summarise_v2
This model is a fine-tuned version of allenai/led-base-16384 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.3235
- Rouge2 Precision: 0.018
- Rouge2 Recall: 0.0916
- Rouge2 Fmeasure: 0.0292
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 |
|---|---|---|---|---|---|---|
| 3.1721 | 0.08 | 10 | 2.7742 | 0.0107 | 0.0671 | 0.0178 |
| 3.0802 | 0.16 | 20 | 2.7914 | 0.0111 | 0.0878 | 0.019 |
| 3.0795 | 0.24 | 30 | 2.6954 | 0.0094 | 0.076 | 0.0157 |
| 2.5806 | 0.32 | 40 | 2.6587 | 0.0028 | 0.0271 | 0.0046 |
| 2.6553 | 0.4 | 50 | 2.5958 | 0.0084 | 0.0566 | 0.0143 |
| 2.689 | 0.48 | 60 | 2.4857 | 0.0089 | 0.0733 | 0.015 |
| 2.6642 | 0.56 | 70 | 2.4205 | 0.0069 | 0.0478 | 0.0116 |
| 2.3768 | 0.64 | 80 | 2.3754 | 0.0127 | 0.0795 | 0.0215 |
| 2.1949 | 0.72 | 90 | 2.3752 | 0.0155 | 0.1013 | 0.0258 |
| 2.3257 | 0.8 | 100 | 2.3509 | 0.0155 | 0.1011 | 0.0261 |
| 2.4053 | 0.88 | 110 | 2.3261 | 0.015 | 0.0901 | 0.0246 |
| 2.9896 | 0.96 | 120 | 2.3235 | 0.018 | 0.0916 | 0.0292 |
Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 1.2.1
- Tokenizers 0.12.1
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