Tweepy VS doccano

How is this much different than what tweepy does?

There are plenty of open source modules you can use, such as tweepy, which will make it a lot easier for you.

I chose Python as a language to handle the streaming, because a) it’s the language I’m strongest in, and b) because of tweepy.

· Tweepy. An easy-to-use Python library for accessing the Twitter API.

As announced in this blog post, the Twitter API v2 supports the ability to get metadata about edited Tweets. In this short guide, I will showcase how developers and researchers can get information about edited Tweets from the Twitter API v2 in Python using Tweepy.

In this tutorial, we will configure AWS CodePipeline to build an ECR image and deploy the latest version to lambda container. The application code will stream tweets using Tweepy, a Python library for accessing the Twitter API. First we need to setup CodePipeline and the various stages to deploy application code to lambda image which will stream tweets when invoked. The intended devops architecture is as below.

That means you need a requirements.txt file in your repo somewhere, and it needs to list the dependencies for your project. A good way of knowing what the dependencies are is to check what you imported at the beginning of your python file. For my bots, I need Tweepy, config, and Python-dotenv Requirements.txt looks like this for Divas, my Twitter Bot Collection.

you can easily make a simple script with tweepy that scrapes the data and puts it into whatever file you need

In order to use the Twitter API v2, you need to apply for a Twitter developer account. Once you have an approved Twitter developer account, follow the instruction here to obtain your BEARER TOKEN that you will use to connect to the Twitter API v2 in you code in Python. We will be using the Tweepy package in Python to get images from the Twitter API v2, so you will need to have Python as well as the Tweepy package installed on your machine. Instructions on installing the Tweepy package can be found in this tutorial.

Huh?

I wrote my own system for classifying a stream of texts in Python, I might Open Source it one of these days but I have to get it to the point where it is modular enough that I can customize it to do the particular things I want without subjecting people to my whims... I use it every day and I'm not afraid to demo it because it is rock solid.

My understanding is that my system would not be hard to adapt to work on images for certain kinds of tasks.

Pytorch is open source, Huggingface is open source. CUDA isn't. This is

https://labelstud.io/

and for annotating text spans there are so many open source tools

https://github.com/doccano/doccano

I worked for a company a few years back that built annotation tools for projects we sold to customers but never quite got to a polished general purpose annotator. Today there are an overwhelming number of companies in this space and products I never heard of, many of which are cloud based or paid. Looks like a gold rush to me.

I followed the guide to have doccano https://github.com/doccano/doccano setup in a notebook server in Kubeflow. It is running and the django connection is established, but it fails to connect with my localmachine so when I try to open the link, it does not respond. Is there a way to connect apps running on remove notebook servers to local in Kubeflow?

Why not just use the docker instructions on the GitHub page? https://github.com/doccano/doccano

doccano is built on a Django backend though I believe it uses extensive front end code for the annotation UI.

I worked at a place where we developed a system for doing this kind of tagging but it was for making training sets and there was no expectation that you could export the document from the system for normal use.

Quite a few NLP annotation systems are out there

https://prodi.gy/

https://brat.nlplab.org/

https://doccano.herokuapp.com/