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import streamlit as st |
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from st_pages import Page, show_pages |
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st.set_page_config(page_title="Question Answering", page_icon="🏠") |
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show_pages( |
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[ |
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Page("app.py", "Home", "🏠"), |
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Page( |
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"SampleQA.py", "Sample in Dataset", "📝" |
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), |
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Page( |
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"QuestionAnswering.py", "Question Answering", "📝" |
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), |
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] |
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) |
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st.title("Project in Text Mining and Application") |
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st.header("Question Answering use a pre-trained model - ELECTRA") |
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st.markdown( |
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""" |
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**Team members:** |
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| Student ID | Full Name | Email | |
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| ---------- | ------------------------ | ------------------------------ | |
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| 1712603 | Lê Quang Nam | 1712603@student.hcmus.edu.vn | |
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| 19120582 | Lê Nhựt Minh | 19120582@student.hcmus.edu.vn | |
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| 19120600 | Bùi Nguyên Nghĩa | 19120600@student.hcmus.edu.vn | |
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| 21120198 | Nguyễn Thị Lan Anh | 21120198@student.hcmus.edu.vn | |
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""" |
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) |
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st.header("The Need for Question Answering") |
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st.markdown( |
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""" |
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In the rapidly advancing field of Natural Language Processing (NLP), the Question Answering (QA) |
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task has become increasingly essential. QA systems are pivotal for efficient information retrieval, |
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enabling users to obtain precise answers to their queries quickly. This is particularly valuable in |
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domains such as customer service, education, and healthcare, where timely and accurate information |
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is crucial. |
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""" |
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) |
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st.header("Technology used") |
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st.markdown( |
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""" |
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The ELECTRA model, specifically the "google/electra-small-discriminator" used here, |
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is a deep learning model in the field of natural language processing (NLP) developed |
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by Google. This model is an intelligent variation of the supervised learning model |
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based on the Transformer architecture, designed to understand and process natural language efficiently. |
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For this Question Answering task, we choose two related classes: ElectraTokenizerFast and |
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TFElectraForQuestionAnswering to implement. |
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""" |
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) |