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Peter
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Commit
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84b8f8b
1
Parent(s):
151824c
add onnx support
Browse filesSigned-off-by: Peter <74869040+pszemraj@users.noreply.github.com>
- app.py +39 -8
- requirements.txt +3 -1
app.py
CHANGED
@@ -1,13 +1,31 @@
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import re
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from cleantext import clean
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import gradio as gr
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from tqdm.auto import tqdm
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from transformers import pipeline
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# pipelines
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checker = pipeline(
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-
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def split_text(text: str) -> list:
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# Split the text into sentences using regex
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@@ -31,6 +49,7 @@ def split_text(text: str) -> list:
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return sentence_batches
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def correct_text(text: str, checker, corrector, separator: str = " ") -> str:
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# Split the text into sentence batches
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sentence_batches = split_text(text)
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@@ -63,23 +82,35 @@ def correct_text(text: str, checker, corrector, separator: str = " ") -> str:
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return corrected_text
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def update(text: str):
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text = clean(text[:4000], lower=False)
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return correct_text(text, checker, corrector)
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with gr.Blocks() as demo:
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gr.Markdown("# <center>Robust Grammar Correction with FLAN-T5</center>")
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gr.Markdown(
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-
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- `textattack/roberta-base-CoLA` for grammar quality detection
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- `pszemraj/flan-t5-large-grammar-synthesis` for grammar correction
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"""
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with gr.Row():
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inp = gr.Textbox(
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out = gr.Textbox(label="output", interactive=False)
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btn = gr.Button("Process")
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btn.click(fn=update, inputs=inp, outputs=out)
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gr.Markdown("---")
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gr.Markdown(
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gr.Markdown("- if experiencing long wait times, feel free to duplicate the space!")
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demo.launch()
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import re
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import os
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from cleantext import clean
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import gradio as gr
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from tqdm.auto import tqdm
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from transformers import pipeline
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checker_model_name = "textattack/roberta-base-CoLA"
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corrector_model_name = "pszemraj/flan-t5-large-grammar-synthesis"
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# pipelines
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checker = pipeline(
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"text-classification",
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checker_model_name,
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)
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if os.environ.get("HF_DEMO_NO_USE_ONNX") is None:
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# load onnx runtime unless HF_DEMO_NO_USE_ONNX is set
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from optimum.pipelines import pipeline
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corrector = pipeline(
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"text2text-generation", model=corrector_model_name, accelerator="ort"
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)
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else:
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corrector = pipeline("text2text-generation", corrector_model_name)
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def split_text(text: str) -> list:
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# Split the text into sentences using regex
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return sentence_batches
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def correct_text(text: str, checker, corrector, separator: str = " ") -> str:
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# Split the text into sentence batches
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sentence_batches = split_text(text)
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return corrected_text
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def update(text: str):
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text = clean(text[:4000], lower=False)
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return correct_text(text, checker, corrector)
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with gr.Blocks() as demo:
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gr.Markdown("# <center>Robust Grammar Correction with FLAN-T5</center>")
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gr.Markdown(
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"**Instructions:** Enter the text you want to correct in the textbox below (_text will be truncated to 4000 characters_). Click 'Process' to run."
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)
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gr.Markdown(
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"""Models:
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- `textattack/roberta-base-CoLA` for grammar quality detection
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- `pszemraj/flan-t5-large-grammar-synthesis` for grammar correction
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"""
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)
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with gr.Row():
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inp = gr.Textbox(
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label="input",
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placeholder="PUT TEXT TO CHECK & CORRECT BROSKI",
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value="I wen to the store yesturday to bye some food. I needd milk, bread, and a few otter things. The store was really crowed and I had a hard time finding everyting I needed. I finaly made it to the check out line and payed for my stuff.",
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)
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out = gr.Textbox(label="output", interactive=False)
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btn = gr.Button("Process")
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btn.click(fn=update, inputs=inp, outputs=out)
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gr.Markdown("---")
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gr.Markdown(
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"- see the [model card](https://huggingface.co/pszemraj/flan-t5-large-grammar-synthesis) for more info"
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)
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gr.Markdown("- if experiencing long wait times, feel free to duplicate the space!")
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demo.launch()
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requirements.txt
CHANGED
@@ -2,4 +2,6 @@ transformers
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gradio
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tqdm
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torch
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clean-text
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gradio
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tqdm
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torch
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clean-text
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accelerate
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optimum[onnxruntime]
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