t5_small_SA_abbr_replaced
This model is a fine-tuned version of t5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5633
- Rouge1: 0.2325
- Rouge2: 0.1112
- Rougel: 0.2228
- Rougelsum: 0.2228
- Gen Len: 13.115
- Bert Score F1: 0.8407
- Bert Score Precision: 0.8551
- Bert Score Recall: 0.8282
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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12
Training results
Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Bert Score F1 | Bert Score Precision | Bert Score Recall |
---|---|---|---|---|---|---|---|---|---|---|---|
0.6839 | 0.95 | 500 | 0.6228 | 0.1495 | 0.0654 | 0.1433 | 0.1421 | 10.3894 | 0.6689 | 0.6835 | 0.656 |
0.5918 | 1.9 | 1000 | 0.6084 | 0.1965 | 0.0775 | 0.1893 | 0.1904 | 13.6991 | 0.8138 | 0.827 | 0.8024 |
0.5917 | 2.85 | 1500 | 0.5943 | 0.2075 | 0.0804 | 0.1975 | 0.1982 | 12.5841 | 0.8293 | 0.8452 | 0.8153 |
0.5524 | 3.8 | 2000 | 0.5859 | 0.2104 | 0.0856 | 0.1994 | 0.2 | 13.4602 | 0.8373 | 0.8523 | 0.8242 |
0.5635 | 4.74 | 2500 | 0.5830 | 0.2069 | 0.0905 | 0.1974 | 0.1984 | 12.1327 | 0.8379 | 0.8549 | 0.8231 |
0.5455 | 5.69 | 3000 | 0.5756 | 0.2113 | 0.0963 | 0.2038 | 0.2032 | 12.6903 | 0.8398 | 0.8545 | 0.8269 |
0.5156 | 6.64 | 3500 | 0.5710 | 0.2147 | 0.0993 | 0.2093 | 0.2096 | 13.115 | 0.8393 | 0.8527 | 0.8278 |
0.5134 | 7.59 | 4000 | 0.5683 | 0.2252 | 0.105 | 0.216 | 0.2168 | 12.4513 | 0.8418 | 0.8572 | 0.8285 |
0.5381 | 8.54 | 4500 | 0.5661 | 0.2228 | 0.1051 | 0.215 | 0.2151 | 13.4602 | 0.8401 | 0.8534 | 0.8287 |
0.5092 | 9.49 | 5000 | 0.5643 | 0.2186 | 0.1027 | 0.2135 | 0.2132 | 13.1593 | 0.8385 | 0.8524 | 0.8265 |
0.5181 | 10.44 | 5500 | 0.5643 | 0.2299 | 0.1087 | 0.2196 | 0.2202 | 13.0 | 0.8398 | 0.8543 | 0.8273 |
0.4824 | 11.39 | 6000 | 0.5633 | 0.2325 | 0.1112 | 0.2228 | 0.2228 | 13.115 | 0.8407 | 0.8551 | 0.8282 |
Framework versions
- Transformers 4.27.2
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
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