Update app.py

app.py

@@ -7,7 +7,17 @@ tokenizer  = AutoTokenizer.from_pretrained('humarin/chatgpt_paraphraser_on_T5_ba
 model = AutoModelForSeq2SeqLM.from_pretrained('humarin/chatgpt_paraphraser_on_T5_base', cache_dir='./Models')
 # torch.quantization.quantize_dynamic(model, {torch.nn.Linear}, dtype=torch.qint8, inplace=True)
 
-def paraphrase(model, text, max_length=128, num_return_sequences=5, num_beams=25, temperature=0.7):
+def paraphrase(
+    text,
+    num_beams=5,
+    num_beam_groups=5,
+    num_return_sequences=5,
+    repetition_penalty=10.0,
+    diversity_penalty=3.0,
+    no_repeat_ngram_size=2,
+    temperature=0.7,
+    max_length=128
+):
     input_ids = tokenizer(
         f'paraphrase: {text}',
         return_tensors="pt", padding="longest",
@@ -16,20 +26,42 @@ def paraphrase(model, text, max_length=128, num_return_sequences=5, num_beams=25
     ).input_ids
     
     outputs = model.generate(
-        input_ids, temperature=temperature, repetition_penalty=1.5,
-        num_return_sequences=num_return_sequences, no_repeat_ngram_size=5, num_beams=num_beams, max_length=max_length
+        input_ids, temperature=temperature, repetition_penalty=repetition_penalty,
+        num_return_sequences=num_return_sequences, no_repeat_ngram_size=no_repeat_ngram_size,
+        num_beams=num_beams, num_beam_groups=num_beam_groups,
+        max_length=max_length, diversity_penalty=diversity_penalty
     )
 
     res = tokenizer.batch_decode(outputs, skip_special_tokens=True)
 
     return res
 
-def fn(text, results_num=5, beams_num=25, temperature=0.7):
-    return '\n'.join(paraphrase(model, text, num_return_sequences=results_num, num_beams=beams_num, temperature=temperature))
+def fn(
+    text,
+    num_beams=5,
+    num_beam_groups=5,
+    num_return_sequences=5,
+    repetition_penalty=10.0,
+    diversity_penalty=3.0,
+    no_repeat_ngram_size=2,
+    temperature=0.7,
+    max_length=128
+):
+    return '\n'.join(paraphrase(text, num_beams, num_beam_groups, num_return_sequences, repetition_penalty, diversity_penalty, no_repeat_ngram_size, temperature, max_length))
 
 demo = gr.Interface(
     fn=fn,
-    inputs=[gr.Textbox(lines=3, placeholder='Enter Text To Paraphrase'), gr.Slider(minimum=1, maximum=10, step=1, value=5), gr.Slider(minimum=1, maximum=50, step=1, value=25), gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=0.7)],
+    inputs=[
+        gr.Textbox(lines=3, placeholder='Enter Text To Paraphrase'),
+        gr.Slider(minimum=1, maximum=25, step=1, value=5),
+        gr.Slider(minimum=1, maximum=25, step=1, value=5),
+        gr.Slider(minimum=1, maximum=20, step=1, value=5),
+        gr.Slider(minimum=0.6, maximum=20.1, step=0.5, value=10.1),
+        gr.Slider(minimum=0.6, maximum=20.1, step=0.5, value=3.1),
+        gr.Slider(minimum=1, maximum=10, step=1, value=2),
+        gr.Slider(minimum=0.0, maximum=1000, step=0.1, value=0.7),
+        gr.Slider(minimum=32, maximum=512, step=1, value=128),
+    ],
     outputs=['text'],
 )