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import gradio as gr | |
from util import summarizer, textproc | |
from util.examples import entries | |
model_collection = summarizer.init_models() | |
patent_summarizer = summarizer.PatentSummarizer(model_collection) | |
iface = gr.Interface( | |
patent_summarizer.pipeline, | |
theme="huggingface", | |
examples=entries, | |
examples_per_page=4, | |
inputs=[ | |
gr.inputs.Textbox(label="Patent Information", | |
placeholder="US10125002B2 or https://patents.google.com/patent/US10125002B2 ...", | |
lines=1, | |
default="US10125002B2"), | |
gr.inputs.CheckboxGroup(summarizer.summary_options, | |
default=summarizer.summary_options, | |
label="Summaries to Generate"), | |
gr.inputs.Dropdown(summarizer.model_names, | |
default=summarizer.model_names[3], | |
label="Abstract Model"), | |
gr.inputs.Dropdown(summarizer.model_names, | |
default=summarizer.model_names[0], | |
label="Background Model"), | |
gr.inputs.Dropdown(summarizer.model_names, | |
default=summarizer.model_names[2], | |
label="Claims Model"), | |
gr.inputs.Checkbox(default=True, | |
label="Collate Claims", | |
optional=False), | |
gr.inputs.Slider(minimum=250, | |
maximum=1000, | |
step=10, | |
default=250, | |
label="Input Document Word Limit"), | |
], | |
outputs=[ | |
gr.outputs.Textbox(label="Abstract Summary"), | |
gr.outputs.Textbox(label="Background Summary"), | |
gr.outputs.Textbox(label="Sample Claims Summary") | |
], | |
title="Patent Summarizer π", | |
description=""" | |
v.1.0.0 | |
Reading through patent documents is oftentimes a long and tedious task. | |
There are cases wherein one has to manually go through several pages in | |
order to determine if the patent is relevant to the prior art search. | |
This application is meant to automate the initial phase of going through | |
patents so that the potential inventor or researcher may lessen the time | |
spent trying to filter documents. | |
The Patent Summarizer: | |
βοΈ Provides an interface for user input | |
π Retrieves and parses the document from Patents Google based on the given patent information | |
π Returns summaries for the abstract, background, and/or claims. | |
Models explored: | |
π€ Big Bird: Transformers for Longer Sequences | |
https://arxiv.org/abs/2007.14062 , https://huggingface.co/google/bigbird-pegasus-large-bigpatent | |
π€ T5 | |
https://arxiv.org/pdf/1910.10683.pdf , https://huggingface.co/cnicu/t5-small-booksum | |
π€ BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension | |
https://arxiv.org/abs/1910.13461, https://huggingface.co/sshleifer/distilbart-cnn-6-6 | |
π€ PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization | |
https://arxiv.org/abs/1912.08777 , https://huggingface.co/google/pegasus-xsum | |
""" | |
) | |
iface.launch() |