metadata

license: apache-2.0
tags:
  - summarization
  - generated_from_trainer
datasets:
  - cnn_dailymail
metrics:
  - rouge
model-index:
  - name: bart-base-finetuned-cnn_dailymail
    results:
      - task:
          type: text2text-generation
          name: Sequence-to-sequence Language Modeling
        dataset:
          name: cnn_dailymail
          type: cnn_dailymail
          config: 3.0.0
          split: train
          args: 3.0.0
        metrics:
          - type: rouge
            value: 0.35105989316705805
            name: Rouge1
      - task:
          type: summarization
          name: Summarization
        dataset:
          name: cnn_dailymail
          type: cnn_dailymail
          config: 3.0.0
          split: test
        metrics:
          - type: rouge
            value: 24.5323
            name: ROUGE-1
            verified: true
            verifyToken: >-
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          - type: rouge
            value: 12.1475
            name: ROUGE-2
            verified: true
            verifyToken: >-
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          - type: rouge
            value: 20.4203
            name: ROUGE-L
            verified: true
            verifyToken: >-
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          - type: rouge
            value: 23.0696
            name: ROUGE-LSUM
            verified: true
            verifyToken: >-
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          - type: loss
            value: 15.141033172607422
            name: loss
            verified: true
            verifyToken: >-
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          - type: gen_len
            value: 20
            name: gen_len
            verified: true
            verifyToken: >-
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bart-base-finetuned-cnn_dailymail

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

Loss: 1.5396
Rouge1: 0.3511
Rouge2: 0.1925
Rougel: 0.3086
Rougelsum: 0.3292

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: 5.6e-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
num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
1.9486	1.0	35890	1.5941	0.3498	0.1893	0.3063	0.3272
1.6706	2.0	71780	1.5601	0.3503	0.1916	0.3079	0.3279
1.4809	3.0	107670	1.5423	0.3520	0.1923	0.3086	0.3295
1.3293	4.0	143560	1.5396	0.3511	0.1925	0.3086	0.3292

Framework versions

Transformers 4.24.0
Pytorch 1.12.1+cu113
Datasets 2.7.1
Tokenizers 0.13.2