Use of pytorch dataset for model inference- GPU

Question

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
tqdm.pandas()

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
  UserWarning,

I have access to GPU. Can somebody provide some pointers to speed up the execution and utilising full capabilities of GPU

--------------------------------EDIT---------------------------------

I tried using pytorch Dataset in the below way, but still the processing is slow:

class CustomD(Dataset):
    
    def __init__(self, text):
        self.text = text
        self.len = text.shape[0]
        
    def __len__(self):
        return self.len
    
    def __getitem__(self, idx):
        text = self.text[idx]
        text = "gec: " + text
        result = happy_tt.generate_text(text, args=beam_settings)
        return result.text 

TD = GramData(df.original_text)
final_data = DataLoader(dataset=TD,
                          batch_size=10,
                          shuffle=False
                         )
import itertools
list_modified=[]
for (idx, batch) in enumerate(final_data):
    list_modified.append(batch)



flat_list = [item for sublist in list_modified for item in sublist]
df["new_text"]=flat_list