readme: ---
  
  pipeline_tag: text-classification
  inference: false
  language: pt
  tags:
  - transformers
  
  ---
  
  # Prompsit/paraphrase-bert-pt
  
  This model allows to evaluate paraphrases for a given phrase.  
  
  We have fine-tuned this model from pretrained "neuralmind/bert-base-portuguese-cased".
  
  Model built under a TSI-100905-2019-4 project, co-financed by Ministry of Economic Affairs and Digital Transformation from the Government of Spain.
  
  # How to use it
  
  The model answer the following question: Is "phrase B" a paraphrase of "phrase A".
  
  Please note that we're considering phrases instead of sentences. Therefore, we must take into account that the model doesn't expect to find punctuation marks or long pieces of text.
  
  Resulting probabilities correspond to classes:  
  
  * 0: Not a paraphrase
  * 1: It's a paraphrase
  
  So, considering the phrase "logo após o homicídio" and a candidate paraphrase like "pouco depois do assassinato", you can use the model like this:
  
  ```
  
  import torch
  
  from transformers import AutoTokenizer, AutoModelForSequenceClassification
  tokenizer = AutoTokenizer.from_pretrained("Prompsit/paraphrase-bert-pt")
  model = AutoModelForSequenceClassification.from_pretrained("Prompsit/paraphrase-bert-pt")
  
  input = tokenizer('logo após o homicídio','pouco depois do assassinato',return_tensors='pt')
  logits = model(**input).logits
  soft = torch.nn.Softmax(dim=1)
  print(soft(logits))
  
  ```
  
  Code output is:
  
   ``` 
  
   tensor([[0.2137, 0.7863]], grad_fn=<SoftmaxBackward>)
  
   ```
  
  As the probability of 1 (=It's a paraphrase) is 0.7863 and the probability of 0 (=It is not a paraphrase) is 0.2137, we can conclude, for our previous example, that "pouco depois do assassinato" is a paraphrase of "logo após o homicidio".
  
  # Evaluation results
  
  We have used as test dataset 16500 pairs of phrases human tagged. 
  
  Metrics obtained are:
  
  ```
  metrics={
   'test_loss': 0.6074697375297546, 
   'test_accuracy': 0.7809, 
   'test_precision': 0.7157638466220329, 
   'test_recall': 0.40551724137931033, 
   'test_f1': 0.5177195685670262, 
   'test_matthews_correlation': 0.41603913834665324, 
   'test_runtime': 16.4585, 
   'test_samples_per_second': 607.587, 
   'test_steps_per_second': 19.017
  }
  ```