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t5-fine-tuned-with-25-yake-keywords

This model is a fine-tuned version of t5-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.7255
  • Rouge1: 25.5531
  • Rouge2: 11.1657
  • Rougel: 20.7513
  • Rougelsum: 24.054
  • Gen Len: 19.0

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: 25

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum Gen Len
1.3097 1.0 604 1.3789 25.5146 11.2077 20.8249 23.9199 19.0
1.1951 2.0 1208 1.3779 25.3347 11.2485 20.6781 23.7106 19.0
1.1081 3.0 1812 1.3903 26.1109 11.8345 21.2205 24.551 18.994
1.0272 4.0 2416 1.4042 26.027 11.5618 21.1159 24.3576 18.992
0.919 5.0 3020 1.4225 25.8294 11.5972 21.0053 24.3003 18.992
0.8643 6.0 3624 1.4410 25.9719 11.6151 21.0454 24.4411 18.99
0.8215 7.0 4228 1.4599 25.68 11.2692 20.9075 24.2681 19.0
0.7931 8.0 4832 1.4926 25.0808 10.9178 20.4053 23.6258 19.0
0.7664 9.0 5436 1.5090 25.458 10.9978 20.6381 23.9113 19.0
0.7053 10.0 6040 1.5259 25.4787 10.8938 20.5842 23.9459 18.998
0.6725 11.0 6644 1.5481 25.2993 10.7172 20.5288 23.8319 19.0
0.6462 12.0 7248 1.5710 25.6251 11.0816 20.7758 24.082 19.0
0.6275 13.0 7852 1.5884 25.8573 11.0737 20.988 24.294 19.0
0.5838 14.0 8456 1.6131 26.1096 11.3973 21.3659 24.6114 19.0
0.5682 15.0 9060 1.6259 25.7213 11.1484 20.8604 24.1114 19.0
0.5629 16.0 9664 1.6473 25.6197 11.2045 20.8956 24.1237 19.0
0.5446 17.0 10268 1.6645 25.4284 10.7362 20.4946 23.9147 19.0
0.5108 18.0 10872 1.6716 25.6986 11.2317 20.8851 24.272 19.0
0.5358 19.0 11476 1.6882 25.8002 11.2396 21.0001 24.2643 19.0
0.4959 20.0 12080 1.7027 25.636 11.2417 20.8785 24.1355 18.992
0.4942 21.0 12684 1.7131 25.6154 11.1795 20.7925 24.1343 19.0
0.4833 22.0 13288 1.7178 25.7708 11.2434 20.9096 24.1974 19.0
0.4702 23.0 13892 1.7227 25.6977 11.2352 20.9147 24.1121 19.0
0.4747 24.0 14496 1.7241 25.6248 11.2042 20.8192 24.1186 19.0
0.4691 25.0 15100 1.7255 25.5531 11.1657 20.7513 24.054 19.0

Framework versions

  • Transformers 4.34.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.14.1
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