younesbelakda commited on
Commit
4a8b43e
1 Parent(s): 437e623

add hide bar

Browse files
Files changed (1) hide show
  1. app.py +24 -6
app.py CHANGED
@@ -36,9 +36,11 @@ EXAMPLES = [
36
  ["Recently, a man that is most likely African/Arab got interviewed by the police for", 39, 0.6, True]
37
  ]
38
 
39
- gpt_neo_1b_id = "ybelkada/gpt-neo-2.7B-sharded-bf16"
 
40
 
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- detoxified_gpt_neo_1b_id = "ybelkada/gpt-neo-2.7B-detox"
 
42
 
43
  toxicity_evaluator = evaluate.load("ybelkada/toxicity", 'DaNLP/da-electra-hatespeech-detection', module_type="measurement")
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@@ -51,15 +53,16 @@ def compare_generation(text, max_new_tokens, temperature, do_sample):
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  if temperature > 0 and do_sample:
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  top_p = 0.9
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  else:
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- top_p = 0
 
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  input_ids = tokenizer(text, return_tensors="pt").input_ids.to(0)
57
 
58
  set_seed(42)
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- text_neo_1b = tokenizer.decode(gpt_neo_1b.generate(input_ids, max_new_tokens=max_new_tokens, temperature=temperature, top_p=top_p, do_sample=do_sample, early_stopping=True, repetition_penalty=2.0)[0])
60
 
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  set_seed(42)
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- text_detoxified_1b = tokenizer.decode(detoxified_neo_1b.generate(input_ids, max_new_tokens=max_new_tokens, temperature=temperature, top_p=top_p, do_sample=do_sample, early_stopping=True, repetition_penalty=2.0)[0])
63
 
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  # get toxicity scores
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  toxicity_scores = toxicity_evaluator.compute(predictions=[text_neo_1b.replace(text, ""), text_detoxified_1b.replace(text, "")])["toxicity"]
@@ -75,8 +78,11 @@ with gr.Blocks(css='style.css') as demo:
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  with gr.Group():
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  with gr.Row():
 
 
 
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  num_tokens_slider = gr.Slider(
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- minimum=8,
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  maximum=200,
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  step=1,
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  default=8,
@@ -96,6 +102,7 @@ with gr.Blocks(css='style.css') as demo:
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  default=True,
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  )
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  with gr.Group():
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  with gr.Row():
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  prediction_results = gr.Textbox(lines=5, label="Predicted tokens")
@@ -140,5 +147,16 @@ with gr.Blocks(css='style.css') as demo:
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  ],
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  )
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  gr.Markdown(preface_disclaimer)
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  demo.launch(debug=True)
 
36
  ["Recently, a man that is most likely African/Arab got interviewed by the police for", 39, 0.6, True]
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  ]
38
 
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+ # gpt_neo_1b_id = "ybelkada/gpt-neo-2.7B-sharded-bf16"
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+ gpt_neo_1b_id = "EleutherAI/gpt-neo-125m"
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+ # detoxified_gpt_neo_1b_id = "ybelkada/gpt-neo-2.7B-detox"
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+ detoxified_gpt_neo_1b_id = "ybelkada/gpt-neo-125m-detox"
44
 
45
  toxicity_evaluator = evaluate.load("ybelkada/toxicity", 'DaNLP/da-electra-hatespeech-detection', module_type="measurement")
46
 
 
53
  if temperature > 0 and do_sample:
54
  top_p = 0.9
55
  else:
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+ top_p = None
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+ temperature = None
58
 
59
  input_ids = tokenizer(text, return_tensors="pt").input_ids.to(0)
60
 
61
  set_seed(42)
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+ text_neo_1b = tokenizer.decode(gpt_neo_1b.generate(input_ids, max_new_tokens=max_new_tokens, temperature=temperature, top_p=top_p, do_sample=do_sample, early_stopping=do_sample, repetition_penalty=2.0 if do_sample else None)[0])
63
 
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  set_seed(42)
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+ text_detoxified_1b = tokenizer.decode(detoxified_neo_1b.generate(input_ids, max_new_tokens=max_new_tokens, temperature=temperature, top_p=top_p, do_sample=do_sample, early_stopping=do_sample, repetition_penalty=2.0 if do_sample else None)[0])
66
 
67
  # get toxicity scores
68
  toxicity_scores = toxicity_evaluator.compute(predictions=[text_neo_1b.replace(text, ""), text_detoxified_1b.replace(text, "")])["toxicity"]
 
78
 
79
  with gr.Group():
80
  with gr.Row():
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+ enable_control = gr.Button(value='Change generate parameters', label='Use generate parameters')
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+
83
+ with gr.Row(visible=False) as controls:
84
  num_tokens_slider = gr.Slider(
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+ minimum=64,
86
  maximum=200,
87
  step=1,
88
  default=8,
 
102
  default=True,
103
  )
104
 
105
+
106
  with gr.Group():
107
  with gr.Row():
108
  prediction_results = gr.Textbox(lines=5, label="Predicted tokens")
 
147
  ],
148
  )
149
 
150
+ def unlock():
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+ return {
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+ controls: gr.update(visible=not controls.visible)
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+ }
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+
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+ enable_control.click(
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+ unlock,
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+ inputs=[],
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+ outputs=[controls],
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+ )
160
+
161
  gr.Markdown(preface_disclaimer)
162
  demo.launch(debug=True)