Unnamed: 0
int64 | category
string | githuburl
string | customtopics
string | customabout
string | customarxiv
string | custompypi
string | featured
float64 | links
string | description
string | _repopath
string | _reponame
string | _stars
int64 | _forks
int64 | _watches
int64 | _language
string | _homepage
string | _github_description
string | _organization
string | _updated_at
string | _created_at
string | _age_weeks
int64 | _stars_per_week
float64 | _avatar_url
string | _description
string | _github_topics
string | _topics
string | _last_commit_date
string | sim
string | _pop_contributor_count
int64 | _pop_contributor_orgs_len
float64 | _pop_contributor_orgs_error
float64 | _pop_commit_frequency
float64 | _pop_updated_issues_count
int64 | _pop_closed_issues_count
int64 | _pop_created_since_days
int64 | _pop_updated_since_days
int64 | _pop_recent_releases_count
int64 | _pop_recent_releases_estimated_tags
int64 | _pop_recent_releases_adjusted_count
int64 | _pop_issue_count
float64 | _pop_comment_count
float64 | _pop_comment_count_lookback_days
float64 | _pop_comment_frequency
float64 | _pop_score
int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1,427
|
llm
|
https://github.com/declare-lab/instruct-eval
|
[]
| null |
[]
|
[]
| null | null | null |
declare-lab/instruct-eval
|
instruct-eval
| 404
| 29
| 12
|
Python
|
https://declare-lab.net/instruct-eval/
|
This repository contains code to quantitatively evaluate instruction-tuned models such as Alpaca and Flan-T5 on held-out tasks.
|
declare-lab
|
2024-01-12
|
2023-03-28
| 44
| 9.181818
|
https://avatars.githubusercontent.com/u/59164695?v=4
|
This repository contains code to quantitatively evaluate instruction-tuned models such as Alpaca and Flan-T5 on held-out tasks.
|
['instruct-tuning', 'llm']
|
['instruct-tuning', 'llm']
|
2023-09-26
|
[('instruction-tuning-with-gpt-4/gpt-4-llm', 0.653624415397644, 'llm', 0), ('tiger-ai-lab/mammoth', 0.6364500522613525, 'llm', 0), ('yizhongw/self-instruct', 0.550851047039032, 'llm', 0), ('hiyouga/llama-factory', 0.5344966650009155, 'llm', 1), ('hiyouga/llama-efficient-tuning', 0.5344966053962708, 'llm', 1), ('zrrskywalker/llama-adapter', 0.5260562896728516, 'llm', 0), ('tatsu-lab/stanford_alpaca', 0.5018754005432129, 'llm', 0)]
| 3
| 1
| null | 2.75
| 7
| 0
| 10
| 4
| 0
| 0
| 0
| 7
| 2
| 90
| 0.3
| 28
|
1,619
|
testing
|
https://github.com/samuelcolvin/pytest-pretty
|
['pytest']
| null |
[]
|
[]
| null | null | null |
samuelcolvin/pytest-pretty
|
pytest-pretty
| 388
| 6
| 7
|
Python
| null |
pytest plugin for pretty printing the test summary.
|
samuelcolvin
|
2024-01-04
|
2022-10-25
| 66
| 5.878788
| null |
pytest plugin for pretty printing the test summary.
|
[]
|
['pytest']
|
2023-05-04
|
[('pytest-dev/pytest-cov', 0.6255528926849365, 'testing', 1), ('pytest-dev/pytest', 0.5827073454856873, 'testing', 0), ('teemu/pytest-sugar', 0.5765059590339661, 'testing', 1), ('pytest-dev/pytest-mock', 0.5683526992797852, 'testing', 1), ('pytest-dev/pytest-xdist', 0.5537928342819214, 'testing', 1), ('inducer/pudb', 0.5497497320175171, 'debug', 1), ('ionelmc/pytest-benchmark', 0.5456304550170898, 'testing', 1), ('kiwicom/pytest-recording', 0.544182538986206, 'testing', 1), ('computationalmodelling/nbval', 0.5243958234786987, 'jupyter', 1), ('samuelcolvin/dirty-equals', 0.5239970088005066, 'util', 1), ('taverntesting/tavern', 0.5094279050827026, 'testing', 1), ('hugovk/pypistats', 0.5067400932312012, 'util', 0), ('nedbat/coveragepy', 0.506611704826355, 'testing', 0)]
| 5
| 4
| null | 0.29
| 0
| 0
| 15
| 8
| 5
| 4
| 5
| 0
| 0
| 90
| 0
| 28
|
708
|
ml-ops
|
https://github.com/unionai-oss/unionml
|
[]
| null |
[]
|
[]
| null | null | null |
unionai-oss/unionml
|
unionml
| 323
| 43
| 4
|
Python
|
https://www.union.ai/unionml
|
UnionML: the easiest way to build and deploy machine learning microservices
|
unionai-oss
|
2024-01-11
|
2021-11-17
| 114
| 2.812189
|
https://avatars.githubusercontent.com/u/94206482?v=4
|
UnionML: the easiest way to build and deploy machine learning microservices
|
['machine-learning', 'mlops']
|
['machine-learning', 'mlops']
|
2023-09-27
|
[('ml-tooling/opyrator', 0.6805552840232849, 'viz', 1), ('polyaxon/polyaxon', 0.6547122597694397, 'ml-ops', 2), ('kubeflow/pipelines', 0.6105091571807861, 'ml-ops', 2), ('fmind/mlops-python-package', 0.5882454514503479, 'template', 1), ('bodywork-ml/bodywork-core', 0.5865074396133423, 'ml-ops', 2), ('titanml/takeoff', 0.56184321641922, 'llm', 0), ('microsoft/nni', 0.5530210137367249, 'ml', 2), ('zenml-io/zenml', 0.5419542193412781, 'ml-ops', 2), ('allegroai/clearml', 0.536503255367279, 'ml-ops', 2), ('mlflow/mlflow', 0.5361176133155823, 'ml-ops', 1), ('ajndkr/lanarky', 0.5264783501625061, 'llm', 0), ('zenml-io/mlstacks', 0.5240914225578308, 'ml-ops', 1), ('janetech-inc/fast-api-admin-template', 0.5197975635528564, 'template', 0), ('kubeflow/fairing', 0.5197477340698242, 'ml-ops', 0), ('flyteorg/flyte', 0.5188993215560913, 'ml-ops', 2), ('onnx/onnx', 0.5090394020080566, 'ml', 1), ('netflix/metaflow', 0.5082710981369019, 'ml-ops', 2), ('bentoml/bentoml', 0.5069453716278076, 'ml-ops', 2), ('alpa-projects/alpa', 0.50523841381073, 'ml-dl', 1), ('automl/auto-sklearn', 0.5017037987709045, 'ml', 0)]
| 16
| 6
| null | 0.08
| 1
| 0
| 26
| 4
| 0
| 8
| 8
| 1
| 0
| 90
| 0
| 28
|
84
|
ml
|
https://github.com/stan-dev/pystan
|
[]
| null |
[]
|
[]
| null | null | null |
stan-dev/pystan
|
pystan
| 296
| 56
| 13
|
Python
| null |
PyStan, a Python interface to Stan, a platform for statistical modeling. Documentation: https://pystan.readthedocs.io
|
stan-dev
|
2024-01-13
|
2017-09-17
| 332
| 0.8908
|
https://avatars.githubusercontent.com/u/3374820?v=4
|
PyStan, a Python interface to Stan, a platform for statistical modeling. Documentation: https://pystan.readthedocs.io
|
[]
|
[]
|
2024-01-05
|
[('firmai/atspy', 0.5848978757858276, 'time-series', 0), ('pandas-dev/pandas', 0.5815196633338928, 'pandas', 0), ('alkaline-ml/pmdarima', 0.5813616514205933, 'time-series', 0), ('eleutherai/pyfra', 0.5657259225845337, 'ml', 0), ('selfexplainml/piml-toolbox', 0.5616912245750427, 'ml-interpretability', 0), ('pymc-devs/pymc3', 0.5608965158462524, 'ml', 0), ('statsmodels/statsmodels', 0.5519436001777649, 'ml', 0), ('crflynn/stochastic', 0.5423630475997925, 'sim', 0), ('rjt1990/pyflux', 0.5393368601799011, 'time-series', 0), ('udst/urbansim', 0.5362882018089294, 'sim', 0), ('pysal/pysal', 0.5347137451171875, 'gis', 0), ('pytoolz/toolz', 0.5338031053543091, 'util', 0), ('pmorissette/ffn', 0.5250624418258667, 'finance', 0), ('uber/orbit', 0.5150203704833984, 'time-series', 0), ('mwaskom/seaborn', 0.5127715468406677, 'viz', 0), ('google/temporian', 0.5121117234230042, 'time-series', 0), ('brokenloop/jsontopydantic', 0.5074247717857361, 'util', 0), ('altair-viz/altair', 0.506462574005127, 'viz', 0), ('rasbt/mlxtend', 0.5054618716239929, 'ml', 0), ('scikit-learn/scikit-learn', 0.5038464665412903, 'ml', 0), ('probml/pyprobml', 0.5016106963157654, 'ml', 0)]
| 14
| 4
| null | 0.17
| 9
| 7
| 77
| 1
| 0
| 3
| 3
| 9
| 12
| 90
| 1.3
| 28
|
945
|
diffusion
|
https://github.com/lunarring/latentblending
|
[]
| null |
[]
|
[]
| null | null | null |
lunarring/latentblending
|
latentblending
| 290
| 23
| 14
|
Python
| null |
Create butter-smooth transitions between prompts, powered by stable diffusion
|
lunarring
|
2024-01-08
|
2022-11-19
| 62
| 4.645309
|
https://avatars.githubusercontent.com/u/78172771?v=4
|
Create butter-smooth transitions between prompts, powered by stable diffusion
|
['animation', 'diffusion', 'stable-diffusion']
|
['animation', 'diffusion', 'stable-diffusion']
|
2024-01-10
|
[('carson-katri/dream-textures', 0.5618883967399597, 'diffusion', 1), ('nateraw/stable-diffusion-videos', 0.5257456302642822, 'diffusion', 1)]
| 12
| 1
| null | 2
| 3
| 2
| 14
| 0
| 0
| 0
| 0
| 3
| 1
| 90
| 0.3
| 28
|
1,705
|
util
|
https://github.com/mtkennerly/dunamai
|
[]
| null |
[]
|
[]
| null | null | null |
mtkennerly/dunamai
|
dunamai
| 276
| 23
| 3
|
Python
|
https://dunamai.readthedocs.io/en/latest
|
Dynamic versioning library and CLI
|
mtkennerly
|
2024-01-13
|
2019-03-26
| 253
| 1.090909
| null |
Dynamic versioning library and CLI
|
['bazaar', 'cli', 'darcs', 'dynamic-version', 'fossil', 'fossil-scm', 'git', 'mercurial', 'pijul', 'semantic-versioning', 'subversion', 'versioning']
|
['bazaar', 'cli', 'darcs', 'dynamic-version', 'fossil', 'fossil-scm', 'git', 'mercurial', 'pijul', 'semantic-versioning', 'subversion', 'versioning']
|
2023-12-09
|
[('mtkennerly/poetry-dynamic-versioning', 0.7415024042129517, 'util', 11), ('pypa/setuptools_scm', 0.6616849303245544, 'util', 2), ('callowayproject/bump-my-version', 0.6012407541275024, 'util', 1), ('python-versioneer/python-versioneer', 0.5904924869537354, 'util', 0), ('pypa/hatch', 0.56247878074646, 'util', 2), ('spack/spack', 0.5440518856048584, 'util', 0)]
| 14
| 4
| null | 0.58
| 1
| 1
| 58
| 1
| 6
| 9
| 6
| 1
| 1
| 90
| 1
| 28
|
454
|
gis
|
https://github.com/graal-research/deepparse
|
[]
| null |
[]
|
[]
| null | null | null |
graal-research/deepparse
|
deepparse
| 265
| 28
| 4
|
Python
|
https://deepparse.org/
|
Deepparse is a state-of-the-art library for parsing multinational street addresses using deep learning
|
graal-research
|
2024-01-04
|
2020-07-01
| 186
| 1.418196
|
https://avatars.githubusercontent.com/u/7155143?v=4
|
Deepparse is a state-of-the-art library for parsing multinational street addresses using deep learning
|
['addresses-parsing', 'machine-learning']
|
['addresses-parsing', 'machine-learning']
|
2023-12-17
|
[('jasonrig/address-net', 0.7487471699714661, 'gis', 1)]
| 8
| 2
| null | 1.38
| 2
| 1
| 43
| 1
| 6
| 14
| 6
| 2
| 3
| 90
| 1.5
| 28
|
207
|
util
|
https://github.com/aws/aws-lambda-python-runtime-interface-client
|
[]
| null |
[]
|
[]
| null | null | null |
aws/aws-lambda-python-runtime-interface-client
|
aws-lambda-python-runtime-interface-client
| 237
| 65
| 17
|
Python
| null | null |
aws
|
2024-01-05
|
2020-09-02
| 177
| 1.33253
|
https://avatars.githubusercontent.com/u/2232217?v=4
|
aws/aws-lambda-python-runtime-interface-client
|
[]
|
[]
|
2023-10-30
|
[('nficano/python-lambda', 0.706771194934845, 'util', 0), ('aws/chalice', 0.672527015209198, 'web', 0), ('geeogi/async-python-lambda-template', 0.6352989673614502, 'template', 0), ('jordaneremieff/mangum', 0.6282299160957336, 'web', 0), ('boto/boto3', 0.6017659902572632, 'util', 0), ('developmentseed/geolambda', 0.5933845043182373, 'gis', 0), ('pynamodb/pynamodb', 0.5816658735275269, 'data', 0), ('rpgreen/apilogs', 0.5472556948661804, 'util', 0), ('samuelcolvin/aioaws', 0.5471480488777161, 'data', 0), ('amzn/ion-python', 0.524419903755188, 'data', 0), ('awslabs/python-deequ', 0.5195323824882507, 'ml', 0)]
| 27
| 4
| null | 0.37
| 19
| 11
| 41
| 3
| 4
| 3
| 4
| 19
| 12
| 90
| 0.6
| 28
|
1,251
|
study
|
https://github.com/stanford-crfm/ecosystem-graphs
|
[]
| null |
[]
|
[]
| null | null | null |
stanford-crfm/ecosystem-graphs
|
ecosystem-graphs
| 214
| 25
| 14
|
JavaScript
| null | null |
stanford-crfm
|
2024-01-08
|
2022-03-10
| 98
| 2.167873
|
https://avatars.githubusercontent.com/u/75054807?v=4
|
stanford-crfm/ecosystem-graphs
|
[]
|
[]
|
2024-01-09
|
[]
| 15
| 4
| null | 3.42
| 13
| 13
| 22
| 0
| 0
| 0
| 0
| 13
| 1
| 90
| 0.1
| 28
|
1,701
|
llm
|
https://github.com/llm-tuning-safety/llms-finetuning-safety
|
[]
| null |
[]
|
[]
| null | null | null |
llm-tuning-safety/llms-finetuning-safety
|
LLMs-Finetuning-Safety
| 110
| 8
| 3
|
Python
|
https://llm-tuning-safety.github.io/
|
We jailbreak GPT-3.5 Turbo’s safety guardrails by fine-tuning it on only 10 adversarially designed examples, at a cost of less than $0.20 via OpenAI’s APIs.
|
llm-tuning-safety
|
2024-01-13
|
2023-10-06
| 16
| 6.637931
| null |
We jailbreak GPT-3.5 Turbo’s safety guardrails by fine-tuning it on only 10 adversarially designed examples, at a cost of less than $0.20 via OpenAI’s APIs.
|
['alignment', 'llm', 'llm-finetuning']
|
['alignment', 'llm', 'llm-finetuning']
|
2023-11-21
|
[('guardrails-ai/guardrails', 0.6136285066604614, 'llm', 1), ('nvidia/nemo-guardrails', 0.5286512970924377, 'llm', 0)]
| 4
| 2
| null | 0.27
| 4
| 4
| 3
| 2
| 0
| 0
| 0
| 4
| 4
| 90
| 1
| 28
|
1,775
|
llm
|
https://github.com/aws-samples/serverless-pdf-chat
|
[]
| null |
[]
|
[]
| null | null | null |
aws-samples/serverless-pdf-chat
|
serverless-pdf-chat
| 96
| 94
| 7
|
TypeScript
|
https://aws.amazon.com/blogs/compute/building-a-serverless-document-chat-with-aws-lambda-and-amazon-bedrock/
|
LLM-powered document chat using Amazon Bedrock and AWS Serverless
|
aws-samples
|
2024-01-09
|
2023-09-30
| 17
| 5.508197
|
https://avatars.githubusercontent.com/u/8931462?v=4
|
LLM-powered document chat using Amazon Bedrock and AWS Serverless
|
['ai', 'amazon-bedrock', 'serverless']
|
['ai', 'amazon-bedrock', 'serverless']
|
2024-01-11
|
[('deep-diver/llm-as-chatbot', 0.5605927109718323, 'llm', 0), ('nomic-ai/gpt4all', 0.5228908061981201, 'llm', 0), ('aws/chalice', 0.5006961226463318, 'web', 1), ('intel/intel-extension-for-transformers', 0.5002910494804382, 'perf', 0)]
| 3
| 1
| null | 0.56
| 20
| 20
| 4
| 0
| 0
| 0
| 0
| 20
| 15
| 90
| 0.8
| 28
|
762
|
ml-dl
|
https://github.com/praw-dev/asyncpraw
|
[]
| null |
[]
|
[]
| null | null | null |
praw-dev/asyncpraw
|
asyncpraw
| 92
| 17
| 4
|
Python
|
https://asyncpraw.readthedocs.io
|
Async PRAW, an abbreviation for "Asynchronous Python Reddit API Wrapper", is a python package that allows for simple access to Reddit's API.
|
praw-dev
|
2023-11-29
|
2019-02-05
| 260
| 0.353846
|
https://avatars.githubusercontent.com/u/1696888?v=4
|
Async PRAW, an abbreviation for "Asynchronous Python Reddit API Wrapper", is a python package that allows for simple access to Reddit's API.
|
['api', 'async', 'asyncpraw', 'oauth', 'praw', 'reddit', 'reddit-api']
|
['api', 'async', 'asyncpraw', 'oauth', 'praw', 'reddit', 'reddit-api']
|
2024-01-10
|
[('praw-dev/praw', 0.8785129189491272, 'data', 5), ('tornadoweb/tornado', 0.5063261389732361, 'web', 0)]
| 233
| 4
| null | 1.04
| 19
| 19
| 60
| 0
| 2
| 2
| 2
| 19
| 6
| 90
| 0.3
| 28
|
325
|
security
|
https://github.com/snyk-labs/pysnyk
|
[]
| null |
[]
|
[]
| null | null | null |
snyk-labs/pysnyk
|
pysnyk
| 73
| 116
| 11
|
Python
|
https://snyk.docs.apiary.io/
|
A Python client for the Snyk API.
|
snyk-labs
|
2023-12-26
|
2019-02-03
| 260
| 0.280461
|
https://avatars.githubusercontent.com/u/47793611?v=4
|
A Python client for the Snyk API.
|
['api', 'snyk']
|
['api', 'snyk']
|
2024-01-13
|
[('simple-salesforce/simple-salesforce', 0.6529852151870728, 'data', 1), ('cohere-ai/cohere-python', 0.6142659187316895, 'util', 0), ('googleapis/google-api-python-client', 0.5522992610931396, 'util', 0), ('encode/httpx', 0.5362645983695984, 'web', 0), ('shishirpatil/gorilla', 0.5202717185020447, 'llm', 1), ('psf/requests', 0.518162727355957, 'web', 0), ('hugapi/hug', 0.515166163444519, 'util', 0), ('falconry/falcon', 0.5076401829719543, 'web', 1), ('meilisearch/meilisearch-python', 0.5068243741989136, 'data', 1), ('python-restx/flask-restx', 0.5058279633522034, 'web', 1), ('ethereum/web3.py', 0.5023728013038635, 'crypto', 0), ('hydrosquall/tiingo-python', 0.5022001266479492, 'finance', 0)]
| 41
| 4
| null | 0.81
| 19
| 14
| 60
| 0
| 14
| 7
| 14
| 19
| 10
| 90
| 0.5
| 28
|
1,024
|
finance
|
https://github.com/borisbanushev/stockpredictionai
|
[]
| null |
[]
|
[]
| null | null | null |
borisbanushev/stockpredictionai
|
stockpredictionai
| 3,844
| 1,627
| 266
| null | null |
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
|
borisbanushev
|
2024-01-13
|
2019-01-09
| 263
| 14.568489
| null |
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
|
[]
|
[]
|
2019-02-11
|
[('ydataai/ydata-synthetic', 0.5105303525924683, 'data', 0)]
| 1
| 0
| null | 0
| 6
| 0
| 61
| 60
| 0
| 0
| 0
| 6
| 4
| 90
| 0.7
| 27
|
596
|
gis
|
https://github.com/plant99/felicette
|
[]
| null |
[]
|
[]
| null | null | null |
plant99/felicette
|
felicette
| 1,810
| 88
| 40
|
Python
| null |
Satellite imagery for dummies.
