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.
machine-learning
data-science
causal-inference
causality
treatment-effects
bayesian-networks
causal-machine-learning
causal-models
do-calculus
graphical-models
python3
Обновлено 2024-11-21 01:27:34 +03:00
Azure Functions Python SDK
Обновлено 2024-11-20 01:54:25 +03:00
Automated static analysis & linting bot for Mozilla repositories
Обновлено 2024-11-19 19:34:39 +03:00
This tool helps automatic generation of grammatically valid synthetic Code-mixed data by utilizing linguistic theories such as Equivalence Constant Theory and Matrix Language Theory.
natural-language-processing
python3
code-switching
linguistics
synthetic-data-generation
code-mixing
data-generation
language-modeling
Обновлено 2024-07-31 00:01:52 +03:00
This code provides word level language identification tool for identifying language for individual words in Code-Mixed text. e.g. The text that includes words from two languages such as Hindi written in roman script, mixed with English.
natural-language-processing
python3
code-mixing
code-switching
language-identification
language-tags
linguistics
mallet
Обновлено 2020-08-12 02:05:32 +03:00