readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: pegasus-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. -->
  
  # pegasus-qmsum-meeting-summarization
  
  This model is a fine-tuned version of [google/pegasus-xsum](https://huggingface.co/google/pegasus-xsum) on the QMSum dataset.
  It achieves the following results on the evaluation set:
  - Loss: 4.2331
  - Rouge1: 32.7156
  - Rouge2: 10.5699
  - Rougel: 23.2759
  - Rougelsum: 29.7903
  - Gen Len: 61.65
  
  ## 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-06
  - 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
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 300
  - label_smoothing_factor: 0.1
  
  ### Training results
  
  | Training Loss | Epoch  | Step  | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
  |:-------------:|:------:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
  | 5.5746        | 1.09   | 100   | 5.1739          | 9.4941  | 1.7868  | 7.2455  | 8.4302    | 29.825  |
  | 5.5784        | 2.17   | 200   | 5.0939          | 9.113   | 1.7887  | 6.9741  | 8.0457    | 26.85   |
  | 5.3777        | 3.26   | 300   | 4.9723          | 9.6387  | 1.9301  | 7.349   | 8.7941    | 25.325  |
  | 5.1884        | 4.35   | 400   | 4.8423          | 10.6045 | 2.4008  | 7.8423  | 9.4593    | 22.625  |
  | 5.0795        | 5.43   | 500   | 4.7313          | 13.7621 | 3.1231  | 9.6944  | 12.2204   | 32.175  |
  | 4.9369        | 6.52   | 600   | 4.6555          | 19.5696 | 4.9121  | 14.2603 | 16.9622   | 46.45   |
  | 4.8926        | 7.61   | 700   | 4.6038          | 22.8411 | 5.9791  | 17.2227 | 20.1173   | 51.825  |
  | 4.7502        | 8.7    | 800   | 4.5659          | 24.0555 | 6.1971  | 18.967  | 20.9143   | 54.25   |
  | 4.6876        | 9.78   | 900   | 4.5379          | 24.7066 | 6.0317  | 19.542  | 21.5774   | 57.575  |
  | 4.6266        | 10.87  | 1000  | 4.5160          | 26.128  | 6.5089  | 20.5573 | 22.5338   | 58.0    |
  | 4.6303        | 11.96  | 1100  | 4.4983          | 26.6639 | 7.1208  | 20.5222 | 23.5783   | 57.925  |
  | 4.6263        | 13.04  | 1200  | 4.4815          | 26.8262 | 7.1029  | 20.5172 | 23.6216   | 57.575  |
  | 4.577         | 14.13  | 1300  | 4.4667          | 27.7952 | 7.8331  | 21.1111 | 24.6086   | 56.95   |
  | 4.5797        | 15.22  | 1400  | 4.4559          | 27.728  | 7.8144  | 21.1519 | 24.4858   | 56.6    |
  | 4.4923        | 16.3   | 1500  | 4.4448          | 28.0998 | 8.1346  | 21.4004 | 25.3769   | 55.975  |
  | 4.4583        | 17.39  | 1600  | 4.4335          | 28.9003 | 8.6135  | 22.0139 | 26.0409   | 56.55   |
  | 4.5036        | 18.48  | 1700  | 4.4246          | 29.2187 | 8.8301  | 22.3569 | 26.1964   | 58.125  |
  | 4.4383        | 19.57  | 1800  | 4.4144          | 28.8424 | 8.9131  | 22.0398 | 25.9214   | 56.75   |
  | 4.4797        | 20.65  | 1900  | 4.4054          | 28.9285 | 8.9298  | 22.222  | 26.0316   | 56.225  |
  | 4.4264        | 21.74  | 2000  | 4.3989          | 29.7184 | 9.0477  | 22.2885 | 26.7439   | 56.225  |
  | 4.3615        | 22.83  | 2100  | 4.3902          | 29.1538 | 8.9529  | 22.0076 | 26.4925   | 57.175  |
  | 4.329         | 23.91  | 2200  | 4.3839          | 29.5186 | 9.2777  | 21.9025 | 26.3141   | 55.5    |
  | 4.3578        | 25.0   | 2300  | 4.3766          | 28.4309 | 8.9423  | 21.0945 | 25.8191   | 53.975  |
  | 4.3748        | 26.09  | 2400  | 4.3707          | 28.3    | 9.0625  | 21.4946 | 25.1966   | 53.0    |
  | 4.3233        | 27.17  | 2500  | 4.3639          | 28.2325 | 8.9889  | 21.6226 | 25.3677   | 54.6    |
  | 4.339         | 28.26  | 2600  | 4.3578          | 28.0744 | 8.774   | 21.2509 | 25.2901   | 54.1    |
  | 4.2798        | 29.35  | 2700  | 4.3532          | 27.772  | 8.7096  | 21.1687 | 25.3345   | 54.025  |
  | 4.2964        | 30.43  | 2800  | 4.3465          | 27.7827 | 8.1597  | 20.8139 | 25.0152   | 54.45   |
  | 4.3365        | 31.52  | 2900  | 4.3423          | 28.2039 | 8.4661  | 21.3546 | 25.6381   | 55.5    |
  | 4.2385        | 32.61  | 3000  | 4.3380          | 28.1098 | 8.6483  | 21.5279 | 25.2009   | 53.95   |
  | 4.2451        | 33.7   | 3100  | 4.3331          | 28.2745 | 8.5024  | 21.4456 | 25.3363   | 52.6    |
  | 4.2393        | 34.78  | 3200  | 4.3289          | 28.7597 | 9.0881  | 21.6532 | 25.8954   | 52.65   |
  | 4.2116        | 35.87  | 3300  | 4.3252          | 29.0463 | 9.1218  | 21.8026 | 26.2037   | 53.65   |
  | 4.2175        | 36.96  | 3400  | 4.3210          | 28.8009 | 9.0188  | 21.8368 | 25.8678   | 52.85   |
  | 4.2071