|
plant99
|
2024-01-12
|
2020-07-12
| 185
| 9.768697
| null |
Satellite imagery for dummies.
|
['earth-observation', 'earth-science', 'geoinformatics', 'geospatial', 'geospatial-data', 'geospatial-visualization', 'gis', 'satellite-data', 'satellite-imagery', 'satellite-images']
|
['earth-observation', 'earth-science', 'geoinformatics', 'geospatial', 'geospatial-data', 'geospatial-visualization', 'gis', 'satellite-data', 'satellite-imagery', 'satellite-images']
|
2021-09-08
|
[('sentinelsat/sentinelsat', 0.6691089272499084, 'gis', 1), ('developmentseed/label-maker', 0.6269397139549255, 'gis', 1), ('microsoft/torchgeo', 0.554535448551178, 'gis', 3), ('fatiando/verde', 0.533496618270874, 'gis', 2), ('giswqs/aws-open-data-geo', 0.5306439995765686, 'gis', 2), ('developmentseed/landsat-util', 0.5221068263053894, 'gis', 0), ('azavea/raster-vision', 0.5094917416572571, 'gis', 1)]
| 6
| 2
| null | 0
| 0
| 0
| 43
| 29
| 0
| 1
| 1
| 0
| 0
| 90
| 0
| 27
|
1,000
|
finance
|
https://github.com/cuemacro/findatapy
|
[]
| null |
[]
|
[]
| 1
| null | null |
cuemacro/findatapy
|
findatapy
| 1,501
| 196
| 91
|
Python
| null |
Python library to download market data via Bloomberg, Eikon, Quandl, Yahoo etc.
|
cuemacro
|
2024-01-13
|
2016-08-03
| 390
| 3.840278
|
https://avatars.githubusercontent.com/u/20479975?v=4
|
Python library to download market data via Bloomberg, Eikon, Quandl, Yahoo etc.
|
['arctic', 'bloomberg', 'dukascopy', 'eikon', 'fred', 'market-data', 'python-api', 'quandl']
|
['arctic', 'bloomberg', 'dukascopy', 'eikon', 'fred', 'market-data', 'python-api', 'quandl']
|
2023-12-01
|
[('hydrosquall/tiingo-python', 0.6793490052223206, 'finance', 0), ('ranaroussi/yfinance', 0.6290664672851562, 'finance', 1), ('jovianml/opendatasets', 0.6065632104873657, 'data', 0), ('gbeced/pyalgotrade', 0.5724738836288452, 'finance', 0), ('quandl/quandl-python', 0.5631470084190369, 'finance', 1), ('hugovk/pypistats', 0.562679648399353, 'util', 0), ('quantopian/zipline', 0.5615792870521545, 'finance', 0), ('nv7-github/googlesearch', 0.5598644614219666, 'util', 0), ('nasdaq/data-link-python', 0.538777232170105, 'finance', 0), ('pydata/pandas-datareader', 0.5381442308425903, 'pandas', 1), ('man-c/pycoingecko', 0.5231452584266663, 'crypto', 0), ('sentinel-hub/sentinelhub-py', 0.5211452841758728, 'gis', 0), ('goldmansachs/gs-quant', 0.5208711624145508, 'finance', 0), ('cuemacro/finmarketpy', 0.5173200368881226, 'finance', 0), ('domokane/financepy', 0.5130403637886047, 'finance', 0), ('pmorissette/ffn', 0.5108827948570251, 'finance', 0), ('matplotlib/mplfinance', 0.5023597478866577, 'finance', 1), ('openai/openai-python', 0.5010272860527039, 'util', 0)]
| 7
| 1
| null | 0.08
| 1
| 1
| 91
| 1
| 3
| 4
| 3
| 1
| 0
| 90
| 0
| 27
|
896
|
crypto
|
https://github.com/ofek/bit
|
[]
| null |
[]
|
[]
| null | null | null |
ofek/bit
|
bit
| 1,181
| 205
| 49
|
Python
|
https://ofek.dev/bit/
|
Bitcoin made easy.
|
ofek
|
2024-01-13
|
2016-11-12
| 376
| 3.137381
| null |
Bitcoin made easy.
|
['bitcoin', 'cryptocurrencies', 'libraries', 'payments']
|
['bitcoin', 'cryptocurrencies', 'libraries', 'payments']
|
2023-11-13
|
[('numerai/example-scripts', 0.5733424425125122, 'finance', 0), ('1200wd/bitcoinlib', 0.5140418410301208, 'crypto', 1)]
| 16
| 1
| null | 0.04
| 6
| 2
| 87
| 2
| 0
| 0
| 0
| 6
| 4
| 90
| 0.7
| 27
|
1,209
|
llm
|
https://github.com/keirp/automatic_prompt_engineer
|
['prompt-engineering', 'language-model']
|
Large Language Models Are Human-Level Prompt Engineers
|
[]
|
[]
| null | null | null |
keirp/automatic_prompt_engineer
|
automatic_prompt_engineer
| 860
| 109
| 16
|
Python
| null | null |
keirp
|
2024-01-13
|
2022-10-24
| 66
| 13.00216
| null |
Large Language Models Are Human-Level Prompt Engineers
|
[]
|
['language-model', 'prompt-engineering']
|
2023-05-25
|
[('hazyresearch/ama_prompting', 0.7995690107345581, 'llm', 1), ('guidance-ai/guidance', 0.7347609996795654, 'llm', 2), ('ctlllll/llm-toolmaker', 0.699556291103363, 'llm', 1), ('neulab/prompt2model', 0.6848369836807251, 'llm', 1), ('microsoft/promptbase', 0.6507399082183838, 'llm', 1), ('kyegomez/tree-of-thoughts', 0.6476341485977173, 'llm', 1), ('srush/minichain', 0.6470367908477783, 'llm', 1), ('thudm/p-tuning-v2', 0.6158888339996338, 'nlp', 0), ('1rgs/jsonformer', 0.5909636616706848, 'llm', 1), ('stanfordnlp/dspy', 0.5835668444633484, 'llm', 0), ('promptslab/promptify', 0.5769301056861877, 'nlp', 1), ('agenta-ai/agenta', 0.5762568116188049, 'llm', 1), ('thudm/chatglm-6b', 0.5738417506217957, 'llm', 1), ('hannibal046/awesome-llm', 0.5738133192062378, 'study', 1), ('spcl/graph-of-thoughts', 0.5692050457000732, 'llm', 1), ('yizhongw/self-instruct', 0.5658340454101562, 'llm', 1), ('lianjiatech/belle', 0.5627188682556152, 'llm', 0), ('ai21labs/lm-evaluation', 0.5610719919204712, 'llm', 1), ('lm-sys/fastchat', 0.5581333041191101, 'llm', 1), ('bigscience-workshop/promptsource', 0.5572788715362549, 'nlp', 0), ('hazyresearch/manifest', 0.5553961396217346, 'llm', 1), ('facebookresearch/shepherd', 0.5500012636184692, 'llm', 1), ('freedomintelligence/llmzoo', 0.5354942083358765, 'llm', 1), ('promptslab/awesome-prompt-engineering', 0.5347884297370911, 'study', 1), ('conceptofmind/toolformer', 0.5263444185256958, 'llm', 1), ('next-gpt/next-gpt', 0.525351881980896, 'llm', 0), ('airi-institute/probing_framework', 0.5204843282699585, 'nlp', 0), ('juncongmoo/pyllama', 0.518515944480896, 'llm', 0), ('suno-ai/bark', 0.5167652368545532, 'ml', 0), ('likenneth/honest_llama', 0.5166599750518799, 'llm', 1), ('jina-ai/thinkgpt', 0.5156129002571106, 'llm', 1), ('openai/finetune-transformer-lm', 0.5153794884681702, 'llm', 0), ('hazyresearch/h3', 0.5113564729690552, 'llm', 0), ('tatsu-lab/stanford_alpaca', 0.511340320110321, 'llm', 1), ('jonasgeiping/cramming', 0.5112678408622742, 'nlp', 1), ('microsoft/autogen', 0.5107211470603943, 'llm', 0), ('young-geng/easylm', 0.5091218948364258, 'llm', 1), ('openbmb/toolbench', 0.5084372758865356, 'llm', 0), ('reasoning-machines/pal', 0.5067288279533386, 'llm', 1), ('baichuan-inc/baichuan-13b', 0.5003429055213928, 'llm', 0)]
| 4
| 0
| null | 0.04
| 6
| 1
| 15
| 8
| 0
| 0
| 0
| 6
| 2
| 90
| 0.3
| 27
|
652
|
util
|
https://github.com/rasbt/watermark
|
[]
| null |
[]
|
[]
| null | null | null |
rasbt/watermark
|
watermark
| 849
| 89
| 13
|
Python
| null |
An IPython magic extension for printing date and time stamps, version numbers, and hardware information
|
rasbt
|
2024-01-09
|
2014-07-30
| 495
| 1.712187
| null |
An IPython magic extension for printing date and time stamps, version numbers, and hardware information
|
['ipython', 'jupyter', 'magic-extension']
|
['ipython', 'jupyter', 'magic-extension']
|
2023-07-02
|
[('python/cpython', 0.5787110328674316, 'util', 0), ('jupyter/nbformat', 0.5627220869064331, 'jupyter', 0), ('wesm/pydata-book', 0.5561281442642212, 'study', 0), ('ipython/ipython', 0.5411689281463623, 'util', 2), ('ipython/ipykernel', 0.541002631187439, 'util', 2), ('erotemic/ubelt', 0.5379260182380676, 'util', 0), ('ipython/ipyparallel', 0.5231406688690186, 'perf', 1), ('faster-cpython/ideas', 0.5214296579360962, 'perf', 0), ('dateutil/dateutil', 0.5191126465797424, 'util', 0), ('pyston/pyston', 0.5138229131698608, 'util', 0), ('pypy/pypy', 0.5103526711463928, 'util', 0), ('faster-cpython/tools', 0.507169783115387, 'perf', 0), ('gotcha/ipdb', 0.5067563652992249, 'debug', 1), ('cohere-ai/notebooks', 0.5048350095748901, 'llm', 0)]
| 19
| 5
| null | 0.38
| 1
| 0
| 115
| 7
| 1
| 2
| 1
| 1
| 1
| 90
| 1
| 27
|
695
|
profiling
|
https://github.com/pythonspeed/filprofiler
|
[]
| null |
[]
|
[]
| null | null | null |
pythonspeed/filprofiler
|
filprofiler
| 802
| 24
| 9
|
Rust
|
https://pythonspeed.com/products/filmemoryprofiler/
|
A Python memory profiler for data processing and scientific computing applications
|
pythonspeed
|
2024-01-14
|
2020-06-18
| 188
| 4.249811
| null |
A Python memory profiler for data processing and scientific computing applications
|
['memory', 'memory-', 'memory-leak', 'memory-leak-detection', 'memory-leak-finder', 'memory-leaks', 'memory-profiler', 'memory-profiling']
|
['memory', 'memory-', 'memory-leak', 'memory-leak-detection', 'memory-leak-finder', 'memory-leaks', 'memory-profiler', 'memory-profiling']
|
2023-03-18
|
[('pympler/pympler', 0.7303571105003357, 'perf', 0), ('bloomberg/memray', 0.7156088352203369, 'profiling', 4), ('benfred/py-spy', 0.7144114971160889, 'profiling', 0), ('pythonprofilers/memory_profiler', 0.664732813835144, 'profiling', 0), ('sumerc/yappi', 0.6349960565567017, 'profiling', 0), ('pyutils/line_profiler', 0.6149646043777466, 'profiling', 0), ('dgilland/cacheout', 0.6149056553840637, 'perf', 0), ('python-cachier/cachier', 0.605586588382721, 'perf', 0), ('plasma-umass/scalene', 0.6020299196243286, 'profiling', 0), ('joblib/joblib', 0.5940987467765808, 'util', 0), ('pyston/pyston', 0.5313695073127747, 'util', 0), ('pytables/pytables', 0.5271565914154053, 'data', 0), ('joerick/pyinstrument', 0.5218971967697144, 'profiling', 0), ('xrudelis/pytrait', 0.5162791013717651, 'util', 0), ('rasbt/mlxtend', 0.5145261883735657, 'ml', 0), ('micropython/micropython', 0.5140711069107056, 'util', 0), ('numpy/numpy', 0.5123228430747986, 'math', 0), ('jiffyclub/snakeviz', 0.5121504664421082, 'profiling', 0), ('cython/cython', 0.5108945369720459, 'util', 0), ('google/pytype', 0.5083953738212585, 'typing', 0), ('spotify/annoy', 0.5058916807174683, 'ml', 0), ('exaloop/codon', 0.5046871900558472, 'perf', 0), ('eleutherai/pyfra', 0.5045285820960999, 'ml', 0), ('p403n1x87/austin', 0.5042270421981812, 'profiling', 0), ('pypy/pypy', 0.5019063353538513, 'util', 0)]
| 6
| 4
| null | 0.33
| 1
| 0
| 44
| 10
| 3
| 18
| 3
| 1
| 0
| 90
| 0
| 27
|
832
|
finance
|
https://github.com/idanya/algo-trader
|
[]
| null |
[]
|
[]
| null | null | null |
idanya/algo-trader
|
algo-trader
| 726
| 90
| 29
|
Python
| null |
Trading bot with support for realtime trading, backtesting, custom strategies and much more.
|
idanya
|
2024-01-13
|
2021-09-14
| 124
| 5.854839
| null |
Trading bot with support for realtime trading, backtesting, custom strategies and much more.
|
['algorithmic-trading', 'backtesting', 'crypto-bot', 'technical-analysis', 'trading-bot', 'trading-strategies']
|
['algorithmic-trading', 'backtesting', 'crypto-bot', 'technical-analysis', 'trading-bot', 'trading-strategies']
|
2023-11-20
|
[('freqtrade/freqtrade', 0.8236120939254761, 'crypto', 2), ('polakowo/vectorbt', 0.6762666702270508, 'finance', 3), ('gbeced/basana', 0.6138817071914673, 'finance', 3), ('quantconnect/lean', 0.5937037467956543, 'finance', 2), ('ccxt/ccxt', 0.5679965019226074, 'crypto', 0), ('blankly-finance/blankly', 0.5535969138145447, 'finance', 2), ('zvtvz/zvt', 0.5369071364402771, 'finance', 5), ('kernc/backtesting.py', 0.522881269454956, 'finance', 3), ('ai4finance-foundation/finrl', 0.5114476084709167, 'finance', 1), ('openbb-finance/openbbterminal', 0.5061081051826477, 'finance', 0)]
| 4
| 2
| null | 0.08
| 1
| 1
| 28
| 2
| 1
| 3
| 1
| 1
| 0
| 90
| 0
| 27
|
305
|
crypto
|
https://github.com/palkeo/panoramix
|
[]
| null |
[]
|
[]
| null | null | null |
palkeo/panoramix
|
panoramix
| 714
| 194
| 35
|
Python
| null |
Ethereum decompiler
|
palkeo
|
2024-01-12
|
2020-02-17
| 206
| 3.463617
| null |
Ethereum decompiler
|
[]
|
[]
|
2023-06-14
|
[('ethtx/ethtx_ce', 0.6499204635620117, 'crypto', 0)]
| 4
| 2
| null | 0.4
| 3
| 1
| 48
| 7
| 0
| 1
| 1
| 3
| 3
| 90
| 1
| 27
|
568
|
gis
|
https://github.com/developmentseed/landsat-util
|
[]
| null |
[]
|
[]
| null | null | null |
developmentseed/landsat-util
|
landsat-util
| 687
| 153
| 127
|
Python
| null |
A utility to search, download and process Landsat 8 satellite imagery
|
developmentseed
|
2024-01-10
|
2014-08-01
| 495
| 1.386278
|
https://avatars.githubusercontent.com/u/92384?v=4
|
A utility to search, download and process Landsat 8 satellite imagery
|
[]
|
[]
|
2018-07-30
|
[('plant99/felicette', 0.5221068263053894, 'gis', 0), ('sentinelsat/sentinelsat', 0.5123438239097595, 'gis', 0)]
| 25
| 7
| null | 0
| 1
| 0
| 115
| 66
| 0
| 2
| 2
| 1
| 3
| 90
| 3
| 27
|
513
|
ml-dl
|
https://github.com/kakaobrain/rq-vae-transformer
|
[]
| null |
[]
|
[]
| null | null | null |
kakaobrain/rq-vae-transformer
|
rq-vae-transformer
| 647
| 74
| 16
|
Jupyter Notebook
| null |
The official implementation of Autoregressive Image Generation using Residual Quantization (CVPR '22)
|
kakaobrain
|
2024-01-12
|
2022-03-03
| 99
| 6.488539
|
https://avatars.githubusercontent.com/u/25736994?v=4
|
The official implementation of Autoregressive Image Generation using Residual Quantization (CVPR '22)
|
[]
|
[]
|
2024-01-03
|
[('stability-ai/stablediffusion', 0.5072489380836487, 'diffusion', 0), ('compvis/latent-diffusion', 0.5072487592697144, 'diffusion', 0)]
| 2
| 2
| null | 0.02
| 1
| 0
| 23
| 0
| 0
| 0
| 0
| 1
| 0
| 90
| 0
| 27
|
1,078
|
ml-ops
|
https://github.com/kubeflow-kale/kale
|
[]
| null |
[]
|
[]
| null | null | null |
kubeflow-kale/kale
|
kale
| 613
| 129
| 17
|
Python
|
http://kubeflow-kale.github.io
|
Kubeflow’s superfood for Data Scientists
|
kubeflow-kale
|
2024-01-05
|
2019-01-24
| 261
| 2.342249
|
https://avatars.githubusercontent.com/u/52384265?v=4
|
Kubeflow’s superfood for Data Scientists
|
['jupyter-notebook', 'kubeflow', 'kubeflow-pipelines', 'machine-learning']
|
['jupyter-notebook', 'kubeflow', 'kubeflow-pipelines', 'machine-learning']
|
2021-10-20
|
[('kubeflow/pipelines', 0.692866325378418, 'ml-ops', 3), ('orchest/orchest', 0.6008453965187073, 'ml-ops', 1), ('getindata/kedro-kubeflow', 0.5977193117141724, 'ml-ops', 2), ('firmai/industry-machine-learning', 0.5946695804595947, 'study', 2), ('determined-ai/determined', 0.5921743512153625, 'ml-ops', 1), ('ploomber/ploomber', 0.5709792971611023, 'ml-ops', 1), ('flyteorg/flyte', 0.5685352683067322, 'ml-ops', 1), ('gradio-app/gradio', 0.5675045847892761, 'viz', 1), ('superduperdb/superduperdb', 0.5663890838623047, 'data', 0), ('polyaxon/polyaxon', 0.5640344619750977, 'ml-ops', 1), ('linealabs/lineapy', 0.5481640100479126, 'jupyter', 0), ('dagworks-inc/hamilton', 0.5480688214302063, 'ml-ops', 1), ('mito-ds/monorepo', 0.5466421842575073, 'jupyter', 0), ('ageron/handson-ml2', 0.5455378293991089, 'ml', 0), ('kedro-org/kedro-viz', 0.5434872508049011, 'ml-ops', 0), ('bodywork-ml/bodywork-core', 0.5426039695739746, 'ml-ops', 1), ('netflix/metaflow', 0.5416892170906067, 'ml-ops', 1), ('backtick-se/cowait', 0.5415117740631104, 'util', 0), ('astronomer/astro-sdk', 0.5413955450057983, 'ml-ops', 0), ('hi-primus/optimus', 0.5404044389724731, 'ml-ops', 1), ('mage-ai/mage-ai', 0.5401076674461365, 'ml-ops', 1), ('jovianml/opendatasets', 0.5395414233207703, 'data', 1), ('dylanhogg/awesome-python', 0.5359322428703308, 'study', 1), ('skops-dev/skops', 0.5353480577468872, 'ml-ops', 1), ('huggingface/datasets', 0.5326496362686157, 'nlp', 1), ('mlflow/mlflow', 0.5298233032226562, 'ml-ops', 1), ('kedro-org/kedro', 0.5225927233695984, 'ml-ops', 1), ('polyaxon/datatile', 0.5204256772994995, 'pandas', 0), ('googlecloudplatform/vertex-ai-samples', 0.5194539427757263, 'ml', 0), ('dask/dask-ml', 0.5168511867523193, 'ml', 0), ('wandb/client', 0.5164470076560974, 'ml', 1), ('kubeflow/fairing', 0.5162668824195862, 'ml-ops', 0), ('merantix-momentum/squirrel-core', 0.5156881809234619, 'ml', 1), ('eventual-inc/daft', 0.5150558352470398, 'pandas', 1), ('airbytehq/airbyte', 0.5148319005966187, 'data', 0), ('koaning/scikit-lego', 0.5133776664733887, 'ml', 1), ('vaexio/vaex', 0.5090245008468628, 'perf', 1), ('ashleve/lightning-hydra-template', 0.5069912075996399, 'util', 0), ('gefyrahq/gefyra', 0.5049176216125488, 'util', 0), ('intake/intake', 0.5041489005088806, 'data', 0), ('fastai/fastcore', 0.5032058358192444, 'util', 0)]
| 10
| 4
| null | 0
| 2
| 0
| 60
| 27
| 0
| 5
| 5
| 2
| 3
| 90
| 1.5
| 27
|
1,349
|
ml
|
https://github.com/ray-project/tune-sklearn
|
[]
| null |
[]
|
[]
| null | null | null |
ray-project/tune-sklearn
|
tune-sklearn
| 462
| 51
| 18
|
Python
|
https://docs.ray.io/en/master/tune/api_docs/sklearn.html
|
A drop-in replacement for Scikit-Learn’s GridSearchCV / RandomizedSearchCV -- but with cutting edge hyperparameter tuning techniques.
