readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: final-squad-bn-qgen-mt5-small-all-metric-v2
    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. -->
  
  # final-squad-bn-qgen-mt5-small-all-metric-v2
  
  This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6559
  - Rouge1 Precision: 31.143
  - Rouge1 Recall: 24.8687
  - Rouge1 Fmeasure: 26.7861
  - Rouge2 Precision: 12.1721
  - Rouge2 Recall: 9.3907
  - Rouge2 Fmeasure: 10.1945
  - Rougel Precision: 29.2741
  - Rougel Recall: 23.4105
  - Rougel Fmeasure: 25.196
  - Rougelsum Precision: 29.2488
  - Rougelsum Recall: 23.3873
  - Rougelsum Fmeasure: 25.1783
  - Bleu-1: 20.2844
  - Bleu-2: 11.7083
  - Bleu-3: 7.2251
  - Bleu-4: 4.6646
  - Meteor: 0.1144
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:-------------------:|:----------------:|:------------------:|:-------:|:-------:|:------:|:------:|:------:|
  | 0.9251        | 1.0   | 6769  | 0.7237          | 26.4973          | 20.6282       | 22.3983         | 9.3138           | 6.9928        | 7.6534          | 24.9538          | 19.4635       | 21.1113         | 24.9713             | 19.4608          | 21.119             | 17.5414 | 9.5172  | 5.6104 | 3.4646 | 0.097  |
  | 0.8214        | 2.0   | 13538 | 0.6804          | 29.524           | 23.4125       | 25.2574         | 11.2954          | 8.6345        | 9.3841          | 27.8173          | 22.1005       | 23.8164         | 27.7939             | 22.0878          | 23.801             | 19.2368 | 10.9056 | 6.6821 | 4.2702 | 0.1074 |
  | 0.7914        | 3.0   | 20307 | 0.6600          | 30.7136          | 24.5527       | 26.4259         | 11.8743          | 9.1634        | 9.9452          | 28.8725          | 23.1161       | 24.859          | 28.8566             | 23.1018          | 24.8457            | 19.9315 | 11.4473 | 7.0613 | 4.5701 | 0.1119 |
  | 0.7895        | 4.0   | 27076 | 0.6559          | 31.1568          | 24.8787       | 26.8004         | 12.1685          | 9.3879        | 10.1929         | 29.2804          | 23.3999       | 25.1925         | 29.2554             | 23.3891          | 25.1818            | 20.2844 | 11.7083 | 7.2251 | 4.6646 | 0.1144 |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.11.0
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  