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BART_pretrained_on_billsum_finetuned_on_small_SCOTUS_extracted_dataset_2
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: 9.5858
- Rouge1: 0.06
- Rouge2: 0.0
- Rougel: 0.06
- Rougelsum: 0.0598
- Gen Len: 20.0
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: 0.02
- 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: 6
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 23.3274 | 0.98 | 10 | 35.8778 | 0.0289 | 0.0 | 0.0288 | 0.0289 | 20.0 |
| 43.999 | 1.98 | 20 | 39.9056 | 0.0475 | 0.0045 | 0.0453 | 0.0453 | 20.0 |
| 32.0042 | 2.98 | 30 | 25.4613 | 0.0516 | 0.0005 | 0.0499 | 0.0499 | 20.0 |
| 21.3151 | 3.98 | 40 | 17.6485 | 0.0021 | 0.0 | 0.0021 | 0.0021 | 20.0 |
| 15.4017 | 4.98 | 50 | 12.6187 | 0.06 | 0.0 | 0.06 | 0.0598 | 20.0 |
| 10.9491 | 5.98 | 60 | 9.5858 | 0.06 | 0.0 | 0.06 | 0.0598 | 20.0 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
