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Text_Summarization

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

  • Loss: 1.4199
  • Rouge1: 0.2439
  • Rouge2: 0.2006
  • Rougel: 0.2365
  • Rougelsum: 0.2366
  • Gen Len: 18.9994

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: 12
  • eval_batch_size: 12
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 15
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum Gen Len
1.9264 1.0 1580 1.6705 0.2329 0.1842 0.223 0.223 18.9994
1.8184 2.0 3160 1.5849 0.2376 0.1894 0.2287 0.2288 18.9988
1.7427 3.0 4740 1.5382 0.2379 0.1914 0.2296 0.2297 18.9994
1.7067 4.0 6320 1.5073 0.2397 0.1943 0.2318 0.2318 19.0
1.6783 5.0 7900 1.4873 0.2406 0.1957 0.2329 0.2329 19.0
1.6585 6.0 9480 1.4716 0.242 0.1976 0.2343 0.2343 19.0
1.6457 7.0 11060 1.4572 0.2427 0.1988 0.2351 0.2351 19.0
1.6129 8.0 12640 1.4488 0.2433 0.1995 0.2357 0.2358 19.0
1.6014 9.0 14220 1.4405 0.2435 0.1999 0.236 0.236 19.0
1.5851 10.0 15800 1.4337 0.2439 0.2002 0.2364 0.2365 18.9994
1.5859 11.0 17380 1.4281 0.2436 0.2 0.2362 0.2362 19.0
1.573 12.0 18960 1.4247 0.244 0.2005 0.2365 0.2366 18.9994
1.5826 13.0 20540 1.4220 0.244 0.2007 0.2365 0.2365 18.9994
1.5674 14.0 22120 1.4205 0.2439 0.2006 0.2365 0.2365 18.9994
1.572 15.0 23700 1.4199 0.2439 0.2006 0.2365 0.2366 18.9994

Framework versions

  • Transformers 4.45.1
  • Pytorch 2.4.0
  • Datasets 3.0.1
  • Tokenizers 0.20.0
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