zwellington
/

bart-cnn-pubhealth-expanded

+---
+license: mit
+base_model: facebook/bart-large-cnn
+tags:
+- generated_from_trainer
+datasets:
+- clupubhealth
+metrics:
+- rouge
+model-index:
+- name: bart-cnn-pubhealth-expanded
+  results:
+  - task:
+      name: Sequence-to-sequence Language Modeling
+      type: text2text-generation
+    dataset:
+      name: clupubhealth
+      type: clupubhealth
+      config: expanded
+      split: test
+      args: expanded
+    metrics:
+    - name: Rouge1
+      type: rouge
+      value: 28.3745
+---
+<!-- 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-cnn-pubhealth-expanded
+This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the clupubhealth dataset.
+It achieves the following results on the evaluation set:
+- Loss: 2.7286
+- Rouge1: 28.3745
+- Rouge2: 8.806
+- Rougel: 19.3896
+- Rougelsum: 20.7149
+- Gen Len: 66.075
+## 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: 16
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+### Training results
+| Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
+|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
+| 2.571         | 0.26  | 500   | 2.2030          | 29.8543 | 10.1926 | 20.7137 | 21.7285   | 66.6    |
+| 2.313         | 0.51  | 1000  | 2.1891          | 29.5708 | 9.5292  | 20.0823 | 21.4907   | 66.87   |
+| 2.1371        | 0.77  | 1500  | 2.1981          | 29.7651 | 9.4575  | 20.412  | 21.2983   | 65.925  |
+| 1.9488        | 1.03  | 2000  | 2.3023          | 29.6158 | 9.4241  | 20.6193 | 21.5966   | 64.745  |
+| 1.7406        | 1.29  | 2500  | 2.2808          | 30.0862 | 9.8179  | 20.5477 | 21.4372   | 65.17   |
+| 1.6732        | 1.54  | 3000  | 2.2953          | 29.65   | 9.693   | 20.3996 | 21.1837   | 64.48   |
+| 1.6349        | 1.8   | 3500  | 2.3093          | 29.9081 | 9.4101  | 20.2955 | 21.381    | 64.605  |
+| 1.4981        | 2.06  | 4000  | 2.3376          | 29.3183 | 9.2161  | 20.4919 | 21.3562   | 64.73   |
+| 1.3951        | 2.32  | 4500  | 2.3323          | 29.9405 | 9.118   | 19.9364 | 21.1458   | 66.425  |
+| 1.3775        | 2.57  | 5000  | 2.3597          | 29.1785 | 8.7657  | 19.6031 | 20.6261   | 65.505  |
+| 1.3426        | 2.83  | 5500  | 2.3744          | 29.1015 | 8.9953  | 20.0223 | 21.1623   | 64.99   |
+| 1.2243        | 3.09  | 6000  | 2.4723          | 28.8329 | 8.8603  | 19.9412 | 21.0484   | 65.655  |
+| 1.1798        | 3.35  | 6500  | 2.4063          | 28.9035 | 8.9915  | 19.8531 | 20.9957   | 65.93   |
+| 1.1926        | 3.6   | 7000  | 2.4110          | 29.4024 | 8.8828  | 19.4321 | 20.763    | 65.9    |
+| 1.1791        | 3.86  | 7500  | 2.4147          | 29.8599 | 9.168   | 20.2613 | 21.4986   | 65.205  |
+| 1.0545        | 4.12  | 8000  | 2.4941          | 27.9696 | 8.1513  | 19.5133 | 20.2316   | 65.26   |
+| 1.0513        | 4.37  | 8500  | 2.4345          | 28.8695 | 8.7627  | 19.8116 | 20.8412   | 64.375  |
+| 1.0516        | 4.63  | 9000  | 2.4550          | 29.3524 | 9.1717  | 20.0134 | 21.1516   | 65.59   |
+| 1.0454        | 4.89  | 9500  | 2.4543          | 29.0709 | 8.8377  | 19.9499 | 20.9215   | 66.055  |
+| 0.9247        | 5.15  | 10000 | 2.5152          | 28.8769 | 8.7619  | 19.5535 | 20.5383   | 65.455  |
+| 0.9529        | 5.4   | 10500 | 2.5192          | 29.4734 | 8.6629  | 19.6803 | 20.9521   | 66.855  |
+| 0.953         | 5.66  | 11000 | 2.5530          | 28.7234 | 8.5991  | 19.235  | 20.3965   | 64.62   |
+| 0.9519        | 5.92  | 11500 | 2.5024          | 28.8013 | 8.8198  | 19.091  | 20.2732   | 65.16   |
+| 0.8492        | 6.18  | 12000 | 2.6300          | 28.8821 | 8.974   | 20.1383 | 21.1273   | 66.16   |
+| 0.8705        | 6.43  | 12500 | 2.6192          | 28.9942 | 9.0923  | 20.0151 | 20.9462   | 66.17   |
+| 0.8489        | 6.69  | 13000 | 2.5758          | 28.5162 | 8.7087  | 19.6472 | 20.6057   | 68.725  |
+| 0.8853        | 6.95  | 13500 | 2.5783          | 29.0936 | 8.8353  | 19.8755 | 20.867    | 65.61   |
+| 0.8043        | 7.21  | 14000 | 2.6668          | 28.198  | 8.5221  | 19.2404 | 20.4359   | 66.84   |
+| 0.8004        | 7.46  | 14500 | 2.6676          | 28.4951 | 8.8535  | 19.8777 | 20.8867   | 65.99   |
+| 0.8067        | 7.72  | 15000 | 2.6136          | 29.2442 | 8.8243  | 19.7428 | 20.9531   | 66.265  |
+| 0.8008        | 7.98  | 15500 | 2.6362          | 28.9875 | 8.8529  | 19.6993 | 20.6463   | 65.83   |
+| 0.7499        | 8.23  | 16000 | 2.6987          | 29.2742 | 9.0804  | 19.8464 | 21.0735   | 65.66   |
+| 0.7556        | 8.49  | 16500 | 2.6859          | 28.5046 | 8.3465  | 19.0813 | 20.2561   | 65.31   |
+| 0.7574        | 8.75  | 17000 | 2.7021          | 29.2861 | 8.8262  | 19.5899 | 20.9786   | 65.735  |
+| 0.7524        | 9.01  | 17500 | 2.7160          | 29.1471 | 8.9296  | 20.0009 | 21.2013   | 66.415  |
+| 0.7124        | 9.26  | 18000 | 2.7418          | 28.8323 | 8.7672  | 19.5686 | 20.5814   | 67.355  |
+| 0.7084        | 9.52  | 18500 | 2.7267          | 28.3833 | 8.7165  | 19.0514 | 20.3386   | 67.075  |
+| 0.7251        | 9.78  | 19000 | 2.7286          | 28.3745 | 8.806   | 19.3896 | 20.7149   | 66.075  |
+### Framework versions
+- Transformers 4.31.0
+- Pytorch 2.0.1+cu117
+- Datasets 2.7.1
+- Tokenizers 0.13.2