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metadata
license: mit
base_model: facebook/bart-large-cnn
tags:
  - generated_from_trainer
datasets:
  - samsum
model-index:
  - name: bart-samsum-finetuned
    results: []
metrics:
  - bertscore
  - bleu

bart-samsum-finetuned

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

  • Loss: 0.1326

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: 1e-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
  • lr_scheduler_warmup_steps: 100
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss
0.1196 1.0 74 0.1362
0.0948 2.0 148 0.1334
0.0738 3.0 222 0.1326

Evaluation results

Rouge Scores:

Metric Precision Recall F-Measure
rouge1 Low - 0.2923 Low - 0.5755 Low - 0.3645
Mid - 0.3012 Mid - 0.5881 Mid - 0.3722
High - 0.3108 High - 0.6011 High - 0.3811
rouge2 Low - 0.1185 Low - 0.2418 Low - 0.1481
Mid - 0.1252 Mid - 0.2545 Mid - 0.1555
High - 0.1321 High - 0.2682 High - 0.1632
rougeL Low - 0.2182 Low - 0.4434 Low - 0.2744
Mid - 0.2251 Mid - 0.4547 Mid - 0.2810
High - 0.2328 High - 0.4679 High - 0.2886
rougeLsum Low - 0.2178 Low - 0.4425 Low - 0.2739
Mid - 0.2249 Mid - 0.4546 Mid - 0.2807
High - 0.2321 High - 0.4679 High - 0.2883

BERTScore:

Precision Recall F1
0.6054495 0.6918860 0.6425597

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.2.1+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2