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8821400
Update QuestionAnswering.py
Browse files- QuestionAnswering.py +5 -5
QuestionAnswering.py
CHANGED
@@ -3,7 +3,7 @@ import streamlit as st
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import tensorflow as tf
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from transformers import ElectraTokenizerFast, TFElectraForQuestionAnswering
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model_hf =
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tokenizer = ElectraTokenizerFast.from_pretrained(model_hf)
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reload_model = TFElectraForQuestionAnswering.from_pretrained(model_hf)
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@@ -27,7 +27,7 @@ def main():
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col1, col2 = st.columns([2, 1])
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col1.title("Question Answering")
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col2.link_button("Explore my model",
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question = st.text_area(
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"QUESTION: Please enter a question:",
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@@ -42,16 +42,16 @@ def main():
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prediction = ""
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upload_file = st.file_uploader("CONTEXT: Or upload a file with some
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if upload_file is not None:
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for line in text.splitlines():
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line = line.strip()
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if not line:
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continue
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prediction = predict(
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st.success(line + "\n\n" + prediction)
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import tensorflow as tf
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from transformers import ElectraTokenizerFast, TFElectraForQuestionAnswering
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model_hf = "nguyennghia0902/bestfailed_electra-small-discriminator_5e-05_16"
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tokenizer = ElectraTokenizerFast.from_pretrained(model_hf)
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reload_model = TFElectraForQuestionAnswering.from_pretrained(model_hf)
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col1, col2 = st.columns([2, 1])
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col1.title("Question Answering")
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col2.link_button("Explore my model", "https://huggingface.co/"+model_hf)
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question = st.text_area(
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"QUESTION: Please enter a question:",
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prediction = ""
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upload_file = st.file_uploader("CONTEXT: Or upload a file with some questions", type=["txt"])
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if upload_file is not None:
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question = upload_file.read().decode("utf-8")
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for line in text.splitlines():
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line = line.strip()
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if not line:
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continue
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prediction = predict(line, text)
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st.success(line + "\n\n" + prediction)
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