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metadata
license: apache-2.0
base_model: google/t5-efficient-tiny
tags:
  - generated_from_trainer
metrics:
  - rouge
model-index:
  - name: denoice-finetuned-xsum
    results: []

denoice-finetuned-xsum

This model is a fine-tuned version of google/t5-efficient-tiny on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 1.0564
  • Rouge1: 63.8802
  • Rouge2: 45.4086
  • Rougel: 63.8882
  • Rougelsum: 63.8316
  • Gen Len: 17.2016

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: 500
  • eval_batch_size: 500
  • 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
No log 1.0 76 1.1573 62.0366 43.6573 62.0258 61.9691 17.2068
No log 2.0 152 1.1458 61.7366 43.5997 61.7261 61.6638 17.2408
No log 3.0 228 1.1342 62.8021 44.3773 62.8168 62.7397 17.178
No log 4.0 304 1.1221 62.5511 44.4096 62.3775 62.3239 17.1518
No log 5.0 380 1.1177 63.0909 44.9863 62.9819 62.9072 17.1702
No log 6.0 456 1.1123 62.5334 44.2764 62.4559 62.4037 17.2173
1.5445 7.0 532 1.1073 62.8456 44.711 62.7463 62.7041 17.2016
1.5445 8.0 608 1.0983 63.0763 44.9468 62.9522 62.9795 17.2147
1.5445 9.0 684 1.0952 62.9383 44.9129 62.8777 62.8081 17.2487
1.5445 10.0 760 1.0947 62.8263 44.5132 62.7596 62.7362 17.233
1.5445 11.0 836 1.0801 63.0087 44.8035 63.0091 62.9498 17.1806
1.5445 12.0 912 1.0781 62.9718 44.6364 62.881 62.8786 17.1832
1.5445 13.0 988 1.0767 63.0711 44.7516 62.9967 62.9834 17.199
1.4815 14.0 1064 1.0722 63.1128 44.8069 63.0483 63.0151 17.2068
1.4815 15.0 1140 1.0719 63.2282 44.9567 63.2052 63.1787 17.2147
1.4815 16.0 1216 1.0684 63.3222 44.916 63.322 63.2505 17.199
1.4815 17.0 1292 1.0668 63.1931 44.9734 63.1833 63.114 17.2251
1.4815 18.0 1368 1.0640 63.5689 45.1652 63.62 63.5671 17.1806
1.4815 19.0 1444 1.0600 63.5552 45.2046 63.5795 63.5295 17.199
1.4452 20.0 1520 1.0593 63.5801 45.2453 63.5856 63.5245 17.199
1.4452 21.0 1596 1.0594 63.6291 45.1114 63.6412 63.5951 17.2042
1.4452 22.0 1672 1.0571 63.9129 45.3688 63.914 63.8618 17.1702
1.4452 23.0 1748 1.0573 63.8608 45.3548 63.857 63.8156 17.2042
1.4452 24.0 1824 1.0571 63.875 45.3997 63.8858 63.8202 17.2094
1.4452 25.0 1900 1.0564 63.8802 45.4086 63.8882 63.8316 17.2016

Framework versions

  • Transformers 4.36.2
  • Pytorch 1.13.1
  • Datasets 2.16.1
  • Tokenizers 0.15.0