readme: ---
  license: apache-2.0
  tags:
  - summarization
  - generated_from_trainer
  datasets:
  - cnn_dailymail
  model-index:
  - name: bart-base-finetuned-summarization-cnn-ver1.2
    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-base-finetuned-summarization-cnn-ver1.2
  
  This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the cnn_dailymail dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.2476
  - Bertscore-mean-precision: 0.8904
  - Bertscore-mean-recall: 0.8611
  - Bertscore-mean-f1: 0.8753
  - Bertscore-median-precision: 0.8891
  - Bertscore-median-recall: 0.8600
  - Bertscore-median-f1: 0.8741
  
  ## 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: 3e-05
  - train_batch_size: 1
  - eval_batch_size: 1
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Bertscore-mean-precision | Bertscore-mean-recall | Bertscore-mean-f1 | Bertscore-median-precision | Bertscore-median-recall | Bertscore-median-f1 |
  |:-------------:|:-----:|:-----:|:---------------:|:------------------------:|:---------------------:|:-----------------:|:--------------------------:|:-----------------------:|:-------------------:|
  | 2.3305        | 1.0   | 5742  | 2.2125          | 0.8845                   | 0.8587                | 0.8713            | 0.8840                     | 0.8577                  | 0.8706              |
  | 1.7751        | 2.0   | 11484 | 2.2028          | 0.8910                   | 0.8616                | 0.8759            | 0.8903                     | 0.8603                  | 0.8744              |
  | 1.4564        | 3.0   | 17226 | 2.2476          | 0.8904                   | 0.8611                | 0.8753            | 0.8891                     | 0.8600                  | 0.8741              |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu113
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  