
    def paraphrase(
        question,
        num_beams=5,
        num_beam_groups=5,
        num_return_sequences=1,
        repetition_penalty=10.0,
        diversity_penalty=3.0,
        no_repeat_ngram_size=2,
        temperature=0.7,
        max_length=128
    ):
      input_ids = tokenizer(
          f'paraphrase: {question}',
          return_tensors="pt", 
          padding="longest",
          max_length=max_length,
          truncation=True,
      ).input_ids
      
      outputs = model.generate(
          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
{"language": ["en"], "metrics": ["bleu", "rouge"], "pipeline_tag": "text2text-generation"}
transformers pytorch t5 text2text-generation en endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #en #endpoints_compatible #text-generation-inference #region-us 


    def paraphrase(
        question,
        num_beams=5,
        num_beam_groups=5,
        num_return_sequences=1,
        repetition_penalty=10.0,
        diversity_penalty=3.0,
        no_repeat_ngram_size=2,
        temperature=0.7,
        max_length=128
    ):
      input_ids = tokenizer(
          f'paraphrase: {question}',
          return_tensors="pt", 
          padding="longest",
          max_length=max_length,
          truncation=True,
      ).input_ids
      
      outputs = model.generate(
          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
TAGS
#transformers #pytorch #t5 #text2text-generation #en #endpoints_compatible #text-generation-inference #region-us 
