dmarx/dmarx-github-stars huggingface dataset:
Repository: wolfgarbe/SymSpell
Description: SymSpell: 1 million times faster spelling correction & fuzzy search through Symmetric Delete spelling correction algorithm
Language: C#
Topics: approximate-string-matching, chinese-text-segmentation, chinese-word-segmentation, damerau-levenshtein, edit-distance, fuzzy-matching, fuzzy-search, levenshtein, levenshtein-distance, spell-check, spellcheck, spelling, spelling-correction, symspell, text-segmentation, word-segmentation
Homepage: https://seekstorm.com/blog/1000x-spelling-correction/
Repository: bab2min/tomotopy
Description: Python package of Tomoto, the Topic Modeling Tool 
Language: C++
Topics: correlated-topic-model, dirichlet-multinomial-regression, hierarchical-dirichlet-processes, latent-dirichlet-allocation, nlp, pachinko-allocation, python-library, supervised-lda, topic-modeling, topic-models
Homepage: https://bab2min.github.io/tomotopy
Repository: MilaNLProc/contextualized-topic-models
Description: A python package to run contextualized topic modeling. CTMs combine contextualized embeddings (e.g., BERT) with topic models to get coherent topics. Published at EACL and ACL 2021.
Language: Python
Topics: bert, embeddings, multilingual-models, multilingual-topic-models, neural-topic-models, nlp, nlp-library, nlp-machine-learning, text-as-data, topic-coherence, topic-modeling, transformer
Repository: ahmedbesbes/cartoonify
Description: Deploy and scale serverless machine learning app - in 4 steps.
Language: Jupyter Notebook
Topics: aws, aws-lambda, cartoongan, cartoonify, computer-vision, deployment, gan, machine-learning-production, netlify, netlify-deployment, production, pytorch, react, scalable-applications, serverless, serverless-framework, streamlit, style-transfer
Homepage: https://cartoonify.netlify.app/
Repository: py-why/dowhy
Description: DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks. 
Language: Python
Topics: bayesian-networks, causal-inference, causal-machine-learning, causal-models, causality, data-science, do-calculus, graphical-models, machine-learning, python3, treatment-effects
Homepage: https://www.pywhy.org/dowhy