Update app.py
Browse files
app.py
CHANGED
@@ -12,7 +12,10 @@ from interpret import InterpretationPrompt
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MAX_PROMPT_TOKENS = 30
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## info
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dataset_info = [{'name': 'Commonsense', 'hf_repo': 'tau/commonsense_qa', 'text_col': 'question'}
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@@ -198,13 +201,15 @@ with gr.Blocks(theme=gr.themes.Default(), css=css) as demo:
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interpretation_prompt = gr.Text(suggested_interpretation_prompts[0], label='Interpretation Prompt')
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gr.Markdown('''
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Here are some examples of prompts we can analyze their internal representations
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''')
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for info in dataset_info:
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with gr.Tab(info['name']):
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num_examples = 10
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dataset = load_dataset(info['hf_repo'], split='train', streaming=True)
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dataset = dataset.shuffle(buffer_size=2000).take(num_examples)
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dataset = [[row[info['text_col']]] for row in dataset]
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gr.Examples(dataset, [original_prompt_raw])
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MAX_PROMPT_TOKENS = 30
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## info
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dataset_info = [{'name': 'Commonsense', 'hf_repo': 'tau/commonsense_qa', 'text_col': 'question'},
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{'name': 'Fact Recall', 'hf_repo': 'azhx/counterfact-filtered-gptj6b', 'text_col': 'subject+predicate',
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'filter': lambda x: x['label'] == 1},
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]
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interpretation_prompt = gr.Text(suggested_interpretation_prompts[0], label='Interpretation Prompt')
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gr.Markdown('''
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Here are some examples of prompts we can analyze their internal representations:
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''')
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for info in dataset_info:
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with gr.Tab(info['name']):
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num_examples = 10
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dataset = load_dataset(info['hf_repo'], split='train', streaming=True)
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if 'filter' in info:
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dataset = dataset.filter(info['filter'])
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dataset = dataset.shuffle(buffer_size=2000).take(num_examples)
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dataset = [[row[info['text_col']]] for row in dataset]
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gr.Examples(dataset, [original_prompt_raw])
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