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bart-paraphrase-finetuned-xsum-v3
This model is a fine-tuned version of eugenesiow/bart-paraphrase on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3377
- Rouge1: 99.9461
- Rouge2: 72.6619
- Rougel: 99.9461
- Rougelsum: 99.9461
- Gen Len: 9.0396
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: 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: 20
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
---|---|---|---|---|---|---|---|---|
No log | 1.0 | 139 | 0.3653 | 96.4972 | 70.8271 | 96.5252 | 96.5085 | 9.7158 |
No log | 2.0 | 278 | 0.6624 | 98.3228 | 72.2829 | 98.2598 | 98.2519 | 9.0612 |
No log | 3.0 | 417 | 0.2880 | 98.2415 | 72.36 | 98.249 | 98.2271 | 9.4496 |
0.5019 | 4.0 | 556 | 0.4188 | 98.1123 | 70.8536 | 98.0746 | 98.0465 | 9.4065 |
0.5019 | 5.0 | 695 | 0.3718 | 98.8882 | 72.6619 | 98.8997 | 98.8882 | 10.7842 |
0.5019 | 6.0 | 834 | 0.4442 | 99.6076 | 72.6619 | 99.6076 | 99.598 | 9.0647 |
0.5019 | 7.0 | 973 | 0.2681 | 99.6076 | 72.6619 | 99.598 | 99.598 | 9.1403 |
0.2751 | 8.0 | 1112 | 0.3577 | 99.2479 | 72.6619 | 99.2536 | 99.2383 | 9.0612 |
0.2751 | 9.0 | 1251 | 0.2481 | 98.8785 | 72.6394 | 98.8882 | 98.8882 | 9.7914 |
0.2751 | 10.0 | 1390 | 0.2339 | 99.6076 | 72.6619 | 99.6076 | 99.6076 | 9.1942 |
0.2051 | 11.0 | 1529 | 0.2472 | 99.9461 | 72.6619 | 99.9461 | 99.9461 | 9.2338 |
0.2051 | 12.0 | 1668 | 0.3948 | 99.6076 | 72.6619 | 99.598 | 99.598 | 9.0468 |
0.2051 | 13.0 | 1807 | 0.4756 | 99.6076 | 72.6619 | 99.6076 | 99.6076 | 9.0576 |
0.2051 | 14.0 | 1946 | 0.3543 | 99.9461 | 72.6619 | 99.9461 | 99.9461 | 9.0396 |
0.1544 | 15.0 | 2085 | 0.2828 | 99.9461 | 72.6619 | 99.9461 | 99.9461 | 9.0576 |
0.1544 | 16.0 | 2224 | 0.2456 | 99.9461 | 72.6619 | 99.9461 | 99.9461 | 9.1079 |
0.1544 | 17.0 | 2363 | 0.2227 | 99.9461 | 72.6394 | 99.9461 | 99.9461 | 9.5072 |
0.1285 | 18.0 | 2502 | 0.3490 | 99.9461 | 72.6619 | 99.9461 | 99.9461 | 9.0396 |
0.1285 | 19.0 | 2641 | 0.3736 | 99.9461 | 72.6619 | 99.9461 | 99.9461 | 9.0396 |
0.1285 | 20.0 | 2780 | 0.3377 | 99.9461 | 72.6619 | 99.9461 | 99.9461 | 9.0396 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1