text_shortening_model_v57
This model is a fine-tuned version of t5-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.8302
- Rouge1: 0.676
- Rouge2: 0.4816
- Rougel: 0.6172
- Rougelsum: 0.6179
- Bert precision: 0.9135
- Bert recall: 0.9082
- Bert f1-score: 0.9104
- Average word count: 8.1161
- Max word count: 16
- Min word count: 4
- Average token count: 12.2946
- % shortened texts with length > 12: 6.25
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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
Training results
Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bert precision | Bert recall | Bert f1-score | Average word count | Max word count | Min word count | Average token count | % shortened texts with length > 12 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2.6374 | 1.0 | 49 | 2.0882 | 0.3209 | 0.1826 | 0.2886 | 0.2875 | 0.7596 | 0.789 | 0.7725 | 9.6562 | 17 | 0 | 15.9018 | 30.3571 |
2.1652 | 2.0 | 98 | 1.7029 | 0.3216 | 0.1848 | 0.2939 | 0.2932 | 0.7531 | 0.7906 | 0.77 | 9.7054 | 17 | 0 | 16.0402 | 25.0 |
1.8731 | 3.0 | 147 | 1.4874 | 0.4234 | 0.277 | 0.3972 | 0.3951 | 0.8117 | 0.8225 | 0.8158 | 8.7455 | 17 | 0 | 14.567 | 21.4286 |
1.6683 | 4.0 | 196 | 1.3346 | 0.5198 | 0.3525 | 0.4889 | 0.4888 | 0.8491 | 0.8338 | 0.8405 | 7.4598 | 17 | 0 | 12.7188 | 13.3929 |
1.5287 | 5.0 | 245 | 1.2321 | 0.5535 | 0.377 | 0.5101 | 0.5109 | 0.8713 | 0.8588 | 0.8643 | 7.7143 | 17 | 0 | 12.6384 | 12.0536 |
1.4303 | 6.0 | 294 | 1.1638 | 0.5773 | 0.3941 | 0.529 | 0.529 | 0.8848 | 0.8703 | 0.8769 | 7.5 | 17 | 0 | 12.2009 | 9.375 |
1.3696 | 7.0 | 343 | 1.1091 | 0.5945 | 0.4125 | 0.5426 | 0.5421 | 0.8946 | 0.8814 | 0.8874 | 7.6473 | 16 | 0 | 12.1875 | 9.375 |
1.305 | 8.0 | 392 | 1.0707 | 0.597 | 0.4082 | 0.5434 | 0.5432 | 0.8959 | 0.883 | 0.8889 | 7.6741 | 16 | 0 | 12.2143 | 8.0357 |
1.2575 | 9.0 | 441 | 1.0384 | 0.6094 | 0.4193 | 0.5521 | 0.553 | 0.8993 | 0.8868 | 0.8925 | 7.6786 | 17 | 0 | 12.125 | 7.1429 |
1.241 | 10.0 | 490 | 1.0125 | 0.617 | 0.423 | 0.5595 | 0.5601 | 0.9038 | 0.8918 | 0.8973 | 7.817 | 17 | 2 | 12.1027 | 7.5893 |
1.17 | 11.0 | 539 | 0.9912 | 0.6173 | 0.4236 | 0.5593 | 0.5591 | 0.9052 | 0.892 | 0.8981 | 7.7455 | 17 | 2 | 11.9911 | 6.25 |
1.1413 | 12.0 | 588 | 0.9750 | 0.6253 | 0.4322 | 0.5661 | 0.5663 | 0.9049 | 0.8935 | 0.8986 | 7.9286 | 17 | 2 | 12.1786 | 7.1429 |
1.1367 | 13.0 | 637 | 0.9586 | 0.63 | 0.4356 | 0.5704 | 0.5704 | 0.9068 | 0.8943 | 0.9 | 7.8705 | 17 | 2 | 12.0357 | 6.6964 |
