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Update src/streamlit_app.py
Browse files- src/streamlit_app.py +1 -0
src/streamlit_app.py
CHANGED
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@@ -79,6 +79,7 @@ st.markdown(
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expander = st.expander("**Important notes**")
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expander.write("""
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**How to Use the HR.ai web app:**
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Type or paste your text into the text area below, then press Ctrl + Enter. Click the 'Results' button to extract and tag entities in your text data.
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This HR.ai web app predicts thirty-seven (37) labels: "Job_with_Date", "Email", "Phone_number", "Street_address", "City", "Country", "Date_of_birth", "Marital_status", "Person", "Full_time", "Part_time", "Contract", "Terminated", "Retired", "Job_title", "Date", "Organization", "Role", "Performance_score", "Leave_of_absence", "Retirement_plan", "Bonus", "Stock_options", "Health_insurance", "Pay_rate", "Annual_salary", "Tax", "Deductions", "Interview_type", "Applicant", "Referral", "Job_board", "Recruiter", "Offer_letter", "Agreement", "Certification", "Skill"
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expander = st.expander("**Important notes**")
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expander.write("""
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**How to Use the HR.ai web app:**
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+
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Type or paste your text into the text area below, then press Ctrl + Enter. Click the 'Results' button to extract and tag entities in your text data.
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This HR.ai web app predicts thirty-seven (37) labels: "Job_with_Date", "Email", "Phone_number", "Street_address", "City", "Country", "Date_of_birth", "Marital_status", "Person", "Full_time", "Part_time", "Contract", "Terminated", "Retired", "Job_title", "Date", "Organization", "Role", "Performance_score", "Leave_of_absence", "Retirement_plan", "Bonus", "Stock_options", "Health_insurance", "Pay_rate", "Annual_salary", "Tax", "Deductions", "Interview_type", "Applicant", "Referral", "Job_board", "Recruiter", "Offer_letter", "Agreement", "Certification", "Skill"
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