew/zero-shot-image-to-text | zero-shot-image-to-text | 237 | 36 | 7 | Python | null | Implementation of Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic | yoadtew | 2024-01-05 | 2021-11-26 | 113 | 2.086792 | null | Implementation of Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic | [] | [] | 2022-09-17 | [('sharonzhou/long_stable_diffusion', 0.5378433465957642, 'diffusion', 0), ('lucidrains/dalle2-pytorch', 0.5140002965927124, 'diffusion', 0)] | 6 | 0 | null | 0 | 2 | 0 | 26 | 16 | 0 | 0 | 0 | 2 | 4 | 90 | 2 | 17 |
1,370 | nlp | https://github.com/lingjzhu/charsiug2p | ['phoneme', 'grapheme'] | null | [] | [] | null | null | null | lingjzhu/charsiug2p | CharsiuG2P | 230 | 22 | 10 | Jupyter Notebook | null | Multilingual G2P in 100 languages | lingjzhu | 2024-01-11 | 2022-01-19 | 105 | 2.17274 | null | Multilingual G2P in 100 languages | [] | ['grapheme', 'phoneme'] | 2023-05-26 | [('thudm/chatglm2-6b', 0.5563612580299377, 'llm', 0), ('hannibal046/awesome-llm', 0.5186607241630554, 'study', 0), ('next-gpt/next-gpt', 0.5078827738761902, 'llm', 0), ('gunthercox/chatterbot-corpus', 0.5040284395217896, 'nlp', 0)] | 5 | 2 | null | 0.04 | 2 | 0 | 24 | 8 | 0 | 0 | 0 | 2 | 0 | 90 | 0 | 17 |
1,823 | util | https://github.com/initialcommit-com/git-story | [] | null | [] | [] | null | null | null | initialcommit-com/git-story | git-story | 226 | 8 | 2 | Python | https://initialcommit.com/tools/git-story | Easily create video animations (.mp4) of your Git commit history, directory from your Git repo. | initialcommit-com | 2024-01-05 | 2022-05-12 | 89 | 2.519108 | https://avatars.githubusercontent.com/u/105462693?v=4 | Easily create video animations (.mp4) of your Git commit history, directory from your Git repo. | ['animation', 'collaboration', 'git', 'gitcommand', 'gitcommands', 'gitrepo', 'gitrepository', 'gitstory', 'software-development', 'video', 'visualization', 'viz'] | ['animation', 'collaboration', 'git', 'gitcommand', 'gitcommands', 'gitrepo', 'gitrepository', 'gitstory', 'software-development', 'video', 'visualization', 'viz'] | 2022-07-20 | [] | 1 | 1 | null | 0 | 1 | 0 | 20 | 18 | 0 | 8 | 8 | 1 | 0 | 90 | 0 | 17 |
927 | ml | https://github.com/jonasgeiping/breaching | [] | null | [] | [] | null | null | null | jonasgeiping/breaching | breaching | 225 | 49 | 4 | Python | null | Breaching privacy in federated learning scenarios for vision and text | jonasgeiping | 2024-01-13 | 2022-02-15 | 102 | 2.205882 | null | Breaching privacy in federated learning scenarios for vision and text | ['decentralized-learning', 'federated-learning', 'machine-learning', 'privacy-audit', 'pytorch', 'security'] | ['decentralized-learning', 'federated-learning', 'machine-learning', 'privacy-audit', 'pytorch', 'security'] | 2023-02-09 | [('nevronai/metisfl', 0.6550554037094116, 'ml', 2), ('adap/flower', 0.6508305668830872, 'ml-ops', 3)] | 17 | 2 | null | 0.02 | 0 | 0 | 23 | 11 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 17 |
1,133 | nlp | https://github.com/amansrivastava17/embedding-as-service | [] | null | [] | [] | null | null | null | amansrivastava17/embedding-as-service | embedding-as-service | 196 | 29 | 11 | Python | null | One-Stop Solution to encode sentence to fixed length vectors from various embedding techniques | amansrivastava17 | 2024-01-04 | 2019-05-29 | 243 | 0.803749 | null | One-Stop Solution to encode sentence to fixed length vectors from various embedding techniques | ['ai', 'albert', 'bert', 'bert-as-service', 'deep-learning', 'embedding', 'embedding-as-service', 'embeddings', 'encoder', 'fasttext', 'glove', 'nlp', 'roberta', 'sentence-encoding', 'tensorflow', 'transformer', 'ulmfit', 'word-embedding', 'word2vec', 'xlnet'] | ['ai', 'albert', 'bert', 'bert-as-service', 'deep-learning', 'embedding', 'embedding-as-service', 'embeddings', 'encoder', 'fasttext', 'glove', 'nlp', 'roberta', 'sentence-encoding', 'tensorflow', 'transformer', 'ulmfit', 'word-embedding', 'word2vec', 'xlnet'] | 2022-10-25 | [('jina-ai/clip-as-service', 0.6331066489219666, 'nlp', 4), ('plasticityai/magnitude', 0.6195082068443298, 'nlp', 5), ('google-research/electra', 0.6076592803001404, 'ml-dl', 3), ('jina-ai/finetuner', 0.5800686478614807, 'ml', 1), ('ukplab/sentence-transformers', 0.5738234519958496, 'nlp', 0), ('ddangelov/top2vec', 0.5621562600135803, 'nlp', 1), ('huggingface/text-embeddings-inference', 0.5573744177818298, 'llm', 2), ('llmware-ai/llmware', 0.5347151756286621, 'llm', 4), ('neuml/txtai', 0.5340142250061035, 'nlp', 2), ('muennighoff/sgpt', 0.5335803627967834, 'llm', 0), ('extreme-bert/extreme-bert', 0.5209690928459167, 'llm', 4), ('jina-ai/vectordb', 0.5125251412391663, 'data', 0), ('alibaba/easynlp', 0.5122250318527222, 'nlp', 3), ('deepset-ai/farm', 0.5068530440330505, 'nlp', 4), ('koaning/whatlies', 0.5058193802833557, 'nlp', 2), ('graykode/nlp-tutorial', 0.5044365525245667, 'study', 4)] | 12 | 4 | null | 0 | 0 | 0 | 56 | 15 | 0 | 2 | 2 | 0 | 0 | 90 | 0 | 17 |
1,134 | gis | https://github.com/martibosch/detectree | [] | null | [] | [] | null | null | null | martibosch/detectree | detectree | 171 | 27 | 8 | Python | https://doi.org/10.21105/joss.02172 | Tree detection from aerial imagery in Python | martibosch | 2024-01-04 | 2019-07-31 | 234 | 0.728102 | null | Tree detection from aerial imagery in Python | ['image-segmentation', 'remote-sensing', 'tree-canopy', 'tree-detection', 'tree-pixels'] | ['image-segmentation', 'remote-sensing', 'tree-canopy', 'tree-detection', 'tree-pixels'] | 2022-10-24 | [] | 2 | 2 | null | 0 | 1 | 1 | 54 | 15 | 0 | 2 | 2 | 1 | 1 | 90 | 1 | 17 |
1,278 | sim | https://github.com/elliotwaite/rule-30-and-game-of-life | [] | null | [] | [] | null | null | null | elliotwaite/rule-30-and-game-of-life | rule-30-and-game-of-life | 156 | 13 | 5 | Python | https://youtu.be/IK7nBOLYzdE | Generates a 2D animation of Rule 30 (or other rules) being fed into Conway's Game of Life. | elliotwaite | 2024-01-10 | 2019-11-06 | 220 | 0.706339 | null | Generates a 2D animation of Rule 30 (or other rules) being fed into Conway's Game of Life. | ['cellular-automata', 'conways-game-of-life', 'game-of-life', 'rule-30'] | ['cellular-automata', 'conways-game-of-life', 'game-of-life', 'rule-30'] | 2024-01-11 | [('ljvmiranda921/seagull', 0.7149527072906494, 'sim', 3), ('alephalpha/golly', 0.6694435477256775, 'sim', 2)] | 2 | 1 | null | 0.06 | 2 | 2 | 51 | 0 | 0 | 0 | 0 | 2 | 1 | 90 | 0.5 | 17 |
774 | gis | https://github.com/ghislainv/forestatrisk | [] | null | [] | [] | null | null | null | ghislainv/forestatrisk | forestatrisk | 108 | 26 | 6 | Python | https://ecology.ghislainv.fr/forestatrisk | :package: :snake: Python package to model and forecast the risk of deforestation | ghislainv | 2023-11-16 | 2016-12-01 | 373 | 0.288991 | null | 📦 🐍 Python package to model and forecast the risk of deforestation | ['biodiversity-scenario', 'co2-emissions', 'deforestation', 'deforestation-risk', 'forecasting', 'forest-cover-change', 'ipbes', 'ipcc', 'land-use-change', 'protected-areas', 'redd', 'roads', 'spatial-analysis', 'spatial-autocorrelation', 'spatial-modelling', 'tropical-forests'] | ['biodiversity-scenario', 'co2-emissions', 'deforestation', 'deforestation-risk', 'forecasting', 'forest-cover-change', 'ipbes', 'ipcc', 'land-use-change', 'protected-areas', 'redd', 'roads', 'spatial-analysis', 'spatial-autocorrelation', 'spatial-modelling', 'tropical-forests'] | 2023-12-19 | [] | 6 | 5 | null | 0.63 | 0 | 0 | 87 | 1 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 17 |
