doccano-mini

doccano-mini is a few-shot annotation tool to assist the development of applications with Large language models (LLMs). Once you annotate a few text, you can solve your task (e.g. text classification) with LLMs via LangChain.

At this time, the following tasks are supported:

• Text classification
• Question answering
• Summarization
• Paraphrasing
• Named Entity Recognition
• Task Free

Note: This is an experimental project.

Installation

pip install doccano-mini

Usage

For this example, we will be using OpenAI’s APIs, so we need to set the environment variable in the terminal.

export OPENAI_API_KEY="..."

Then, we can run the server.

doccano-mini

Now, we can open the browser and go to http://localhost:8501/ to see the interface.

Step1: Annotate a few text

In this step, we will annotate a few text. We can add a new text by clicking the + button. Try it out by double-clicking on any cell. You'll notice you can edit all cell values.

The editor also supports pasting in tabular data from Google Sheets, Excel, and many other similar tools.

Step2: Test your task

In this step, we will test your task. We can enter a new test to the text box and click the Predict button. Then, we can see the result of the test.

Step3: Download the config

In this step, we will download the LangChain's config. We can click the Download button to download it. After loading the config file, we can predict a label for the new text.

from langchain.chains import load_chain

chain = load_chain("chain.yaml")
chain.run("YOUR TEXT")

Development

poetry install
streamlit run doccano_mini/home.py