readme: ---
  annotations_creators: []
  language:
  - ro
  language_creators:
  - machine-generated
  license: 
  - apache-2.0
  multilinguality:
  - monolingual
  pretty_name: BlackKakapo/t5-small-paraphrase-ro
  size_categories:
  - 10K<n<100K
  source_datasets:
  - original
  tags: []
  task_categories:
  - text2text-generation
  task_ids: []
  ---
  # Romanian paraphrase
  
  ![v2.0](https://img.shields.io/badge/V.2-17.08.2022-brightgreen)
  
  Fine-tune t5-small-paraphrase-ro model for paraphrase. Since there is no Romanian dataset for paraphrasing, I had to create my own [dataset](https://huggingface.co/datasets/BlackKakapo/paraphrase-ro-v2). The dataset contains ~30k examples.
  
  ### How to use
  
  ```python
  from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
  
  tokenizer = AutoTokenizer.from_pretrained("BlackKakapo/t5-small-paraphrase-ro-v2")
  model = AutoModelForSeq2SeqLM.from_pretrained("BlackKakapo/t5-small-paraphrase-ro-v2")
  ```
  
  ### Or
  
  ```python
  from transformers import T5ForConditionalGeneration, T5TokenizerFast 
  
  model = T5ForConditionalGeneration.from_pretrained("BlackKakapo/t5-small-paraphrase-ro-v2")
  tokenizer = T5TokenizerFast.from_pretrained("BlackKakapo/t5-small-paraphrase-ro-v2")
  ```
  
  ### Generate
  
  ```python
  text = "Am impresia că fac multe greșeli."
  
  encoding = tokenizer.encode_plus(text, pad_to_max_length=True, return_tensors="pt")
  input_ids, attention_masks = encoding["input_ids"], encoding["attention_mask"]
  
  beam_outputs = model.generate(
      input_ids=input_ids, 
      attention_mask=attention_masks,
      do_sample=True,
      max_length=256,
      top_k=20,
      top_p=0.9,
      early_stopping=False,
      num_return_sequences=5
  )
  
  final_outputs = []
  
  for beam_output in beam_outputs:
      text_para = tokenizer.decode(beam_output, skip_special_tokens=True,clean_up_tokenization_spaces=True)
      
      if text.lower() != text_para.lower() or text not in final_outputs:
          final_outputs.append(text_para)
          
  
  print(final_outputs)      
  ```
  ### Output
  
  ```out
  ['Am impresia că fac multe erori.']
  ```