Annotation meets Large Language Models.
Project description
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
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