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updated description and Title
Browse files
app.py
CHANGED
@@ -182,13 +182,18 @@ def interface():
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# prompt = template_single.format(tab_name, textbox_prompt)
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with gr.Tab("Linguistic Entities"):
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gr.Markdown("
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# Inputs
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ling_ents_prompt = gr.Textbox(show_label=False, placeholder="Write a prompt and press enter")
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# outputs=[linguistic_entities, ling_ents_prompt, gpt_ling_ents_chatbot])
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with gr.Tab("POS/Chunking"):
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gr.Markdown("
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# Inputs
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task_prompt = gr.Textbox(show_label=False, placeholder="Write a prompt and press enter")
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# outputs=[task, task_prompt, gpt_S3_chatbot])
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with gr.Blocks(theme=gr.themes.Soft()) as demo:
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gr.Markdown("
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# load interface
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interface()
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# prompt = template_single.format(tab_name, textbox_prompt)
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with gr.Tab("Linguistic Entities"):
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gr.Markdown("""
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##π Step-By-Step Instructions
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- Enter a sentence for three models to process (Vicuna-7b, LLaMA-7b and GPT-3.5).
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- Enter your OpenAI Api Key and click on 'Submit Key'.
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- Select a Linguistic Entity from the Dropdown.
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- Click 'Submit' to send your inputs to the models.
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- Scroll to the bottom and click 'Clear' to start again.
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###π€ Now the models will output the linguistic entities found in your prompt based on your selections!
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""")
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# Inputs
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ling_ents_prompt = gr.Textbox(show_label=False, placeholder="Write a prompt and press enter")
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# outputs=[linguistic_entities, ling_ents_prompt, gpt_ling_ents_chatbot])
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with gr.Tab("POS/Chunking"):
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gr.Markdown("""
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##π Step-By-Step Instructions
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- Enter a sentence for three models to process (Vicuna-7b, LLaMA-7b and GPT-3.5).
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- Enter your OpenAI Api Key and click on 'Submit Key'.
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- Select a Task from the Dropdown.
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- Select a Linguistic Entity from the Dropdown.
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- Click 'Submit' to send your inputs to the models.
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- Scroll to the bottom and click 'Clear' to start again.
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### π€ Now the models will output the POS Tagging or Chunking in your prompt with three Strategies based on your selections!
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""")
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# Inputs
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task_prompt = gr.Textbox(show_label=False, placeholder="Write a prompt and press enter")
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# outputs=[task, task_prompt, gpt_S3_chatbot])
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with gr.Blocks(theme=gr.themes.Soft()) as demo:
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gr.Markdown("""
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# Assessing the Articulate
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## A Comparative Analysis of the Core Linguistic Knowledge in Large Language Models
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""")
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# load interface
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interface()
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