|
ray-project
|
2024-01-06
|
2019-11-28
| 217
| 2.122047
|
https://avatars.githubusercontent.com/u/22125274?v=4
|
A drop-in replacement for Scikit-Learn’s GridSearchCV / RandomizedSearchCV -- but with cutting edge hyperparameter tuning techniques.
|
['automl', 'bayesian-optimization', 'gridsearchcv', 'hyperparameter-tuning', 'scikit-learn']
|
['automl', 'bayesian-optimization', 'gridsearchcv', 'hyperparameter-tuning', 'scikit-learn']
|
2023-11-04
|
[('automl/auto-sklearn', 0.6818026304244995, 'ml', 4), ('microsoft/flaml', 0.6497718095779419, 'ml', 2), ('kubeflow/katib', 0.626979410648346, 'ml', 0), ('microsoft/nni', 0.580021321773529, 'ml', 3), ('mljar/mljar-supervised', 0.5580164194107056, 'ml', 2), ('scikit-optimize/scikit-optimize', 0.550593912601471, 'ml', 3), ('google/vizier', 0.5394313931465149, 'ml', 2), ('determined-ai/determined', 0.5187373161315918, 'ml-ops', 1), ('optuna/optuna', 0.5114966034889221, 'ml', 0), ('rasbt/machine-learning-book', 0.5028691291809082, 'study', 1)]
| 14
| 3
| null | 0.15
| 12
| 8
| 50
| 2
| 2
| 3
| 2
| 12
| 3
| 90
| 0.2
| 27
|
1,115
|
web
|
https://github.com/pylons/webob
|
['wsgi']
| null |
[]
|
[]
| null | null | null |
pylons/webob
|
webob
| 428
| 189
| 20
|
Python
|
https://webob.org/
|
WSGI request and response objects
|
pylons
|
2024-01-12
|
2011-09-17
| 645
| 0.663125
|
https://avatars.githubusercontent.com/u/452227?v=4
|
WSGI request and response objects
|
[]
|
['wsgi']
|
2023-09-05
|
[('pallets/werkzeug', 0.6051927804946899, 'web', 1), ('pylons/waitress', 0.5591251254081726, 'web', 0), ('requests/toolbelt', 0.5443535447120667, 'util', 0), ('benoitc/gunicorn', 0.5041623711585999, 'web', 1)]
| 110
| 3
| null | 0.13
| 3
| 0
| 150
| 4
| 0
| 6
| 6
| 3
| 3
| 90
| 1
| 27
|
516
|
gis
|
https://github.com/scikit-geometry/scikit-geometry
|
[]
| null |
[]
|
[]
| null | null | null |
scikit-geometry/scikit-geometry
|
scikit-geometry
| 398
| 53
| 14
|
Jupyter Notebook
|
https://scikit-geometry.github.io/scikit-geometry
|
Scientific Python Geometric Algorithms Library
|
scikit-geometry
|
2024-01-09
|
2016-03-28
| 409
| 0.972765
|
https://avatars.githubusercontent.com/u/59055868?v=4
|
Scientific Python Geometric Algorithms Library
|
['cgal', 'geometric-algorithms', 'geometry', 'wrapper']
|
['cgal', 'geometric-algorithms', 'geometry', 'wrapper']
|
2023-12-04
|
[('scipy/scipy', 0.6300176382064819, 'math', 0), ('shapely/shapely', 0.5902884602546692, 'gis', 2), ('pysal/pysal', 0.5866171717643738, 'gis', 0), ('fredrik-johansson/mpmath', 0.5644670724868774, 'math', 0), ('albahnsen/pycircular', 0.5635026693344116, 'math', 0), ('marcomusy/vedo', 0.5506011843681335, 'viz', 0), ('numpy/numpy', 0.5440155267715454, 'math', 0), ('sympy/sympy', 0.5399286150932312, 'math', 0), ('kornia/kornia', 0.5317684412002563, 'ml-dl', 0), ('artelys/geonetworkx', 0.5264812707901001, 'gis', 0), ('benbovy/spherely', 0.5209958553314209, 'gis', 2), ('dfki-ric/pytransform3d', 0.5100734233856201, 'math', 0)]
| 18
| 6
| null | 0.02
| 9
| 4
| 95
| 1
| 0
| 0
| 0
| 9
| 11
| 90
| 1.2
| 27
|
840
|
gis
|
https://github.com/mapbox/mercantile
|
[]
| null |
[]
|
[]
| null | null | null |
mapbox/mercantile
|
mercantile
| 384
| 62
| 124
|
Python
| null |
Spherical mercator tile and coordinate utilities
|
mapbox
|
2024-01-14
|
2014-02-12
| 519
| 0.738664
|
https://avatars.githubusercontent.com/u/600935?v=4
|
Spherical mercator tile and coordinate utilities
|
['imagery', 'pxm', 'satellite']
|
['imagery', 'pxm', 'satellite']
|
2023-11-02
|
[]
| 23
| 5
| null | 0
| 1
| 1
| 121
| 2
| 0
| 4
| 4
| 1
| 1
| 90
| 1
| 27
|
1,157
|
gamedev
|
https://github.com/libtcod/python-tcod
|
[]
| null |
[]
|
[]
| null | null | null |
libtcod/python-tcod
|
python-tcod
| 382
| 37
| 19
|
Python
| null |
A high-performance Python port of libtcod. Includes the libtcodpy module for backwards compatibility with older projects.
|
libtcod
|
2024-01-13
|
2015-03-14
| 463
| 0.824291
|
https://avatars.githubusercontent.com/u/40313210?v=4
|
A high-performance Python port of libtcod. Includes the libtcodpy module for backwards compatibility with older projects.
|
['field-of-view', 'libtcod', 'libtcodpy', 'pathfinding', 'pypy', 'pypy3', 'python-tcod']
|
['field-of-view', 'libtcod', 'libtcodpy', 'pathfinding', 'pypy', 'pypy3', 'python-tcod']
|
2024-01-08
|
[('pypy/pypy', 0.6247069835662842, 'util', 0), ('pyodide/micropip', 0.5887089967727661, 'util', 0), ('pyodide/pyodide', 0.5775824785232544, 'util', 0), ('cython/cython', 0.5713533759117126, 'util', 0), ('1200wd/bitcoinlib', 0.5591480731964111, 'crypto', 0), ('pyston/pyston', 0.5553798079490662, 'util', 0), ('pyo3/maturin', 0.5518137812614441, 'util', 1), ('erotemic/ubelt', 0.5495151877403259, 'util', 0), ('hoffstadt/dearpygui', 0.5448720455169678, 'gui', 0), ('pdm-project/pdm', 0.5411107540130615, 'util', 0), ('pytoolz/toolz', 0.5379080176353455, 'util', 0), ('fastai/fastcore', 0.5340726375579834, 'util', 0), ('pypa/hatch', 0.5316311120986938, 'util', 0), ('pytest-dev/pytest-bdd', 0.5265241265296936, 'testing', 0), ('pympler/pympler', 0.52317214012146, 'perf', 0), ('klen/py-frameworks-bench', 0.5221824049949646, 'perf', 0), ('dgilland/cacheout', 0.5180550813674927, 'perf', 0), ('primal100/pybitcointools', 0.5110588669776917, 'crypto', 0), ('dosisod/refurb', 0.5105434060096741, 'util', 0), ('beeware/toga', 0.5042990446090698, 'gui', 0), ('paramiko/paramiko', 0.502030611038208, 'util', 0)]
| 23
| 1
| null | 2.67
| 10
| 10
| 108
| 0
| 10
| 20
| 10
| 10
| 1
| 90
| 0.1
| 27
|
1,874
|
ml
|
https://github.com/oneil512/insight
|
[]
| null |
[]
|
[]
| null | null | null |
oneil512/insight
|
INSIGHT
| 373
| 54
| 13
|
Python
| null |
INSIGHT is an autonomous AI that can do medical research!
|
oneil512
|
2024-01-12
|
2023-04-08
| 42
| 8.791246
| null |
INSIGHT is an autonomous AI that can do medical research!
|
['agent', 'ai', 'chatgpt', 'gpt', 'llm', 'medical', 'ml']
|
['agent', 'ai', 'chatgpt', 'gpt', 'llm', 'medical', 'ml']
|
2023-10-21
|
[('lucidrains/medical-chatgpt', 0.6099081635475159, 'llm', 0), ('torantulino/auto-gpt', 0.5754587054252625, 'llm', 1), ('mindsdb/mindsdb', 0.5716148614883423, 'data', 4), ('microsoft/lmops', 0.5636782050132751, 'llm', 2), ('assafelovic/gpt-researcher', 0.5495372414588928, 'llm', 0), ('gventuri/pandas-ai', 0.5358114242553711, 'pandas', 2), ('google-research/google-research', 0.5356377363204956, 'ml', 1), ('prefecthq/marvin', 0.5314114093780518, 'nlp', 3), ('project-monai/monai', 0.5232803225517273, 'ml', 0), ('oegedijk/explainerdashboard', 0.5162001848220825, 'ml-interpretability', 0), ('antonosika/gpt-engineer', 0.5132570862770081, 'llm', 1), ('alirezadir/machine-learning-interview-enlightener', 0.5009233355522156, 'study', 1)]
| 3
| 0
| null | 1.42
| 3
| 3
| 9
| 3
| 0
| 0
| 0
| 3
| 2
| 90
| 0.7
| 27
|
1,351
|
util
|
https://github.com/nv7-github/googlesearch
|
['google-crawler']
| null |
[]
|
[]
| null | null | null |
nv7-github/googlesearch
|
googlesearch
| 345
| 91
| 6
|
Python
|
https://pypi.org/project/googlesearch-python/
|
A Python library for scraping the Google search engine.
|
nv7-github
|
2024-01-12
|
2020-07-05
| 186
| 1.851994
| null |
A Python library for scraping the Google search engine.
|
[]
|
['google-crawler']
|
2023-05-30
|
[('scrapy/scrapy', 0.7149690985679626, 'data', 0), ('alirezamika/autoscraper', 0.7142531275749207, 'data', 0), ('googleapis/google-api-python-client', 0.6702570915222168, 'util', 0), ('serpapi/google-search-results-python', 0.6652640700340271, 'util', 1), ('roniemartinez/dude', 0.6479972004890442, 'util', 0), ('binux/pyspider', 0.6098272204399109, 'data', 0), ('scholarly-python-package/scholarly', 0.6054853796958923, 'data', 0), ('jovianml/opendatasets', 0.5820246338844299, 'data', 0), ('clips/pattern', 0.5700188279151917, 'nlp', 0), ('cuemacro/findatapy', 0.5598644614219666, 'finance', 0), ('goldsmith/wikipedia', 0.5323612093925476, 'data', 0), ('meilisearch/meilisearch-python', 0.5313740968704224, 'data', 0), ('requests/toolbelt', 0.5127140879631042, 'util', 0), ('psf/requests', 0.5044116973876953, 'web', 0), ('qdrant/qdrant-client', 0.5012821555137634, 'util', 0)]
| 8
| 4
| null | 0.15
| 9
| 1
| 43
| 8
| 0
| 1
| 1
| 9
| 18
| 90
| 2
| 27
|
341
|
term
|
https://github.com/rockhopper-technologies/enlighten
|
[]
| null |
[]
|
[]
| null | null | null |
rockhopper-technologies/enlighten
|
enlighten
| 335
| 23
| 5
|
Python
|
https://python-enlighten.readthedocs.io
|
Enlighten Progress Bar for Python Console Apps
|
rockhopper-technologies
|
2024-01-11
|
2017-09-22
| 331
| 1.01034
|
https://avatars.githubusercontent.com/u/20388549?v=4
|
Enlighten Progress Bar for Python Console Apps
|
[]
|
[]
|
2023-12-25
|
[('wolph/python-progressbar', 0.7673426270484924, 'util', 0), ('tqdm/tqdm', 0.7503482699394226, 'term', 0), ('urwid/urwid', 0.5648614764213562, 'term', 0), ('rsalmei/alive-progress', 0.5524495244026184, 'util', 0), ('inducer/pudb', 0.5411313772201538, 'debug', 0), ('jquast/blessed', 0.5384596586227417, 'term', 0), ('willmcgugan/rich', 0.5262578725814819, 'term', 0), ('alexmojaki/heartrate', 0.5111202597618103, 'debug', 0), ('teemu/pytest-sugar', 0.5103371739387512, 'testing', 0)]
| 6
| 2
| null | 0.92
| 2
| 2
| 77
| 1
| 6
| 5
| 6
| 2
| 3
| 90
| 1.5
| 27
|
1,649
|
llm
|
https://github.com/lchen001/llmdrift
|
['drift', 'language-model']
|
LLM Drifts: How Is ChatGPT’s Behavior Changing over Time?
|
[]
|
[]
| null | null | null |
lchen001/llmdrift
|
LLMDrift
| 320
| 28
| 15
|
Jupyter Notebook
| null | null |
lchen001
|
2024-01-12
|
2023-07-18
| 28
| 11.428571
| null |
LLM Drifts: How Is ChatGPT’s Behavior Changing over Time?
|
[]
|
['drift', 'language-model']
|
2024-01-03
|
[('thudm/chatglm2-6b', 0.5880559086799622, 'llm', 0), ('hwchase17/langchain', 0.5510751008987427, 'llm', 1), ('microsoft/autogen', 0.5390075445175171, 'llm', 0), ('nomic-ai/gpt4all', 0.5283440351486206, 'llm', 1), ('fasteval/fasteval', 0.5002023577690125, 'llm', 0)]
| 5
| 0
| null | 0.48
| 1
| 1
| 6
| 0
| 0
| 0
| 0
| 1
| 0
| 90
| 0
| 27
|
986
|
time-series
|
https://github.com/microprediction/microprediction
|
[]
| null |
[]
|
[]
| null | null | null |
microprediction/microprediction
|
microprediction
| 311
| 55
| 15
|
Jupyter Notebook
|
http://www.microprediction.org
|
If you can measure it, consider it predicted
|
microprediction
|
2024-01-09
|
2020-02-20
| 205
| 1.511806
| null |
If you can measure it, consider it predicted
|
['fbprophet', 'filterpy', 'hmmlearn', 'kalman-filter', 'keras', 'nowcasting', 'online-algorithms', 'pmdarima', 'time-series', 'timeseries', 'timeseries-analysis', 'timeseries-clustering', 'timeseries-data', 'timeseries-database', 'timeseries-forecasting', 'timeseries-prediction', 'tsfresh', 'tslearn']
|
['fbprophet', 'filterpy', 'hmmlearn', 'kalman-filter', 'keras', 'nowcasting', 'online-algorithms', 'pmdarima', 'time-series', 'timeseries', 'timeseries-analysis', 'timeseries-clustering', 'timeseries-data', 'timeseries-database', 'timeseries-forecasting', 'timeseries-prediction', 'tsfresh', 'tslearn']
|
2024-01-05
|
[('ourownstory/neural_prophet', 0.5824498534202576, 'ml', 3), ('alkaline-ml/pmdarima', 0.5424190759658813, 'time-series', 2), ('awslabs/gluonts', 0.5331199765205383, 'time-series', 2), ('unit8co/darts', 0.5138264298439026, 'time-series', 1), ('salesforce/merlion', 0.5122169852256775, 'time-series', 1), ('firmai/atspy', 0.5109694004058838, 'time-series', 1), ('sktime/sktime', 0.5038242340087891, 'time-series', 1)]
| 14
| 3
| null | 4.37
| 0
| 0
| 47
| 0
| 5
| 36
| 5
| 0
| 0
| 90
| 0
| 27
|
1,448
|
util
|
https://github.com/salesforce/logai
|
[]
| null |
[]
|
[]
| null | null | null |
salesforce/logai
|
logai
| 298
| 39
| 15
|
Python
| null |
LogAI - An open-source library for log analytics and intelligence
|
salesforce
|
2024-01-10
|
2022-10-27
| 65
| 4.534783
|
https://avatars.githubusercontent.com/u/453694?v=4
|
LogAI - An open-source library for log analytics and intelligence
|
['ai', 'aiops', 'anomaly-detection', 'benchmarking', 'log-analysis', 'log-intelligence', 'machine-learning']
|
['ai', 'aiops', 'anomaly-detection', 'benchmarking', 'log-analysis', 'log-intelligence', 'machine-learning']
|
2023-03-02
|
[('whylabs/whylogs', 0.7454851269721985, 'util', 1), ('aimhubio/aim', 0.5716543197631836, 'ml-ops', 2), ('polyaxon/datatile', 0.5616445541381836, 'pandas', 0), ('pycaret/pycaret', 0.5456848740577698, 'ml', 2), ('ray-project/ray', 0.5410916805267334, 'ml-ops', 1), ('mindsdb/mindsdb', 0.5326856970787048, 'data', 2), ('metachris/logzero', 0.5321155786514282, 'util', 0), ('yzhao062/pyod', 0.5268727540969849, 'data', 2), ('mlflow/mlflow', 0.5248235464096069, 'ml-ops', 2), ('larsbaunwall/bricky', 0.5161862969398499, 'llm', 1), ('oegedijk/explainerdashboard', 0.5160885453224182, 'ml-interpretability', 0), ('unit8co/darts', 0.5133403539657593, 'time-series', 2), ('nebuly-ai/nebullvm', 0.5123262405395508, 'perf', 1), ('netflix/metaflow', 0.5108799934387207, 'ml-ops', 2), ('alirezadir/machine-learning-interview-enlightener', 0.5083866119384766, 'study', 2), ('wandb/client', 0.5036634206771851, 'ml', 1), ('fmind/mlops-python-package', 0.5032975077629089, 'template', 1)]
| 5
| 2
| null | 0.92
| 2
| 0
| 15
| 11
| 4
| 4
| 4
| 2
| 2
| 90
| 1
| 27
|
1,620
|
data
|
https://github.com/samuelcolvin/rtoml
|
['toml', 'rust']
| null |
[]
|
[]
| null | null | null |
samuelcolvin/rtoml
|
rtoml
| 285
| 29
| 8
|
Python
|
https://pypi.org/project/rtoml/
|
A fast TOML library for python implemented in rust.
|
samuelcolvin
|
2024-01-12
|
2020-01-07
| 212
| 1.34434
| null |
A fast TOML library for python implemented in rust.
