semvec/pypi-packages huggingface dataset:
antannotator 0.0.3
JupyterLab tool for data annotations for machine learning routines
Requires Python: >=3.7

# AntAnnotator

JupyterLab tool for data annotations for machine learning routines.

Just drop your python data into widged and label it.

![AnnotationWidget.png](./docs/AnnotationWidget.png)

# Features

**Data persistence** Data is automatically persisted on disk in .json format.

**UI** Annotation UI witch allow to do annotation

# Usage example

```
from antannotator.annotation_controller import AnnotationController
from antannotator.persistence import AutoEventStorage, AutoAnnotationTaskStorage, AnnotationSample

# Select directory where annnotation task data will be stored

event_storage = AutoEventStorage.get_event_storage("/tmp/annotator/events")
task_storage = AutoAnnotationTaskStorage.get_task_storage("/tmp/annotator/tasks")
controller = AnnotationController(task_storage=task_storage, event_storage=event_storage)

# 
options = {"literature": "Classical literature",
            "songs": "20th Century Songs", 
            "other":"Other"}


# load samples to annotate
samples = [ AnnotationSample(sample_id=f"id_1", 
            task_data=f"We passed upon the stair\nWe spoke of was and when\nAlthough I wasn't there\nHe said I was his friend", 
            available_options=options, 
            hidden_info=None,
            visible_info=None),
        AnnotationSample(sample_id=f"id_2", 
            task_data=f"It was many and many a year ago,\nIn a kingdom by the sea", 
            available_options=options, 
            hidden_info=None,
            visible_info=None),
        AnnotationSample(sample_id=f"id_3", 
            task_data=f"On a dark desert highway, cool wind in my hair\nWarm smell of colitas, rising up through the air", 
            available_options=options, 
            hidden_info=None,
            visible_info=None)]

controller.append_samples(samples)

# start annotations process
controller.do_annotaions()
```

# Installation 

```
pip install antannotator
```

# Roadmap

- [x] Multichoice text classification  
Multiclass, multichoise text classification


- [ ] One choice text classification  
One choice classification. (Task adavance immediately user celect a option)

- [ ] Free input  
Free user input. (For example for summarization task)