Hugging Face's logo

GlycerinLOL
/
LLM_Teached_Pegasus_50k

Text2Text Generation
Transformers
TensorBoard
Safetensors
pegasus
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
LLM_Teached_Pegasus_50k / README.md
GlycerinLOL's picture
GlycerinLOL
Model save
5bcd218 verified
preview code
|
raw
history blame
3.76 kB
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: 1.6378
  • Rouge1: 0.4698
  • Rouge2: 0.2197
  • Rougel: 0.385
  • Rougelsum: 0.3849
  • Gen Len: 26.5251
  • Precision: 0.9107
  • Recall: 0.909
  • F1: 0.9096

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: 16
  • 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 1.6508 0.4668 0.2175 0.3823 0.3822 26.4393 0.9103 0.9084 0.9092
1.7165 13.0 5071 1.6451 0.4687 0.2191 0.3834 0.3834 26.6385 0.9103 0.9089 0.9094
1.7165 14.0 5462 1.6405 0.4691 0.2193 0.3845 0.3844 26.4156 0.9106 0.9087 0.9095
1.7068 15.0 5853 1.6383 0.4699 0.2204 0.3853 0.3853 26.4571 0.9108 0.9089 0.9097
1.7004 15.99 6240 1.6378 0.4698 0.2197 0.385 0.3849 26.5251 0.9107 0.909 0.9096

Framework versions

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