268 | term | https://github.com/deeplook/sparklines | [] | null | [] | [] | null | null | null | deeplook/sparklines | sparklines | 102 | 5 | 3 | Python | null | Text-based sparklines for the command line mimicking those of Edward Tuft. | deeplook | 2024-01-05 | 2016-05-17 | 402 | 0.253731 | null | Text-based sparklines for the command line mimicking those of Edward Tuft. | ['ascii', 'command-line-tool', 'graphs', 'sparkline-graphs', 'sparklines'] | ['ascii', 'command-line-tool', 'graphs', 'sparkline-graphs', 'sparklines'] | 2023-10-20 | [('kellyjonbrazil/jc', 0.529207170009613, 'util', 1), ('plotly/plotly.py', 0.5089108943939209, 'viz', 0)] | 7 | 3 | null | 0.25 | 5 | 4 | 93 | 3 | 0 | 0 | 0 | 5 | 1 | 90 | 0.2 | 17 |
917 | gis | https://github.com/benbovy/spherely | ['geometric-algorithms', 'geometry'] | null | [] | [] | null | null | null | benbovy/spherely | spherely | 97 | 4 | 6 | C++ | https://spherely.readthedocs.io/ | Manipulation and analysis of geometric objects on the sphere. | benbovy | 2024-01-04 | 2022-11-24 | 61 | 1.571759 | null | Manipulation and analysis of geometric objects on the sphere. | [] | ['geometric-algorithms', 'geometry'] | 2023-03-20 | [('shapely/shapely', 0.873152494430542, 'gis', 2), ('scikit-geometry/scikit-geometry', 0.5209958553314209, 'gis', 2)] | 4 | 2 | null | 0.13 | 1 | 0 | 14 | 10 | 0 | 0 | 0 | 1 | 1 | 90 | 1 | 17 |
1,039 | finance | https://github.com/numerai/numerai-cli | [] | null | [] | [] | null | null | null | numerai/numerai-cli | numerai-cli | 90 | 29 | 21 | Python | null | Fully automated submission workflow in the cloud for <$1/mo | numerai | 2024-01-11 | 2019-05-22 | 244 | 0.367561 | https://avatars.githubusercontent.com/u/15222762?v=4 | Fully automated submission workflow in the cloud for <$1/mo | [] | [] | 2023-12-09 | [('prefecthq/server', 0.5039493441581726, 'util', 0)] | 14 | 2 | null | 0.52 | 7 | 5 | 57 | 1 | 0 | 0 | 0 | 7 | 0 | 90 | 0 | 17 |
1,031 | finance | https://github.com/wilsonfreitas/python-bizdays | [] | null | [] | [] | null | null | null | wilsonfreitas/python-bizdays | python-bizdays | 73 | 34 | 9 | Jupyter Notebook | http://wilsonfreitas.github.io/python-bizdays/ | Business days calculations and utilities | wilsonfreitas | 2024-01-13 | 2013-09-01 | 543 | 0.134368 | null | Business days calculations and utilities | [] | [] | 2023-12-29 | [] | 7 | 1 | null | 0.27 | 10 | 9 | 126 | 1 | 0 | 0 | 0 | 10 | 5 | 90 | 0.5 | 17 |
492 | ml-dl | https://github.com/hysts/pytorch_image_classification | [] | null | [] | [] | null | null | null | hysts/pytorch_image_classification | pytorch_image_classification | 1,288 | 296 | 27 | Python | null | PyTorch implementation of image classification models for CIFAR-10/CIFAR-100/MNIST/FashionMNIST/Kuzushiji-MNIST/ImageNet | hysts | 2024-01-13 | 2017-12-09 | 320 | 4.019617 | null | PyTorch implementation of image classification models for CIFAR-10/CIFAR-100/MNIST/FashionMNIST/Kuzushiji-MNIST/ImageNet | ['cifar10', 'computer-vision', 'fashion-mnist', 'imagenet', 'pytorch'] | ['cifar10', 'computer-vision', 'fashion-mnist', 'imagenet', 'pytorch'] | 2021-12-12 | [('lucidrains/imagen-pytorch', 0.6431624889373779, 'ml-dl', 0), ('nvlabs/gcvit', 0.6284846067428589, 'diffusion', 1), ('skorch-dev/skorch', 0.619719386100769, 'ml-dl', 1), ('pytorch/ignite', 0.615060031414032, 'ml-dl', 1), ('rwightman/pytorch-image-models', 0.5974335074424744, 'ml-dl', 1), ('intel/intel-extension-for-pytorch', 0.5713286399841309, 'perf', 1), ('nyandwi/modernconvnets', 0.5707684755325317, 'ml-dl', 1), ('huggingface/accelerate', 0.5682789087295532, 'ml', 0), ('nvidia/apex', 0.5612041354179382, 'ml-dl', 0), ('lucidrains/vit-pytorch', 0.5473499298095703, 'ml-dl', 1), ('salesforce/blip', 0.5436573624610901, 'diffusion', 0), ('lightly-ai/lightly', 0.5435891151428223, 'ml', 2), ('roboflow/supervision', 0.538576066493988, 'ml', 2), ('deci-ai/super-gradients', 0.5351361632347107, 'ml-dl', 3), ('lutzroeder/netron', 0.5342903137207031, 'ml', 1), ('rasbt/machine-learning-book', 0.5264284610748291, 'study', 1), ('pytorch/captum', 0.5230705142021179, 'ml-interpretability', 0), ('lucidrains/dalle2-pytorch', 0.519826352596283, 'diffusion', 0), ('pyg-team/pytorch_geometric', 0.5179129242897034, 'ml-dl', 1), ('mrdbourke/pytorch-deep-learning', 0.5127461552619934, 'study', 1), ('keras-team/keras-cv', 0.5079523921012878, 'ml-dl', 1), ('mchong6/jojogan', 0.5051544308662415, 'data', 0), ('mcahny/deep-video-inpainting', 0.5014491677284241, 'ml-dl', 0)] | 1 | 0 | null | 0 | 0 | 0 | 74 | 25 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 16 |
781 | study | https://github.com/mynameisfiber/high_performance_python_2e | [] | null | [] | [] | null | null | null | mynameisfiber/high_performance_python_2e | high_performance_python_2e | 371 | 129 | 9 | Python | null | Code for the book "High Performance Python 2e" by Micha Gorelick and Ian Ozsvald with OReilly | mynameisfiber | 2024-01-04 | 2020-04-12 | 198 | 1.871037 | null | Code for the book "High Performance Python 2e" by Micha Gorelick and Ian Ozsvald with OReilly | ['code-samples', 'high-performance', 'oreilly', 'oreilly-books'] | ['code-samples', 'high-performance', 'oreilly', 'oreilly-books'] | 2023-01-18 | [('fchollet/deep-learning-with-python-notebooks', 0.6630170941352844, 'study', 0), ('wesm/pydata-book', 0.6341950297355652, 'study', 0), ('probml/pyprobml', 0.6130873560905457, 'ml', 0), ('python/cpython', 0.5944276452064514, 'util', 0), ('cohere-ai/notebooks', 0.5679949522018433, 'llm', 0), ('gbeced/pyalgotrade', 0.5649918913841248, 'finance', 0), ('eleutherai/pyfra', 0.5524687170982361, 'ml', 0), ('pypy/pypy', 0.549612820148468, 'util', 0), ('klen/py-frameworks-bench', 0.5484030842781067, 'perf', 0), ('jakevdp/pythondatasciencehandbook', 0.5477574467658997, 'study', 0), ('sympy/sympy', 0.5473185777664185, 'math', 0), ('astral-sh/ruff', 0.542158305644989, 'util', 0), ('pytoolz/toolz', 0.5383166670799255, 'util', 0), ('brandon-rhodes/python-patterns', 0.5350989699363708, 'util', 0), ('gerdm/prml', 0.5304524302482605, 'study', 0), ('ageron/handson-ml2', 0.5303263068199158, 'ml', 0), ('ta-lib/ta-lib-python', 0.5237243175506592, 'finance', 0), ('pyston/pyston', 0.5236924290657043, 'util', 0), ('amaargiru/pyroad', 0.5223120450973511, 'study', 0), ('faster-cpython/tools', 0.5218332409858704, 'perf', 0), ('realpython/python-guide', 0.5209663510322571, 'study', 0), ('google/latexify_py', 0.518977165222168, 'util', 0), ('rubik/radon', 0.5166555643081665, 'util', 0), ('cuemacro/finmarketpy', 0.513410210609436, 'finance', 0), ('scipy/scipy', 0.5127685070037842, 'math', 0), ('google/pytype', 0.5118113160133362, 'typing', 0), ('google/yapf', 0.5080813765525818, 'util', 0), ('renpy/renpy', 0.5067214369773865, 'viz', 0), ('adafruit/circuitpython', 0.5031493902206421, 'util', 0), ('faster-cpython/ideas', 0.5015802979469299, 'perf', 0)] | 2 | 2 | null | 0 | 0 | 0 | 46 | 12 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 16 |
462 | data | https://github.com/dmarx/psaw | [] | null | [] | [] | null | null | null | dmarx/psaw | psaw | 358 | 58 | 9 | Python | null | Python Pushshift.io API Wrapper (for comment/submission search) | dmarx | 2024-01-12 | 2018-04-15 | 302 | 1.18431 | null | Python Pushshift.io API Wrapper (for comment/submission search) | [] | [] | 2022-07-09 | [('meilisearch/meilisearch-python', 0.5636150240898132, 'data', 0)] | 8 | 3 | null | 0 | 0 | 0 | 70 | 18 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 16 |
583 | data | https://github.com/tokern/data-lineage | [] | null | [] | [] | null | null | null | tokern/data-lineage | data-lineage | 291 | 41 | 8 | Python | https://tokern.io/data-lineage/ | Generate and Visualize Data Lineage from query history | tokern | 2024-01-04 | 2020-03-17 | 202 | 1.440594 | https://avatars.githubusercontent.com/u/57188591?v=4 | Generate and Visualize Data Lineage from query history | ['data-governance', 'data-lineage', 'jupyter', 'postgresql'] | ['data-governance', 'data-lineage', 'jupyter', 'postgresql'] | 2023-08-04 | [('airbytehq/airbyte', 0.5245165824890137, 'data', 1), ('man-group/dtale', 0.5029366612434387, 'viz', 0)] | 5 | 0 | null | 0.02 | 0 | 0 | 47 | 5 | 0 | 7 | 7 | 0 | 0 | 90 | 0 | 16 |