|
['deserialization', 'parser', 'rust', 'toml']
|
['deserialization', 'parser', 'rust', 'toml']
|
2023-12-21
|
[('astral-sh/ruff', 0.5641447305679321, 'util', 1), ('rustpython/rustpython', 0.5515300035476685, 'util', 1), ('yukinarit/pyserde', 0.5448687076568604, 'util', 1), ('pyo3/pyo3', 0.5289204716682434, 'util', 1), ('marshmallow-code/marshmallow', 0.5252963900566101, 'util', 1), ('deepmind/chex', 0.5042668581008911, 'ml-dl', 0), ('sfu-db/connector-x', 0.5036177039146423, 'data', 1)]
| 14
| 3
| null | 0.13
| 8
| 6
| 49
| 1
| 1
| 3
| 1
| 8
| 8
| 90
| 1
| 27
|
521
|
util
|
https://github.com/venth/aws-adfs
|
[]
| null |
[]
|
[]
| null | null | null |
venth/aws-adfs
|
aws-adfs
| 283
| 96
| 11
|
Python
| null |
Command line tool to ease aws cli authentication against ADFS (multi factor authentication with active directory)
|
venth
|
2024-01-11
|
2016-06-25
| 396
| 0.713874
| null |
Command line tool to ease aws cli authentication against ADFS (multi factor authentication with active directory)
|
['adfs', 'aws', 'command-line', 'duo-security', 'multi-factor-authentication', 'tools']
|
['adfs', 'aws', 'command-line', 'duo-security', 'multi-factor-authentication', 'tools']
|
2023-12-16
|
[]
| 50
| 2
| null | 0.87
| 15
| 12
| 92
| 1
| 6
| 16
| 6
| 15
| 7
| 90
| 0.5
| 27
|
726
|
ml
|
https://github.com/autonlab/auton-survival
|
[]
| null |
[]
|
[]
| null | null | null |
autonlab/auton-survival
|
auton-survival
| 275
| 70
| 8
|
Python
|
http://autonlab.github.io/auton-survival
|
Auton Survival - an open source package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Events
|
autonlab
|
2024-01-12
|
2020-04-01
| 199
| 1.375983
|
https://avatars.githubusercontent.com/u/11739208?v=4
|
Auton Survival - an open source package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Events
|
['causal-inference', 'counterfactual-inference', 'data-science', 'deep-learning', 'graphical-models', 'machine-learning', 'regression', 'reliability-analysis', 'survival-analysis', 'time-to-event']
|
['causal-inference', 'counterfactual-inference', 'data-science', 'deep-learning', 'graphical-models', 'machine-learning', 'regression', 'reliability-analysis', 'survival-analysis', 'time-to-event']
|
2023-10-16
|
[]
| 12
| 4
| null | 0.31
| 26
| 2
| 46
| 3
| 2
| 1
| 2
| 24
| 26
| 90
| 1.1
| 27
|
1,786
|
util
|
https://github.com/cohere-ai/cohere-python
|
[]
| null |
[]
|
[]
| null | null | null |
cohere-ai/cohere-python
|
cohere-python
| 157
| 33
| 23
|
Python
|
https://docs.cohere.ai
|
Python Library for Accessing the Cohere API
|
cohere-ai
|
2024-01-09
|
2021-01-20
| 157
| 0.99457
|
https://avatars.githubusercontent.com/u/54850923?v=4
|
Python Library for Accessing the Cohere API
|
['sdk']
|
['sdk']
|
2024-01-10
|
[('snyk-labs/pysnyk', 0.6142659187316895, 'security', 0), ('cohere-ai/notebooks', 0.5595293641090393, 'llm', 0), ('simple-salesforce/simple-salesforce', 0.5442338585853577, 'data', 0), ('openai/openai-python', 0.5279492735862732, 'util', 0), ('open-telemetry/opentelemetry-python', 0.5118368864059448, 'util', 1), ('anthropics/anthropic-sdk-python', 0.5114951133728027, 'util', 1), ('googleapis/google-api-python-client', 0.5053079128265381, 'util', 0), ('kubeflow/fairing', 0.5045545697212219, 'ml-ops', 0)]
| 37
| 2
| null | 2.9
| 48
| 38
| 36
| 0
| 0
| 0
| 0
| 48
| 30
| 90
| 0.6
| 27
|
1,546
|
llm
|
https://github.com/luohongyin/sail
|
['search-augmentation', 'search', 'language-model']
| null |
[]
|
[]
| null | null | null |
luohongyin/sail
|
SAIL
| 147
| 14
| 3
|
Python
| null |
SAIL: Search Augmented Instruction Learning
|
luohongyin
|
2024-01-04
|
2023-05-24
| 35
| 4.099602
| null |
SAIL: Search Augmented Instruction Learning
|
[]
|
['language-model', 'search', 'search-augmentation']
|
2023-06-06
|
[('ai21labs/in-context-ralm', 0.5805896520614624, 'llm', 1), ('openbmb/toolbench', 0.5190768241882324, 'llm', 0), ('intellabs/fastrag', 0.5123240947723389, 'nlp', 0), ('yizhongw/self-instruct', 0.5114395022392273, 'llm', 1), ('srush/minichain', 0.5050071477890015, 'llm', 0)]
| 2
| 1
| null | 0.19
| 3
| 2
| 8
| 7
| 0
| 0
| 0
| 3
| 19
| 90
| 6.3
| 27
|
904
|
security
|
https://github.com/abnamro/repository-scanner
|
[]
| null |
[]
|
[]
| null | null | null |
abnamro/repository-scanner
|
repository-scanner
| 141
| 13
| 7
|
Python
| null |
Tool to detect secrets in source code management systems.
|
abnamro
|
2024-01-09
|
2022-09-08
| 72
| 1.939096
|
https://avatars.githubusercontent.com/u/42280701?v=4
|
Tool to detect secrets in source code management systems.
|
[]
|
[]
|
2023-12-20
|
[('ionelmc/python-hunter', 0.5003989338874817, 'debug', 0)]
| 10
| 1
| null | 4.21
| 23
| 22
| 16
| 1
| 9
| 8
| 9
| 23
| 5
| 90
| 0.2
| 27
|
1,600
|
llm
|
https://github.com/krohling/bondai
|
['autonomous-agents', 'agents']
|
Open-source framework tailored for integrating and customizing Conversational AI Agents
|
[]
|
[]
| null | null | null |
krohling/bondai
|
bondai
| 128
| 20
| 11
|
Python
| null | null |
krohling
|
2024-01-12
|
2023-07-16
| 28
| 4.525253
| null |
Open-source framework tailored for integrating and customizing Conversational AI Agents
|
[]
|
['agents', 'autonomous-agents']
|
2024-01-14
|
[('rasahq/rasa', 0.7222825288772583, 'llm', 0), ('nvidia/nemo', 0.7121801972389221, 'nlp', 0), ('facebookresearch/parlai', 0.680033266544342, 'nlp', 0), ('aiwaves-cn/agents', 0.6704409718513489, 'nlp', 1), ('deeppavlov/deeppavlov', 0.6658996939659119, 'nlp', 0), ('prefecthq/marvin', 0.6641014814376831, 'nlp', 1), ('rcgai/simplyretrieve', 0.6295303702354431, 'llm', 0), ('embedchain/embedchain', 0.6167078018188477, 'llm', 0), ('openlmlab/moss', 0.6103507876396179, 'llm', 0), ('togethercomputer/openchatkit', 0.596705973148346, 'nlp', 0), ('minimaxir/simpleaichat', 0.5760908722877502, 'llm', 0), ('larsbaunwall/bricky', 0.5706357359886169, 'llm', 0), ('cheshire-cat-ai/core', 0.5621045827865601, 'llm', 0), ('chatarena/chatarena', 0.5611550807952881, 'llm', 0), ('google-research/language', 0.5564771294593811, 'nlp', 0), ('antonosika/gpt-engineer', 0.5529053211212158, 'llm', 0), ('transformeroptimus/superagi', 0.5482898354530334, 'llm', 2), ('lm-sys/fastchat', 0.5441566109657288, 'llm', 0), ('thilinarajapakse/simpletransformers', 0.534867525100708, 'nlp', 0), ('operand/agency', 0.5317063331604004, 'llm', 2), ('nomic-ai/gpt4all', 0.5283734798431396, 'llm', 0), ('smol-ai/developer', 0.5269186496734619, 'llm', 0), ('run-llama/rags', 0.5235958099365234, 'llm', 0), ('humanoidagents/humanoidagents', 0.5202670693397522, 'sim', 1), ('laion-ai/open-assistant', 0.5133852362632751, 'llm', 0), ('unity-technologies/ml-agents', 0.5124642252922058, 'ml-rl', 0), ('gunthercox/chatterbot', 0.5118715763092041, 'nlp', 0), ('microsoft/chatgpt-robot-manipulation-prompts', 0.5104005932807922, 'llm', 0), ('deepset-ai/haystack', 0.5101301074028015, 'llm', 0), ('minimaxir/aitextgen', 0.5016161799430847, 'llm', 0)]
| 2
| 0
| null | 2.62
| 7
| 6
| 6
| 0
| 0
| 71
| 71
| 7
| 1
| 90
| 0.1
| 27
|
558
|
gis
|
https://github.com/geopandas/xyzservices
|
[]
| null |
[]
|
[]
| null | null | null |
geopandas/xyzservices
|
xyzservices
| 125
| 23
| 14
|
Python
|
https://xyzservices.readthedocs.io/
|
Source of XYZ tiles providers
|
geopandas
|
2024-01-09
|
2021-05-21
| 140
| 0.889228
|
https://avatars.githubusercontent.com/u/8130715?v=4
|
Source of XYZ tiles providers
|
[]
|
[]
|
2023-12-15
|
[]
| 18
| 5
| null | 0.77
| 5
| 3
| 32
| 1
| 5
| 8
| 5
| 5
| 3
| 90
| 0.6
| 27
|
1,445
|
util
|
https://github.com/pypa/installer
|
['wheel']
| null |
[]
|
[]
| null | null | null |
pypa/installer
|
installer
| 105
| 51
| 15
|
Python
|
https://installer.readthedocs.io/
|
A low-level library for installing from a Python wheel distribution.
|
pypa
|
2024-01-08
|
2020-04-11
| 198
| 0.529158
|
https://avatars.githubusercontent.com/u/647025?v=4
|
A low-level library for installing from a Python wheel distribution.
|
['wheel']
|
['wheel']
|
2024-01-05
|
[('pyo3/maturin', 0.6027328372001648, 'util', 0), ('pyodide/micropip', 0.5991305708885193, 'util', 0), ('pytoolz/toolz', 0.5778533220291138, 'util', 0), ('getsentry/milksnake', 0.5740995407104492, 'util', 0), ('indygreg/pyoxidizer', 0.5681904554367065, 'util', 0), ('erotemic/ubelt', 0.5646870136260986, 'util', 0), ('pdm-project/pdm', 0.5561202168464661, 'util', 0), ('pypy/pypy', 0.5516636371612549, 'util', 0), ('ofek/pyapp', 0.5315470695495605, 'util', 0), ('pyston/pyston', 0.5297871828079224, 'util', 0), ('mitsuhiko/rye', 0.5275384187698364, 'util', 0), ('pypi/warehouse', 0.5235101580619812, 'util', 0), ('legrandin/pycryptodome', 0.5224993228912354, 'util', 0), ('python-poetry/poetry', 0.522213339805603, 'util', 0), ('scitools/cartopy', 0.5148464441299438, 'gis', 0), ('python/cpython', 0.505916953086853, 'util', 0), ('urwid/urwid', 0.5019884705543518, 'term', 0), ('jquast/blessed', 0.5001348853111267, 'term', 0)]
| 24
| 3
| null | 0.63
| 16
| 11
| 46
| 0
| 0
| 3
| 3
| 16
| 16
| 90
| 1
| 27
|
994
|
finance
|
https://github.com/quantopian/research_public
|
[]
| null |
[]
|
[]
| null | null | null |
quantopian/research_public
|
research_public
| 2,262
| 1,544
| 201
|
Jupyter Notebook
|
https://www.quantopian.com/lectures
|
Quantitative research and educational materials
|
quantopian
|
2024-01-13
|
2015-02-26
| 465
| 4.857055
|
https://avatars.githubusercontent.com/u/1393215?v=4
|
Quantitative research and educational materials
|
[]
|
[]
|
2020-10-30
|
[]
| 52
| 4
| null | 0
| 0
| 0
| 108
| 39
| 0
| 0
| 0
| 0
| 0
| 90
| 0
| 26
|
266
|
nlp
|
https://github.com/arxiv-vanity/arxiv-vanity
|
[]
| null |
[]
|
[]
| null | null | null |
arxiv-vanity/arxiv-vanity
|
arxiv-vanity
| 1,575
| 102
| 23
|
Python
|
https://www.arxiv-vanity.com
|
Renders papers from arXiv as responsive web pages so you don't have to squint at a PDF.
|
arxiv-vanity
|
2024-01-12
|
2017-08-12
| 337
| 4.667655
|
https://avatars.githubusercontent.com/u/31142715?v=4
|
Renders papers from arXiv as responsive web pages so you don't have to squint at a PDF.
|
['academic-publishing', 'arxiv', 'latex', 'science']
|
['academic-publishing', 'arxiv', 'latex', 'science']
|
2022-01-18
|
[('lukasschwab/arxiv.py', 0.5270992517471313, 'util', 1)]
| 9
| 3
| null | 0
| 2
| 0
| 78
| 24
| 0
| 0
| 0
| 2
| 1
| 90
| 0.5
| 26
|
1,124
|
nlp
|
https://github.com/gunthercox/chatterbot-corpus
|
[]
| null |
[]
|
[]
| null | null | null |
gunthercox/chatterbot-corpus
|
chatterbot-corpus
| 1,333
| 1,151
| 69
|
Python
|
http://chatterbot-corpus.readthedocs.io
|
A multilingual dialog corpus
|
gunthercox
|
2024-01-12
|
2017-01-11
| 367
| 3.623689
| null |
A multilingual dialog corpus
|
['chatterbot', 'corpus', 'dialog', 'language', 'yaml']
|
['chatterbot', 'corpus', 'dialog', 'language', 'yaml']
|
2020-08-24
|
[('lm-sys/fastchat', 0.6321564316749573, 'llm', 0), ('deeppavlov/deeppavlov', 0.6254500150680542, 'nlp', 0), ('thudm/chatglm-6b', 0.6088327765464783, 'llm', 0), ('rasahq/rasa', 0.6020722985267639, 'llm', 0), ('gunthercox/chatterbot', 0.5911657214164734, 'nlp', 2), ('langchain-ai/chat-langchain', 0.5771211981773376, 'llm', 0), ('fasteval/fasteval', 0.5639832019805908, 'llm', 0), ('nvidia/nemo', 0.5635098218917847, 'nlp', 0), ('bigscience-workshop/promptsource', 0.5633988976478577, 'nlp', 0), ('nomic-ai/gpt4all', 0.5547274947166443, 'llm', 0), ('thudm/chatglm2-6b', 0.5543924570083618, 'llm', 0), ('openlmlab/moss', 0.5456989407539368, 'llm', 0), ('killianlucas/open-interpreter', 0.5440534949302673, 'llm', 0), ('databrickslabs/dolly', 0.541381299495697, 'llm', 0), ('pemistahl/lingua-py', 0.5412878394126892, 'nlp', 0), ('run-llama/rags', 0.5412566065788269, 'llm', 0), ('srush/minichain', 0.5370650291442871, 'llm', 0), ('facebookresearch/parlai', 0.5316453576087952, 'nlp', 0), ('nltk/nltk', 0.5303866267204285, 'nlp', 0), ('togethercomputer/openchatkit', 0.5263169407844543, 'nlp', 0), ('pndurette/gtts', 0.5201086401939392, 'util', 0), ('blinkdl/chatrwkv', 0.5196929574012756, 'llm', 0), ('suno-ai/bark', 0.5169947743415833, 'ml', 0), ('facebookresearch/seamless_communication', 0.5149349570274353, 'nlp', 0), ('guidance-ai/guidance', 0.5125293135643005, 'llm', 0), ('embedchain/embedchain', 0.5088998079299927, 'llm', 0), ('aiwaves-cn/agents', 0.5078116059303284, 'nlp', 0), ('minimaxir/simpleaichat', 0.5073516964912415, 'llm', 0), ('rcgai/simplyretrieve', 0.5069754123687744, 'llm', 0), ('lingjzhu/charsiug2p', 0.5040284395217896, 'nlp', 0), ('explosion/spacy-models', 0.5032789707183838, 'nlp', 0)]
| 72
| 5
| null | 0
| 0
| 0
| 85
| 41
| 0
| 1
| 1
| 0
| 0
| 90
| 0
| 26
|
1,426
|
util
|
https://github.com/py4j/py4j
|
[]
| null |
[]
|
[]
| null | null | null |
py4j/py4j
|
py4j
| 1,123
| 206
| 41
|
Java
|
https://www.py4j.org
|
Py4J enables Python programs to dynamically access arbitrary Java objects
|
py4j
|
2024-01-13
|
2010-11-02
| 691
| 1.625181
|
https://avatars.githubusercontent.com/u/99001623?v=4
|
Py4J enables Python programs to dynamically access arbitrary Java objects
|
['distributed-systems', 'java', 'programming-languages']
|
['distributed-systems', 'java', 'programming-languages']
|
2023-02-12
|
[('pympler/pympler', 0.5723164677619934, 'perf', 0), ('pyston/pyston', 0.5638821721076965, 'util', 0), ('oracle/graalpython', 0.5628305077552795, 'util', 1), ('pypy/pypy', 0.5454596877098083, 'util', 0), ('micropython/micropython', 0.5158247947692871, 'util', 0), ('backtick-se/cowait', 0.5144885182380676, 'util', 0), ('numba/llvmlite', 0.5133116245269775, 'util', 0), ('pyglet/pyglet', 0.5067098140716553, 'gamedev', 0), ('secdev/scapy', 0.5051878690719604, 'util', 0), ('hoffstadt/dearpygui', 0.5022397041320801, 'gui', 0)]
| 38
| 5
| null | 0.02
| 6
| 1
| 161
| 11
| 0
| 2
| 2
| 6
| 2
| 90
| 0.3
| 26
|
1,681
|
util
|
https://github.com/klen/pylama
|
['linter']
| null |
[]
|
[]
| null | null | null |
klen/pylama
|
pylama
| 1,034
| 102
| 20
|
Python
| null |
Code audit tool for python.
|
klen
|
2024-01-09
|
2012-08-17
| 597
| 1.730337
| null |
Code audit tool for python.
