readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: bart-qmsum-meeting-summarization
    results: []
  datasets:
  - yawnick/QMSum
  ---
  
  <!-- 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-qmsum-meeting-summarization
  
  This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on the QMSum dataset.
  It achieves the following results on the evaluation set:
  - Loss: 4.3354
  - Rouge1: 39.5539
  - Rouge2: 12.1134
  - Rougel: 23.9163
  - Rougelsum: 36.0299
  - Gen Len: 117.225
  
  ## 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-07
  - train_batch_size: 4
  - eval_batch_size: 4
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 200
  - label_smoothing_factor: 0.1
  
  ### Training results
  
  | Training Loss | Epoch  | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
  |:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
  | 5.5573        | 2.17   | 100  | 5.4074          | 23.6282 | 4.1122  | 14.584  | 21.2263   | 84.75   |
  | 5.4721        | 4.35   | 200  | 5.2899          | 24.61   | 4.272   | 15.2096 | 22.2997   | 87.2    |
  | 5.3407        | 6.52   | 300  | 5.1360          | 25.8272 | 4.3314  | 15.9926 | 23.3416   | 87.95   |
  | 5.1527        | 8.7    | 400  | 4.9751          | 27.7207 | 5.31    | 16.7055 | 24.8357   | 88.35   |
  | 5.0058        | 10.87  | 500  | 4.8372          | 30.1847 | 6.8615  | 18.934  | 27.2424   | 89.95   |
  | 4.8807        | 13.04  | 600  | 4.7488          | 33.1208 | 9.1784  | 20.655  | 30.1198   | 101.3   |
  | 4.7931        | 15.22  | 700  | 4.6891          | 33.2266 | 8.4253  | 20.0334 | 30.4093   | 108.925 |
  | 4.7272        | 17.39  | 800  | 4.6467          | 35.0475 | 9.326   | 21.0655 | 31.8413   | 111.7   |
  | 4.6904        | 19.57  | 900  | 4.6102          | 34.869  | 9.6046  | 21.395  | 32.4346   | 115.05  |
  | 4.6547        | 21.74  | 1000 | 4.5829          | 36.3392 | 10.9936 | 22.1524 | 33.6863   | 119.875 |
  | 4.594         | 23.91  | 1100 | 4.5602          | 35.9717 | 10.3827 | 21.6118 | 32.8302   | 119.5   |
  | 4.5714        | 26.09  | 1200 | 4.5424          | 36.3656 | 10.6282 | 22.2187 | 33.6494   | 118.0   |
  | 4.542         | 28.26  | 1300 | 4.5256          | 36.7386 | 10.615  | 22.2487 | 34.1927   | 115.675 |
  | 4.5092        | 30.43  | 1400 | 4.5116          | 37.1597 | 10.7751 | 22.6747 | 34.396    | 118.55  |
  | 4.5031        | 32.61  | 1500 | 4.4981          | 37.6108 | 10.9732 | 22.8342 | 34.6833   | 117.125 |
  | 4.4682        | 34.78  | 1600 | 4.4875          | 37.5057 | 11.1328 | 22.8973 | 34.7114   | 117.65  |
  | 4.4387        | 36.96  | 1700 | 4.4775          | 38.1278 | 11.3597 | 23.1307 | 35.1869   | 115.65  |
  | 4.4085        | 39.13  | 1800 | 4.4682          | 37.9578 | 11.4355 | 23.1149 | 35.4961   | 119.6   |
  | 4.4166        | 41.3   | 1900 | 4.4592          | 38.1467 | 11.3208 | 23.045  | 35.0824   | 120.05  |
  | 4.3971        | 43.48  | 2000 | 4.4517          | 37.9922 | 11.5071 | 23.3983 | 34.6918   | 114.425 |
  | 4.3638        | 45.65  | 2100 | 4.4438          | 38.1666 | 11.4985 | 23.5518 | 35.1484   | 117.2   |
  | 4.3522        | 47.83  | 2200 | 4.4377          | 37.7572 | 11.3984 | 23.4437 | 35.0453   | 113.725 |
  | 4.3398        | 50.0   | 2300 | 4.4320          | 38.5833 | 11.4575 | 23.6411 | 35.3437   | 116.125 |
  | 4.3341        | 52.17  | 2400 | 4.4247          | 38.2705 | 12.0374 | 23.5807 | 34.9985   | 110.8   |
  | 4.3024        | 54.35  | 2500 | 4.4201          | 39.0206 | 12.2041 | 23.4394 | 35.6291   | 114.5   |
  | 4.3117        | 56.52  | 2600 | 4.4147          | 38.6555 | 12.1079 | 23.5655 | 35.5287   | 111.325 |
  | 4.2659        | 58.7   | 2700 | 4.4107          | 39.2235 | 12.025  | 23.934  | 36.2243   | 113.3   |
  | 4.2946        | 60.87  | 2800 | 4.4055          | 39.0301 | 12.1833 | 23.8999 | 36.0487   | 110.325 |
  | 4.2431        | 63.04  | 2900 | 4.4009          | 39.0498 | 12.3215 | 23.9686 | 36.0277   | 112.775 |
  | 4.2439        | 65.22  | 3000 | 4.3968          | 38.8786 | 12.0985 | 23.8308 | 35.8575   | 115.175 |
  | 4.2244        | 67.39  | 3100 | 4.3922          | 38.7614 | 12.1721 | 23.7736 | 35.6744   | 113.55  |
  | 4.235         | 69.57  | 3200 | 4.3895          | 38.6858 | 11.3994 | 23.6392 | 35.3456   | 114.125 |
  | 4.2064        | 71.74  | 3300 | 4.3859          | 39.0258 | 12.0435 | 24.2528 | 35.8378   | 113.5   |
  | 4.1934        | 73.91  | 3400 | 4.3835          | 39.0467 | 11.5556 | 23.6704 | 35.5643   | 111.5   |
  | 4.1859