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bart-base-xsum

Summarization
Transformers
PyTorch
Safetensors
bart
text2text-generation
Generated from Trainer
Eval Results
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bart-base-xsum / README.md
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Add evaluation results on the default config and test split of xsum
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
  - summarization
model-index:
  - name: bart-base-xsum
    results:
      - task:
          type: summarization
          name: Summarization
        dataset:
          name: xsum
          type: xsum
          config: default
          split: test
        metrics:
          - name: ROUGE-1
            type: rouge
            value: 38.643
            verified: true
          - name: ROUGE-2
            type: rouge
            value: 17.7546
            verified: true
          - name: ROUGE-L
            type: rouge
            value: 32.2114
            verified: true
          - name: ROUGE-LSUM
            type: rouge
            value: 32.2207
            verified: true
          - name: loss
            type: loss
            value: 1.8224396705627441
            verified: true
          - name: gen_len
            type: gen_len
            value: 19.7028
            verified: true
    dataset:
      type:
        xsum: null
      name:
        xsum: null

bart-base-xsum

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

  • Loss: 0.8051
  • R1: 0.5643
  • R2: 0.3017
  • Rl: 0.5427
  • Rlsum: 0.5427

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: 5e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss R1 R2 Rl Rlsum
0.8983 1.0 6377 0.8145 0.5443 0.2724 0.5212 0.5211
0.8211 2.0 12754 0.7940 0.5519 0.2831 0.5295 0.5295
0.7701 3.0 19131 0.7839 0.5569 0.2896 0.5347 0.5348
0.7046 4.0 25508 0.7792 0.5615 0.2956 0.5394 0.5393
0.6837 5.0 31885 0.7806 0.5631 0.2993 0.5416 0.5416
0.6412 6.0 38262 0.7816 0.5643 0.301 0.5427 0.5426
0.6113 7.0 44639 0.7881 0.5645 0.3017 0.5428 0.5428
0.5855 8.0 51016 0.7921 0.5651 0.303 0.5433 0.5432
0.5636 9.0 57393 0.7972 0.5649 0.3032 0.5433 0.5433
0.5482 10.0 63770 0.7996 0.565 0.3036 0.5436 0.5435

Framework versions

  • Transformers 4.22.1
  • Pytorch 1.11.0+cu113
  • Datasets 2.0.0
  • Tokenizers 0.11.6