1.1101 | 14.0 | 686 | 0.9458 | 0.6273 | 0.4355 | 0.5665 | 0.5671 | 0.9057 | 0.8949 | 0.8998 | 7.942 | 17 | 2 | 12.1384 | 6.6964 |
1.0711 | 15.0 | 735 | 0.9374 | 0.6357 | 0.4424 | 0.5718 | 0.5721 | 0.9068 | 0.8969 | 0.9013 | 8.0179 | 17 | 2 | 12.1875 | 6.6964 |
1.0553 | 16.0 | 784 | 0.9282 | 0.6378 | 0.4455 | 0.5752 | 0.5756 | 0.9084 | 0.8969 | 0.9022 | 7.8571 | 16 | 2 | 12.0536 | 4.9107 |
1.047 | 17.0 | 833 | 0.9188 | 0.6439 | 0.4525 | 0.5821 | 0.5825 | 0.9085 | 0.8996 | 0.9035 | 7.9955 | 16 | 2 | 12.1741 | 5.3571 |
1.0201 | 18.0 | 882 | 0.9104 | 0.643 | 0.4536 | 0.5832 | 0.5836 | 0.9083 | 0.8997 | 0.9035 | 8.0134 | 16 | 2 | 12.2098 | 5.8036 |
1.0228 | 19.0 | 931 | 0.9023 | 0.6471 | 0.4601 | 0.5862 | 0.5865 | 0.9101 | 0.902 | 0.9056 | 8.0 | 16 | 2 | 12.1652 | 4.9107 |
0.9896 | 20.0 | 980 | 0.8936 | 0.6497 | 0.463 | 0.5882 | 0.5888 | 0.9103 | 0.9017 | 0.9055 | 8.0491 | 16 | 2 | 12.1741 | 5.3571 |
0.9815 | 21.0 | 1029 | 0.8873 | 0.6555 | 0.4659 | 0.5937 | 0.5948 | 0.9106 | 0.9025 | 0.9061 | 8.0402 | 16 | 2 | 12.2411 | 5.8036 |
0.9877 | 22.0 | 1078 | 0.8828 | 0.6618 | 0.4728 | 0.6005 | 0.6007 | 0.9125 | 0.9047 | 0.9081 | 8.1205 | 16 | 2 | 12.308 | 6.25 |
0.9696 | 23.0 | 1127 | 0.8774 | 0.661 | 0.4679 | 0.6 | 0.5994 | 0.9128 | 0.9046 | 0.9082 | 8.0938 | 16 | 3 | 12.2902 | 6.25 |
0.9556 | 24.0 | 1176 | 0.8737 | 0.6613 | 0.4717 | 0.6023 | 0.6022 | 0.913 | 0.9052 | 0.9086 | 8.0893 | 16 | 3 | 12.3036 | 6.25 |
0.95 | 25.0 | 1225 | 0.8703 | 0.6636 | 0.4725 | 0.6044 | 0.6041 | 0.913 | 0.9055 | 0.9088 | 8.1384 | 16 | 3 | 12.3616 | 6.25 |
0.9464 | 26.0 | 1274 | 0.8660 | 0.6629 | 0.4723 | 0.6057 | 0.6052 | 0.9125 | 0.9053 | 0.9085 | 8.1562 | 16 | 3 | 12.3482 | 6.25 |
0.9189 | 27.0 | 1323 | 0.8605 | 0.6633 | 0.4746 | 0.6084 | 0.6079 | 0.9124 | 0.9052 | 0.9083 | 8.0848 | 16 | 3 | 12.2723 | 6.25 |
0.9277 | 28.0 | 1372 | 0.8583 | 0.662 | 0.4731 | 0.6059 | 0.6057 | 0.9118 | 0.9059 | 0.9084 | 8.1607 | 16 | 3 | 12.3304 | 6.25 |
0.9142 | 29.0 | 1421 | 0.8550 | 0.6663 | 0.4784 | 0.6106 | 0.6104 | 0.9126 | 0.9073 | 0.9095 | 8.1786 | 16 | 4 | 12.3482 | 6.6964 |
0.913 | 30.0 | 1470 | 0.8529 | 0.6656 | 0.477 | 0.6073 | 0.607 | 0.9123 | 0.9073 | 0.9093 | 8.2589 | 16 | 4 | 12.4241 | 7.1429 |
0.8984 | 31.0 | 1519 | 0.8507 | 0.6708 | 0.4804 | 0.6114 | 0.6116 | 0.9128 | 0.9083 | 0.9101 | 8.2098 | 16 | 4 | 12.3973 | 6.6964 |
0.903 | 32.0 | 1568 | 0.8479 | 0.6728 | 0.4777 | 0.6096 | 0.6096 | 0.9133 | 0.9081 | 0.9103 | 8.2232 | 16 | 4 | 12.3973 | 7.1429 |
