Creating this repository because I have the feeling GPT3.5 with constrained generation, and some calibration with conformal prediction could do very well on topic classification.
Either way, it's generic so you can use whatever topic classification you want.
I called it topcon^2 because it's 11pm, I'm tired, and lacking imagination apparently.
Install with poetry:
pip install poetry
poetry installRequires calibrating via a calibration set before prediction. There is a huggingface datasets class-method for ease of use. Below is an example.
from topcon.predict import topic_proba
from topcon.calibration import ConformalPredictor
topic_conformer = ConformalPredictor.from_hf_datasets(
hf_repo_name='yahoo_answers_topics',
topic_column='topic',
text_columns=['question_title', 'question_content'],
topic_proba=topic_proba, #the topic classification function
calibration_size=1000,
save_path='save.pkl',
)You can then predict on new text:
topic_conformer.get_prediction_sets(
text='I think football is really amazing because man kick ball good',
)Can also load a previously calibrated model:
topic_conformer = ConformalPredictor.load('save.pkl')- Add Tests
- Perform extensive evaluation
- Get a job at DeepMind