readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: mT5_multilingual_XLSum-sumarizacao-PTBR
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # mT5_multilingual_XLSum-sumarizacao-PTBR
  
  This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https://huggingface.co/csebuetnlp/mT5_multilingual_XLSum) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.3870
  - Rouge1: 42.0195
  - Rouge2: 24.9493
  - Rougel: 32.3653
  - Rougelsum: 37.9982
  - Gen Len: 77.0
  
  ## Let's see the model in action!
  ```python
  from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
  
  WHITESPACE_HANDLER = lambda k: re.sub('\s+', ' ', re.sub('\n+', ' ', k.strip()))
  
  model_name = "GiordanoB/mT5_multilingual_XLSum-sumarizacao-PTBR"
  tokenizer = AutoTokenizer.from_pretrained(model_name)
  model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
  
      input_ids = tokenizer(
          [WHITESPACE_HANDLER(sumariosDuplos[i])],
          return_tensors="pt",
          padding="max_length",
          truncation=True,
          max_length=512
      )["input_ids"]
  
      output_ids = model.generate(
          input_ids=input_ids,
          max_length=200,
          min_length=75,
          no_repeat_ngram_size=2,
          num_beams=5
      )[0]
  
      summary = tokenizer.decode(
          output_ids,
          skip_special_tokens=True,
          clean_up_tokenization_spaces=False
      )
  
      sumariosFinal.append(summary)
      print(i,"\n",summary,"\n")
     ```
      
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 2e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
  | No log        | 1.0   | 15   | 1.5687          | 32.2316 | 18.9289 | 23.918  | 27.7216   | 51.5714 |
  | No log        | 2.0   | 30   | 1.4530          | 41.2297 | 26.1883 | 30.8012 | 37.1727   | 69.5714 |
  | No log        | 3.0   | 45   | 1.4043          | 40.8986 | 24.4993 | 31.349  | 36.8782   | 72.2143 |
  | No log        | 4.0   | 60   | 1.3908          | 42.1019 | 25.5555 | 32.9018 | 38.0202   | 74.5    |
  | No log        | 5.0   | 75   | 1.3870          | 42.0195 | 24.9493 | 32.3653 | 37.9982   | 77.0    |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.11.0
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  