57 | term | https://github.com/click-contrib/click-completion | ['click', 'shell'] | null | [] | [] | null | null | null | click-contrib/click-completion | click-completion | 281 | 32 | 8 | Python | null | Add or enhance bash, fish, zsh and powershell completion in Click | click-contrib | 2024-01-13 | 2016-07-23 | 392 | 0.716054 | https://avatars.githubusercontent.com/u/13245136?v=4 | Add or enhance bash, fish, zsh and powershell completion in Click | [] | ['click', 'shell'] | 2022-05-09 | [('textualize/trogon', 0.5366969704627991, 'term', 1)] | 16 | 4 | null | 0 | 0 | 0 | 91 | 21 | 0 | 1 | 1 | 0 | 0 | 90 | 0 | 16 |
229 | data | https://github.com/microsoft/genalog | [] | null | [] | [] | null | null | null | microsoft/genalog | genalog | 280 | 25 | 12 | Jupyter Notebook | https://microsoft.github.io/genalog/ | Genalog is an open source, cross-platform python package allowing generation of synthetic document images with custom degradations and text alignment capabilities. | microsoft | 2024-01-12 | 2020-06-15 | 189 | 1.480363 | https://avatars.githubusercontent.com/u/6154722?v=4 | Genalog is an open source, cross-platform python package allowing generation of synthetic document images with custom degradations and text alignment capabilities. | ['data-generation', 'data-science', 'machine-learning', 'ner', 'ocr-recognition', 'synthetic-data', 'synthetic-data-generation', 'synthetic-images', 'text-alignment'] | ['data-generation', 'data-science', 'machine-learning', 'ner', 'ocr-recognition', 'synthetic-data', 'synthetic-data-generation', 'synthetic-images', 'text-alignment'] | 2023-02-14 | [('pyfpdf/fpdf2', 0.5020582675933838, 'util', 0)] | 7 | 1 | null | 0.02 | 1 | 0 | 44 | 11 | 0 | 2 | 2 | 1 | 0 | 90 | 0 | 16 |
1,262 | data | https://github.com/weaviate/semantic-search-through-wikipedia-with-weaviate | ['vector-search'] | null | [] | [] | null | null | null | weaviate/semantic-search-through-wikipedia-with-weaviate | semantic-search-through-wikipedia-with-weaviate | 235 | 22 | 8 | Python | null | Semantic search through a vectorized Wikipedia (SentenceBERT) with the Weaviate vector search engine | weaviate | 2024-01-04 | 2021-10-26 | 118 | 1.991525 | https://avatars.githubusercontent.com/u/37794290?v=4 | Semantic search through a vectorized Wikipedia (SentenceBERT) with the Weaviate vector search engine | [] | ['vector-search'] | 2023-05-31 | [('goldsmith/wikipedia', 0.5848309397697449, 'data', 0), ('weaviate/demo-text2vec-openai', 0.55460125207901, 'util', 1), ('harangju/wikinet', 0.5428258776664734, 'data', 0), ('muennighoff/sgpt', 0.5272648334503174, 'llm', 0), ('neuml/txtai', 0.5210995674133301, 'nlp', 1), ('qdrant/vector-db-benchmark', 0.5176312923431396, 'perf', 1)] | 2 | 2 | null | 0.12 | 0 | 0 | 27 | 8 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 16 |
891 | gis | https://github.com/kuanb/peartree | [] | null | [] | [] | null | null | null | kuanb/peartree | peartree | 201 | 20 | 13 | Python | null | peartree: A library for converting transit data into a directed graph for sketch network analysis. | kuanb | 2024-01-11 | 2017-11-12 | 324 | 0.619824 | null | peartree: A library for converting transit data into a directed graph for sketch network analysis. | ['gis', 'graphs', 'gtfs', 'modeling', 'network-analysis', 'spatial-analysis', 'transit'] | ['gis', 'graphs', 'gtfs', 'modeling', 'network-analysis', 'spatial-analysis', 'transit'] | 2021-01-18 | [('networkx/networkx', 0.532261312007904, 'graph', 0), ('graphistry/pygraphistry', 0.5183374285697937, 'data', 1), ('westhealth/pyvis', 0.5152437090873718, 'graph', 0), ('h4kor/graph-force', 0.5150353908538818, 'graph', 0), ('artelys/geonetworkx', 0.5083203911781311, 'gis', 0), ('pygraphviz/pygraphviz', 0.5017895698547363, 'viz', 0)] | 5 | 2 | null | 0 | 3 | 0 | 75 | 36 | 0 | 2 | 2 | 3 | 2 | 90 | 0.7 | 16 |
1,863 | sim | https://github.com/inspirai/timechamber | [] | null | [] | [] | null | null | null | inspirai/timechamber | TimeChamber | 177 | 21 | 8 | Python | null | A Massively Parallel Large Scale Self-Play Framework | inspirai | 2024-01-11 | 2022-08-17 | 75 | 2.333333 | https://avatars.githubusercontent.com/u/44988657?v=4 | A Massively Parallel Large Scale Self-Play Framework | ['deep-reinforcement-learning', 'isaac-gym', 'multi-agent', 'reinforcement-learning', 'self-play'] | ['deep-reinforcement-learning', 'isaac-gym', 'multi-agent', 'reinforcement-learning', 'self-play'] | 2023-01-09 | [('salesforce/warp-drive', 0.6329183578491211, 'ml-rl', 1), ('unity-technologies/ml-agents', 0.6286770105361938, 'ml-rl', 2), ('thu-ml/tianshou', 0.5735207796096802, 'ml-rl', 0), ('pettingzoo-team/pettingzoo', 0.5665203332901001, 'ml-rl', 1), ('farama-foundation/gymnasium', 0.5623535513877869, 'ml-rl', 1), ('nvidia-omniverse/isaacgymenvs', 0.56003338098526, 'sim', 0), ('facebookresearch/habitat-lab', 0.5581255555152893, 'sim', 2), ('nvidia-omniverse/omniisaacgymenvs', 0.5575793981552124, 'sim', 0), ('minedojo/voyager', 0.5301423072814941, 'llm', 0), ('pytorch/rl', 0.5206745862960815, 'ml-rl', 1), ('keras-rl/keras-rl', 0.5203887224197388, 'ml-rl', 1), ('operand/agency', 0.5097473859786987, 'llm', 0), ('humancompatibleai/imitation', 0.50629061460495, 'ml-rl', 0), ('google/trax', 0.5059916377067566, 'ml-dl', 2), ('openai/baselines', 0.5040830969810486, 'ml-rl', 0), ('google/dopamine', 0.5013951063156128, 'ml-rl', 0)] | 4 | 2 | null | 0 | 0 | 0 | 17 | 12 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 16 |
661 | gis | https://github.com/zorzi-s/polyworldpretrainednetwork | [] | null | [] | [] | null | null | null | zorzi-s/polyworldpretrainednetwork | PolyWorldPretrainedNetwork | 146 | 27 | 6 | Python | null | PolyWorld: Polygonal Building Extraction with Graph Neural Networks in Satellite Images | zorzi-s | 2024-01-10 | 2022-03-23 | 96 | 1.507375 | null | PolyWorld: Polygonal Building Extraction with Graph Neural Networks in Satellite Images | [] | [] | 2022-11-10 | [('lydorn/polygonization-by-frame-field-learning', 0.6778478026390076, 'gis', 0), ('microsoft/globalmlbuildingfootprints', 0.600134015083313, 'gis', 0)] | 1 | 0 | null | 0 | 4 | 1 | 22 | 14 | 0 | 0 | 0 | 4 | 13 | 90 | 3.2 | 16 |
1,340 | llm | https://github.com/prefecthq/langchain-prefect | ['langchain'] | null | [] | [] | null | null | null | prefecthq/langchain-prefect | langchain-prefect | 92 | 3 | 3 | Python | https://prefecthq.github.io/langchain-prefect/ | Tools for using Langchain with Prefect | prefecthq | 2024-01-04 | 2023-03-06 | 47 | 1.951515 | https://avatars.githubusercontent.com/u/39270919?v=4 | Tools for using Langchain with Prefect | ['langchain', 'large-language-models', 'prefect'] | ['langchain', 'large-language-models', 'prefect'] | 2023-06-08 | [('gkamradt/langchain-tutorials', 0.7797976732254028, 'study', 0), ('hannibal046/awesome-llm', 0.6407128572463989, 'study', 0), ('ctlllll/llm-toolmaker', 0.6256077885627747, 'llm', 0), ('langchain-ai/langgraph', 0.6234740018844604, 'llm', 1), ('lianjiatech/belle', 0.6042580604553223, 'llm', 0), ('freedomintelligence/llmzoo', 0.5867060422897339, 'llm', 0), ('ai21labs/lm-evaluation', 0.5636144280433655, 'llm', 0), ('alphasecio/langchain-examples', 0.5599607229232788, 'llm', 1), ('cg123/mergekit', 0.5573033094406128, 'llm', 0), ('guidance-ai/guidance', 0.5558692812919617, 'llm', 0), ('logspace-ai/langflow', 0.548227071762085, 'llm', 2), ('juncongmoo/pyllama', 0.5437901616096497, 'llm', 0), ('baichuan-inc/baichuan-13b', 0.5365051627159119, 'llm', 1), ('conceptofmind/toolformer', 0.536091685295105, 'llm', 0), ('salesforce/xgen', 0.5343795418739319, 'llm', 1), ('togethercomputer/redpajama-data', 0.5340298414230347, 'llm', 0), ('hiyouga/llama-factory', 0.5244020819664001, 'llm', 1), ('hiyouga/llama-efficient-tuning', 0.5244019627571106, 'llm', 1), ('lm-sys/fastchat', 0.5175867080688477, 'llm', 0), ('microsoft/autogen', 0.5145857334136963, 'llm', 0), ('langchain-ai/langsmith-sdk', 0.5083007216453552, 'llm', 0), ('hwchase17/langchain', 0.5060675740242004, 'llm', 1), ('nat/openplayground', 0.5041869282722473, 'llm', 0), ('oobabooga/text-generation-webui', 0.5021174550056458, 'llm', 0), ('infinitylogesh/mutate', 0.50165855884552, 'nlp', 0)] | 2 | 1 | null | 1.27 | 0 | 0 | 10 | 7 | 3 | 4 | 3 | 0 | 0 | 90 | 0 | 16 |
