Peter
remove re-showing input, increase max words
7a2e137
raw
history blame
5.62 kB
import logging
import re
from pathlib import Path
import time
import gradio as gr
import nltk
from cleantext import clean
from summarize import load_model_and_tokenizer, summarize_via_tokenbatches
from utils import load_examples, truncate_word_count
_here = Path(__file__).parent
nltk.download("stopwords") # TODO=find where this requirement originates from
import transformers
transformers.logging.set_verbosity_error()
logging.basicConfig()
def proc_submission(
input_text: str,
model_size: str,
num_beams,
token_batch_length,
length_penalty,
repetition_penalty,
no_repeat_ngram_size,
max_input_length: int = 768,
):
"""
proc_submission - a helper function for the gradio module
Parameters
----------
input_text : str, required, the text to be processed
max_input_length : int, optional, the maximum length of the input text, default=512
Returns
-------
str of HTML, the interactive HTML form for the model
"""
settings = {
"length_penalty": length_penalty,
"repetition_penalty": repetition_penalty,
"no_repeat_ngram_size": no_repeat_ngram_size,
"encoder_no_repeat_ngram_size": 4,
"num_beams": num_beams,
"min_length": 4,
"max_length": int(token_batch_length // 4),
"early_stopping": True,
"do_sample": False,
}
st = time.perf_counter()
history = {}
clean_text = clean(input_text, lower=False)
max_input_length = 1024 if model_size == "base" else max_input_length
processed = truncate_word_count(clean_text, max_input_length)
if processed["was_truncated"]:
tr_in = processed["truncated_text"]
msg = f"Input text was truncated to {max_input_length} words (based on whitespace)"
logging.warning(msg)
history["WARNING"] = msg
else:
tr_in = input_text
_summaries = summarize_via_tokenbatches(
tr_in,
model_sm if model_size == "base" else model,
tokenizer_sm if model_size == "base" else tokenizer,
batch_length=token_batch_length,
**settings,
)
sum_text = [f"Section {i}: " + s["summary"][0] for i, s in enumerate(_summaries)]
sum_scores = [
f"\n - Section {i}: {round(s['summary_score'],4)}"
for i, s in enumerate(_summaries)
]
history["Summary Text"] = "<br>".join(sum_text)
history["Summary Scores"] = "The summary scores can be thought of as representing the quality of the summary. less-negative numbers (closer to 0) are better.<br><br>"
history["Summary Scores"] += "\n".join(sum_scores)
html = ""
rt = round((time.perf_counter() - st) / 60, 2)
print(f"Runtime: {rt} minutes")
html += f"<p>Runtime: {rt} minutes on CPU</p>"
for name, item in history.items():
html += (
f"<h2>{name}:</h2><hr><b>{item}</b><br><br>"
if "summary" not in name.lower()
else f"<h2>{name}:</h2><hr>{item}<br><br>"
)
html += ""
return html
if __name__ == "__main__":
model, tokenizer = load_model_and_tokenizer("pszemraj/led-large-book-summary")
model_sm, tokenizer_sm = load_model_and_tokenizer("pszemraj/led-base-book-summary")
title = "Long-Form Summarization: LED & BookSum"
description = "A simple demo of how to use a fine-tuned LED model to summarize long-form text. [This model](https://huggingface.co/pszemraj/led-large-book-summary) is a fine-tuned version of [allenai/led-large-16384](https://huggingface.co/allenai/led-large-16384) on the [BookSum dataset](https://arxiv.org/abs/2105.08209).The goal was to create a model that can generalize well and is useful in summarizing lots of text in academic and daily usage. See [model card](https://huggingface.co/pszemraj/led-large-book-summary) for a notebook with GPU inference (much faster) on Colab."
gr.Interface(
proc_submission,
inputs=[
gr.inputs.Textbox(
lines=10,
label="input text",
placeholder="Enter text to summarize, the text will be cleaned and truncated on Spaces. Narrative, academic (both papers and lecture transcription), and article text work well. May take a bit to generate depending on the input text :)",
),
gr.inputs.Radio(
choices=["base", "large"], label="model size", default="large"
),
gr.inputs.Slider(
minimum=2, maximum=4, label="num_beams", default=2, step=1
),
gr.inputs.Slider(
minimum=512,
maximum=1024,
label="token_batch_length",
default=512,
step=256,
),
gr.inputs.Slider(
minimum=0.5, maximum=1.1, label="length_penalty", default=0.7, step=0.05
),
gr.inputs.Slider(
minimum=1.0,
maximum=5.0,
label="repetition_penalty",
default=3.5,
step=0.1,
),
gr.inputs.Slider(
minimum=2, maximum=4, label="no_repeat_ngram_size", default=3, step=1
),
],
outputs="html",
examples_per_page=2,
title=title,
description=description,
article="The model can be used with tag [pszemraj/led-large-book-summary](https://huggingface.co/pszemraj/led-large-book-summary). See the model card for details on usage & a notebook for a tutorial.",
examples=load_examples(_here / "examples"),
cache_examples=True,
).launch()