readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - ccdv/arxiv-summarization
  metrics:
  - rouge
  model-index:
  - name: arxiv-summarization-fb-bart-base-2022-09-21
    results:
    - task:
        name: Summarization
        type: summarization
      dataset:
        name: ccdv/arxiv-summarization
        type: ccdv/arxiv-summarization
        config: section
        split: train
        args: section
      metrics:
      - name: Rouge1
        type: rouge
        value: 42.9082
  ---
  
  <!-- 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. -->
  
  # arxiv-summarization-fb-bart-base-2022-09-21
  
  This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the ccdv/arxiv-summarization dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.1597
  - Rouge1: 42.9082
  - Rouge2: 15.7763
  - Rougel: 25.9239
  - Rougelsum: 37.7957
  - Gen Len: 110.5816
  
  ## 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: 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: 3.0
  
  ### Training results
  
  | Training Loss | Epoch | Step   | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum | Gen Len |
  |:-------------:|:-----:|:------:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
  | 2.9142        | 0.05  | 10000  | 2.7522          | 17.073  | 6.7502 | 13.6779 | 15.6668   | 20.0    |
  | 2.7876        | 0.1   | 20000  | 2.6888          | 16.7954 | 6.7038 | 13.4939 | 15.3426   | 19.9992 |
  | 2.715         | 0.15  | 30000  | 2.6308          | 17.3324 | 6.8771 | 13.7918 | 15.7839   | 20.0    |
  | 2.6431        | 0.2   | 40000  | 2.5858          | 16.7055 | 6.8108 | 13.4796 | 15.2959   | 20.0    |
  | 2.6381        | 0.25  | 50000  | 2.5393          | 17.4643 | 7.0687 | 13.9507 | 16.012    | 20.0    |
  | 2.6269        | 0.3   | 60000  | 2.5159          | 17.5934 | 7.0022 | 13.9394 | 16.0203   | 20.0    |
  | 2.5482        | 0.34  | 70000  | 2.4894          | 17.5428 | 7.1822 | 13.9788 | 16.0355   | 20.0    |
  | 2.4962        | 0.39  | 80000  | 2.4476          | 17.3587 | 7.1501 | 13.9215 | 15.8637   | 20.0    |
  | 2.513         | 0.44  | 90000  | 2.4309          | 18.0806 | 7.5429 | 14.4201 | 16.561    | 20.0    |
  | 2.4464        | 0.49  | 100000 | 2.4128          | 17.9813 | 7.5454 | 14.3403 | 16.52     | 19.9989 |
  | 2.4969        | 0.54  | 110000 | 2.4114          | 17.3353 | 7.1382 | 13.9109 | 15.873    | 20.0    |
  | 2.4417        | 0.59  | 120000 | 2.3866          | 18.0241 | 7.553  | 14.3892 | 16.5077   | 19.9980 |
  | 2.4333        | 0.64  | 130000 | 2.3903          | 18.0578 | 7.4999 | 14.3901 | 16.5134   | 20.0    |
  | 2.4296        | 0.69  | 140000 | 2.3793          | 17.7742 | 7.5182 | 14.2794 | 16.2879   | 20.0    |
  | 2.4277        | 0.74  | 150000 | 2.3571          | 17.8015 | 7.4677 | 14.226  | 16.3288   | 20.0    |
  | 2.4258        | 0.79  | 160000 | 2.3539          | 17.5335 | 7.399  | 14.09   | 16.0936   | 20.0    |
  | 2.4006        | 0.84  | 170000 | 2.3469          | 17.5983 | 7.4285 | 14.1315 | 16.1385   | 20.0    |
  | 2.367         | 0.89  | 180000 | 2.3344          | 17.297  | 7.2361 | 13.9286 | 15.8352   | 20.0    |
  | 2.373         | 0.94  | 190000 | 2.3377          | 17.7189 | 7.4993 | 14.2603 | 16.2546   | 19.9980 |
  | 2.3762        | 0.99  | 200000 | 2.3106          | 17.7883 | 7.4766 | 14.2675 | 16.3115   | 20.0    |
  | 2.2538        | 1.03  | 210000 | 2.3197          | 17.4487 | 7.4171 | 14.0473 | 15.9771   | 20.0    |
  | 2.268         | 1.08  | 220000 | 2.3044          | 17.9603 | 7.5806 | 14.3755 | 16.4328   | 20.0    |
  | 2.2986        | 1.13  | 230000 | 2.3002          | 17.9268 | 7.5321 | 14.3503 | 16.4191   | 20.0    |
  | 2.241         | 1.18  | 240000 | 2.3059          | 17.4542 | 7.3224 | 14.0578 | 16.0157   | 20.0    |
  | 2.2534        | 1.23  | 250000 | 2.2927          | 17.8039 | 7.6232 | 14.2916 | 16.3442   | 20.0    |
  | 2.26          | 1.28  | 260000 | 2.2910          | 17.8607 | 7.5645 | 14.318  | 16.3336   | 19.9983 |
  | 2.3           | 1.33  | 270000 | 2.2818          | 17.8203 | 7.4815 | 14.3171 | 16.3309   | 20.0    |
  | 2.2964        | 1.38  | 280000 | 2.2721          | 17.983  | 7.6867 | 14.3971 | 16.493    | 20.0    |
  | 2.2564        | 1.43  | 290000 | 2.2701          | 18.059  | 7.7273 | 14.4806 | 16.5792   | 19.9988 |
  | 2.2576        | 1.48  | 300000 | 2.2663          | 17.5706 | 7.4424 | 14.1424 | 16.1297   | 20.0    |
  | 2.2605        | 1.53  | 310000 | 2.2607          | 17.8057 | 7.5219 | 14.3226 | 16.3355   | 19.9988 |
  | 2.2587        | 1.58  | 320000 | 2.2552          | 18.0396 | 7.7064 | 14