Coreference is amazing but the data required for training a model is very scarce. In our case, the available training for non-English languages also proved to be poorly annotated. Crosslingual Coreference, therefore, uses the assumption a trained model with English data and cross-lingual embeddings should work for languages with similar sentence structures. pip install crosslingual-coreference

Simple Python wrapper kernel for Crystal language. ICrystal is the widely used Jupyter kernel for Crystal, which uses ICR. On the other hand, this crystal_kernel uses the official Crystal interpreter. Forked from bash_kernel installation Make sure the Crystal’s interpreter starts with crystal i. Then type the following commands. pip install crystal_kernel python -m crystal_kernel.install Development Something is better than nothing. GitHub View Github    

A functional, easy to use Python wrapper of Notion Api. pip install notion-potion Quick Start Authentication from potion import Request, NotionHeader token = ” nh = NotionHeader(authorization=token) req = Request(nh.headers) Create an integration follow this official tutorial to get token. A ‘retrieve’ example. (Full code can be found here) from potion.api import * print(req.get(

ROS2 wrapper package of VOICEROID2 Windows Only Installation Install VOICEROID2 (x64). Install Python 3.4 (x64) or later. Install ROS2 foxy or later. pip install simpleaudio pip install git+https://github.com/Nkyoku/pyvcroid2.git Clone this repository. Build this repository.colcon build –merge-install –packages-select voiceroid2 Usage Source this package../install/local_setup.ps1 Run the publisher node.ros2 run voiceroid2 talker There are several parameters. language : stringName of the language library.If language is not specified, default value will be used. voice : stringName of the voice library.If voice is not specified, […]

Gensim 4.x and sklearn Introduction General informations Methods Description GridSearch and BayesSearch Usage BayesSearch Example Prod2Vec or Item2Vec produces embedding for items in a latent space. The method is capable of inferring item-item relations even when user information is not available. It’s based on NLP model Word2Vec. Click here to know more This project provide a class that encapsulates Item2Vec model (word2vec gensim model) as a sklearn estimator. It allows the simple and efficient use of the Item2Vec model by […]

Visrl (pronounced “visceral”) is a simple wrapper to analyse and visualise reinforcement learning agents’ behaviour in the environment. Reinforcement learning requires a lot of overhead code to inspect an agent’s behaviour visually, typically through env.render(). Visrl allows users to easily intervene and switch between agent control and human control, and allows inserting a breakpoint in the game state to pause only at a relevant state of interest. Features Set action hotkeys Human intervention: Take actions 1 step at a time […]

This is a simple wrapper of Javascript engines, it wraps the Javascriptinterpreter for Python use. There are two ways to call interpreters, via dynamic library loading is internalcall which is faster than the other one, via subprocess is external call. System’s built-in Javascript interpreter: macOS: JavascriptCoreLinux: Gjs on Gnome, CJS on Cinnamon, etc.Windows: Chakra (internal call, but not applicable to Windows 7) Two Python bindings (Recommend, internal call): PyChakra,QuickJS Any installed external Javascript interpreters, e.g. SpiderMonkey, Node.js, QuickJS, etc. JSEngine […]

from python_flutterwave import payment payment.token = ‘YOUR_SECRET_KEY’ uri = payment.initiate_payment(tx_ref=”qwerty”, amount=100, redirect_url=’your_callback_url’, payment_options=’mpesa’, customer_email='[email protected]’, customer_phone_number=’0123456789′, currency=’KES’, customer_name=’John Doe’, title=’Demo Payment’, description=’Just pay me…’) print(uri) details = payment.get_payment_details(transaction_id)