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Update app.py
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app.py
CHANGED
@@ -150,13 +150,13 @@ STYLE = """
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}
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/*Hover-Section*/
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.tree li a:hover, .tree li a:hover+ul li a {
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background: var(--primary-
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}
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.tree li a:hover+ul li::after, .tree li a:hover+ul li::before, .tree li a:hover+ul::before, .tree li a:hover+ul ul::before, .tree li a:hover+ul a::before {
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border-color: var(--primary-
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}
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.end-of-text, .chosen-token {
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background-color: var(--primary-
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}
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.end-of-text {
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width:auto!important;
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@@ -166,7 +166,7 @@ STYLE = """
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min-width: 280px;
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}
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.selected-sequence {
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background-color: var(--secondary-
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}
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"""
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@@ -399,8 +399,8 @@ def get_beam_search_html(input_text, number_steps, number_beams, length_penalty)
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)
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markdown = "The conclusive sequences are the ones that end in an `<|endoftext|>` token or at the end of generation."
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markdown += "\n\nThey are ranked by their scores, as given by the formula `score = cumulative_score / (output_length ** length_penalty)`.\n\n"
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markdown += "Only the top `num_beams` scoring sequences are returned: in the tree they are highlighted in **<span style='color:var(--secondary-
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markdown += " The non-selected sequences are also shown in the tree, highlighted in **<span style='color:var(--primary-
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markdown += "\n#### <span style='color:var(--secondary-600)!important'>Output sequences:</span>"
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# Sequences are padded anyway so you can batch decode them
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decoded_sequences = tokenizer.batch_decode(outputs.sequences)
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@@ -425,11 +425,11 @@ with gr.Blocks(
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css=STYLE,
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) as demo:
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gr.Markdown(
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"""# <span style='color:var(--primary-
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Play with the parameters below to understand how beam search decoding works!
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#### <span style='color:var(--primary-
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- **Sentence to decode from** (`inputs`): the input sequence to your decoder.
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- **Number of steps** (`max_new_tokens`): the number of tokens to generate
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- **Number of beams** (`num_beams`): the number of beams to use
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}
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/*Hover-Section*/
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.tree li a:hover, .tree li a:hover+ul li a {
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background: var(--primary-600);
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}
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.tree li a:hover+ul li::after, .tree li a:hover+ul li::before, .tree li a:hover+ul::before, .tree li a:hover+ul ul::before, .tree li a:hover+ul a::before {
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border-color: var(--primary-400);
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}
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.end-of-text, .chosen-token {
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background-color: var(--primary-500);
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}
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.end-of-text {
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width:auto!important;
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min-width: 280px;
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}
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.selected-sequence {
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background-color: var(--secondary-500)!important;
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}
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"""
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)
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markdown = "The conclusive sequences are the ones that end in an `<|endoftext|>` token or at the end of generation."
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markdown += "\n\nThey are ranked by their scores, as given by the formula `score = cumulative_score / (output_length ** length_penalty)`.\n\n"
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markdown += "Only the top `num_beams` scoring sequences are returned: in the tree they are highlighted in **<span style='color:var(--secondary-500)!important'>blue</span>**."
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markdown += " The non-selected sequences are also shown in the tree, highlighted in **<span style='color:var(--primary-500)!important'>yellow</span>**."
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markdown += "\n#### <span style='color:var(--secondary-600)!important'>Output sequences:</span>"
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# Sequences are padded anyway so you can batch decode them
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decoded_sequences = tokenizer.batch_decode(outputs.sequences)
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css=STYLE,
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) as demo:
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gr.Markdown(
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+
"""# <span style='color:var(--primary-500)!important'>Beam Search Visualizer</span>
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Play with the parameters below to understand how beam search decoding works!
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#### <span style='color:var(--primary-500)!important'>Parameters:</span>
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- **Sentence to decode from** (`inputs`): the input sequence to your decoder.
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- **Number of steps** (`max_new_tokens`): the number of tokens to generate
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- **Number of beams** (`num_beams`): the number of beams to use
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