1,604 | term | https://github.com/kellyjonbrazil/jellex | [] | null | [] | [] | null | null | null | kellyjonbrazil/jellex | jellex | 91 | 1 | 2 | Python | null | TUI to filter JSON and JSON Lines data with Python syntax | kellyjonbrazil | 2023-12-28 | 2021-06-29 | 135 | 0.674074 | null | TUI to filter JSON and JSON Lines data with Python syntax | ['filter', 'json', 'json-lines', 'process', 'query', 'tui'] | ['filter', 'json', 'json-lines', 'process', 'query', 'tui'] | 2023-10-24 | [('kellyjonbrazil/jello', 0.7509535551071167, 'util', 5)] | 2 | 0 | null | 0.04 | 1 | 1 | 31 | 3 | 0 | 7 | 7 | 1 | 1 | 90 | 1 | 16 |
451 | gis | https://github.com/googlecloudplatform/dataflow-geobeam | [] | null | [] | [] | null | null | null | googlecloudplatform/dataflow-geobeam | dataflow-geobeam | 85 | 28 | 11 | Python | null | null | googlecloudplatform | 2023-11-24 | 2021-02-04 | 155 | 0.545872 | https://avatars.githubusercontent.com/u/2810941?v=4 | googlecloudplatform/dataflow-geobeam | [] | [] | 2023-07-10 | [('googlecloudplatform/practical-ml-vision-book', 0.504852831363678, 'study', 0)] | 7 | 4 | null | 0.29 | 0 | 0 | 36 | 6 | 0 | 2 | 2 | 0 | 0 | 90 | 0 | 16 |
234 | crypto | https://github.com/blockchainsllc/in3 | [] | null | [] | [] | null | null | null | blockchainsllc/in3 | in3 | 73 | 29 | 13 | C | https://in3.readthedocs.io/en/develop/index.html | The IN3 client (written in C). | blockchainsllc | 2023-11-19 | 2019-09-17 | 228 | 0.320175 | https://avatars.githubusercontent.com/u/12978006?v=4 | The IN3 client (written in C). | ['blockchain', 'crypto-economic', 'ethereum', 'ipfs', 'verify'] | ['blockchain', 'crypto-economic', 'ethereum', 'ipfs', 'verify'] | 2022-04-01 | [] | 35 | 3 | null | 0 | 0 | 0 | 53 | 22 | 0 | 21 | 21 | 0 | 0 | 90 | 0 | 16 |
1,100 | template | https://github.com/giswqs/pypackage | [] | null | [] | [] | null | null | null | giswqs/pypackage | pypackage | 45 | 16 | 2 | Python | https://giswqs.github.io/pypackage | Cookiecutter template creating a Python package with mkdocs | giswqs | 2023-12-16 | 2020-11-15 | 167 | 0.269001 | null | Cookiecutter template creating a Python package with mkdocs | ['cookiecutter', 'cookiecutter-template', 'mkdocs', 'mkdocs-material', 'template', 'template-project'] | ['cookiecutter', 'cookiecutter-template', 'mkdocs', 'mkdocs-material', 'template', 'template-project'] | 2023-07-31 | [('ionelmc/cookiecutter-pylibrary', 0.8761537075042725, 'template', 3), ('lyz-code/cookiecutter-python-project', 0.815499484539032, 'template', 1), ('tedivm/robs_awesome_python_template', 0.7693808078765869, 'template', 1), ('cookiecutter/cookiecutter', 0.7356756329536438, 'template', 1), ('buuntu/fastapi-react', 0.6124856472015381, 'template', 1), ('cjolowicz/cookiecutter-hypermodern-python', 0.5635570883750916, 'template', 0), ('tezromach/python-package-template', 0.5540108680725098, 'template', 2)] | 109 | 5 | null | 0.21 | 0 | 0 | 38 | 6 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 16 |
979 | sim | https://github.com/alephalpha/golly | [] | null | [] | [] | null | null | null | alephalpha/golly | golly | 40 | 8 | 5 | C++ | http://sourceforge.net/projects/golly/ | Golly, a Game of Life simulator (unofficial mirror from SourceForge) | alephalpha | 2023-11-24 | 2018-07-10 | 290 | 0.137931 | null | Golly, a Game of Life simulator (unofficial mirror from SourceForge) | ['cellular-automata', 'game-of-life'] | ['cellular-automata', 'game-of-life'] | 2023-11-04 | [('ljvmiranda921/seagull', 0.7014067769050598, 'sim', 2), ('elliotwaite/rule-30-and-game-of-life', 0.6694435477256775, 'sim', 2), ('projectmesa/mesa', 0.5125412344932556, 'sim', 0), ('lordmauve/pgzero', 0.5084664821624756, 'gamedev', 0), ('pokepetter/ursina', 0.5002817511558533, 'gamedev', 0)] | 22 | 4 | null | 0.54 | 0 | 0 | 67 | 2 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 16 |
1,795 | ml | https://github.com/hazyresearch/hgcn | [] | null | [] | [] | null | null | null | hazyresearch/hgcn | hgcn | 526 | 111 | 27 | Python | null | Hyperbolic Graph Convolutional Networks in PyTorch. | hazyresearch | 2024-01-12 | 2019-09-30 | 226 | 2.325963 | https://avatars.githubusercontent.com/u/2165246?v=4 | Hyperbolic Graph Convolutional Networks in PyTorch. | [] | [] | 2020-10-03 | [('pyg-team/pytorch_geometric', 0.6979220509529114, 'ml-dl', 0), ('danielegrattarola/spektral', 0.5537418723106384, 'ml-dl', 0), ('pytorch/ignite', 0.551753580570221, 'ml-dl', 0), ('dmlc/dgl', 0.5476505160331726, 'ml-dl', 0), ('mrdbourke/pytorch-deep-learning', 0.5278249979019165, 'study', 0), ('graphistry/pygraphistry', 0.525396466255188, 'data', 0), ('hamed1375/exphormer', 0.5110289454460144, 'graph', 0), ('tensorflow/mesh', 0.5072011351585388, 'ml-dl', 0), ('nvidia/apex', 0.502190113067627, 'ml-dl', 0), ('nicolas-chaulet/torch-points3d', 0.5006260871887207, 'ml', 0), ('stellargraph/stellargraph', 0.500386118888855, 'graph', 0)] | 2 | 0 | null | 0 | 4 | 0 | 52 | 40 | 0 | 0 | 0 | 4 | 3 | 90 | 0.8 | 15 |
495 | ml-dl | https://github.com/mcahny/deep-video-inpainting | [] | null | [] | [] | null | null | null | mcahny/deep-video-inpainting | Deep-Video-Inpainting | 488 | 93 | 14 | Python | null | Official pytorch implementation for "Deep Video Inpainting" (CVPR 2019) | mcahny | 2024-01-04 | 2019-05-22 | 244 | 1.992999 | null | Official pytorch implementation for "Deep Video Inpainting" (CVPR 2019) | [] | [] | 2020-12-10 | [('researchmm/sttn', 0.6987395882606506, 'ml-dl', 0), ('nvlabs/gcvit', 0.5919110774993896, 'diffusion', 0), ('vt-vl-lab/fgvc', 0.5231187343597412, 'ml-dl', 0), ('nvidia/apex', 0.514928936958313, 'ml-dl', 0), ('hysts/pytorch_image_classification', 0.5014491677284241, 'ml-dl', 0), ('timothybrooks/instruct-pix2pix', 0.500446081161499, 'diffusion', 0)] | 3 | 2 | null | 0 | 0 | 0 | 57 | 38 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 15 |
471 | ml-dl | https://github.com/nyandwi/modernconvnets | [] | null | [] | [] | null | null | null | nyandwi/modernconvnets | ModernConvNets | 323 | 36 | 8 | Jupyter Notebook | null | Revisions and implementations of modern Convolutional Neural Networks architectures in TensorFlow and Keras | nyandwi | 2024-01-09 | 2022-02-10 | 102 | 3.144645 | null | Revisions and implementations of modern Convolutional Neural Networks architectures in TensorFlow and Keras | ['cnns', 'computer-vision', 'convnets', 'convolutional-neural-networks', 'deep-learning-algorithms', 'image-classification', 'keras', 'neural-networks', 'tensorflow'] | ['cnns', 'computer-vision', 'convnets', 'convolutional-neural-networks', 'deep-learning-algorithms', 'image-classification', 'keras', 'neural-networks', 'tensorflow'] | 2022-10-05 | [('rwightman/pytorch-image-models', 0.6331978440284729, 'ml-dl', 0), ('tensorflow/tensorflow', 0.6226397752761841, 'ml-dl', 1), ('keras-team/keras', 0.6126129627227783, 'ml-dl', 2), ('onnx/onnx', 0.6126094460487366, 'ml', 2), ('keras-team/keras-cv', 0.6089338064193726, 'ml-dl', 2), ('danielegrattarola/spektral', 0.6024564504623413, 'ml-dl', 2), ('microsoft/onnxruntime', 0.5876009464263916, 'ml', 2), ('roboflow/supervision', 0.5865331292152405, 'ml', 2), ('ddbourgin/numpy-ml', 0.5848450660705566, 'ml', 1), ('lutzroeder/netron', 0.584083616733551, 'ml', 2), ('keras-rl/keras-rl', 0.58319491147995, 'ml-rl', 3), ('deci-ai/super-gradients', 0.5777014493942261, 'ml-dl', 