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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 (#6)
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
  - summarization
base_model: facebook/bart-base
model-index:
  - name: bart-base-xsum
    results:
      - task:
          type: summarization
          name: Summarization
        dataset:
          name: xsum
          type: xsum
          config: default
          split: test
        metrics:
          - type: rouge
            value: 38.6513
            name: ROUGE-1
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      - task:
          type: summarization
          name: Summarization
        dataset:
          name: xsum
          type: xsum
          config: default
          split: validation
        metrics:
          - type: rouge
            value: 38.7415
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      - task:
          type: summarization
          name: Summarization
        dataset:
          name: cnn_dailymail
          type: cnn_dailymail
          config: 3.0.0
          split: test
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bart-base-xsum

Training: The model has been trained using the script provided in the following repository https://github.com/MorenoLaQuatra/transformers-tasks-templates

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