remove BetterTransformer for classifier

already integrated into transformers based on error

app.py

@@ -1,38 +1,30 @@
-import re
-import os
 import gc
+import logging
+import os
+import re
 
+import torch
 from cleantext import clean
 import gradio as gr
 from tqdm.auto import tqdm
 from transformers import pipeline
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
+logging.basicConfig(level=logging.INFO)
+logging.info(f"torch version:\t{torch.__version__}")
 
 checker_model_name = "textattack/roberta-base-CoLA"
 corrector_model_name = "pszemraj/flan-t5-large-grammar-synthesis"
 
 # pipelines
-
-
-if os.environ.get("HF_DEMO_NO_USE_ONNX") is None:
-    from optimum.bettertransformer import BetterTransformer
-
-    model_hf = AutoModelForSequenceClassification.from_pretrained(checker_model_name)
-    tokenizer = AutoTokenizer.from_pretrained(checker_model_name)
-    model = BetterTransformer.transform(model_hf, keep_original_model=False)
-
-    checker = pipeline(
-        "text-classification",
-        model=model,
-        tokenizer=tokenizer,
-    )
-else:
-    checker = pipeline(
+checker = pipeline(
         "text-classification",
         checker_model_name,
     )
+checker.model = torch.compile(checker.model)
+
 gc.collect()
+
 if os.environ.get("HF_DEMO_NO_USE_ONNX") is None:
     # load onnx runtime unless HF_DEMO_NO_USE_ONNX is set
     from optimum.pipelines import pipeline
@@ -130,4 +122,4 @@ with gr.Blocks() as demo:
         "- see the [model card](https://huggingface.co/pszemraj/flan-t5-large-grammar-synthesis) for more info"
     )
     gr.Markdown("- if experiencing long wait times, feel free to duplicate the space!")
-demo.launch()
+demo.launch(debug=True)