|
[]
|
['linter']
|
2022-08-08
|
[('pycqa/pyflakes', 0.6928360462188721, 'util', 1), ('landscapeio/prospector', 0.5915239453315735, 'util', 0), ('nedbat/coveragepy', 0.5741574764251709, 'testing', 0), ('google/pytype', 0.5735052824020386, 'typing', 1), ('instagram/fixit', 0.5459169745445251, 'util', 1), ('rubik/radon', 0.5425077676773071, 'util', 0), ('trailofbits/pip-audit', 0.5406633019447327, 'security', 0), ('gaogaotiantian/viztracer', 0.5285407304763794, 'profiling', 0), ('pycqa/pylint-django', 0.5170671939849854, 'util', 1), ('astral-sh/ruff', 0.5164267420768738, 'util', 1), ('pycqa/pylint', 0.5042141675949097, 'util', 1), ('alexmojaki/snoop', 0.500612735748291, 'debug', 0)]
| 46
| 5
| null | 0
| 3
| 0
| 139
| 17
| 0
| 12
| 12
| 3
| 0
| 90
| 0
| 26
|
1,020
|
finance
|
https://github.com/enthought/pyql
|
[]
| null |
[]
|
[]
| null | null | null |
enthought/pyql
|
pyql
| 889
| 192
| 108
|
Cython
| null |
Cython QuantLib wrappers
|
enthought
|
2024-01-12
|
2012-03-08
| 620
| 1.432221
|
https://avatars.githubusercontent.com/u/539651?v=4
|
Cython QuantLib wrappers
|
['cython', 'quantlib']
|
['cython', 'quantlib']
|
2023-11-22
|
[('lballabio/quantlib-swig', 0.5862199664115906, 'finance', 0)]
| 24
| 4
| null | 1.37
| 12
| 12
| 144
| 2
| 0
| 0
| 0
| 12
| 0
| 90
| 0
| 26
|
503
|
gis
|
https://github.com/scikit-mobility/scikit-mobility
|
[]
| null |
[]
|
[]
| null | null | null |
scikit-mobility/scikit-mobility
|
scikit-mobility
| 672
| 151
| 29
|
Python
|
https://scikit-mobility.github.io/scikit-mobility/
|
scikit-mobility: mobility analysis in Python
|
scikit-mobility
|
2024-01-09
|
2019-04-30
| 248
| 2.709677
|
https://avatars.githubusercontent.com/u/45601440?v=4
|
scikit-mobility: mobility analysis in Python
|
['complex-systems', 'data-analysis', 'data-science', 'human-mobility', 'mobility-analysis', 'mobility-flows', 'network-science', 'risk-assessment', 'scikit-mobility', 'statistics', 'synthetic-flows']
|
['complex-systems', 'data-analysis', 'data-science', 'human-mobility', 'mobility-analysis', 'mobility-flows', 'network-science', 'risk-assessment', 'scikit-mobility', 'statistics', 'synthetic-flows']
|
2023-01-20
|
[('ranaroussi/quantstats', 0.6154873967170715, 'finance', 0), ('networkx/networkx', 0.614628255367279, 'graph', 0), ('statsmodels/statsmodels', 0.5975031852722168, 'ml', 3), ('scikit-learn/scikit-learn', 0.5857199430465698, 'ml', 3), ('pandas-dev/pandas', 0.5808542966842651, 'pandas', 2), ('goldmansachs/gs-quant', 0.5793623328208923, 'finance', 0), ('wesm/pydata-book', 0.5627614259719849, 'study', 0), ('plotly/dash', 0.5490847826004028, 'viz', 1), ('firmai/atspy', 0.5477433800697327, 'time-series', 0), ('eleutherai/pyfra', 0.5473421216011047, 'ml', 0), ('thealgorithms/python', 0.5455461740493774, 'study', 0), ('atsushisakai/pythonrobotics', 0.5399863719940186, 'sim', 0), ('anitagraser/movingpandas', 0.5389496684074402, 'gis', 0), ('geopandas/geopandas', 0.5326095819473267, 'gis', 0), ('krzjoa/awesome-python-data-science', 0.5322821736335754, 'study', 3), ('fatiando/verde', 0.531174898147583, 'gis', 0), ('pysal/pysal', 0.5261431336402893, 'gis', 0), ('rasbt/mlxtend', 0.5235726833343506, 'ml', 1), ('python-odin/odin', 0.5235087275505066, 'util', 0), ('pycaret/pycaret', 0.5205168128013611, 'ml', 1), ('online-ml/river', 0.5196621417999268, 'ml', 1), ('dagworks-inc/hamilton', 0.5164218544960022, 'ml-ops', 2), ('ta-lib/ta-lib-python', 0.5160692930221558, 'finance', 0), ('makepath/xarray-spatial', 0.5155532956123352, 'gis', 0), ('sympy/sympy', 0.5121610164642334, 'math', 0), ('facebook/pyre-check', 0.5081936120986938, 'typing', 0), ('projectmesa/mesa', 0.5076199769973755, 'sim', 1), ('opengeos/leafmap', 0.5058870911598206, 'gis', 1), ('cuemacro/finmarketpy', 0.5044008493423462, 'finance', 0), ('keon/algorithms', 0.5026717782020569, 'util', 0), ('alkaline-ml/pmdarima', 0.5019405484199524, 'time-series', 0), ('amaargiru/pyroad', 0.5013498067855835, 'study', 0), ('quantecon/quantecon.py', 0.5013092756271362, 'sim', 0), ('artelys/geonetworkx', 0.5012951493263245, 'gis', 0), ('pytoolz/toolz', 0.5007225871086121, 'util', 0)]
| 23
| 3
| null | 0
| 9
| 1
| 57
| 12
| 0
| 2
| 2
| 9
| 2
| 90
| 0.2
| 26
|
1,204
|
util
|
https://github.com/serpapi/google-search-results-python
|
[]
| null |
[]
|
[]
| null | null | null |
serpapi/google-search-results-python
|
google-search-results-python
| 474
| 89
| 14
|
Python
| null |
Google Search Results via SERP API pip Python Package
|
serpapi
|
2024-01-12
|
2018-01-10
| 315
| 1.500678
|
https://avatars.githubusercontent.com/u/34724717?v=4
|
Google Search Results via SERP API pip Python Package
|
['bing-image', 'google-crawler', 'google-images', 'scraping', 'serp-api', 'serpapi', 'web-scraping']
|
['bing-image', 'google-crawler', 'google-images', 'scraping', 'serp-api', 'serpapi', 'web-scraping']
|
2023-09-01
|
[('nv7-github/googlesearch', 0.6652640700340271, 'util', 1), ('alirezamika/autoscraper', 0.5220003724098206, 'data', 2), ('googleapis/google-api-python-client', 0.5091744065284729, 'util', 0)]
| 17
| 2
| null | 0.17
| 12
| 7
| 73
| 4
| 0
| 1
| 1
| 12
| 12
| 90
| 1
| 26
|
1,114
|
util
|
https://github.com/pylons/colander
|
[]
| null |
[]
|
[]
| null | null | null |
pylons/colander
|
colander
| 438
| 146
| 28
|
Python
|
https://docs.pylonsproject.org/projects/colander/en/latest/
|
A serialization/deserialization/validation library for strings, mappings and lists.
|
pylons
|
2024-01-03
|
2011-02-16
| 675
| 0.648066
|
https://avatars.githubusercontent.com/u/452227?v=4
|
A serialization/deserialization/validation library for strings, mappings and lists.
|
['deserialization', 'forms', 'serialization', 'validation']
|
['deserialization', 'forms', 'serialization', 'validation']
|
2023-09-09
|
[('yukinarit/pyserde', 0.6523554921150208, 'util', 1), ('marshmallow-code/marshmallow', 0.6454058885574341, 'util', 3), ('python-odin/odin', 0.63074791431427, 'util', 1), ('pyeve/cerberus', 0.5786774754524231, 'data', 0), ('pydantic/pydantic', 0.531548798084259, 'util', 2), ('lidatong/dataclasses-json', 0.5289405584335327, 'util', 0), ('google/flatbuffers', 0.5039762258529663, 'perf', 1)]
| 111
| 4
| null | 0.02
| 3
| 2
| 157
| 4
| 0
| 4
| 4
| 3
| 2
| 90
| 0.7
| 26
|
820
|
gis
|
https://github.com/datasystemslab/geotorch
|
[]
| null |
[]
|
[]
| null | null | null |
datasystemslab/geotorch
|
GeoTorchAI
| 433
| 31
| 13
|
Jupyter Notebook
|
https://kanchanchy.github.io/geotorchai/
|
GeoTorchAI: A Framework for Training and Using Spatiotemporal Deep Learning Models at Scale
|
datasystemslab
|
2024-01-10
|
2022-05-23
| 88
| 4.91248
|
https://avatars.githubusercontent.com/u/92130061?v=4
|
GeoTorchAI: A Framework for Training and Using Spatiotemporal Deep Learning Models at Scale
|
['classification-model', 'convlstm-pytorch', 'deep-learning', 'deep-neural-networks', 'deepsat', 'prediction-model', 'raster-data', 'satellite-classification', 'satellite-images', 'segmentation-models', 'sequence-models', 'spatial-data-analysis', 'spatio-temporal-analysis', 'spatio-temporal-models', 'spatio-temporal-prediction', 'st-resnet']
|
['classification-model', 'convlstm-pytorch', 'deep-learning', 'deep-neural-networks', 'deepsat', 'prediction-model', 'raster-data', 'satellite-classification', 'satellite-images', 'segmentation-models', 'sequence-models', 'spatial-data-analysis', 'spatio-temporal-analysis', 'spatio-temporal-models', 'spatio-temporal-prediction', 'st-resnet']
|
2023-10-22
|
[('azavea/raster-vision', 0.6860873103141785, 'gis', 1), ('microsoft/torchgeo', 0.6509654521942139, 'gis', 1), ('developmentseed/label-maker', 0.5983750224113464, 'gis', 1), ('tensorflow/tensorflow', 0.5481935739517212, 'ml-dl', 2), ('nyandwi/modernconvnets', 0.5321657061576843, 'ml-dl', 0), ('rwightman/pytorch-image-models', 0.5261633992195129, 'ml-dl', 0), ('rasbt/deeplearning-models', 0.5235190391540527, 'ml-dl', 0), ('kevinmusgrave/pytorch-metric-learning', 0.5219647884368896, 'ml', 1), ('roboflow/notebooks', 0.5124086141586304, 'study', 2), ('aiqc/aiqc', 0.5082912445068359, 'ml-ops', 0), ('aistream-peelout/flow-forecast', 0.5054202675819397, 'time-series', 2)]
| 5
| 2
| null | 1.21
| 1
| 0
| 20
| 3
| 0
| 1
| 1
| 1
| 0
| 90
| 0
| 26
|
749
|
ml-dl
|
https://github.com/samuela/git-re-basin
|
[]
| null |
[]
|
[]
| null | null | null |
samuela/git-re-basin
|
git-re-basin
| 429
| 36
| 8
|
Python
|
https://arxiv.org/abs/2209.04836
|
Code release for "Git Re-Basin: Merging Models modulo Permutation Symmetries"
|
samuela
|
2024-01-12
|
2022-09-13
| 72
| 5.958333
| null |
Code release for "Git Re-Basin: Merging Models modulo Permutation Symmetries"
|
['deep-learning', 'deeplearning', 'jax', 'machine-learning', 'neural-networks']
|
['deep-learning', 'deeplearning', 'jax', 'machine-learning', 'neural-networks']
|
2023-03-07
|
[('rasbt/machine-learning-book', 0.564606785774231, 'study', 3), ('huggingface/transformers', 0.5258132219314575, 'nlp', 3), ('alpa-projects/alpa', 0.507290780544281, 'ml-dl', 3)]
| 2
| 0
| null | 0.12
| 1
| 1
| 16
| 10
| 0
| 0
| 0
| 1
| 4
| 90
| 4
| 26
|
559
|
gis
|
https://github.com/geopandas/dask-geopandas
|
[]
| null |
[]
|
[]
| null | null | null |
geopandas/dask-geopandas
|
dask-geopandas
| 424
| 45
| 23
|
Python
|
https://dask-geopandas.readthedocs.io/
|
Parallel GeoPandas with Dask
|
geopandas
|
2024-01-05
|
2020-02-13
| 206
| 2.05114
|
https://avatars.githubusercontent.com/u/8130715?v=4
|
Parallel GeoPandas with Dask
|
[]
|
[]
|
2023-05-19
|
[('dask/dask', 0.5673131942749023, 'perf', 0), ('nalepae/pandarallel', 0.5014110207557678, 'pandas', 0)]
| 20
| 7
| null | 0.19
| 3
| 0
| 48
| 8
| 2
| 4
| 2
| 3
| 1
| 90
| 0.3
| 26
|
1,668
|
testing
|
https://github.com/jamielennox/requests-mock
|
['mocking']
| null |
[]
|
[]
| null | null | null |
jamielennox/requests-mock
|
requests-mock
| 400
| 65
| 5
|
Python
|
https://requests-mock.readthedocs.io
|
Mocked responses for the requests library
|
jamielennox
|
2024-01-11
|
2014-12-16
| 476
| 0.840336
| null |
Mocked responses for the requests library
|
[]
|
['mocking']
|
2023-11-04
|
[('getsentry/responses', 0.7649958729743958, 'testing', 1), ('kevin1024/vcrpy', 0.6570014953613281, 'testing', 1), ('lundberg/respx', 0.6144221425056458, 'testing', 1)]
| 51
| 5
| null | 0.17
| 13
| 4
| 110
| 3
| 1
| 3
| 1
| 13
| 4
| 90
| 0.3
| 26
|
12
|
nlp
|
https://github.com/dialogflow/dialogflow-python-client-v2
|
[]
| null |
[]
|
[]
| null | null | null |
dialogflow/dialogflow-python-client-v2
|
python-dialogflow
| 398
| 187
| 56
| null |
https://dialogflow.com/
|
This library has moved to https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-dialogflow
|
dialogflow
|
2024-01-10
|
2017-10-24
| 327
| 1.217125
|
https://avatars.githubusercontent.com/u/16785467?v=4
|
This library has moved to https://github.com/googleapis/google-cloud-python/tree/main/packages/google-cloud-dialogflow
|
['dialogflow', 'machine-learning']
|
['dialogflow', 'machine-learning']
|
2023-09-21
|
[('googleapis/python-speech', 0.743192732334137, 'ml', 0), ('googleapis/google-api-python-client', 0.5611771941184998, 'util', 0), ('deeppavlov/deeppavlov', 0.5524868369102478, 'nlp', 1), ('pndurette/gtts', 0.5459315776824951, 'util', 0), ('googlecloudplatform/vertex-ai-samples', 0.5129502415657043, 'ml', 0), ('google/vizier', 0.5073549151420593, 'ml', 1)]
| 37
| 5
| null | 0.96
| 0
| 0
| 76
| 4
| 10
| 9
| 10
| 0
| 0
| 90
| 0
| 26
|
1,190
|
llm
|
https://github.com/microsoft/chatgpt-robot-manipulation-prompts
|
[]
| null |
[]
|
[]
| null | null | null |
microsoft/chatgpt-robot-manipulation-prompts
|
ChatGPT-Robot-Manipulation-Prompts
| 304
| 30
| 8
| null | null | null |
microsoft
|
2024-01-10
|
2023-04-06
| 42
| 7.117057
|
https://avatars.githubusercontent.com/u/6154722?v=4
|
microsoft/ChatGPT-Robot-Manipulation-Prompts
|
[]
|
[]
|
2023-11-28
|
[('embedchain/embedchain', 0.5969848036766052, 'llm', 0), ('microsoft/promptcraft-robotics', 0.580747663974762, 'sim', 0), ('togethercomputer/openchatkit', 0.5749940872192383, 'nlp', 0), ('minimaxir/simpleaichat', 0.5525240302085876, 'llm', 0), ('weaviate/verba', 0.5466259121894836, 'llm', 0), ('run-llama/rags', 0.5412412881851196, 'llm', 0), ('cheshire-cat-ai/core', 0.5411517024040222, 'llm', 0), ('promptslab/promptify', 0.541034460067749, 'nlp', 0), ('prefecthq/marvin', 0.5360046029090881, 'nlp', 0), ('nomic-ai/gpt4all', 0.5312781929969788, 'llm', 0), ('promptslab/awesome-prompt-engineering', 0.5258796811103821, 'study', 0), ('rcgai/simplyretrieve', 0.5226764678955078, 'llm', 0), ('killianlucas/open-interpreter', 0.5223796367645264, 'llm', 0), ('humanoidagents/humanoidagents', 0.5163865685462952, 'sim', 0), ('krohling/bondai', 0.5104005932807922, 'llm', 0), ('gunthercox/chatterbot', 0.5103239417076111, 'nlp', 0), ('chatarena/chatarena', 0.5086156725883484, 'llm', 0), ('guidance-ai/guidance', 0.5060887932777405, 'llm', 0), ('microsoft/autogen', 0.5033249258995056, 'llm', 0), ('xtekky/gpt4free', 0.50308758020401, 'llm', 0)]
| 3
| 1
| null | 0.42
| 2
| 2
| 9
| 2
| 0
| 0
| 0
| 2
| 1
| 90
| 0.5
| 26
|
834
|
jupyter
|
https://github.com/cmudig/autoprofiler
|
[]
| null |
[]
|
[]
| null | null | null |
cmudig/autoprofiler
|
AutoProfiler
| 294
| 8
| 6
|
Svelte
| null |
Automatically profile dataframes in the Jupyter sidebar
|
cmudig
|
2024-01-12
|
2022-03-24
| 96
| 3.039882
|
https://avatars.githubusercontent.com/u/56060038?v=4
|
Automatically profile dataframes in the Jupyter sidebar
|
['jupyter', 'pandas']
|
['jupyter', 'pandas']
|
2023-09-26
|
[('tkrabel/bamboolib', 0.6205199956893921, 'pandas', 1), ('quantopian/qgrid', 0.5721290111541748, 'jupyter', 0), ('lux-org/lux', 0.5497778058052063, 'viz', 2), ('koaning/drawdata', 0.5204150080680847, 'jupyter', 1), ('adamerose/pandasgui', 0.5127207040786743, 'pandas', 1), ('jakevdp/pythondatasciencehandbook', 0.5069370865821838, 'study', 1)]
| 4
| 2
| null | 1.17
| 1
| 0
| 22
| 4
| 0
| 0
| 0
| 1
| 2
| 90
| 2
| 26
|
915
|
gis
|
https://github.com/giswqs/aws-open-data-geo
|
[]
| null |
[]
|
[]
| null | null | null |
giswqs/aws-open-data-geo
|
aws-open-data-geo
| 263
| 7
| 11
|
Python
| null |
A list of open geospatial datasets on AWS
|
giswqs
|
2024-01-05
|
2022-12-18
| 58
| 4.512255
|
https://avatars.githubusercontent.com/u/129896036?v=4
|
A list of open geospatial datasets on AWS
|
['aws', 'environment', 'geospatial', 'mapping', 'open-data', 'satellite-imagery', 'sustainability']
|
['aws', 'environment', 'geospatial', 'mapping', 'open-data', 'satellite-imagery', 'sustainability']
|
2024-01-13
|
[('sentinelsat/sentinelsat', 0.6044768691062927, 'gis', 2), ('plant99/felicette', 0.5306439995765686, 'gis', 2), ('developmentseed/geolambda', 0.507233738899231, 'gis', 0)]
| 2
| 2
| null | 2.15
| 1
| 1
| 13
| 0
| 0
| 0
| 0
| 1
| 0
| 90
| 0
| 26
|
1,590
|
util
|
https://github.com/soft-matter/pims
|
['formats', 'video']
| null |
[]
|
[]
| null | null | null |
soft-matter/pims
|
pims
| 256
| 66
| 14
|
Python
|
http://soft-matter.github.io/pims/
|
Python Image Sequence: Load video and sequential images in many formats with a simple, consistent interface.
|
soft-matter
|
2024-01-04
|
2013-11-12
| 533
| 0.4803
|
https://avatars.githubusercontent.com/u/5857177?v=4
|
Python Image Sequence: Load video and sequential images in many formats with a simple, consistent interface.
|
[]
|
['formats', 'video']
|
2023-11-26
|
[('zulko/moviepy', 0.6140278577804565, 'util', 1), ('imageio/imageio', 0.5806184411048889, 'util', 1)]
| 38
| 5
| null | 0.1
| 3
| 1
| 124
| 2
| 0
| 2
| 2
| 3
| 3
| 90
| 1
| 26
|
1,648
|
nlp
|
https://github.com/microsoft/vert-papers
|
[]
| null |
[]
|
[]
| null | null | null |
microsoft/vert-papers
|
vert-papers
| 256
| 90
| 12
|
Python
| null |
This repository contains code and datasets related to entity/knowledge papers from the VERT (Versatile Entity Recognition & disambiguation Toolkit) project, by the Knowledge Computing group at Microsoft Research Asia (MSRA).
|
microsoft
|
2024-01-08
|
2019-07-25
| 235
| 1.086061
|
https://avatars.githubusercontent.com/u/6154722?v=4
|
This repository contains code and datasets related to entity/knowledge papers from the VERT (Versatile Entity Recognition & disambiguation Toolkit) project, by the Knowledge Computing group at Microsoft Research Asia (MSRA).
