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  1. app.py +68 -0
  2. requirements.txt +1 -0
app.py ADDED
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+ import gradio as gr
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+
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+ default_question = """
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+ We're going to use the <a href="https://huggingface.co/datasets/wikitext"><code>wikitext (link)</a></code> dataset with the <code><a href="https://huggingface.co/bert-base-cased?">bert-base-cased (link)</a></code> model checkpoint.
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+
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+ <br/><br/>
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+
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+ Start by loading the <code>wikitext-2-raw-v1</code> version of that dataset, and take the 11th example (index 10) of the <code>train</code> split.<br/>
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+ We'll tokenize this using the appropriate tokenizer, and we'll mask the sixth token (index 5) the sequence.
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+
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+ <br/><br/>
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+
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+ When using the <code>bert-base-cased</code> checkpoint to unmask that token, what is the most probable prediction?
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+ """
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+
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+ internships = {
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+ 'Accelerate': default_question,
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+ 'Diffusion distillation': default_question,
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+ 'Skops & Scikit-Learn': default_question,
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+ "Code Generation": default_question,
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+ "Document AI Democratization": default_question,
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+ "Evaluate": default_question,
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+ "ASR": default_question,
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+ "Efficient video pretraining": default_question,
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+ "Embodied AI": default_question,
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+ "Emergence of scene and text understanding": default_question,
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+ "Everything is multimodal": default_question,
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+ "Everything is vision": default_question,
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+ "Retrieval augmentation as prompting": default_question,
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+ "Social impact evaluations": default_question,
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+ "Toolkit for detecting distribution shift": default_question,
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+ "AI Art Tooling Residency": default_question,
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+ "Gradio as an ecosystem": default_question,
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+ }
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+
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+
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+ with gr.Blocks() as demo:
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+ gr.Markdown(
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+ """
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+ # Internship introduction
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+ Please select the internship you would like to apply to and answer the question asked in the Answer box.
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+ """
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+ )
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+
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+ internship_choice = gr.Dropdown(label='Internship', choices=list(internships.keys()))
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+
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+ with gr.Column(visible=False) as details_col:
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+ summary = gr.HTML(label='Question')
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+ details = gr.Textbox(label="Answer")
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+ username = gr.Textbox(label="Hugging Face Username")
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+ generate_btn = gr.Button("Submit")
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+ output = gr.Label()
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+
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+ def filter_species(species):
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+ return gr.Label.update(
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+ internships[species]
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+ ), gr.update(visible=True)
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+
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+ internship_choice.change(filter_species, internship_choice, [summary, details_col])
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+
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+ def on_click(_details, _username):
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+ return f"Submitted: '{_details}' for user '{_username}'"
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+
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+ generate_btn.click(on_click, inputs=[details, username], outputs=[output])
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+
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+
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+ if __name__ == "__main__":
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+ demo.launch()
requirements.txt ADDED
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+ gradio