Amitontheweb commited on
Commit
18abbba
1 Parent(s): 0754c2a

Update app.py

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Files changed (1) hide show
  1. app.py +5 -5
app.py CHANGED
@@ -36,7 +36,7 @@ def generate(input_text, number_steps, number_beams, number_beam_groups, diversi
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  top_p=top_p if top_p_flag else None,
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  top_k=top_k if top_k_flag else None,
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  no_repeat_ngram_size = no_repeat_ngram_size,
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- repetition_penalty = repetition_penalty if (repetition_penalty > 0) else None,
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  output_scores=False,
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  do_sample=True
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  )
@@ -58,7 +58,7 @@ def generate(input_text, number_steps, number_beams, number_beam_groups, diversi
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  length_penalty=length_penalty,
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  temperature=temperature if beam_temp_flag else None,
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  no_repeat_ngram_size = no_repeat_ngram_size,
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- repetition_penalty = repetition_penalty if (repetition_penalty > 0) else None,
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  early_stopping = True if early_stop_flag else False,
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  output_scores=False,
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  do_sample=True if beam_temp_flag else False
@@ -94,7 +94,7 @@ def generate(input_text, number_steps, number_beams, number_beam_groups, diversi
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  return_dict_in_generate=False,
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  length_penalty=length_penalty,
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  no_repeat_ngram_size = no_repeat_ngram_size,
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- repetition_penalty = repetition_penalty if (repetition_penalty > 0) else None,
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  early_stopping = True if early_stop_flag else False,
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  output_scores=False,
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  )
@@ -117,7 +117,7 @@ def generate(input_text, number_steps, number_beams, number_beam_groups, diversi
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  penalty_alpha=penalty_alpha,
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  top_k=top_k if top_k_flag else None,
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  no_repeat_ngram_size = no_repeat_ngram_size,
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- repetition_penalty = repetition_penalty if (repetition_penalty > 0) else None,
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  output_scores=False,
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  do_sample=True
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  )
@@ -418,7 +418,7 @@ with gr.Blocks() as demo:
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  ## Strategies:
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- Given some text as input, a decoder-only model hunts for the most popular continuation - whether the continuation makes sense or not - using various search strategies.
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  Example:
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  top_p=top_p if top_p_flag else None,
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  top_k=top_k if top_k_flag else None,
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  no_repeat_ngram_size = no_repeat_ngram_size,
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+ repetition_penalty = float(repetition_penalty) if (repetition_penalty > 0) else None,
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  output_scores=False,
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  do_sample=True
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  )
 
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  length_penalty=length_penalty,
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  temperature=temperature if beam_temp_flag else None,
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  no_repeat_ngram_size = no_repeat_ngram_size,
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+ repetition_penalty = float(repetition_penalty) if (repetition_penalty > 0) else None,
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  early_stopping = True if early_stop_flag else False,
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  output_scores=False,
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  do_sample=True if beam_temp_flag else False
 
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  return_dict_in_generate=False,
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  length_penalty=length_penalty,
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  no_repeat_ngram_size = no_repeat_ngram_size,
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+ repetition_penalty = float(repetition_penalty) if (repetition_penalty > 0) else None,
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  early_stopping = True if early_stop_flag else False,
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  output_scores=False,
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  )
 
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  penalty_alpha=penalty_alpha,
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  top_k=top_k if top_k_flag else None,
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  no_repeat_ngram_size = no_repeat_ngram_size,
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+ repetition_penalty = float(repetition_penalty) if (repetition_penalty > 0) else None,
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  output_scores=False,
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  do_sample=True
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  )
 
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  ## Strategies:
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+ Given some text as input, a decoder-only model hunts for a continuation using various search strategies. (Whether the continuation makes sense or not is for us to determine.)
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  Example:
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