|
['bertel', 'can-ner', 'cross-lingual-ner', 'entity-disambiguation', 'entity-extraction', 'entity-linking', 'entity-resolution', 'grn', 'language-understanding', 'linkingpark', 'ml', 'named-entity-recognition', 'ner', 'nlp', 'nlp-resources', 'unitrans', 'xl-ner']
|
['bertel', 'can-ner', 'cross-lingual-ner', 'entity-disambiguation', 'entity-extraction', 'entity-linking', 'entity-resolution', 'grn', 'language-understanding', 'linkingpark', 'ml', 'named-entity-recognition', 'ner', 'nlp', 'nlp-resources', 'unitrans', 'xl-ner']
|
2023-10-07
|
[('zjunlp/deepke', 0.5728095769882202, 'ml', 3), ('babelscape/rebel', 0.5618175864219666, 'nlp', 2), ('franck-dernoncourt/neuroner', 0.5278816223144531, 'nlp', 2), ('neuml/txtai', 0.5036661028862, 'nlp', 1), ('dylanhogg/llmgraph', 0.5032587647438049, 'ml', 0)]
| 13
| 5
| null | 0.13
| 2
| 1
| 54
| 3
| 0
| 0
| 0
| 2
| 5
| 90
| 2.5
| 26
|
580
|
gis
|
https://github.com/spatialucr/geosnap
|
[]
| null |
[]
|
[]
| null | null | null |
spatialucr/geosnap
|
geosnap
| 218
| 31
| 17
|
Python
|
https://oturns.github.io/geosnap/
|
The Geospatial Neighborhood Analysis Package
|
spatialucr
|
2024-01-04
|
2018-09-19
| 279
| 0.778969
|
https://avatars.githubusercontent.com/u/122131626?v=4
|
The Geospatial Neighborhood Analysis Package
|
['geodemographics', 'neighborhood-dynamics', 'spatial-analysis', 'urban-modeling']
|
['geodemographics', 'neighborhood-dynamics', 'spatial-analysis', 'urban-modeling']
|
2023-12-11
|
[('udst/urbansim', 0.5915238857269287, 'sim', 0), ('pysal/momepy', 0.5846999287605286, 'gis', 0), ('gregorhd/mapcompare', 0.54576176404953, 'gis', 0), ('mcordts/cityscapesscripts', 0.5296457409858704, 'gis', 0)]
| 9
| 6
| null | 1.15
| 7
| 6
| 65
| 1
| 5
| 6
| 5
| 7
| 1
| 90
| 0.1
| 26
|
1,785
|
llm
|
https://github.com/cohere-ai/notebooks
|
['notebooks', 'cohere']
| null |
[]
|
[]
| null | null | null |
cohere-ai/notebooks
|
notebooks
| 204
| 54
| 12
|
Jupyter Notebook
| null |
Code examples and jupyter notebooks for the Cohere Platform
|
cohere-ai
|
2024-01-12
|
2021-10-06
| 120
| 1.687943
|
https://avatars.githubusercontent.com/u/54850923?v=4
|
Code examples and jupyter notebooks for the Cohere Platform
|
[]
|
['cohere', 'notebooks']
|
2024-01-14
|
[('fchollet/deep-learning-with-python-notebooks', 0.7323339581489563, 'study', 0), ('jupyter/nbformat', 0.7138713598251343, 'jupyter', 0), ('aws/graph-notebook', 0.6492716073989868, 'jupyter', 0), ('jupyter/notebook', 0.6436967253684998, 'jupyter', 0), ('jupyter-widgets/ipywidgets', 0.6400914192199707, 'jupyter', 0), ('mwouts/jupytext', 0.6391822695732117, 'jupyter', 1), ('jakevdp/pythondatasciencehandbook', 0.6190292835235596, 'study', 0), ('jupyter/nbconvert', 0.6110220551490784, 'jupyter', 0), ('python/cpython', 0.603980302810669, 'util', 0), ('voila-dashboards/voila', 0.60094153881073, 'jupyter', 0), ('wesm/pydata-book', 0.5929689407348633, 'study', 0), ('jupyter-lsp/jupyterlab-lsp', 0.5917092561721802, 'jupyter', 0), ('jupyter/nbgrader', 0.5908788442611694, 'jupyter', 0), ('ageron/handson-ml2', 0.5897052884101868, 'ml', 0), ('jupyterlab/jupyterlab-desktop', 0.5881903767585754, 'jupyter', 0), ('faster-cpython/ideas', 0.5839570760726929, 'perf', 0), ('koaning/calm-notebooks', 0.5802757143974304, 'study', 0), ('nteract/papermill', 0.5747708082199097, 'jupyter', 1), ('mynameisfiber/high_performance_python_2e', 0.5679949522018433, 'study', 0), ('alphasecio/langchain-examples', 0.5620157718658447, 'llm', 0), ('vizzuhq/ipyvizzu', 0.5615155100822449, 'jupyter', 0), ('jupyter/nbdime', 0.559609591960907, 'jupyter', 0), ('cohere-ai/cohere-python', 0.5595293641090393, 'util', 0), ('ipython/ipyparallel', 0.5586426258087158, 'perf', 0), ('quantopian/qgrid', 0.5572461485862732, 'jupyter', 0), ('ipython/ipykernel', 0.5502215623855591, 'util', 0), ('nbqa-dev/nbqa', 0.550182044506073, 'jupyter', 0), ('opengeos/leafmap', 0.546214759349823, 'gis', 0), ('jupyterlab/jupyterlab', 0.5374837517738342, 'jupyter', 0), ('brandtbucher/specialist', 0.5358579158782959, 'perf', 0), ('adafruit/circuitpython', 0.5329226851463318, 'util', 0), ('huggingface/notebooks', 0.529309868812561, 'ml', 0), ('masoniteframework/masonite', 0.5226303935050964, 'web', 0), ('maartenbreddels/ipyvolume', 0.5213759541511536, 'jupyter', 0), ('eleutherai/pyfra', 0.5172991752624512, 'ml', 0), ('giswqs/mapwidget', 0.51350337266922, 'gis', 0), ('holoviz/panel', 0.5109737515449524, 'viz', 0), ('pypy/pypy', 0.5104973316192627, 'util', 0), ('faster-cpython/tools', 0.5094420313835144, 'perf', 0), ('mito-ds/monorepo', 0.5074410438537598, 'jupyter', 0), ('rasbt/watermark', 0.5048350095748901, 'util', 0), ('willmcgugan/textual', 0.5048252940177917, 'term', 0), ('wxwidgets/phoenix', 0.5029077529907227, 'gui', 0), ('timofurrer/awesome-asyncio', 0.5014423131942749, 'study', 0), ('pytoolz/toolz', 0.5008567571640015, 'util', 0)]
| 9
| 3
| null | 1.56
| 33
| 25
| 28
| 0
| 0
| 0
| 0
| 33
| 7
| 90
| 0.2
| 26
|
1,099
|
gis
|
https://github.com/giswqs/mapwidget
|
[]
| null |
[]
|
[]
| null | null | null |
giswqs/mapwidget
|
mapwidget
| 201
| 12
| 9
|
Python
|
http://mapwidget.gishub.org
|
Custom Jupyter widgets for creating interactive 2D/3D maps using popular JavaScript libraries with bidirectional communication, such as Cesium, Mapbox, MapLibre, Leaflet, and OpenLayers
|
giswqs
|
2024-01-04
|
2023-01-21
| 53
| 3.762032
|
https://avatars.githubusercontent.com/u/129896036?v=4
|
Custom Jupyter widgets for creating interactive 2D/3D maps using popular JavaScript libraries with bidirectional communication, such as Cesium, Mapbox, MapLibre, Leaflet, and OpenLayers
|
['anywidget', 'cesium', 'geopython', 'geospatial', 'ipywidgets', 'jupyter', 'leaflet', 'mapbox', 'maplibre', 'mapping', 'openlayers']
|
['anywidget', 'cesium', 'geopython', 'geospatial', 'ipywidgets', 'jupyter', 'leaflet', 'mapbox', 'maplibre', 'mapping', 'openlayers']
|
2023-03-24
|
[('jupyter-widgets/ipyleaflet', 0.6565911769866943, 'gis', 2), ('maartenbreddels/ipyvolume', 0.6517302989959717, 'jupyter', 1), ('jupyter-widgets/ipywidgets', 0.6259199976921082, 'jupyter', 0), ('opengeos/leafmap', 0.5904461741447449, 'gis', 4), ('python-visualization/folium', 0.5697619915008545, 'gis', 0), ('vizzuhq/ipyvizzu', 0.5461418032646179, 'jupyter', 1), ('bokeh/bokeh', 0.5409641861915588, 'viz', 1), ('voila-dashboards/voila', 0.5279869437217712, 'jupyter', 1), ('wxwidgets/phoenix', 0.5239470601081848, 'gui', 0), ('aws/graph-notebook', 0.5196599364280701, 'jupyter', 1), ('jupyterlab/jupyterlab-desktop', 0.5143319964408875, 'jupyter', 1), ('cohere-ai/notebooks', 0.51350337266922, 'llm', 0), ('plotly/plotly.py', 0.504154622554779, 'viz', 0)]
| 1
| 1
| null | 0.9
| 1
| 0
| 12
| 10
| 8
| 8
| 8
| 1
| 2
| 90
| 2
| 26
|
739
|
data
|
https://github.com/ktrueda/parquet-tools
|
[]
| null |
[]
|
[]
| null | null | null |
ktrueda/parquet-tools
|
parquet-tools
| 136
| 18
| 4
|
Python
| null |
easy install parquet-tools
|
ktrueda
|
2024-01-04
|
2020-05-02
| 195
| 0.695906
| null |
easy install parquet-tools
|
['cli', 'parquet', 'parquet-tools']
|
['cli', 'parquet', 'parquet-tools']
|
2024-01-02
|
[('dask/fastparquet', 0.5958738327026367, 'data', 0)]
| 14
| 2
| null | 0.13
| 7
| 6
| 45
| 0
| 4
| 5
| 4
| 7
| 10
| 90
| 1.4
| 26
|
1,903
|
data
|
https://github.com/typesense/typesense-python
|
['search-engine', 'sdk', 'api']
|
Open Source alternative to to ElasticSearch. Fast, typo tolerant, in-memory fuzzy Search Engine.
|
[]
|
[]
| null | null | null |
typesense/typesense-python
|
typesense-python
| 125
| 28
| 5
|
Python
| null |
Python client for Typesense: https://github.com/typesense/typesense
|
typesense
|
2024-01-16
|
2018-01-30
| 313
| 0.399361
|
https://avatars.githubusercontent.com/u/19822348?v=4
|
Python client for Typesense: https://github.com/typesense/typesense
|
[]
|
['api', 'sdk', 'search-engine']
|
2024-01-03
|
[('meilisearch/meilisearch-python', 0.5989935994148254, 'data', 3), ('googleapis/google-api-python-client', 0.5588173866271973, 'util', 0), ('qdrant/qdrant-client', 0.5577874779701233, 'util', 0), ('tiangolo/typer', 0.5246941447257996, 'term', 0), ('strawberry-graphql/strawberry', 0.5120099782943726, 'web', 0), ('simple-salesforce/simple-salesforce', 0.503971517086029, 'data', 1)]
| 14
| 5
| null | 0.4
| 8
| 5
| 73
| 0
| 0
| 0
| 0
| 8
| 17
| 90
| 2.1
| 26
|
1,430
|
sim
|
https://github.com/srivatsankrishnan/oss-arch-gym
|
['architecture', 'simulator']
| null |
[]
|
[]
| null | null | null |
srivatsankrishnan/oss-arch-gym
|
oss-arch-gym
| 91
| 15
| 6
|
Jupyter Notebook
| null |
Open source version of ArchGym project.
|
srivatsankrishnan
|
2024-01-04
|
2023-04-11
| 42
| 2.166667
| null |
Open source version of ArchGym project.
|
[]
|
['architecture', 'simulator']
|
2023-12-28
|
[]
| 14
| 2
| null | 5.33
| 28
| 22
| 9
| 1
| 0
| 0
| 0
| 28
| 14
| 90
| 0.5
| 26
|
884
|
util
|
https://github.com/pyodide/micropip
|
[]
| null |
[]
|
[]
| null | null | null |
pyodide/micropip
|
micropip
| 42
| 12
| 6
|
Python
|
https://micropip.pyodide.org
|
A lightweight Python package installer for Pyodide
|
pyodide
|
2024-01-03
|
2022-09-15
| 71
| 0.585657
|
https://avatars.githubusercontent.com/u/77002075?v=4
|
A lightweight Python package installer for Pyodide
|
['package-installer', 'pyodide', 'webassembly']
|
['package-installer', 'pyodide', 'webassembly']
|
2024-01-03
|
[('pyodide/pyodide', 0.7593028545379639, 'util', 1), ('indygreg/pyoxidizer', 0.6752342581748962, 'util', 0), ('ofek/pyapp', 0.6668835282325745, 'util', 0), ('mitsuhiko/rye', 0.6547753810882568, 'util', 0), ('pypi/warehouse', 0.6480095386505127, 'util', 0), ('pypa/flit', 0.6164460778236389, 'util', 0), ('python-poetry/poetry', 0.609188437461853, 'util', 0), ('pdm-project/pdm', 0.6069954037666321, 'util', 0), ('pypa/installer', 0.5991305708885193, 'util', 0), ('pypa/hatch', 0.5937002897262573, 'util', 0), ('pypy/pypy', 0.5889347195625305, 'util', 0), ('libtcod/python-tcod', 0.5887089967727661, 'gamedev', 0), ('bottlepy/bottle', 0.5800544023513794, 'web', 0), ('webpy/webpy', 0.5706849694252014, 'web', 0), ('regebro/pyroma', 0.5652766227722168, 'util', 0), ('beeware/briefcase', 0.561262845993042, 'util', 0), ('mamba-org/mamba', 0.5484160780906677, 'util', 0), ('pallets/flask', 0.5379033088684082, 'web', 0), ('pyo3/maturin', 0.5349652171134949, 'util', 0), ('conda/constructor', 0.5295533537864685, 'util', 0), ('hoffstadt/dearpygui', 0.5266197919845581, 'gui', 0), ('pyinstaller/pyinstaller', 0.5264869332313538, 'util', 0), ('pytables/pytables', 0.5223343968391418, 'data', 0), ('malloydata/malloy-py', 0.5204178690910339, 'data', 0), ('pypa/build', 0.5155296325683594, 'util', 0), ('tezromach/python-package-template', 0.5130401253700256, 'template', 0), ('tox-dev/pipdeptree', 0.5127100944519043, 'util', 0), ('linkedin/shiv', 0.5121049284934998, 'util', 0), ('pyinfra-dev/pyinfra', 0.5114571452140808, 'util', 0), ('pomponchik/instld', 0.5101442337036133, 'util', 0), ('hugovk/pypistats', 0.5070849657058716, 'util', 0), ('erotemic/ubelt', 0.5013461709022522, 'util', 0), ('pypa/virtualenv', 0.5000215172767639, 'util', 0)]
| 8
| 2
| null | 0.48
| 5
| 2
| 16
| 0
| 0
| 5
| 5
| 5
| 14
| 90
| 2.8
| 26
|
903
|
data
|
https://github.com/malloydata/malloy-py
|
[]
| null |
[]
|
[]
| null | null | null |
malloydata/malloy-py
|
malloy-py
| 15
| 6
| 8
|
JavaScript
| null |
Python package for executing Malloy
|
malloydata
|
2024-01-12
|
2022-11-02
| 64
| 0.231278
|
https://avatars.githubusercontent.com/u/115666028?v=4
|
Python package for executing Malloy
|
['business-analytics', 'business-intelligence', 'data', 'data-modeling', 'semantic-modeling', 'sql']
|
['business-analytics', 'business-intelligence', 'data', 'data-modeling', 'semantic-modeling', 'sql']
|
2024-01-12
|
[('tiangolo/sqlmodel', 0.6022137403488159, 'data', 1), ('ibis-project/ibis', 0.5992289185523987, 'data', 1), ('plotly/dash', 0.5759779810905457, 'viz', 0), ('sqlalchemy/sqlalchemy', 0.5755621790885925, 'data', 1), ('tobymao/sqlglot', 0.5748046040534973, 'data', 1), ('krzjoa/awesome-python-data-science', 0.5664848685264587, 'study', 0), ('eleutherai/pyfra', 0.5658804178237915, 'ml', 0), ('willmcgugan/textual', 0.5623626708984375, 'term', 0), ('pympler/pympler', 0.5531739592552185, 'perf', 0), ('pypa/hatch', 0.547518789768219, 'util', 0), ('kubeflow/fairing', 0.5466133952140808, 'ml-ops', 0), ('goldmansachs/gs-quant', 0.5456441044807434, 'finance', 0), ('fastai/fastcore', 0.5410973429679871, 'util', 0), ('pdm-project/pdm', 0.5352970957756042, 'util', 0), ('gradio-app/gradio', 0.5345299243927002, 'viz', 0), ('dagworks-inc/hamilton', 0.532253086566925, 'ml-ops', 0), ('python-odin/odin', 0.5316958427429199, 'util', 0), ('pandas-dev/pandas', 0.5308995246887207, 'pandas', 0), ('pytables/pytables', 0.5297611951828003, 'data', 0), ('indygreg/pyoxidizer', 0.5247126221656799, 'util', 0), ('omry/omegaconf', 0.5221198201179504, 'util', 0), ('ploomber/ploomber', 0.5213688015937805, 'ml-ops', 0), ('pyodide/micropip', 0.5204178690910339, 'util', 0), ('holoviz/panel', 0.5195130109786987, 'viz', 0), ('wesm/pydata-book', 0.5186687707901001, 'study', 0), ('ranaroussi/quantstats', 0.5185449123382568, 'finance', 0), ('amaargiru/pyroad', 0.5178565979003906, 'study', 0), ('ta-lib/ta-lib-python', 0.5163371562957764, 'finance', 0), ('machow/siuba', 0.513921856880188, 'pandas', 1), ('nteract/papermill', 0.5121511220932007, 'jupyter', 0), ('dylanhogg/awesome-python', 0.5110769867897034, 'study', 1), ('spotify/luigi', 0.5109559893608093, 'ml-ops', 0), ('macbre/sql-metadata', 0.5103316307067871, 'data', 1), ('pypy/pypy', 0.5085586309432983, 'util', 0), ('python/cpython', 0.5069926381111145, 'util', 0), ('pytoolz/toolz', 0.5053905844688416, 'util', 0), ('saulpw/visidata', 0.5046626925468445, 'term', 0)]
| 9
| 4
| null | 4.63
| 16
| 15
| 15
| 0
| 46
| 92
| 46
| 16
| 10
| 90
| 0.6
| 26
|
160
|
data
|
https://github.com/goldsmith/wikipedia
|
[]
| null |
[]
|
[]
| null | null | null |
goldsmith/wikipedia
|
Wikipedia
| 2,774
| 569
| 83
|
Python
|
https://wikipedia.readthedocs.org/
|
A Pythonic wrapper for the Wikipedia API
|
goldsmith
|
2024-01-12
|
2013-08-20
| 545
| 5.089908
| null |
A Pythonic wrapper for the Wikipedia API
|
[]
|
[]
|
2020-10-09
|
[('harangju/wikinet', 0.7525506615638733, 'data', 0), ('mediawiki-client-tools/mediawiki-dump-generator', 0.709117591381073, 'data', 0), ('mediawiki-client-tools/wikitools3', 0.708003044128418, 'data', 0), ('weaviate/semantic-search-through-wikipedia-with-weaviate', 0.5848309397697449, 'data', 0), ('urschrei/pyzotero', 0.5380213856697083, 'util', 0), ('facebookresearch/drqa', 0.5370073318481445, 'nlp', 0), ('nv7-github/googlesearch', 0.5323612093925476, 'util', 0), ('meilisearch/meilisearch-python', 0.5276321768760681, 'data', 0), ('dit/dit', 0.525985062122345, 'math', 0), ('googleapis/google-api-python-client', 0.5227052569389343, 'util', 0), ('pytoolz/toolz', 0.5172760486602783, 'util', 0), ('scholarly-python-package/scholarly', 0.5016988515853882, 'data', 0)]
| 23
| 3
| null | 0
| 0
| 0
| 127
| 40
| 0
| 0
| 0
| 0
| 0
| 90
| 0
| 25
|
1,256
|
llm
|
https://github.com/openai/finetune-transformer-lm
|
[]
| null |
[]
|
[]
| null | null | null |
openai/finetune-transformer-lm
|
finetune-transformer-lm
| 1,996
| 485
| 73
|
Python
|
https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf
|
Code and model for the paper "Improving Language Understanding by Generative Pre-Training"
|
openai
|
2024-01-12
|
2018-06-11
| 294
| 6.785818
|
https://avatars.githubusercontent.com/u/14957082?v=4
|
Code and model for the paper "Improving Language Understanding by Generative Pre-Training"
|
['paper']
|
['paper']
|
2018-11-22
|
[('openai/gpt-2', 0.6553829312324524, 'llm', 1), ('srush/minichain', 0.6227275133132935, 'llm', 0), ('openai/image-gpt', 0.6050621867179871, 'llm', 0), ('huawei-noah/pretrained-language-model', 0.5976941585540771, 'nlp', 0), ('thudm/glm-130b', 0.590366780757904, 'llm', 0), ('yizhongw/self-instruct', 0.5861937999725342, 'llm', 0), ('jonasgeiping/cramming', 0.5850217342376709, 'nlp', 0), ('salesforce/blip', 0.5705159902572632, 'diffusion', 0), ('microsoft/unilm', 0.5644842982292175, 'nlp', 0), ('facebookresearch/shepherd', 0.5617777705192566, 'llm', 0), ('yueyu1030/attrprompt', 0.5512840747833252, 'llm', 0), ('togethercomputer/redpajama-data', 0.5497167706489563, 'llm', 0), ('qanastek/drbert', 0.5415375828742981, 'llm', 0), ('suno-ai/bark', 0.5396863222122192, 'ml', 0), ('google-research/electra', 0.5301187634468079, 'ml-dl', 0), ('tatsu-lab/stanford_alpaca', 0.5281931757926941, 'llm', 0), ('hannibal046/awesome-llm', 0.527682363986969, 'study', 0), ('cg123/mergekit', 0.5270984768867493, 'llm', 0), ('openai/clip', 0.5225598812103271, 'ml-dl', 0), ('microsoft/lora', 0.520248532295227, 'llm', 0), ('kyegomez/tree-of-thoughts', 0.5154716968536377, 'llm', 0), ('keirp/automatic_prompt_engineer', 0.5153794884681702, 'llm', 0), ('huggingface/text-generation-inference', 0.5149126648902893, 'llm', 0), ('lupantech/chameleon-llm', 0.5111390352249146, 'llm', 0), ('graykode/nlp-tutorial', 0.5100629329681396, 'study', 1), ('bigscience-workshop/megatron-deepspeed', 0.5082074999809265, 'llm', 0), ('microsoft/megatron-deepspeed', 0.5082074999809265, 'llm', 0), ('guidance-ai/guidance', 0.5050269961357117, 'llm', 0), ('bigscience-workshop/biomedical', 0.5047228336334229, 'data', 0), ('extreme-bert/extreme-bert', 0.504263699054718, 'llm', 0), ('thudm/codegeex', 0.5009891390800476, 'llm', 0)]
| 5
| 1
| null | 0
| 1
| 0
| 68
| 63
| 0
| 0
| 0
| 1
| 1
| 90
| 1
| 25
|
205
|
debug
|
https://github.com/alexmojaki/heartrate
|
[]
| null |
[]
|
[]
| null | null | null |
alexmojaki/heartrate
|
heartrate
| 1,685
| 124
| 33
|
Python
| null |
Simple real time visualisation of the execution of a Python program.
