readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: squad-bn-mt5-base2
    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. -->
  
  # squad-bn-mt5-base2
  
  This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5309
  - Rouge1 Precision: 37.5039
  - Rouge1 Recall: 30.4476
  - Rouge1 Fmeasure: 32.6695
  - Rouge2 Precision: 16.2843
  - Rouge2 Recall: 12.9093
  - Rouge2 Fmeasure: 13.9246
  - Rougel Precision: 35.2648
  - Rougel Recall: 28.6919
  - Rougel Fmeasure: 30.7578
  - Rougelsum Precision: 35.2646
  - Rougelsum Recall: 28.6829
  - Rougelsum Fmeasure: 30.7527
  - Bleu-1: 23.9098
  - Bleu-2: 14.7458
  - Bleu-3: 9.684
  - Bleu-4: 6.6217
  - Meteor: 0.142
  
  ## Model description
  
  More information needed
  
  ## 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: 5e-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: 4
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Rouge1 Precision | Rouge1 Recall | Rouge1 Fmeasure | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | Rougel Precision | Rougel Recall | Rougel Fmeasure | Rougelsum Precision | Rougelsum Recall | Rougelsum Fmeasure | Bleu-1  | Bleu-2  | Bleu-3 | Bleu-4 | Meteor |
  |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:-------------------:|:----------------:|:------------------:|:-------:|:-------:|:------:|:------:|:------:|
  | 0.698         | 1.0   | 6769  | 0.5654          | 35.1173          | 28.5689       | 30.6164         | 14.7565          | 11.6885       | 12.6012         | 33.0241          | 26.9309       | 28.8245         | 33.0061             | 26.9075          | 28.807             | 22.6163 | 13.6841 | 8.8346 | 5.926  | 0.1314 |
  | 0.6202        | 2.0   | 13538 | 0.5437          | 36.3795          | 29.5116       | 31.6675         | 15.5398          | 12.3022       | 13.2805         | 34.3036          | 27.8749       | 29.8881         | 34.2498             | 27.8384          | 29.8439            | 23.2744 | 14.1999 | 9.2715 | 6.2908 | 0.1364 |
  | 0.5878        | 3.0   | 20307 | 0.5322          | 37.2522          | 30.1185       | 32.3701         | 16.0437          | 12.6396       | 13.6664         | 35.0062          | 28.3657       | 30.4487         | 34.9742             | 28.3319          | 30.4195            | 23.7569 | 14.5781 | 9.5429 | 6.52   | 0.1407 |
  | 0.5761        | 4.0   | 27076 | 0.5309          | 37.5             | 30.4513       | 32.6723         | 16.2813          | 12.9079       | 13.9284         | 35.2662          | 28.6924       | 30.755          | 35.2509             | 28.6759          | 30.7444            | 23.9098 | 14.7458 | 9.684  | 6.6217 | 0.142  |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.2
  