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LLM_Teached_Pegasus_50k

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metadata
base_model: google/pegasus-xsum
tags:
  - generated_from_trainer
metrics:
  - rouge
  - precision
  - recall
  - f1
model-index:
  - name: LLM_Teached_Pegasus_50k
    results: []

LLM_Teached_Pegasus_50k

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

  • Loss: 1.6258
  • Rouge1: 0.4708
  • Rouge2: 0.2214
  • Rougel: 0.3861
  • Rougelsum: 0.3863
  • Gen Len: 26.5411
  • Precision: 0.9108
  • Recall: 0.9093
  • F1: 0.9099

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: 32
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step F1 Gen Len Validation Loss Precision Recall Rouge1 Rouge2 Rougel Rougelsum
No log 1.0 390 0.9034 26.2967 1.8258 0.9049 0.9023 0.4338 0.1906 0.3496 0.3498
2.1621 2.0 781 0.9054 26.2727 1.7537 0.9068 0.9044 0.4449 0.2005 0.3633 0.3633
1.8794 3.0 1172 0.9066 26.4345 1.7268 0.9078 0.9058 0.4518 0.2061 0.3696 0.3695
1.8271 4.0 1560 0.9069 26.3971 1.7157 0.9082 0.906 0.4539 0.2075 0.3716 0.3714
1.8271 5.0 1951 0.9074 26.3015 1.7033 0.9087 0.9065 0.4561 0.2098 0.3735 0.3734
1.8067 6.0 2340 0.9077 26.4389 1.6897 0.9089 0.9069 0.4592 0.2114 0.3762 0.3759
1.7833 7.0 2731 0.9079 26.3745 1.6819 0.9092 0.9071 0.4598 0.2115 0.3764 0.376
1.7683 8.0 3120 0.9083 26.6204 1.6763 0.9094 0.9076 0.4621 0.2133 0.3791 0.3789
1.7559 9.0 3511 0.9086 26.424 1.6662 0.9098 0.9078 0.4632 0.215 0.38 0.3799
1.7559 10.0 3902 0.9089 26.5425 1.6594 0.9099 0.9082 0.4651 0.2168 0.3812 0.3812
1.7357 11.0 4293 0.9091 26.6051 1.6555 0.91 0.9086 0.4663 0.2178 0.3824 0.3823
1.7297 12.0 4680 0.9092 26.4393 1.6508 0.9103 0.9084 0.4668 0.2175 0.3823 0.3822
1.7165 13.0 5071 0.9094 26.6385 1.6451 0.9103 0.9089 0.4687 0.2191 0.3834 0.3834
1.7165 14.0 5462 0.9095 26.4156 1.6405 0.9106 0.9087 0.4691 0.2193 0.3845 0.3844
1.7068 15.0 5853 0.9097 26.4571 1.6383 0.9108 0.9089 0.4699 0.2204 0.3853 0.3853
1.7004 16.0 6240 1.6346 0.4703 0.2204 0.385 0.385 26.4247 0.9108 0.9089 0.9097
1.6923 17.0 6631 1.6305 0.4706 0.221 0.3855 0.3856 26.4436 0.911 0.9091 0.9099
1.6839 18.0 7022 1.6285 0.4712 0.2215 0.3862 0.3864 26.612 0.9106 0.9094 0.9098
1.6839 19.0 7413 1.6263 0.4709 0.2217 0.3862 0.3864 26.5291 0.9108 0.9093 0.9099
1.6743 19.99 7800 1.6258 0.4708 0.2214 0.3861 0.3863 26.5411 0.9108 0.9093 0.9099

Framework versions

  • Transformers 4.36.0
  • Pytorch 2.0.1+cu117
  • Datasets 2.14.5
  • Tokenizers 0.15.0