Happytransformer Inference on dataset


I am running T5-base-grammar-correction for grammer correction on my dataframe with text column

from happytransformer import HappyTextToText
from happytransformer import TTSettings
from tqdm.notebook import tqdm

happy_tt = HappyTextToText("T5",  "./t5-base-grammar-correction")
beam_settings =  TTSettings(num_beams=5, min_length=1, max_length=30)
def grammer_pipeline(text):
    text = "gec: " + text
    result = happy_tt.generate_text(text, args=beam_settings)
    return result.text

df['new_text'] =  df['original_text'].progress_apply(grammer_pipeline)

Pandas apply function, though runs and provides required results, but runs quite slow .

Also I get the below warning while executing the code

/home/.local/lib/python3.6/site-packages/transformers/pipelines/base.py:908: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset

How to use Dataset to speed up things?