|
alexmojaki
|
2024-01-13
|
2019-04-24
| 248
| 6.770953
| null |
Simple real time visualisation of the execution of a Python program.
|
['debugger', 'visualization']
|
['debugger', 'visualization']
|
2021-11-13
|
[('gaogaotiantian/viztracer', 0.6707364320755005, 'profiling', 1), ('altair-viz/altair', 0.6655700206756592, 'viz', 1), ('alexmojaki/snoop', 0.6388193964958191, 'debug', 1), ('pympler/pympler', 0.57415771484375, 'perf', 0), ('holoviz/holoviz', 0.573762834072113, 'viz', 0), ('bokeh/bokeh', 0.571494460105896, 'viz', 1), ('inducer/pudb', 0.5674254894256592, 'debug', 1), ('brandtbucher/specialist', 0.56365966796875, 'perf', 0), ('mwaskom/seaborn', 0.5591291785240173, 'viz', 0), ('alexmojaki/birdseye', 0.5575025081634521, 'debug', 1), ('p403n1x87/austin', 0.5422681570053101, 'profiling', 0), ('pythonprofilers/memory_profiler', 0.5310880541801453, 'profiling', 0), ('kanaries/pygwalker', 0.5303294658660889, 'pandas', 1), ('holoviz/geoviews', 0.5235070586204529, 'gis', 0), ('pyutils/line_profiler', 0.522416353225708, 'profiling', 0), ('samuelcolvin/python-devtools', 0.5222712755203247, 'debug', 0), ('pyqtgraph/pyqtgraph', 0.5196880102157593, 'viz', 1), ('has2k1/plotnine', 0.5188327431678772, 'viz', 0), ('ionelmc/python-hunter', 0.5184060335159302, 'debug', 1), ('pyglet/pyglet', 0.5163092613220215, 'gamedev', 0), ('rockhopper-technologies/enlighten', 0.5111202597618103, 'term', 0), ('plotly/plotly.py', 0.5061368942260742, 'viz', 1), ('nschloe/perfplot', 0.5038774013519287, 'perf', 0), ('residentmario/geoplot', 0.5028942823410034, 'gis', 0), ('holoviz/panel', 0.5002499222755432, 'viz', 0)]
| 3
| 0
| null | 0
| 1
| 1
| 58
| 26
| 0
| 0
| 0
| 1
| 2
| 90
| 2
| 25
|
1,503
|
util
|
https://github.com/asweigart/pyperclip
|
['clipboard']
| null |
[]
|
[]
| null | null | null |
asweigart/pyperclip
|
pyperclip
| 1,497
| 184
| 35
|
Python
|
https://pypi.python.org/pypi/pyperclip
|
Python module for cross-platform clipboard functions.
|
asweigart
|
2024-01-14
|
2011-06-15
| 658
| 2.272116
| null |
Python module for cross-platform clipboard functions.
|
[]
|
['clipboard']
|
2021-10-12
|
[('taylorsmarks/playsound', 0.5268693566322327, 'util', 0), ('pytoolz/toolz', 0.5117189288139343, 'util', 0), ('hoffstadt/dearpygui', 0.5023604035377502, 'gui', 0), ('p403n1x87/austin', 0.5013977885246277, 'profiling', 0)]
| 34
| 3
| null | 0
| 4
| 1
| 153
| 27
| 0
| 0
| 0
| 4
| 4
| 90
| 1
| 25
|
995
|
finance
|
https://github.com/quantopian/empyrical
|
[]
| null |
[]
|
[]
| null | null | null |
quantopian/empyrical
|
empyrical
| 1,189
| 365
| 71
|
Python
|
https://quantopian.github.io/empyrical
|
Common financial risk and performance metrics. Used by zipline and pyfolio.
|
quantopian
|
2024-01-13
|
2016-03-18
| 410
| 2.895964
|
https://avatars.githubusercontent.com/u/1393215?v=4
|
Common financial risk and performance metrics. Used by zipline and pyfolio.
|
[]
|
[]
|
2020-10-14
|
[('quantopian/pyfolio', 0.6045350432395935, 'finance', 0)]
| 22
| 4
| null | 0
| 3
| 0
| 95
| 40
| 0
| 4
| 4
| 3
| 0
| 90
| 0
| 25
|
615
|
testing
|
https://github.com/wolever/parameterized
|
[]
| null |
[]
|
[]
| null | null | null |
wolever/parameterized
|
parameterized
| 797
| 104
| 18
|
Python
| null |
Parameterized testing with any Python test framework
|
wolever
|
2024-01-13
|
2012-03-10
| 620
| 1.284596
| null |
Parameterized testing with any Python test framework
|
[]
|
[]
|
2023-03-27
|
[('nedbat/coveragepy', 0.6841293573379517, 'testing', 0), ('pmorissette/bt', 0.620602011680603, 'finance', 0), ('getsentry/responses', 0.6193545460700989, 'testing', 0), ('ionelmc/pytest-benchmark', 0.6030191779136658, 'testing', 0), ('klen/py-frameworks-bench', 0.5862182974815369, 'perf', 0), ('spulec/freezegun', 0.5846211314201355, 'testing', 0), ('locustio/locust', 0.5832534432411194, 'testing', 0), ('buildbot/buildbot', 0.5751279592514038, 'util', 0), ('pytest-dev/pytest', 0.5712449550628662, 'testing', 0), ('pytest-dev/pytest-bdd', 0.5707379579544067, 'testing', 0), ('eleutherai/pyfra', 0.5701524615287781, 'ml', 0), ('taverntesting/tavern', 0.5629963874816895, 'testing', 0), ('pytest-dev/pytest-xdist', 0.5527563095092773, 'testing', 0), ('eugeneyan/python-collab-template', 0.5470243692398071, 'template', 0), ('cobrateam/splinter', 0.5428141355514526, 'testing', 0), ('computationalmodelling/nbval', 0.5427061319351196, 'jupyter', 0), ('seleniumbase/seleniumbase', 0.5371958613395691, 'testing', 0), ('pyeve/cerberus', 0.5334105491638184, 'data', 0), ('pytoolz/toolz', 0.5330343842506409, 'util', 0), ('mementum/backtrader', 0.5309708714485168, 'finance', 0), ('samuelcolvin/dirty-equals', 0.524022102355957, 'util', 0), ('requests/toolbelt', 0.5131222009658813, 'util', 0), ('pytest-dev/pytest-mock', 0.5089722275733948, 'testing', 0), ('unionai-oss/pandera', 0.5074113607406616, 'pandas', 0), ('cuemacro/finmarketpy', 0.5073219537734985, 'finance', 0), ('pympler/pympler', 0.5015774965286255, 'perf', 0)]
| 31
| 5
| null | 0.27
| 6
| 0
| 144
| 10
| 0
| 1
| 1
| 6
| 4
| 90
| 0.7
| 25
|
1,388
|
nlp
|
https://github.com/keredson/wordninja
|
['tokeniser']
| null |
[]
|
[]
| null | null | null |
keredson/wordninja
|
wordninja
| 743
| 107
| 10
|
Python
| null |
Probabilistically split concatenated words using NLP based on English Wikipedia unigram frequencies.
|
keredson
|
2024-01-11
|
2017-04-20
| 353
| 2.100565
| null |
Probabilistically split concatenated words using NLP based on English Wikipedia unigram frequencies.
|
[]
|
['tokeniser']
|
2023-02-14
|
[]
| 6
| 3
| null | 0
| 1
| 0
| 82
| 11
| 0
| 0
| 0
| 1
| 2
| 90
| 2
| 25
|
169
|
gis
|
https://github.com/openeventdata/mordecai
|
[]
| null |
[]
|
[]
| null | null | null |
openeventdata/mordecai
|
mordecai
| 722
| 98
| 34
|
Python
| null |
Full text geoparsing as a Python library
|
openeventdata
|
2024-01-04
|
2016-06-23
| 396
| 1.81995
|
https://avatars.githubusercontent.com/u/1460393?v=4
|
Full text geoparsing as a Python library
|
['geocoding', 'geonames', 'geoparsing', 'nlp', 'spacy', 'toponym-resolution']
|
['geocoding', 'geonames', 'geoparsing', 'nlp', 'spacy', 'toponym-resolution']
|
2021-02-01
|
[('geopandas/geopandas', 0.6333655714988708, 'gis', 0), ('kagisearch/vectordb', 0.5354642868041992, 'data', 0), ('opengeos/leafmap', 0.5265222191810608, 'gis', 0), ('artelys/geonetworkx', 0.5222339630126953, 'gis', 0), ('pemistahl/lingua-py', 0.5192466974258423, 'nlp', 1)]
| 6
| 3
| null | 0
| 2
| 0
| 92
| 36
| 0
| 1
| 1
| 2
| 7
| 90
| 3.5
| 25
|
165
|
nlp
|
https://github.com/explosion/spacy-stanza
|
[]
| null |
[]
|
[]
| null | null | null |
explosion/spacy-stanza
|
spacy-stanza
| 705
| 57
| 26
|
Python
| null |
💥 Use the latest Stanza (StanfordNLP) research models directly in spaCy
|
explosion
|
2024-01-04
|
2019-01-31
| 260
| 2.70411
|
https://avatars.githubusercontent.com/u/20011530?v=4
|
💥 Use the latest Stanza (StanfordNLP) research models directly in spaCy
|
['corenlp', 'data-science', 'machine-learning', 'natural-language-processing', 'nlp', 'spacy', 'spacy-pipeline', 'stanford-corenlp', 'stanford-machine-learning', 'stanford-nlp', 'stanza']
|
['corenlp', 'data-science', 'machine-learning', 'natural-language-processing', 'nlp', 'spacy', 'spacy-pipeline', 'stanford-corenlp', 'stanford-machine-learning', 'stanford-nlp', 'stanza']
|
2023-10-09
|
[('explosion/spacy-models', 0.7454922199249268, 'nlp', 4), ('huggingface/neuralcoref', 0.6504446864128113, 'nlp', 4), ('explosion/spacy-transformers', 0.6275186538696289, 'llm', 5), ('iclrandd/blackstone', 0.5856739282608032, 'nlp', 1), ('explosion/spacy-llm', 0.5577221512794495, 'llm', 4), ('explosion/spacy-streamlit', 0.5425686240196228, 'nlp', 4), ('norskregnesentral/skweak', 0.5375661849975586, 'nlp', 3), ('explosion/spacy', 0.5188982486724854, 'nlp', 5)]
| 8
| 4
| null | 0.17
| 0
| 0
| 60
| 3
| 2
| 3
| 2
| 0
| 0
| 90
| 0
| 25
|
1,413
|
llm
|
https://github.com/hazyresearch/ama_prompting
|
['prompt-engineering']
| null |
[]
|
[]
| null | null | null |
hazyresearch/ama_prompting
|
ama_prompting
| 522
| 45
| 24
|
Python
| null |
Ask Me Anything language model prompting
|
hazyresearch
|
2024-01-09
|
2022-10-01
| 69
| 7.518519
|
https://avatars.githubusercontent.com/u/2165246?v=4
|
Ask Me Anything language model prompting
|
[]
|
['prompt-engineering']
|
2023-07-05
|
[('keirp/automatic_prompt_engineer', 0.7995690107345581, 'llm', 1), ('microsoft/promptbase', 0.7119161486625671, 'llm', 1), ('neulab/prompt2model', 0.687862753868103, 'llm', 0), ('guidance-ai/guidance', 0.6410424709320068, 'llm', 1), ('hazyresearch/manifest', 0.5880966782569885, 'llm', 1), ('srush/minichain', 0.5800570845603943, 'llm', 1), ('1rgs/jsonformer', 0.5634579062461853, 'llm', 1), ('promptslab/promptify', 0.5550077557563782, 'nlp', 1), ('stanfordnlp/dspy', 0.540431022644043, 'llm', 0), ('suno-ai/bark', 0.5254032015800476, 'ml', 0), ('bigscience-workshop/promptsource', 0.512933611869812, 'nlp', 0), ('ctlllll/llm-toolmaker', 0.5080617666244507, 'llm', 0), ('kyegomez/tree-of-thoughts', 0.5045536160469055, 'llm', 1), ('agenta-ai/agenta', 0.5027536153793335, 'llm', 1)]
| 6
| 2
| null | 0.02
| 0
| 0
| 16
| 6
| 0
| 0
| 0
| 0
| 0
| 90
| 0
| 25
|
1,088
|
graph
|
https://github.com/rampasek/graphgps
|
[]
| null |
[]
|
[]
| null | null | null |
rampasek/graphgps
|
GraphGPS
| 520
| 95
| 9
|
Python
| null |
Recipe for a General, Powerful, Scalable Graph Transformer
|
rampasek
|
2024-01-12
|
2022-05-24
| 88
| 5.909091
| null |
Recipe for a General, Powerful, Scalable Graph Transformer
|
['graph-neural-network', 'graph-representation-learning', 'graph-transformer', 'long-range-dependence']
|
['graph-neural-network', 'graph-representation-learning', 'graph-transformer', 'long-range-dependence']
|
2023-02-17
|
[('hamed1375/exphormer', 0.6791350841522217, 'graph', 0), ('pyg-team/pytorch_geometric', 0.6243663430213928, 'ml-dl', 0), ('danielegrattarola/spektral', 0.5940183997154236, 'ml-dl', 0), ('stellargraph/stellargraph', 0.5865841507911682, 'graph', 0), ('dmlc/dgl', 0.5740870833396912, 'ml-dl', 0), ('chandlerbang/awesome-self-supervised-gnn', 0.5653940439224243, 'study', 0), ('graphistry/pygraphistry', 0.5173921585083008, 'data', 0)]
| 2
| 0
| null | 0.13
| 12
| 6
| 20
| 11
| 1
| 1
| 1
| 12
| 13
| 90
| 1.1
| 25
|
1,830
|
data
|
https://github.com/koaning/doubtlab
|
['data-quality']
| null |
[]
|
[]
| null | null | null |
koaning/doubtlab
|
doubtlab
| 485
| 19
| 7
|
Python
|
https://koaning.github.io/doubtlab/
|
Doubt your data, find bad labels.
|
koaning
|
2024-01-09
|
2021-11-05
| 116
| 4.160539
| null |
Doubt your data, find bad labels.
|
[]
|
['data-quality']
|
2022-11-25
|
[('koaning/bulk', 0.5618610382080078, 'data', 1), ('ydataai/ydata-quality', 0.5491899251937866, 'data', 0)]
| 6
| 3
| null | 0
| 2
| 2
| 27
| 14
| 0
| 4
| 4
| 2
| 0
| 90
| 0
| 25
|
735
|
nlp
|
https://github.com/koaning/whatlies
|
[]
| null |
[]
|
[]
| null | null | null |
koaning/whatlies
|
whatlies
| 463
| 53
| 15
|
Python
|
https://koaning.github.io/whatlies/
|
Toolkit to help understand "what lies" in word embeddings. Also benchmarking!
|
koaning
|
2024-01-04
|
2020-02-22
| 205
| 2.253825
| null |
Toolkit to help understand "what lies" in word embeddings. Also benchmarking!
|
['embeddings', 'nlp', 'visualisations']
|
['embeddings', 'nlp', 'visualisations']
|
2023-02-06
|
[('plasticityai/magnitude', 0.6194629669189453, 'nlp', 2), ('koaning/embetter', 0.6023291945457458, 'data', 0), ('qdrant/fastembed', 0.5915380120277405, 'ml', 1), ('ddangelov/top2vec', 0.5833550095558167, 'nlp', 0), ('sebischair/lbl2vec', 0.5767074823379517, 'nlp', 1), ('allenai/allennlp', 0.570354700088501, 'nlp', 1), ('alibaba/easynlp', 0.5676613450050354, 'nlp', 1), ('jina-ai/clip-as-service', 0.553788423538208, 'nlp', 0), ('chroma-core/chroma', 0.5464308261871338, 'data', 1), ('jalammar/ecco', 0.5461040735244751, 'ml-interpretability', 1), ('huggingface/text-embeddings-inference', 0.5444438457489014, 'llm', 1), ('flairnlp/flair', 0.5374947190284729, 'nlp', 1), ('paddlepaddle/paddlenlp', 0.5240222215652466, 'llm', 1), ('maartengr/bertopic', 0.5192699432373047, 'nlp', 1), ('neuml/txtai', 0.5187655687332153, 'nlp', 2), ('milvus-io/bootcamp', 0.5146546363830566, 'data', 2), ('explosion/spacy-models', 0.5144206881523132, 'nlp', 1), ('jbesomi/texthero', 0.5086743831634521, 'nlp', 1), ('muennighoff/sgpt', 0.5074957013130188, 'llm', 0), ('amansrivastava17/embedding-as-service', 0.5058193802833557, 'nlp', 2), ('ukplab/sentence-transformers', 0.5054284334182739, 'nlp', 0), ('mitvis/vistext', 0.5051085948944092, 'data', 0), ('jina-ai/vectordb', 0.5039339661598206, 'data', 0), ('cvxgrp/pymde', 0.5038027167320251, 'ml', 0), ('llmware-ai/llmware', 0.501112163066864, 'llm', 2)]
| 13
| 6
| null | 0.06
| 0
| 0
| 47
| 11
| 0
| 7
| 7
| 0
| 0
| 90
| 0
| 25
|
699
|
ml-ops
|
https://github.com/bodywork-ml/bodywork-core
|
[]
| null |
[]
|
[]
| null | null | null |
bodywork-ml/bodywork-core
|
bodywork-core
| 431
| 22
| 11
|
Python
|
https://bodywork.readthedocs.io/en/latest/
|
ML pipeline orchestration and model deployments on Kubernetes.
|
bodywork-ml
|
2024-01-04
|
2020-11-17
| 167
| 2.580838
|
https://avatars.githubusercontent.com/u/74599515?v=4
|
ML pipeline orchestration and model deployments on Kubernetes.
|
['batch', 'cicd', 'continuous-deployment', 'data-science', 'devops', 'framework', 'kubernetes', 'machine-learning', 'mlops', 'orchestration', 'pipeline', 'serving']
|
['batch', 'cicd', 'continuous-deployment', 'data-science', 'devops', 'framework', 'kubernetes', 'machine-learning', 'mlops', 'orchestration', 'pipeline', 'serving']
|
2022-07-04
|
[('kubeflow/pipelines', 0.8104010820388794, 'ml-ops', 5), ('polyaxon/polyaxon', 0.6861110925674438, 'ml-ops', 4), ('flyteorg/flyte', 0.6820202469825745, 'ml-ops', 4), ('getindata/kedro-kubeflow', 0.6706922650337219, 'ml-ops', 1), ('orchest/orchest', 0.6645437479019165, 'ml-ops', 3), ('allegroai/clearml', 0.6049355268478394, 'ml-ops', 3), ('bentoml/bentoml', 0.5959450602531433, 'ml-ops', 3), ('zenml-io/zenml', 0.5911761522293091, 'ml-ops', 3), ('unionai-oss/unionml', 0.5865074396133423, 'ml-ops', 2), ('netflix/metaflow', 0.5834348201751709, 'ml-ops', 4), ('dagster-io/dagster', 0.5800526142120361, 'ml-ops', 3), ('mage-ai/mage-ai', 0.5791720151901245, 'ml-ops', 4), ('ploomber/ploomber', 0.5688678026199341, 'ml-ops', 3), ('gefyrahq/gefyra', 0.5484521389007568, 'util', 1), ('backtick-se/cowait', 0.547731876373291, 'util', 2), ('kubeflow-kale/kale', 0.5426039695739746, 'ml-ops', 1), ('skypilot-org/skypilot', 0.5361641049385071, 'llm', 2), ('kestra-io/kestra', 0.5349816083908081, 'ml-ops', 2), ('feast-dev/feast', 0.5267590284347534, 'ml-ops', 3), ('zenml-io/mlstacks', 0.5243047475814819, 'ml-ops', 1), ('jina-ai/jina', 0.523324728012085, 'ml', 6), ('tox-dev/tox', 0.5215947031974792, 'testing', 0), ('avaiga/taipy', 0.5173805952072144, 'data', 3), ('apache/airflow', 0.5161139965057373, 'ml-ops', 4), ('onnx/onnx', 0.5088913440704346, 'ml', 1)]
| 4
| 2
| null | 0
| 1
| 1
| 38
| 19
| 0
| 19
| 19
| 1
| 1
| 90
| 1
| 25
|
1,227
|
time-series
|
https://github.com/microsoft/robustlearn
|
[]
| null |
[]
|
[]
| null | null | null |
microsoft/robustlearn
|
robustlearn
| 384
| 45
| 7
|
Python
|
http://aka.ms/roblearn
|
Robust machine learning for responsible AI
|
microsoft
|
2024-01-13
|
2022-10-20
| 66
| 5.755889
|
https://avatars.githubusercontent.com/u/6154722?v=4
|
Robust machine learning for responsible AI
|
[]
|
[]
|
2023-10-08
|
[('seldonio/alibi', 0.5054094791412354, 'ml-interpretability', 0), ('maif/shapash', 0.5023934841156006, 'ml', 0)]
| 8
| 1
| null | 1.54
| 1
| 1
| 15
| 3
| 0
| 0
| 0
| 1
| 0
| 90
| 0
| 25
|
1,364
|
gamedev
|
https://github.com/renpy/pygame_sdl2
|
['pygame', 'sdl2']
| null |
[]
|
[]
| null | null | null |
renpy/pygame_sdl2
|
pygame_sdl2
| 311
| 63
| 29
|
Python
| null |
Reimplementation of portions of the pygame API using SDL2.
