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flan-t5-small-coref

This model is a fine-tuned version of google/flan-t5-small on the winograd_wsc dataset.

The model was trained on the task of coreference resolution.

It achieves the following results on the evaluation set:

  • Loss: 0.5656
  • Rouge1: 0.906
  • Rouge2: 0.8192
  • Rougel: 0.9016
  • Rougelsum: 0.9026
  • Gen Len: 23.1724

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: 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: 20

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum Gen Len
No log 1.0 16 1.0901 0.6849 0.561 0.6734 0.6746 18.4483
No log 2.0 32 0.9083 0.8512 0.7509 0.8438 0.8437 21.1379
No log 3.0 48 0.8132 0.8638 0.7728 0.8588 0.8595 21.8276
No log 4.0 64 0.7590 0.8786 0.7842 0.8744 0.876 22.2069
No log 5.0 80 0.7225 0.8846 0.7928 0.8805 0.8817 22.3793
No log 6.0 96 0.6920 0.886 0.7942 0.8821 0.8827 22.4483
No log 7.0 112 0.6660 0.8861 0.7922 0.8816 0.8827 22.5172
No log 8.0 128 0.6470 0.8879 0.7953 0.8836 0.8849 22.6897
No log 9.0 144 0.6318 0.8968 0.806 0.8923 0.8933 23.069
No log 10.0 160 0.6160 0.8968 0.806 0.8923 0.8933 23.069
No log 11.0 176 0.6055 0.9056 0.822 0.9014 0.9021 23.1724
No log 12.0 192 0.5962 0.9056 0.822 0.9014 0.9021 23.1724
No log 13.0 208 0.5884 0.9074 0.8246 0.9033 0.9042 23.2069
No log 14.0 224 0.5825 0.9049 0.8182 0.9005 0.9016 23.2414
No log 15.0 240 0.5769 0.9049 0.8182 0.9005 0.9016 23.2414
No log 16.0 256 0.5727 0.903 0.8132 0.8991 0.8997 23.1724
No log 17.0 272 0.5698 0.906 0.8192 0.9016 0.9026 23.1724
No log 18.0 288 0.5673 0.906 0.8192 0.9016 0.9026 23.1724
No log 19.0 304 0.5661 0.906 0.8192 0.9016 0.9026 23.1724
No log 20.0 320 0.5656 0.906 0.8192 0.9016 0.9026 23.1724

Framework versions

  • Transformers 4.25.1
  • Pytorch 1.13.0+cu116
  • Datasets 2.7.1
  • Tokenizers 0.13.2
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