longformer_summarise
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 | 2.8969 | 0.09 | 0.1439 | 0.0953 |
| 2.615 | 0.16 | 20 | 2.6182 | 0.1232 | 0.0865 | 0.0924 |
| 2.581 | 0.24 | 30 | 2.4687 | 0.1357 | 0.0733 | 0.09 |
| 2.1294 | 0.32 | 40 | 2.5215 | 0.1495 | 0.0932 | 0.1044 |
| 2.8083 | 0.4 | 50 | 2.3870 | 0.1794 | 0.1054 | 0.1224 |
| 3.0704 | 0.48 | 60 | 2.3676 | 0.1572 | 0.0989 | 0.1108 |
| 2.4716 | 0.56 | 70 | 2.3554 | 0.1707 | 0.1039 | 0.1198 |
| 2.454 | 0.64 | 80 | 2.3411 | 0.1619 | 0.0943 | 0.1115 |
| 2.3046 | 0.72 | 90 | 2.3105 | 0.1547 | 0.0965 | 0.1116 |
| 1.7467 | 0.8 | 100 | 2.3417 | 0.1551 | 0.0877 | 0.1046 |
| 2.7696 | 0.88 | 110 | 2.3226 | 0.1543 | 0.0954 | 0.1085 |
| 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