readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - pub_med_summarization_dataset
  metrics:
  - rouge
  model-index:
  - name: bart-large-finetuned-pubmed
    results:
    - task:
        name: Sequence-to-sequence Language Modeling
        type: text2text-generation
      dataset:
        name: pub_med_summarization_dataset
        type: pub_med_summarization_dataset
        args: document
      metrics:
      - name: Rouge1
        type: rouge
        value: 10.946
  ---
  
  <!-- 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-large-finetuned-pubmed
  
  This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the pub_med_summarization_dataset dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.8135
  - Rouge1: 10.946
  - Rouge2: 5.0933
  - Rougel: 9.5608
  - Rougelsum: 10.4259
  - Gen Len: 19.0495
  
  ## 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: 2e-05
  - train_batch_size: 2
  - eval_batch_size: 2
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2 | Rougel | Rougelsum | Gen Len |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:------:|:---------:|:-------:|
  | 2.0861        | 1.0   | 4000  | 1.8909          | 8.7344  | 3.6919 | 7.8804 | 8.3305    | 20.0    |
  | 1.8996        | 2.0   | 8000  | 1.8261          | 10.2124 | 4.6212 | 8.9842 | 9.7417    | 17.632  |
  | 1.7459        | 3.0   | 12000 | 1.8160          | 9.4933  | 4.4117 | 8.3977 | 9.0758    | 16.4775 |
  | 1.6258        | 4.0   | 16000 | 1.8136          | 10.8248 | 5.0335 | 9.4286 | 10.3123   | 18.724  |
  | 1.5214        | 5.0   | 20000 | 1.8135          | 10.946  | 5.0933 | 9.5608 | 10.4259   | 19.0495 |
  
  
  ### Framework versions
  
  - Transformers 4.16.2
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.3
  - Tokenizers 0.11.6
  