2), ('hysts/pytorch_image_classification', 0.5707684755325317, 'ml-dl', 1), ('lucidrains/imagen-pytorch', 0.5683515667915344, 'ml-dl', 0), ('tensorflow/addons', 0.567768931388855, 'ml', 1), ('neuralmagic/sparseml', 0.567727267742157, 'ml-dl', 4), ('horovod/horovod', 0.5646082162857056, 'ml-ops', 2), ('intel/intel-extension-for-pytorch', 0.564269483089447, 'perf', 0), ('huggingface/datasets', 0.5611058473587036, 'nlp', 2), ('xl0/lovely-tensors', 0.5606785416603088, 'ml-dl', 0), ('pytorchlightning/pytorch-lightning', 0.5557078123092651, 'ml-dl', 0), ('pytorch/ignite', 0.5554874539375305, 'ml-dl', 0), ('pyg-team/pytorch_geometric', 0.5547471046447754, 'ml-dl', 0), ('nvlabs/gcvit', 0.5547017455101013, 'diffusion', 0), ('arogozhnikov/einops', 0.5543664693832397, 'ml-dl', 2), ('tensorlayer/tensorlayer', 0.5535702705383301, 'ml-rl', 1), ('matterport/mask_rcnn', 0.5529733300209045, 'ml-dl', 2), ('pytorch/pytorch', 0.5467002391815186, 'ml-dl', 0), ('nvidia/deeplearningexamples', 0.5466137528419495, 'ml-dl', 2), ('keras-team/keras-nlp', 0.5431491136550903, 'nlp', 2), ('neuralmagic/deepsparse', 0.5420705080032349, 'nlp', 1), ('rasbt/deeplearning-models', 0.5375327467918396, 'ml-dl', 0), ('christoschristofidis/awesome-deep-learning', 0.5358662605285645, 'study', 0), ('amanchadha/coursera-deep-learning-specialization', 0.5350536704063416, 'study', 3), ('explosion/thinc', 0.5349946618080139, 'ml-dl', 1), ('huggingface/transformers', 0.5339410305023193, 'nlp', 1), ('datasystemslab/geotorch', 0.5321657061576843, 'gis', 0), ('aistream-peelout/flow-forecast', 0.5294846296310425, 'time-series', 0), ('rasbt/machine-learning-book', 0.5279679298400879, 'study', 1), ('towhee-io/towhee', 0.5275624394416809, 'ml-ops', 1), ('tensorflow/similarity', 0.5236626863479614, 'ml-dl', 1), ('roboflow/notebooks', 0.5212321877479553, 'study', 2), ('determined-ai/determined', 0.5204993486404419, 'ml-ops', 2), ('tensorly/tensorly', 0.5165544748306274, 'ml-dl', 1), ('mrdbourke/pytorch-deep-learning', 0.5134690999984741, 'study', 0), ('fepegar/torchio', 0.5130273699760437, 'ml-dl', 0), ('mosaicml/composer', 0.5126325488090515, 'ml-dl', 1), ('tlkh/tf-metal-experiments', 0.5091290473937988, 'perf', 1), ('skorch-dev/skorch', 0.509011447429657, 'ml-dl', 0), ('alpa-projects/alpa', 0.5051084160804749, 'ml-dl', 0), ('microsoft/semi-supervised-learning', 0.5045065879821777, 'ml', 1), ('blakeblackshear/frigate', 0.5042876601219177, 'util', 1), ('google/tf-quant-finance', 0.501559853553772, 'finance', 1), ('mrdbourke/tensorflow-deep-learning', 0.5007387399673462, 'study', 1)] | 1 | 1 | null | 0 | 0 | 0 | 23 | 16 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 15 |
1,839 | finance | https://github.com/stefmolin/stock-analysis | [] | null | [] | [] | null | null | null | stefmolin/stock-analysis | stock-analysis | 244 | 98 | 5 | Python | null | Simple to use interfaces for basic technical analysis of stocks. | stefmolin | 2024-01-14 | 2019-01-27 | 261 | 0.933844 | null | Simple to use interfaces for basic technical analysis of stocks. | ['bitcoin-price', 'stock-analysis', 'stock-data', 'stock-indexes', 'stock-market', 'stock-model', 'stock-prediction', 'stock-price-prediction', 'stock-prices', 'stock-visualizer', 'technical-analysis'] | ['bitcoin-price', 'stock-analysis', 'stock-data', 'stock-indexes', 'stock-market', 'stock-model', 'stock-prediction', 'stock-price-prediction', 'stock-prices', 'stock-visualizer', 'technical-analysis'] | 2023-01-22 | [('polakowo/vectorbt', 0.5499805212020874, 'finance', 0), ('ranaroussi/quantstats', 0.5322091579437256, 'finance', 0), ('hydrosquall/tiingo-python', 0.5315351486206055, 'finance', 2), ('ranaroussi/yfinance', 0.5187216401100159, 'finance', 1), ('openbb-finance/openbbterminal', 0.518646776676178, 'finance', 0), ('zvtvz/zvt', 0.5058038830757141, 'finance', 2), ('twopirllc/pandas-ta', 0.5048727989196777, 'finance', 2)] | 1 | 1 | null | 0.02 | 2 | 2 | 60 | 12 | 0 | 1 | 1 | 2 | 1 | 90 | 0.5 | 15 |
1,621 | data | https://github.com/samuelcolvin/aioaws | [] | null | [] | [] | null | null | null | samuelcolvin/aioaws | aioaws | 164 | 13 | 7 | Python | https://pypi.org/project/aioaws/ | Asyncio compatible SDK for aws services. | samuelcolvin | 2023-12-18 | 2020-03-25 | 200 | 0.816501 | null | Asyncio compatible SDK for aws services. | ['asyncio', 'aws', 'python38', 'python39', 's3', 'ses'] | ['asyncio', 'aws', 'python38', 'python39', 's3', 'ses'] | 2023-01-11 | [('jordaneremieff/mangum', 0.7040007710456848, 'web', 2), ('aio-libs/aiobotocore', 0.6990792155265808, 'util', 2), ('geeogi/async-python-lambda-template', 0.687824547290802, 'template', 0), ('boto/boto3', 0.6758776307106018, 'util', 1), ('aio-libs/aiohttp', 0.6308304667472839, 'web', 1), ('pallets/quart', 0.5853649377822876, 'web', 1), ('aws/chalice', 0.5785471200942993, 'web', 1), ('timofurrer/awesome-asyncio', 0.5785270929336548, 'study', 1), ('encode/httpx', 0.5720120072364807, 'web', 1), ('aws/aws-lambda-python-runtime-interface-client', 0.5471480488777161, 'util', 0), ('magicstack/uvloop', 0.5443870425224304, 'util', 1), ('aio-libs/aiokafka', 0.5397475957870483, 'data', 1), ('nficano/python-lambda', 0.5388737916946411, 'util', 1), ('samuelcolvin/arq', 0.5388724207878113, 'data', 1), ('alirn76/panther', 0.5380411148071289, 'web', 0), ('pytest-dev/pytest-asyncio', 0.5367757081985474, 'testing', 1), ('encode/uvicorn', 0.5331629514694214, 'web', 1), ('pynamodb/pynamodb', 0.521031379699707, 'data', 1), ('agronholm/anyio', 0.5206239223480225, 'perf', 1), ('terrycain/aioboto3', 0.5090085864067078, 'util', 1), ('alex-sherman/unsync', 0.507796049118042, 'util', 0), ('python-trio/trio', 0.5013687610626221, 'perf', 0)] | 5 | 2 | null | 0 | 0 | 0 | 46 | 12 | 0 | 3 | 3 | 0 | 0 | 90 | 0 | 15 |
315 | util | https://github.com/irmen/pyminiaudio | [] | null | [] | [] | null | null | null | irmen/pyminiaudio | pyminiaudio | 152 | 19 | 6 | C | null | python interface to the miniaudio audio playback, recording, decoding and conversion library | irmen | 2024-01-04 | 2019-06-30 | 239 | 0.635224 | null | python interface to the miniaudio audio playback, recording, decoding and conversion library | [] | [] | 2023-06-13 | [('spotify/pedalboard', 0.7280553579330444, 'util', 0), ('bastibe/python-soundfile', 0.6631749868392944, 'util', 0), ('uberi/speech_recognition', 0.659464955329895, 'ml', 0), ('taylorsmarks/playsound', 0.6150918006896973, 'util', 0), ('quodlibet/mutagen', 0.5700762867927551, 'util', 0), ('kiwicom/pytest-recording', 0.5641629099845886, 'testing', 0), ('pndurette/gtts', 0.5513820648193359, 'util', 0), ('pytoolz/toolz', 0.5377234816551208, 'util', 0), ('nateshmbhat/pyttsx3', 0.536530077457428, 'util', 0), ('pyston/pyston', 0.5274853110313416, 'util', 0), ('imageio/imageio', 0.5260134339332581, 'util', 0), ('pypy/pypy', 0.5216100215911865, 'util', 0), ('urwid/urwid', 0.51679927110672, 'term', 0)] | 6 | 1 | null | 0.25 | 1 | 0 | 55 | 7 | 4 | 8 | 4 | 1 | 0 | 90 | 0 | 15 |
