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BART_pretrained_on_billsum_finetuned_on_small_SCOTUS_extracted_dataset_3
This model is a fine-tuned version of bheshaj/bart-large-billsum-epochs20 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.5827
- Rouge1: 0.1251
- Rouge2: 0.037
- Rougel: 0.1002
- Rougelsum: 0.1003
- Gen Len: 19.4207
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 6.7036 | 0.98 | 10 | 4.9345 | 0.1041 | 0.0255 | 0.0865 | 0.0864 | 20.0 |
| 4.9048 | 1.98 | 20 | 4.4400 | 0.1201 | 0.0283 | 0.0973 | 0.0974 | 19.9573 |
| 4.544 | 2.98 | 30 | 4.2661 | 0.1272 | 0.0298 | 0.1013 | 0.1007 | 19.9695 |
| 4.3585 | 3.98 | 40 | 4.1790 | 0.1257 | 0.0289 | 0.1007 | 0.1006 | 19.8293 |
| 4.226 | 4.98 | 50 | 4.1156 | 0.1229 | 0.0302 | 0.0994 | 0.0994 | 19.6098 |
| 4.1417 | 5.98 | 60 | 4.0503 | 0.1225 | 0.0293 | 0.0985 | 0.0987 | 19.6037 |
| 4.0406 | 6.98 | 70 | 3.9856 | 0.1208 | 0.0309 | 0.0975 | 0.0976 | 19.6159 |
| 3.9409 | 7.98 | 80 | 3.9266 | 0.1231 | 0.0299 | 0.0982 | 0.0984 | 19.5976 |
| 3.8492 | 8.98 | 90 | 3.8767 | 0.1212 | 0.0299 | 0.0953 | 0.0954 | 19.6037 |
| 3.7571 | 9.98 | 100 | 3.8241 | 0.1196 | 0.0313 | 0.097 | 0.0972 | 19.811 |
| 3.6975 | 10.98 | 110 | 3.7959 | 0.121 | 0.0303 | 0.0963 | 0.0963 | 19.6768 |
| 3.5923 | 11.98 | 120 | 3.7628 | 0.115 | 0.0315 | 0.0959 | 0.0962 | 19.7012 |
| 3.5505 | 12.98 | 130 | 3.7352 | 0.1166 | 0.034 | 0.0952 | 0.0957 | 19.6829 |
| 3.5027 | 13.98 | 140 | 3.7157 | 0.1222 | 0.0347 | 0.1004 | 0.1005 | 19.6341 |
| 3.456 | 14.98 | 150 | 3.6983 | 0.1198 | 0.032 | 0.0968 | 0.097 | 19.6524 |
| 3.4088 | 15.98 | 160 | 3.6644 | 0.1204 | 0.0321 | 0.0969 | 0.0969 | 19.4695 |
| 3.3511 | 16.98 | 170 | 3.6545 | 0.1224 | 0.035 | 0.1001 | 0.1004 | 19.8171 |
| 3.3167 | 17.98 | 180 | 3.6415 | 0.1223 | 0.0363 | 0.1006 | 0.1007 | 19.7683 |
| 3.2786 | 18.98 | 190 | 3.6286 | 0.1234 | 0.0345 | 0.1004 | 0.1005 | 19.9756 |
| 3.2437 | 19.98 | 200 | 3.6239 | 0.124 | 0.0368 | 0.1006 | 0.1009 | 19.6829 |
| 3.2114 | 20.98 | 210 | 3.6138 | 0.1256 | 0.0393 | 0.1035 | 0.104 | 19.7256 |
| 3.1935 | 21.98 | 220 | 3.6025 | 0.1241 | 0.0359 | 0.1016 | 0.1016 | 19.5122 |
| 3.175 | 22.98 | 230 | 3.5939 | 0.1213 | 0.0356 | 0.1008 | 0.1011 | 19.4024 |
| 3.1572 | 23.98 | 240 | 3.5979 | 0.124 | 0.0355 | 0.1008 | 0.1007 | 19.7256 |
| 3.1346 | 24.98 | 250 | 3.5909 | 0.1247 | 0.0356 | 0.1011 | 0.1014 | 19.6037 |
| 3.1202 | 25.98 | 260 | 3.5877 | 0.1299 | 0.0372 | 0.1042 | 0.1045 | 19.7866 |
| 3.1095 | 26.98 | 270 | 3.5876 | 0.13 | 0.0381 | 0.1056 | 0.1059 | 19.7866 |
| 3.0919 | 27.98 | 280 | 3.5851 | 0.1286 | 0.04 | 0.1037 | 0.1037 | 19.6951 |
| 3.1089 | 28.98 | 290 | 3.5830 | 0.1253 | 0.0376 | 0.1001 | 0.1002 | 19.4207 |
| 3.0915 | 29.98 | 300 | 3.5827 | 0.1251 | 0.037 | 0.1002 | 0.1003 | 19.4207 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
