readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: BART_corrector
    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. -->
  
  # BART_corrector
  
  This model is a fine-tuned version of [ainize/bart-base-cnn](https://huggingface.co/ainize/bart-base-cnn) on a homemade dataset. Each sample of the dataset is an english sentence that has been duplicated 10 times and where random errors (7%) were added.
  
  It achieves the following results on the evaluation set:
  - Loss: 0.0025
  - Rouge1: 81.4214
  - Rouge2: 80.2027
  - Rougel: 81.4202
  - Rougelsum: 81.4241
  - Gen Len: 19.3962
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  The goal of this model is to correct a sentence, given several versions of it with various mistakes.
  
  Text sample :
  _TheIdeSbgn of thh Eiffel Toweg is aYtribeted to Ma. . ahd design of The Eijfel Tower is attribQtedBto ta. . The designYof the EifZel Tower Vs APtWibuteQ to Ma. . The xeQign oC the EiffelXTower ik attributed to Ma. . ghebFesign of theSbiffel TJwer is atMributed to Ma. . The desOBn of thQ Eiffel ToweP isfattributnd toBMa. . The design of the EBfUel Fower is JtAriOuted tx Ma. . The design of Jhe ENffel LoweF is aptrVbuted Lo Ma. . The deslgX of the lPffel Towermis attributedhtohMa. . The desRgn of thekSuffel Tower is Ttkribufed to Ma. ._
  
  ## 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: 4
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
  | 0.0071        | 1.0   | 2365 | 0.0039          | 81.3664 | 80.0861 | 81.3601 | 81.3667   | 19.3967 |
  | 0.0033        | 2.0   | 4730 | 0.0029          | 81.3937 | 80.1548 | 81.3902 | 81.3974   | 19.3961 |
  | 0.0018        | 3.0   | 7095 | 0.0029          | 81.3838 | 80.1404 | 81.385  | 81.3878   | 19.3965 |
  | 0.001         | 4.0   | 9460 | 0.0025          | 81.4214 | 80.2027 | 81.4202 | 81.4241   | 19.3962 |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  