464 | nlp | https://github.com/infinitylogesh/mutate | [] | null | [] | [] | null | null | null | infinitylogesh/mutate | mutate | 148 | 9 | 5 | Python | null | A library to synthesize text datasets using Large Language Models (LLM) | infinitylogesh | 2024-01-10 | 2021-12-29 | 108 | 1.35958 | null | A library to synthesize text datasets using Large Language Models (LLM) | ['data-augmentation', 'data-labeling', 'language-model', 'nlp-library', 'text-generation'] | ['data-augmentation', 'data-labeling', 'language-model', 'nlp-library', 'text-generation'] | 2023-01-17 | [('huggingface/text-generation-inference', 0.6840097904205322, 'llm', 0), ('cg123/mergekit', 0.6713061332702637, 'llm', 0), ('explosion/spacy-llm', 0.6443644762039185, 'llm', 0), ('salesforce/xgen', 0.621751070022583, 'llm', 1), ('eleutherai/the-pile', 0.6196421980857849, 'data', 0), ('togethercomputer/redpajama-data', 0.6091659069061279, 'llm', 0), ('paddlepaddle/paddlenlp', 0.6022129654884338, 'llm', 0), ('databrickslabs/dolly', 0.5938010811805725, 'llm', 0), ('ctlllll/llm-toolmaker', 0.5922251343727112, 'llm', 1), ('hannibal046/awesome-llm', 0.5907807350158691, 'study', 1), ('minimaxir/textgenrnn', 0.5876234769821167, 'nlp', 1), ('minimaxir/gpt-2-simple', 0.5839410424232483, 'llm', 1), ('freedomintelligence/llmzoo', 0.5752876996994019, 'llm', 1), ('allenai/allennlp', 0.5738117098808289, 'nlp', 0), ('lianjiatech/belle', 0.5713385939598083, 'llm', 0), ('argilla-io/argilla', 0.5702595114707947, 'nlp', 0), ('young-geng/easylm', 0.566120445728302, 'llm', 1), ('bytedance/lightseq', 0.562773585319519, 'nlp', 0), ('llmware-ai/llmware', 0.5625592470169067, 'llm', 0), ('tatsu-lab/stanford_alpaca', 0.5620525479316711, 'llm', 1), ('openlmlab/moss', 0.5608090758323669, 'llm', 2), ('princeton-nlp/alce', 0.5599058866500854, 'llm', 0), ('juncongmoo/pyllama', 0.5594502687454224, 'llm', 0), ('bigscience-workshop/biomedical', 0.5588589310646057, 'data', 0), ('yueyu1030/attrprompt', 0.5573945045471191, 'llm', 0), ('microsoft/lora', 0.5570184588432312, 'llm', 1), ('makcedward/nlpaug', 0.5548282265663147, 'nlp', 0), ('bobazooba/xllm', 0.5542972087860107, 'llm', 0), ('aiwaves-cn/agents', 0.5541864037513733, 'nlp', 1), ('google-research/electra', 0.553767204284668, 'ml-dl', 0), ('srush/minichain', 0.5535112023353577, 'llm', 0), ('ai21labs/lm-evaluation', 0.5527203679084778, 'llm', 1), ('lm-sys/fastchat', 0.5509554743766785, 'llm', 1), ('mooler0410/llmspracticalguide', 0.5493302345275879, 'study', 0), ('explosion/spacy-models', 0.5492627620697021, 'nlp', 0), ('huggingface/datasets', 0.5476288199424744, 'nlp', 0), ('reasoning-machines/pal', 0.5475483536720276, 'llm', 1), ('next-gpt/next-gpt', 0.5463948845863342, 'llm', 0), ('rasahq/rasa', 0.5458459258079529, 'llm', 0), ('thudm/chatglm2-6b', 0.5451502203941345, 'llm', 0), ('hiyouga/llama-efficient-tuning', 0.5444034934043884, 'llm', 1), ('hiyouga/llama-factory', 0.5444034337997437, 'llm', 1), ('alibaba/easynlp', 0.5399907231330872, 'nlp', 0), ('neuml/txtai', 0.5397577285766602, 'nlp', 1), ('squeezeailab/squeezellm', 0.5389288067817688, 'llm', 1), ('norskregnesentral/skweak', 0.5382066965103149, 'nlp', 1), ('tigerlab-ai/tiger', 0.5381948947906494, 'llm', 1), ('deepset-ai/haystack', 0.5364488959312439, 'llm', 1), ('nltk/nltk', 0.5343964695930481, 'nlp', 0), ('extreme-bert/extreme-bert', 0.5343960523605347, 'llm', 1), ('huggingface/text-embeddings-inference', 0.531061053276062, 'llm', 0), ('microsoft/unilm', 0.5307291746139526, 'nlp', 0), ('nomic-ai/gpt4all', 0.5304908752441406, 'llm', 1), ('facebookresearch/seamless_communication', 0.5298645496368408, 'nlp', 0), ('koaning/embetter', 0.5295093059539795, 'data', 0), ('deepset-ai/farm', 0.5289068818092346, 'nlp', 1), ('microsoft/autogen', 0.5268688201904297, 'llm', 0), ('conceptofmind/toolformer', 0.5267210602760315, 'llm', 1), ('intellabs/fastrag', 0.526527464389801, 'nlp', 0), ('optimalscale/lmflow', 0.524427056312561, 'llm', 1), ('ofa-sys/ofa', 0.514673113822937, 'llm', 0), ('jonasgeiping/cramming', 0.5127670168876648, 'nlp', 1), ('google/sentencepiece', 0.5126572847366333, 'nlp', 0), ('jbesomi/texthero', 0.5125301480293274, 'nlp', 0), ('yizhongw/self-instruct', 0.5120193362236023, 'llm', 1), ('lexpredict/lexpredict-lexnlp', 0.5115991234779358, 'nlp', 0), ('flairnlp/flair', 0.5080118775367737, 'nlp', 0), ('lucidrains/dalle2-pytorch', 0.5058514475822449, 'diffusion', 0), ('nebuly-ai/nebullvm', 0.5052860379219055, 'perf', 0), ('prefecthq/langchain-prefect', 0.50165855884552, 'llm', 0)] | 2 | 1 | null | 0 | 1 | 0 | 25 | 12 | 0 | 0 | 0 | 1 | 1 | 90 | 1 | 15 |
1,037 | finance | https://github.com/numerai/numerox | [] | null | [] | [] | null | null | null | numerai/numerox | numerox | 135 | 36 | 30 | Python | null | Numerai tournament toolbox written in Python | numerai | 2024-01-06 | 2017-10-18 | 327 | 0.411765 | https://avatars.githubusercontent.com/u/15222762?v=4 | Numerai tournament toolbox written in Python | ['numerai'] | ['numerai'] | 2020-12-08 | [('nuitka/nuitka', 0.5074256062507629, 'util', 0)] | 15 | 3 | null | 0 | 0 | 0 | 76 | 38 | 0 | 8 | 8 | 0 | 0 | 90 | 0 | 15 |
1,052 | util | https://github.com/xl0/lovely-numpy | [] | null | [] | [] | null | null | null | xl0/lovely-numpy | lovely-numpy | 60 | 3 | 4 | Jupyter Notebook | https://xl0.github.io/lovely-numpy | NumPy arrays, ready for human consumption | xl0 | 2023-10-31 | 2022-11-17 | 62 | 0.95672 | null | NumPy arrays, ready for human consumption | ['deep-learning', 'numpy', 'statistics', 'visualization'] | ['deep-learning', 'numpy', 'statistics', 'visualization'] | 2023-10-31 | [('xl0/lovely-tensors', 0.7344928979873657, 'ml-dl', 3), ('ddbourgin/numpy-ml', 0.5750322937965393, 'ml', 0), ('luispedro/mahotas', 0.5749441981315613, 'viz', 1), ('numpy/numpy', 0.5545908808708191, 'math', 1), ('pytorch/pytorch', 0.5318784713745117, 'ml-dl', 2), ('blaze/blaze', 0.5316489934921265, 'pandas', 0), ('gradio-app/gradio', 0.5306854248046875, 'viz', 1), ('ageron/handson-ml2', 0.52412348985672, 'ml', 0), ('ml-explore/mlx', 0.5240765810012817, 'ml', 1), ('pyqtgraph/pyqtgraph', 0.5235827565193176, 'viz', 2), ('huggingface/datasets', 0.519147515296936, 'nlp', 2), ('lightly-ai/lightly', 0.5120965242385864, 'ml', 1), ('scikit-learn/scikit-learn', 0.5066837072372437, 'ml', 1), ('google/tensorstore', 0.5052934288978577, 'data', 0), ('cupy/cupy', 0.5024915933609009, 'math', 1)] | 2 | 1 | null | 0.12 | 0 | 0 | 14 | 2 | 0 | 8 | 8 | 0 | 0 | 90 | 0 | 15 |
916 | gis | https://github.com/radiantearth/radiant-mlhub | [] | null | [] | [] | null | null | null | radiantearth/radiant-mlhub | radiant-mlhub | 50 | 8 | 5 | Python | https://radiant-mlhub.readthedocs.io/ | A Python client for the Radiant MLHub API (https://mlhub.earth). | radiantearth | 2024-01-04 | 2020-10-13 | 172 | 0.290698 | https://avatars.githubusercontent.com/u/25801078?v=4 | A Python client for the Radiant MLHub API (https://mlhub.earth). | ['machine-learning', 'python-client', 'satellite-imagery', 'stac'] | ['machine-learning', 'python-client', 'satellite-imagery', 'stac'] | 2023-02-13 | [('sentinel-hub/sentinelhub-py', 0.6164925694465637, 'gis', 1), ('huggingface/huggingface_hub', 0.6118897199630737, 'ml', 1), ('cloudsen12/easystac', 0.6044291853904724, 'gis', 1), ('pytroll/satpy', 0.5896352529525757, 'gis', 0), ('sentinel-hub/eo-learn', 0.5761727094650269, 'gis', 1), ('googleapis/google-api-python-client', 0.5603718757629395, 'util', 0), ('aws/sagemaker-python-sdk', 0.5445782542228699, 'ml', 1), ('kubeflow/fairing', 0.5332103371620178, 'ml-ops', 0), ('encode/httpx', 0.5330476760864258, 'web', 0), ('weecology/deepforest', 0.5279979705810547, 'gis', 0), ('hugapi/hug', 0.5193449854850769, 'util', 0), ('ml-tooling/opyrator', 0.5183095932006836, 'viz', 1), ('google/vizier', 0.5126688480377197, 'ml', 1), ('falconry/falcon', 0.5079211592674255, 'web', 0), ('simple-salesforce/simple-salesforce', 0.5078471899032593, 'data', 0), ('python-pillow/pillow', 0.5047886967658997, 'util', 0), ('giswqs/geemap', 0.5028256773948669, 'gis', 0), ('spotify/voyager', 0.5025808811187744, 'ml', 1), ('jovianml/opendatasets', 0.5003646016120911, 'data', 1)] | 10 | 3 | null | 0.02 | 1 | 0 | 40 | 11 | 0 | 6 | 6 | 1 | 0 | 90 | 0 | 15 |