|
renpy
|
2023-12-27
|
2014-10-23
| 483
| 0.642942
|
https://avatars.githubusercontent.com/u/1900740?v=4
|
Reimplementation of portions of the pygame API using SDL2.
|
[]
|
['pygame', 'sdl2']
|
2023-12-20
|
[('pygame/pygame', 0.7130681872367859, 'gamedev', 2), ('lordmauve/pgzero', 0.5067479610443115, 'gamedev', 1)]
| 26
| 1
| null | 0.44
| 3
| 3
| 112
| 1
| 0
| 33
| 33
| 3
| 2
| 90
| 0.7
| 25
|
990
|
util
|
https://github.com/stub42/pytz
|
[]
| null |
[]
|
[]
| null | null | null |
stub42/pytz
|
pytz
| 294
| 80
| 15
|
C
| null |
pytz Python historical timezone library and database
|
stub42
|
2024-01-13
|
2016-07-12
| 394
| 0.746193
| null |
pytz Python historical timezone library and database
|
[]
|
[]
|
2023-09-05
|
[('sdispater/pendulum', 0.646413266658783, 'util', 0), ('dateutil/dateutil', 0.621961236000061, 'util', 0), ('arrow-py/arrow', 0.5504962205886841, 'util', 0), ('rjt1990/pyflux', 0.5127301812171936, 'time-series', 0)]
| 21
| 3
| null | 0.23
| 3
| 1
| 91
| 4
| 0
| 10
| 10
| 3
| 2
| 90
| 0.7
| 25
|
1,096
|
ml
|
https://github.com/eleutherai/oslo
|
[]
| null |
[]
|
[]
| null | null | null |
eleutherai/oslo
|
oslo
| 169
| 29
| 5
|
Python
|
https://oslo.eleuther.ai
|
OSLO: Open Source for Large-scale Optimization
|
eleutherai
|
2024-01-10
|
2022-08-25
| 74
| 2.26195
|
https://avatars.githubusercontent.com/u/68924597?v=4
|
OSLO: Open Source for Large-scale Optimization
|
[]
|
[]
|
2023-09-09
|
[('determined-ai/determined', 0.56780606508255, 'ml-ops', 0), ('optuna/optuna', 0.5354965925216675, 'ml', 0), ('tensorflow/tensorflow', 0.5216479301452637, 'ml-dl', 0), ('microsoft/olive', 0.5006281733512878, 'ml', 0)]
| 50
| 2
| null | 1.33
| 1
| 0
| 17
| 4
| 0
| 7
| 7
| 1
| 0
| 90
| 0
| 25
|
1,011
|
finance
|
https://github.com/daxm/fmpsdk
|
[]
| null |
[]
|
[]
| null | null | null |
daxm/fmpsdk
|
fmpsdk
| 125
| 48
| 8
|
Python
| null |
SDK for Financial Modeling Prep's (FMP) API
|
daxm
|
2024-01-12
|
2020-12-06
| 164
| 0.76087
| null |
SDK for Financial Modeling Prep's (FMP) API
|
[]
|
[]
|
2024-01-13
|
[('pmorissette/ffn', 0.5664402842521667, 'finance', 0)]
| 15
| 3
| null | 0.4
| 6
| 6
| 38
| 0
| 0
| 0
| 0
| 6
| 8
| 90
| 1.3
| 25
|
1,658
|
data
|
https://github.com/unstructured-io/pipeline-sec-filings
|
['unstructured', 'sec', 'pipeline']
| null |
[]
|
[]
| null | null | null |
unstructured-io/pipeline-sec-filings
|
pipeline-sec-filings
| 119
| 21
| 12
|
Jupyter Notebook
| null |
Preprocessing pipeline notebooks and API supporting text extraction from SEC documents
|
unstructured-io
|
2024-01-04
|
2022-09-27
| 70
| 1.7
|
https://avatars.githubusercontent.com/u/108372208?v=4
|
Preprocessing pipeline notebooks and API supporting text extraction from SEC documents
|
[]
|
['pipeline', 'sec', 'unstructured']
|
2023-10-02
|
[('linealabs/lineapy', 0.6072432994842529, 'jupyter', 0), ('unstructured-io/unstructured-api', 0.5717188715934753, 'data', 1), ('paperswithcode/sota-extractor', 0.537769615650177, 'data', 0)]
| 15
| 5
| null | 0.4
| 14
| 9
| 16
| 3
| 0
| 0
| 0
| 14
| 9
| 90
| 0.6
| 25
|
1,756
|
ml
|
https://github.com/rom1504/embedding-reader
|
['filesystem', 'embeddings']
| null |
[]
|
[]
| null | null | null |
rom1504/embedding-reader
|
embedding-reader
| 77
| 16
| 4
|
Python
| null |
Efficiently read embedding in streaming from any filesystem
|
rom1504
|
2024-01-09
|
2022-02-27
| 100
| 0.767806
| null |
Efficiently read embedding in streaming from any filesystem
|
[]
|
['embeddings', 'filesystem']
|
2024-01-11
|
[('vhranger/nodevectors', 0.53115314245224, 'viz', 0)]
| 8
| 3
| null | 0.19
| 8
| 8
| 23
| 0
| 3
| 12
| 3
| 8
| 10
| 90
| 1.2
| 25
|
1,723
|
study
|
https://github.com/giswqs/geog-414
|
[]
| null |
[]
|
[]
| null | null | null |
giswqs/geog-414
|
geog-414
| 66
| 16
| 7
|
HTML
|
https://geog-414.gishub.org
|
A repo for GEOG-414 (Spatial Data Management) at the University of Tennessee
|
giswqs
|
2023-12-31
|
2023-08-16
| 23
| 2.766467
| null |
A repo for GEOG-414 (Spatial Data Management) at the University of Tennessee
|
['database', 'earthengine', 'geospatial', 'postgis']
|
['database', 'earthengine', 'geospatial', 'postgis']
|
2023-12-04
|
[('apache/incubator-sedona', 0.560769259929657, 'gis', 1)]
| 1
| 1
| null | 1.08
| 1
| 1
| 5
| 1
| 0
| 0
| 0
| 1
| 4
| 90
| 4
| 25
|
1,226
|
util
|
https://github.com/joowani/binarytree
|
[]
| null |
[]
|
[]
| null | null | null |
joowani/binarytree
|
binarytree
| 1,796
| 173
| 46
|
Python
|
http://binarytree.readthedocs.io
|
Python Library for Studying Binary Trees
|
joowani
|
2024-01-12
|
2016-09-20
| 384
| 4.677083
| null |
Python Library for Studying Binary Trees
|
['algorithm', 'binary-search-tree', 'binary-tree', 'binary-trees', 'bst', 'data-structure', 'data-structures', 'heap', 'heaps', 'interview', 'interview-practice', 'learning', 'practise']
|
['algorithm', 'binary-search-tree', 'binary-tree', 'binary-trees', 'bst', 'data-structure', 'data-structures', 'heap', 'heaps', 'interview', 'interview-practice', 'learning', 'practise']
|
2022-06-28
|
[('keon/algorithms', 0.6219033002853394, 'util', 2), ('krzjoa/awesome-python-data-science', 0.5454331636428833, 'study', 0), ('pandas-dev/pandas', 0.5415753722190857, 'pandas', 0), ('thealgorithms/python', 0.5343596935272217, 'study', 2), ('pyparsing/pyparsing', 0.5169668197631836, 'util', 0)]
| 9
| 1
| null | 0
| 1
| 0
| 89
| 19
| 0
| 2
| 2
| 1
| 0
| 90
| 0
| 24
|
1,131
|
ml
|
https://github.com/scikit-learn-contrib/lightning
|
[]
| null |
[]
|
[]
| null | null | null |
scikit-learn-contrib/lightning
|
lightning
| 1,695
| 215
| 38
|
Python
|
https://contrib.scikit-learn.org/lightning/
|
Large-scale linear classification, regression and ranking in Python
|
scikit-learn-contrib
|
2024-01-11
|
2012-01-11
| 628
| 2.695366
|
https://avatars.githubusercontent.com/u/17349883?v=4
|
Large-scale linear classification, regression and ranking in Python
|
['machine-learning']
|
['machine-learning']
|
2022-01-30
|
[('scikit-learn/scikit-learn', 0.6304967403411865, 'ml', 1), ('scikit-learn-contrib/metric-learn', 0.6158003807067871, 'ml', 1), ('dask/dask-ml', 0.5908797979354858, 'ml', 0), ('scikit-learn-contrib/imbalanced-learn', 0.5669341087341309, 'ml', 1), ('rasbt/mlxtend', 0.5572295188903809, 'ml', 1), ('pycaret/pycaret', 0.5490888953208923, 'ml', 1), ('amzn/pecos', 0.5417201519012451, 'ml', 0), ('lmcinnes/pynndescent', 0.5341041684150696, 'ml', 0), ('huggingface/evaluate', 0.5336165428161621, 'ml', 1), ('ggerganov/ggml', 0.5173808336257935, 'ml', 1), ('ageron/handson-ml2', 0.5109522938728333, 'ml', 0), ('gradio-app/gradio', 0.5077913403511047, 'viz', 1), ('catboost/catboost', 0.5035675764083862, 'ml', 1)]
| 17
| 6
| null | 0
| 0
| 0
| 146
| 24
| 0
| 1
| 1
| 0
| 0
| 90
| 0
| 24
|
144
|
nlp
|
https://github.com/plasticityai/magnitude
|
[]
| null |
[]
|
[]
| null | null | null |
plasticityai/magnitude
|
magnitude
| 1,608
| 117
| 38
|
Python
| null |
A fast, efficient universal vector embedding utility package.
|
plasticityai
|
2024-01-12
|
2018-02-24
| 309
| 5.196676
|
https://avatars.githubusercontent.com/u/36324344?v=4
|
A fast, efficient universal vector embedding utility package.
|
['embeddings', 'fast', 'fasttext', 'gensim', 'glove', 'machine-learning', 'machine-learning-library', 'memory-efficient', 'natural-language-processing', 'nlp', 'vectors', 'word-embeddings', 'word2vec']
|
['embeddings', 'fast', 'fasttext', 'gensim', 'glove', 'machine-learning', 'machine-learning-library', 'memory-efficient', 'natural-language-processing', 'nlp', 'vectors', 'word-embeddings', 'word2vec']
|
2020-07-17
|
[('qdrant/fastembed', 0.6370154619216919, 'ml', 1), ('amansrivastava17/embedding-as-service', 0.6195082068443298, 'nlp', 5), ('koaning/whatlies', 0.6194629669189453, 'nlp', 2), ('sebischair/lbl2vec', 0.5967115163803101, 'nlp', 4), ('ddangelov/top2vec', 0.584179162979126, 'nlp', 1), ('jina-ai/vectordb', 0.5780693888664246, 'data', 0), ('chroma-core/chroma', 0.5727373957633972, 'data', 1), ('jina-ai/clip-as-service', 0.5636166334152222, 'nlp', 0), ('huggingface/text-embeddings-inference', 0.5618109703063965, 'llm', 1), ('llmware-ai/llmware', 0.5475419759750366, 'llm', 3), ('jina-ai/finetuner', 0.5438824892044067, 'ml', 0), ('facebookresearch/faiss', 0.5381598472595215, 'ml', 1), ('kagisearch/vectordb', 0.5367014408111572, 'data', 1), ('koaning/embetter', 0.5330700278282166, 'data', 0), ('flairnlp/flair', 0.5328223705291748, 'nlp', 4), ('allenai/allennlp', 0.5297778248786926, 'nlp', 2), ('neuml/txtai', 0.526243269443512, 'nlp', 3), ('muennighoff/sgpt', 0.5156716704368591, 'llm', 0), ('awslabs/dgl-ke', 0.5072051286697388, 'ml', 1), ('extreme-bert/extreme-bert', 0.5061821341514587, 'llm', 3), ('paddlepaddle/paddlenlp', 0.5057362914085388, 'llm', 1), ('google-research/electra', 0.500442385673523, 'ml-dl', 1)]
| 4
| 1
| null | 0
| 1
| 0
| 72
| 42
| 0
| 23
| 23
| 1
| 0
| 90
| 0
| 24
|
541
|
ml
|
https://github.com/borealisai/advertorch
|
[]
| null |
[]
|
[]
| null | null | null |
borealisai/advertorch
|
advertorch
| 1,243
| 192
| 27
|
Jupyter Notebook
| null |
A Toolbox for Adversarial Robustness Research
|
borealisai
|
2024-01-09
|
2018-11-29
| 269
| 4.608581
|
https://avatars.githubusercontent.com/u/38730800?v=4
|
A Toolbox for Adversarial Robustness Research
|
['adversarial-attacks', 'adversarial-example', 'adversarial-examples', 'adversarial-learning', 'adversarial-machine-learning', 'adversarial-perturbations', 'benchmarking', 'machine-learning', 'pytorch', 'robustness', 'security', 'toolbox']
|
['adversarial-attacks', 'adversarial-example', 'adversarial-examples', 'adversarial-learning', 'adversarial-machine-learning', 'adversarial-perturbations', 'benchmarking', 'machine-learning', 'pytorch', 'robustness', 'security', 'toolbox']
|
2022-05-29
|
[('cleverhans-lab/cleverhans', 0.7116900086402893, 'ml', 3), ('ml-for-high-risk-apps-book/machine-learning-for-high-risk-applications-book', 0.5085124969482422, 'study', 2)]
| 21
| 3
| null | 0
| 0
| 0
| 62
| 20
| 0
| 0
| 0
| 0
| 0
| 90
| 0
| 24
|
447
|
gis
|
https://github.com/residentmario/geoplot
|
[]
| null |
[]
|
[]
| 1
| null | null |
residentmario/geoplot
|
geoplot
| 1,101
| 97
| 35
|
Python
|
https://residentmario.github.io/geoplot/index.html
|
High-level geospatial data visualization library for Python.
|
residentmario
|
2024-01-13
|
2016-06-29
| 395
| 2.781306
| null |
High-level geospatial data visualization library for Python.
|
['geopandas', 'geospatial-data', 'geospatial-visualization', 'matplotlib', 'spatial-analysis']
|
['geopandas', 'geospatial-data', 'geospatial-visualization', 'matplotlib', 'spatial-analysis']
|
2023-07-05
|
[('geopandas/geopandas', 0.7451832890510559, 'gis', 1), ('mwaskom/seaborn', 0.7261803150177002, 'viz', 1), ('gregorhd/mapcompare', 0.7236021161079407, 'gis', 0), ('raphaelquast/eomaps', 0.707546055316925, 'gis', 1), ('holoviz/holoviz', 0.6968002319335938, 'viz', 0), ('holoviz/geoviews', 0.6962321400642395, 'gis', 0), ('opengeos/leafmap', 0.6929628252983093, 'gis', 0), ('contextlab/hypertools', 0.686218798160553, 'ml', 0), ('altair-viz/altair', 0.6802260875701904, 'viz', 0), ('giswqs/geemap', 0.6778126358985901, 'gis', 0), ('man-group/dtale', 0.6714649796485901, 'viz', 0), ('scitools/iris', 0.6692157983779907, 'gis', 0), ('scitools/cartopy', 0.6602010726928711, 'gis', 1), ('holoviz/panel', 0.6484428644180298, 'viz', 1), ('earthlab/earthpy', 0.63930344581604, 'gis', 0), ('artelys/geonetworkx', 0.6383180022239685, 'gis', 0), ('bokeh/bokeh', 0.6318153738975525, 'viz', 0), ('enthought/mayavi', 0.6169453859329224, 'viz', 0), ('holoviz/hvplot', 0.6143922805786133, 'pandas', 0), ('holoviz/spatialpandas', 0.6135755777359009, 'pandas', 1), ('kanaries/pygwalker', 0.6120375394821167, 'pandas', 1), ('matplotlib/matplotlib', 0.6070235967636108, 'viz', 1), ('makepath/xarray-spatial', 0.59319007396698, 'gis', 1), ('has2k1/plotnine', 0.5918059349060059, 'viz', 0), ('pyqtgraph/pyqtgraph', 0.5889087915420532, 'viz', 0), ('plotly/plotly.py', 0.5845940709114075, 'viz', 0), ('pysal/pysal', 0.579075276851654, 'gis', 0), ('pyproj4/pyproj', 0.5774697065353394, 'gis', 0), ('visgl/deck.gl', 0.5753275156021118, 'viz', 0), ('dfki-ric/pytransform3d', 0.5743772983551025, 'math', 1), ('anitagraser/movingpandas', 0.5739251375198364, 'gis', 1), ('pandas-dev/pandas', 0.5717509984970093, 'pandas', 0), ('graphistry/pygraphistry', 0.56211256980896, 'data', 0), ('jakevdp/pythondatasciencehandbook', 0.5592259168624878, 'study', 1), ('cuemacro/chartpy', 0.5584993958473206, 'viz', 1), ('wesm/pydata-book', 0.5581743121147156, 'study', 0), ('lux-org/lux', 0.5569419860839844, 'viz', 0), ('plotly/dash', 0.5490462779998779, 'viz', 0), ('vispy/vispy', 0.5477085709571838, 'viz', 0), ('marcomusy/vedo', 0.5391072034835815, 'viz', 0), ('vaexio/vaex', 0.5356773734092712, 'perf', 0), ('marceloprates/prettymaps', 0.5294914245605469, 'viz', 1), ('maartenbreddels/ipyvolume', 0.5270797610282898, 'jupyter', 0), ('imageio/imageio', 0.525743305683136, 'util', 0), ('pyvista/pyvista', 0.5253320336341858, 'viz', 0), ('nomic-ai/deepscatter', 0.5221992135047913, 'viz', 0), ('opengeos/segment-geospatial', 0.5210736989974976, 'gis', 0), ('osgeo/gdal', 0.5153622627258301, 'gis', 1), ('krzjoa/awesome-python-data-science', 0.5134770274162292, 'study', 0), ('eleutherai/pyfra', 0.506131649017334, 'ml', 0), ('vizzuhq/ipyvizzu', 0.5058284997940063, 'jupyter', 0), ('matplotlib/mplfinance', 0.5038678050041199, 'finance', 1), ('alexmojaki/heartrate', 0.5028942823410034, 'debug', 0), ('mckinsey/vizro', 0.5022686719894409, 'viz', 0), ('federicoceratto/dashing', 0.5019742846488953, 'term', 0), ('uber/h3-py', 0.500649094581604, 'gis', 0)]
| 6
| 2
| null | 0.04
| 1
| 0
| 92
| 6
| 0
| 3
| 3
| 1
| 0
| 90
| 0
| 24
|
1,580
|
data
|
https://github.com/brettkromkamp/contextualise
|
['knowledge-graph']
| null |
[]
|
[]
| null | null | null |
brettkromkamp/contextualise
|
contextualise
| 1,023
| 43
| 26
|
Python
|
https://contextualise.dev/
|
Contextualise is an effective tool particularly suited for organising information-heavy projects and activities consisting of unstructured and widely diverse data and information resources
|
brettkromkamp
|
2024-01-04
|
2019-04-22
| 249
| 4.106078
| null |
Contextualise is an effective tool particularly suited for organising information-heavy projects and activities consisting of unstructured and widely diverse data and information resources
|
['cms-backend', 'commonplace-book', 'content-management-system', 'flask-application', 'knowledge-graph', 'knowledge-management-graph', 'metamodel', 'research-tool', 'semantic-web', 'sqlite-database', 'vuejs']
|
['cms-backend', 'commonplace-book', 'content-management-system', 'flask-application', 'knowledge-graph', 'knowledge-management-graph', 'metamodel', 'research-tool', 'semantic-web', 'sqlite-database', 'vuejs']
|
2023-09-30
|
[('wagtail/wagtail', 0.5667254328727722, 'web', 0), ('indico/indico', 0.5198062658309937, 'web', 0), ('zenodo/zenodo', 0.5124539136886597, 'util', 0), ('airbnb/knowledge-repo', 0.5030013918876648, 'data', 0)]
| 5
| 2
| null | 0.37
| 0
| 0
| 58
| 4
| 0
| 0
| 0
| 0
| 0
| 90
| 0
| 24
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.