Text_Summarization_model_15042024

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: 1.5948
  • Rouge1: 0.2374
  • Rouge2: 0.1905
  • Rougel: 0.2302
  • Rougelsum: 0.2302
  • 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: 8
  • eval_batch_size: 8
  • 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
2.4344 0.5 500 1.9250 0.2184 0.1678 0.2088 0.2088 18.9925
2.0598 1.0 1000 1.8118 0.2247 0.1755 0.2155 0.2155 18.9955
1.9648 1.5 1500 1.7581 0.2303 0.1802 0.2206 0.2206 19.0
1.9119 2.0 2000 1.7214 0.2315 0.1822 0.2221 0.2221 19.0
1.8624 2.5 2500 1.6953 0.2337 0.185 0.2253 0.2253 19.0
1.8508 3.0 3000 1.6769 0.2346 0.186 0.2266 0.2266 19.0
1.8092 3.5 3500 1.6563 0.2353 0.1871 0.2278 0.2279 19.0
1.8065 4.0 4000 1.6377 0.2359 0.188 0.2284 0.2284 19.0
1.7724 4.5 4500 1.6309 0.237 0.1895 0.2297 0.2298 19.0
1.7703 5.0 5000 1.6165 0.2376 0.1899 0.2302 0.2303 19.0
1.7468 5.5 5500 1.6082 0.2374 0.1902 0.2303 0.2303 19.0
1.7347 6.0 6000 1.5992 0.2374 0.1906 0.2303 0.2304 19.0
1.7162 6.5 6500 1.5948 0.2374 0.1905 0.2302 0.2302 19.0

Framework versions

  • Transformers 4.39.3
  • Pytorch 2.1.2
  • Datasets 2.18.0
  • Tokenizers 0.15.2
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