1,079 | ml-ops | https://github.com/getindata/kedro-kubeflow | [] | null | [] | [] | null | null | null | getindata/kedro-kubeflow | kedro-kubeflow | 42 | 20 | 11 | Python | https://kedro-kubeflow.readthedocs.io | Kedro Plugin to support running workflows on Kubeflow Pipelines | getindata | 2023-11-04 | 2020-12-18 | 162 | 0.258348 | https://avatars.githubusercontent.com/u/9497597?v=4 | Kedro Plugin to support running workflows on Kubeflow Pipelines | ['ai-pipelines', 'kedro', 'kedro-kubeflow', 'kedro-plugin', 'kubeflow', 'kubeflow-pipelines', 'machinelearning', 'mlops'] | ['ai-pipelines', 'kedro', 'kedro-kubeflow', 'kedro-plugin', 'kubeflow', 'kubeflow-pipelines', 'machinelearning', 'mlops'] | 2023-06-01 | [('kubeflow/pipelines', 0.7283107042312622, 'ml-ops', 3), ('bodywork-ml/bodywork-core', 0.6706922650337219, 'ml-ops', 1), ('kedro-org/kedro', 0.660918653011322, 'ml-ops', 2), ('flyteorg/flyte', 0.6279534697532654, 'ml-ops', 1), ('kubeflow-kale/kale', 0.5977193117141724, 'ml-ops', 2), ('zenml-io/zenml', 0.5848532915115356, 'ml-ops', 1), ('polyaxon/polyaxon', 0.5841188430786133, 'ml-ops', 1), ('allegroai/clearml', 0.5791431069374084, 'ml-ops', 2), ('kedro-org/kedro-viz', 0.578177273273468, 'ml-ops', 2), ('orchest/orchest', 0.5747969746589661, 'ml-ops', 0), ('apache/airflow', 0.5596566200256348, 'ml-ops', 1), ('prefecthq/prefect', 0.5550775527954102, 'ml-ops', 0), ('mage-ai/mage-ai', 0.5537773370742798, 'ml-ops', 0), ('astronomer/astro-sdk', 0.541987419128418, 'ml-ops', 0), ('kestra-io/kestra', 0.5321413278579712, 'ml-ops', 0)] | 15 | 2 | null | 0.06 | 4 | 0 | 37 | 8 | 1 | 9 | 1 | 4 | 2 | 90 | 0.5 | 15 |
563 | sim | https://github.com/gboeing/pynamical | [] | null | [] | [] | null | null | null | gboeing/pynamical | pynamical | 606 | 113 | 32 | Python | https://geoffboeing.com/publications/nonlinear-chaos-fractals-prediction/ | Pynamical is a Python package for modeling and visualizing discrete nonlinear dynamical systems, chaos, and fractals. | gboeing | 2024-01-04 | 2014-09-28 | 487 | 1.243624 | null | Pynamical is a Python package for modeling and visualizing discrete nonlinear dynamical systems, chaos, and fractals. | ['animation', 'bifurcation-diagram', 'chaos', 'cobweb-plot', 'fractal', 'fractals', 'ipynb', 'logistic', 'math', 'matplotlib', 'modeling', 'nonlinear', 'numba', 'numpy', 'pandas', 'phase-diagram', 'physics', 'systems', 'visualization'] | ['animation', 'bifurcation-diagram', 'chaos', 'cobweb-plot', 'fractal', 'fractals', 'ipynb', 'logistic', 'math', 'matplotlib', 'modeling', 'nonlinear', 'numba', 'numpy', 'pandas', 'phase-diagram', 'physics', 'systems', 'visualization'] | 2022-05-24 | [('artemyk/dynpy', 0.5693354606628418, 'sim', 0), ('viblo/pymunk', 0.5553194880485535, 'sim', 0), ('pysal/pysal', 0.5425774455070496, 'gis', 0), ('has2k1/plotnine', 0.5370927453041077, 'viz', 0), ('altair-viz/altair', 0.5344410538673401, 'viz', 1), ('projectmesa/mesa', 0.530546247959137, 'sim', 0), ('marcomusy/vedo', 0.5265044569969177, 'viz', 2), ('plotly/plotly.py', 0.5207886099815369, 'viz', 1), ('pyglet/pyglet', 0.5189253091812134, 'gamedev', 0), ('dfki-ric/pytransform3d', 0.5125102400779724, 'math', 2), ('crflynn/stochastic', 0.509087860584259, 'sim', 0), ('albahnsen/pycircular', 0.503804087638855, 'math', 0)] | 1 | 1 | null | 0 | 0 | 0 | 113 | 20 | 0 | 1 | 1 | 0 | 0 | 90 | 0 | 14 |
470 | nlp | https://github.com/google-research/byt5 | [] | null | [] | [] | null | null | null | google-research/byt5 | byt5 | 451 | 27 | 12 | Python | null | null | google-research | 2024-01-04 | 2021-05-26 | 139 | 3.224719 | https://avatars.githubusercontent.com/u/43830688?v=4 | google-research/byt5 | [] | [] | 2023-06-07 | [('google-research/t5x', 0.8195183277130127, 'ml', 0), ('google-research/google-research', 0.6097835898399353, 'ml', 0)] | 2 | 0 | null | 0 | 0 | 0 | 32 | 20 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 14 |
497 | ml-dl | https://github.com/researchmm/sttn | [] | null | [] | [] | null | null | null | researchmm/sttn | STTN | 414 | 73 | 19 | Jupyter Notebook | https://arxiv.org/abs/2007.10247 | [ECCV'2020] STTN: Learning Joint Spatial-Temporal Transformations for Video Inpainting | researchmm | 2024-01-11 | 2020-07-10 | 185 | 2.230947 | https://avatars.githubusercontent.com/u/49016198?v=4 | [ECCV'2020] STTN: Learning Joint Spatial-Temporal Transformations for Video Inpainting | ['completing-videos', 'spatial-temporal', 'transformer', 'video-inpainting'] | ['completing-videos', 'spatial-temporal', 'transformer', 'video-inpainting'] | 2021-07-26 | [('mcahny/deep-video-inpainting', 0.6987395882606506, 'ml-dl', 0), ('vt-vl-lab/fgvc', 0.6461269855499268, 'ml-dl', 0), ('zulko/moviepy', 0.5262514352798462, 'util', 0), ('facebookresearch/augly', 0.5215305089950562, 'data', 0)] | 2 | 1 | null | 0 | 1 | 0 | 43 | 30 | 0 | 0 | 0 | 1 | 0 | 90 | 0 | 14 |
686 | util | https://github.com/paperswithcode/axcell | [] | null | [] | [] | null | null | null | paperswithcode/axcell | axcell | 370 | 57 | 14 | Python | null | Tools for extracting tables and results from Machine Learning papers | paperswithcode | 2024-01-07 | 2019-06-27 | 239 | 1.543504 | https://avatars.githubusercontent.com/u/40305508?v=4 | Tools for extracting tables and results from Machine Learning papers | [] | [] | 2021-06-23 | [('camelot-dev/camelot', 0.577544093132019, 'util', 0), ('huggingface/evaluate', 0.5434747338294983, 'ml', 0), ('lean-dojo/leandojo', 0.5234335660934448, 'math', 0), ('mljar/mljar-supervised', 0.5180116891860962, 'ml', 0), ('tensorflow/data-validation', 0.5049012899398804, 'ml-ops', 0)] | 7 | 2 | null | 0 | 0 | 0 | 55 | 31 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 14 |
930 | web | https://github.com/dmontagu/fastapi_client | [] | null | [] | [] | null | null | null | dmontagu/fastapi_client | fastapi_client | 321 | 42 | 8 | Python | null | FastAPI client generator | dmontagu | 2024-01-04 | 2019-08-03 | 234 | 1.369287 | null | FastAPI client generator | [] | [] | 2021-02-11 | [('koxudaxi/fastapi-code-generator', 0.6843157410621643, 'web', 0), ('asacristani/fastapi-rocket-boilerplate', 0.6545476913452148, 'template', 0), ('fastapi-users/fastapi-users', 0.6202594637870789, 'web', 0), ('s3rius/fastapi-template', 0.6021080613136292, 'web', 0), ('tiangolo/fastapi', 0.5745565891265869, 'web', 0), ('tiangolo/full-stack-fastapi-postgresql', 0.5413647890090942, 'template', 0), ('awtkns/fastapi-crudrouter', 0.5253075361251831, 'web', 0), ('zhanymkanov/fastapi-best-practices', 0.5196253061294556, 'study', 0), ('samuelcolvin/fastui', 0.5091067552566528, 'gui', 0), ('long2ice/fastapi-cache', 0.5032126307487488, 'web', 0)] | 8 | 2 | null | 0 | 0 | 0 | 54 | 36 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 14 |
976 | nlp | https://github.com/yixinl7/brio | [] | null | [] | [] | null | null | null | yixinl7/brio | BRIO | 306 | 42 | 2 | Python | null | ACL 2022: BRIO: Bringing Order to Abstractive Summarization | yixinl7 | 2024-01-09 | 2022-03-15 | 98 | 3.122449 | null | ACL 2022: BRIO: Bringing Order to Abstractive Summarization | ['nlp', 'text-summarization'] | ['nlp', 'text-summarization'] | 2023-05-23 | [] | 1 | 0 | null | 0.02 | 0 | 0 | 22 | 8 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 14 |
570 | gis | https://github.com/developmentseed/geolambda | [] | null | [] | [] | null | null | null | developmentseed/geolambda | geolambda | 295 | 87 | 49 | Dockerfile | null | Create and deploy Geospatial AWS Lambda functions | developmentseed | 2024-01-12 | 2017-05-02 | 352 | 0.838068 | https://avatars.githubusercontent.com/u/92384?v=4 | Create and deploy Geospatial AWS Lambda functions | [] | [] | 2021-02-16 | [('nficano/python-lambda', 0.6138771176338196, 'util', 0), ('aws/aws-lambda-python-runtime-interface-client', 0.5933845043182373, 'util', 0), ('jordaneremieff/mangum', 0.5730166435241699, 'web', 0), ('geeogi/async-python-lambda-template', 0.5692357420921326, 'template', 0), ('rpgreen/apilogs', 0.5092775225639343, 'util', 0), ('giswqs/aws-open-data-geo', 0.507233738899231, 'gis', 0), ('aws/chalice', 0.5061563849449158, 'web', 0)] | 6 | 3 | null | 0 | 0 | 0 | 82 | 35 | 0 | 1 | 1 | 0 | 0 | 90 | 0 | 14 |