0.8947 | 33.0 | 1617 | 0.8452 | 0.6741 | 0.4785 | 0.6119 | 0.6116 | 0.9132 | 0.9081 | 0.9101 | 8.1741 | 16 | 4 | 12.3616 | 7.1429 |
0.8883 | 34.0 | 1666 | 0.8424 | 0.6733 | 0.4766 | 0.6108 | 0.6107 | 0.9125 | 0.9072 | 0.9094 | 8.1607 | 16 | 4 | 12.3438 | 7.1429 |
0.877 | 35.0 | 1715 | 0.8403 | 0.6742 | 0.4799 | 0.6141 | 0.6145 | 0.9133 | 0.908 | 0.9102 | 8.1429 | 16 | 4 | 12.3304 | 6.6964 |
0.8612 | 36.0 | 1764 | 0.8393 | 0.6737 | 0.4808 | 0.6141 | 0.6143 | 0.9133 | 0.908 | 0.9102 | 8.1384 | 16 | 4 | 12.3259 | 6.6964 |
0.8848 | 37.0 | 1813 | 0.8363 | 0.673 | 0.478 | 0.6131 | 0.6133 | 0.9124 | 0.9074 | 0.9095 | 8.1384 | 16 | 4 | 12.3214 | 6.6964 |
0.8717 | 38.0 | 1862 | 0.8363 | 0.6729 | 0.478 | 0.613 | 0.6132 | 0.9129 | 0.9075 | 0.9097 | 8.0848 | 16 | 4 | 12.2545 | 5.8036 |
0.8739 | 39.0 | 1911 | 0.8355 | 0.6711 | 0.4775 | 0.6115 | 0.6118 | 0.913 | 0.9072 | 0.9096 | 8.0714 | 16 | 4 | 12.2366 | 5.8036 |
0.8569 | 40.0 | 1960 | 0.8343 | 0.672 | 0.4772 | 0.6125 | 0.6128 | 0.9132 | 0.9074 | 0.9098 | 8.0804 | 16 | 4 | 12.2366 | 5.8036 |
0.8601 | 41.0 | 2009 | 0.8342 | 0.675 | 0.4831 | 0.6163 | 0.6165 | 0.9139 | 0.9081 | 0.9105 | 8.0982 | 16 | 4 | 12.2634 | 5.8036 |
0.8519 | 42.0 | 2058 | 0.8330 | 0.6743 | 0.481 | 0.6147 | 0.6152 | 0.9137 | 0.908 | 0.9104 | 8.1027 | 16 | 4 | 12.2723 | 5.8036 |
0.8713 | 43.0 | 2107 | 0.8322 | 0.6757 | 0.4844 | 0.617 | 0.6172 | 0.9133 | 0.9079 | 0.9102 | 8.125 | 16 | 4 | 12.2857 | 6.25 |
0.8554 | 44.0 | 2156 | 0.8313 | 0.6746 | 0.4809 | 0.6151 | 0.6154 | 0.9132 | 0.9079 | 0.9101 | 8.1384 | 16 | 4 | 12.2857 | 6.25 |
0.8559 | 45.0 | 2205 | 0.8314 | 0.6773 | 0.4849 | 0.6184 | 0.6189 | 0.9137 | 0.9085 | 0.9106 | 8.125 | 16 | 4 | 12.2857 | 6.25 |
0.847 | 46.0 | 2254 | 0.8312 | 0.6767 | 0.4829 | 0.6175 | 0.6176 | 0.9136 | 0.9084 | 0.9105 | 8.125 | 16 | 4 | 12.2857 | 6.25 |
0.8588 | 47.0 | 2303 | 0.8306 | 0.6754 | 0.4814 | 0.6163 | 0.6163 | 0.9131 | 0.9082 | 0.9102 | 8.1473 | 16 | 4 | 12.3214 | 6.6964 |
0.8484 | 48.0 | 2352 | 0.8304 | 0.676 | 0.4816 | 0.6172 | 0.6179 | 0.9135 | 0.9082 | 0.9104 | 8.1161 | 16 | 4 | 12.2946 | 6.25 |
0.8514 | 49.0 | 2401 | 0.8303 | 0.676 | 0.4816 | 0.6172 | 0.6179 | 0.9135 | 0.9082 | 0.9104 | 8.1161 | 16 | 4 | 12.2946 | 6.25 |
0.8562 | 50.0 | 2450 | 0.8302 | 0.676 | 0.4816 | 0.6172 | 0.6179 | 0.9135 | 0.9082 | 0.9104 | 8.1161 | 16 | 4 | 12.2946 | 6.25 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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Base model
google-t5/t5-small