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LLM_Teached_Bart_50k

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

  • Loss: 1.5590
  • Rouge1: 0.4909
  • Rouge2: 0.2303
  • Rougel: 0.3967
  • Rougelsum: 0.3965
  • Gen Len: 38.2287
  • Precision: 0.9063
  • Recall: 0.9187
  • F1: 0.9123

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: 4
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum Gen Len Precision Recall F1
No log 1.0 390 1.6214 0.4804 0.2218 0.3873 0.3873 38.3549 0.9049 0.9166 0.9106
1.5842 2.0 781 1.5548 0.4874 0.2283 0.3945 0.3945 37.8604 0.9059 0.9171 0.9113
1.3014 3.0 1172 1.5461 0.49 0.2294 0.3975 0.3974 37.7564 0.9064 0.918 0.912
1.18 3.99 1560 1.5590 0.4909 0.2303 0.3967 0.3965 38.2287 0.9063 0.9187 0.9123

Framework versions

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