• Documentation: https://labeltext.readthedocs.io/en/latest/

After installing labeltext,

1. create a TextAnnotation object (or restore from earlier annotation session),
2. start annotating by calling the .annotate() method.

pip install labeltext

task = TextAnnotation(
    records=["Albert Einstein", "Stephen King", "Marie Curie"],
    labels=["male", "female"],
    output="scientists.csv"
)
print(task)

• records: List of text records to be annotated
• labels: List of class labels (up to 16)
• output: The CSV file where annotations will be saved (default: annotations.csv)

It'll probably be more natural to read the records from a (csv) file somewhere.

import pandas as pd
df = pd.read_csv("example.csv")

task = TextAnnotation(
    records=list(df.text.values), # `text` is a column in df
    labels=["male", "female"],
    output="scientists.csv"
)
print(task)

task.annotate(user_name="@dataBiryani", update_freq=2)

This function starts an interactive annotation session.

• user_name (optional): A project may have multiple annotators. If not provided, the user will be asked for a user_name
• update_freq (optional): New annotations are not immediately saved to disk. They are saved once every update_freq annotations (default 5), or if the user ends the annotation session, or if no records are left to annotate.

Note: The output of the annotation session will be written to a csv file that you can feed into your modeling pipeline. The current state of annotation will also be saved in a pickle file (with the same filename as the csv file, but with .pkl extension). You can use the .pkl file to continue annotation in future sessions.

task = TextAnnotation()("annotations.pkl")
task.annotate(user_name="@dataBiryani")