242 | ml | https://github.com/carla-recourse/carla | [] | null | [] | [] | null | null | null | carla-recourse/carla | CARLA | 260 | 57 | 6 | Python | null | CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms | carla-recourse | 2024-01-04 | 2020-12-09 | 163 | 1.586748 | https://avatars.githubusercontent.com/u/88393731?v=4 | CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms | ['artificial-intelligence', 'benchmark', 'benchmarking', 'counterfactual', 'counterfactual-explanations', 'counterfactuals', 'explainability', 'explainable-ai', 'explainable-ml', 'machine-learning', 'pytorch', 'recourse', 'tensorflow', 'tensorflow2'] | ['artificial-intelligence', 'benchmark', 'benchmarking', 'counterfactual', 'counterfactual-explanations', 'counterfactuals', 'explainability', 'explainable-ai', 'explainable-ml', 'machine-learning', 'pytorch', 'recourse', 'tensorflow', 'tensorflow2'] | 2023-02-22 | [('seldonio/alibi', 0.6956292986869812, 'ml-interpretability', 2), ('teamhg-memex/eli5', 0.6284937262535095, 'ml', 1), ('rafiqhasan/auto-tensorflow', 0.5945912003517151, 'ml-dl', 2), ('reloadware/reloadium', 0.5905767679214478, 'profiling', 1), ('klen/py-frameworks-bench', 0.5568965077400208, 'perf', 1), ('oegedijk/explainerdashboard', 0.5436306595802307, 'ml-interpretability', 0), ('tensorflow/lucid', 0.5387822389602661, 'ml-interpretability', 2), ('koaning/human-learn', 0.5305505394935608, 'data', 2), ('rasbt/mlxtend', 0.5272185802459717, 'ml', 1), ('maif/shapash', 0.5270743370056152, 'ml', 3), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.5270025730133057, 'study', 2), ('pytoolz/toolz', 0.5241925716400146, 'util', 0), ('tensorflow/data-validation', 0.5232628583908081, 'ml-ops', 0), ('csinva/imodels', 0.5205970406532288, 'ml', 4), ('polyaxon/datatile', 0.5203560590744019, 'pandas', 3), ('huggingface/evaluate', 0.5139058232307434, 'ml', 1), ('mckinsey/causalnex', 0.5116320252418518, 'math', 1), ('pyutils/line_profiler', 0.5099355578422546, 'profiling', 0), ('interpretml/interpret', 0.5091050863265991, 'ml-interpretability', 5), ('rasbt/machine-learning-book', 0.5052952766418457, 'study', 2), ('allenai/allennlp', 0.5022084712982178, 'nlp', 1), ('pypy/pypy', 0.5005764365196228, 'util', 0)] | 7 | 1 | null | 0.08 | 0 | 0 | 38 | 11 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 14 |
247 | util | https://github.com/rpgreen/apilogs | [] | null | [] | [] | null | null | null | rpgreen/apilogs | apilogs | 252 | 19 | 10 | Python | null | Easy logging and debugging for Amazon API Gateway and AWS Lambda Serverless APIs | rpgreen | 2024-01-03 | 2016-09-07 | 385 | 0.653091 | null | Easy logging and debugging for Amazon API Gateway and AWS Lambda Serverless APIs | ['api', 'api-gateway', 'aws', 'aws-apigateway', 'aws-lambda', 'cloudwatch-logs', 'gateway', 'lambda', 'logging'] | ['api', 'api-gateway', 'aws', 'aws-apigateway', 'aws-lambda', 'cloudwatch-logs', 'gateway', 'lambda', 'logging'] | 2019-11-13 | [('nficano/python-lambda', 0.635263204574585, 'util', 2), ('aws/chalice', 0.6253986954689026, 'web', 4), ('jordaneremieff/mangum', 0.595600962638855, 'web', 4), ('jorgebastida/awslogs', 0.5721848607063293, 'util', 0), ('aws/aws-lambda-python-runtime-interface-client', 0.5472556948661804, 'util', 0), ('localstack/localstack', 0.5111103653907776, 'util', 1), ('developmentseed/geolambda', 0.5092775225639343, 'gis', 0)] | 23 | 4 | null | 0 | 0 | 0 | 89 | 51 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 14 |
1,581 | data | https://github.com/brettkromkamp/topic-db | ['knowledge-graph'] | null | [] | [] | null | null | null | brettkromkamp/topic-db | topic-db | 245 | 13 | 9 | Python | null | TopicDB is a topic maps-based semantic graph store (using SQLite for persistence) | brettkromkamp | 2024-01-03 | 2016-12-21 | 370 | 0.660632 | null | TopicDB is a topic maps-based semantic graph store (using SQLite for persistence) | ['graph-database', 'knowledge-base', 'knowledge-graph', 'knowledge-management', 'linked-data', 'semantic-web', 'sqlite3', 'sqlite3-database', 'topic-map'] | ['graph-database', 'knowledge-base', 'knowledge-graph', 'knowledge-management', 'linked-data', 'semantic-web', 'sqlite3', 'sqlite3-database', 'topic-map'] | 2023-08-15 | [('rare-technologies/gensim', 0.539949893951416, 'nlp', 0)] | 3 | 1 | null | 0.02 | 0 | 0 | 86 | 5 | 0 | 1 | 1 | 0 | 0 | 90 | 0 | 14 |
1,683 | util | https://github.com/pycqa/eradicate | ['linting', 'styling'] | null | [] | [] | null | null | null | pycqa/eradicate | eradicate | 192 | 24 | 5 | Python | https://pypi.python.org/pypi/eradicate | Removes commented-out code from Python files | pycqa | 2024-01-12 | 2012-12-23 | 579 | 0.331443 | https://avatars.githubusercontent.com/u/8749848?v=4 | Removes commented-out code from Python files | [] | ['linting', 'styling'] | 2023-06-12 | [('pycqa/autoflake', 0.5524262189865112, 'util', 0), ('landscapeio/prospector', 0.5313110947608948, 'util', 2)] | 13 | 1 | null | 0.17 | 0 | 0 | 135 | 7 | 2 | 2 | 2 | 0 | 0 | 90 | 0 | 14 |
414 | ml-dl | https://github.com/rafiqhasan/auto-tensorflow | [] | null | [] | [] | null | null | null | rafiqhasan/auto-tensorflow | auto-tensorflow | 179 | 39 | 13 | Python | null | Build Low Code Automated Tensorflow explainable models in just 3 lines of code. Library created by: Hasan Rafiq - https://www.linkedin.com/in/sam04/ | rafiqhasan | 2023-11-26 | 2021-07-05 | 134 | 1.334398 | null | Build Low Code Automated Tensorflow explainable models in just 3 lines of code. 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964 | data | https://github.com/nickreynke/python-gedcom | [] | null | [] | [] | null | null | null | nickreynke/python-gedcom | python-gedcom | 142 | 37 | 17 | Python | https://nickreynke.github.io/python-gedcom/gedcom/index.html | Python module for parsing, analyzing, and manipulating GEDCOM files | nickreynke | 2024-01-06 | 2018-01-09 | 316 | 0.449367 | null | Python module for parsing, analyzing, and manipulating GEDCOM files | ['gedcom', 'gedcom-parser', 'parser'] | ['gedcom', 'gedcom-parser', 'parser'] | 2021-06-03 | [('pytoolz/toolz', 0.5138238072395325, 'util', 0), ('pympler/pympler', 0.5089355111122131, 'perf', 0)] | 16 | 3 | null | 0 | 1 | 0 | 73 | 32 | 0 | 2 | 2 | 1 | 0 | 90 | 0 | 14 |
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1,202 | util | https://github.com/kuimono/openapi-schema-pydantic | [] | null | [] | [] | null | null | null | kuimono/openapi-schema-pydantic | openapi-schema-pydantic | 100 | 16 | 4 | Python | null | OpenAPI (v3) specification schema as pydantic class | kuimono | 2024-01-14 | 2020-05-14 | 193 | 0.516224 | null | OpenAPI (v3) specification schema as pydantic class | ['openapi3', 'pydantic'] | ['openapi3', 'pydantic'] | 2022-06-29 | [('koxudaxi/fastapi-code-generator', 0.6038326025009155, 'web', 1), ('openai/openai-python', 0.52321857213974, 'util', 0)] | 7 | 3 | null | 0 | 0 | 0 | 45 | 19 | 0 | 3 | 3 | 0 | 0 | 90 | 0 | 14 |
1,138 | study | https://github.com/nomic-ai/semantic-search-app-template | [] | null | [] | [] | null | null | null | nomic-ai/semantic-search-app-template | semantic-search-app-template | 96 | 19 | 6 | Python | null | Tutorial and template for a semantic search app powered by the Atlas Embedding Database, Langchain, OpenAI and FastAPI | nomic-ai | 2024-01-13 | 2023-03-20 | 45 | 2.126582 | https://avatars.githubusercontent.com/u/102670180?v=4 | Tutorial and template for a semantic search app powered by the Atlas Embedding Database, Langchain, OpenAI and FastAPI | ['fastapi', 'openai', 'react', 'semantic-search', 'tutorial'] | ['fastapi', 'openai', 'react', 'semantic-search', 'tutorial'] | 2023-09-12 | [('neuml/txtai', 0.6002048254013062, 'nlp', 1), ('freedmand/semantra', 0.5121299624443054, 'ml', 1), ('zilliztech/gptcache', 0.5088302493095398, 'llm', 2), ('qdrant/fastembed', 0.5012847781181335, 'ml', 1), ('chroma-core/chroma', 0.5006744265556335, 'data', 0), ('paddlepaddle/paddlenlp', 0.5003646016120911, 'llm', 0)] | 2 | 1 | null | 0.38 | 0 | 0 | 10 | 4 | 0 | 0 | 0 | 0 | 0 | 90 | 0 | 14 |