summarise_v3
This model is a fine-tuned version of allenai/led-base-16384 on the scientific_papers dataset. It achieves the following results on the evaluation set:
- Loss: 2.3003
- Rouge2 Precision: 0.1654
- Rouge2 Recall: 0.0966
- Rouge2 Fmeasure: 0.1118
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: 5e-05
- 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
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|---|---|---|---|---|---|---|
| 2.909 | 0.08 | 10 | 2.8968 | 0.0887 | 0.143 | 0.0945 |
| 2.6151 | 0.16 | 20 | 2.6183 | 0.1205 | 0.0854 | 0.0907 |
| 2.5809 | 0.24 | 30 | 2.4685 | 0.1371 | 0.0748 | 0.0911 |
| 2.1297 | 0.32 | 40 | 2.5209 | 0.1481 | 0.092 | 0.1029 |
| 2.8083 | 0.4 | 50 | 2.3871 | 0.1785 | 0.1047 | 0.1217 |
| 3.0703 | 0.48 | 60 | 2.3674 | 0.1576 | 0.0985 | 0.1103 |
| 2.4715 | 0.56 | 70 | 2.3555 | 0.1703 | 0.1036 | 0.1194 |
| 2.4538 | 0.64 | 80 | 2.3411 | 0.1619 | 0.0935 | 0.1108 |
| 2.3046 | 0.72 | 90 | 2.3105 | 0.152 | 0.0975 | 0.1107 |
| 1.7466 | 0.8 | 100 | 2.3416 | 0.1534 | 0.0872 | 0.1038 |
| 2.7695 | 0.88 | 110 | 2.3227 | 0.154 | 0.095 | 0.1081 |
| 2.4999 | 0.96 | 120 | 2.3003 | 0.1654 | 0.0966 | 0.1118 |
Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 1.2.1
- Tokenizers 0.12.1