README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - multi_news
metrics:
  - rouge
model-index:
  - name: finetuned_multi_news_bart_text_summarisation
    results:
      - task:
          name: Sequence-to-sequence Language Modeling
          type: textsummarization
        dataset:
          name: multi_news
          type: multi_news
          config: default
          split: test
          args: default
        metrics:
          - name: Rouge1
            type: rouge
            value: 0.4038
pipeline_tag: summarization

finetuned_multi_news_bart_text_summarisation

This model is a fine-tuned version of slauw87/bart_summarisation on the multi_news dataset. It achieves the following results on the evaluation set:

• Loss: 2.8952
• Rouge1: 0.4038
• Rouge2: 0.1389
• Rougel: 0.2155
• Rougelsum: 0.2147
• Gen Len: 138.7667

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: 2
• eval_batch_size: 2
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
No log	1.0	15	2.9651	0.3903	0.134	0.21	0.2098	137.6
No log	2.0	30	2.8952	0.4038	0.1389	0.2155	0.2147	138.7667

Framework versions

• Transformers 4.30.2
• Pytorch 2.0.1+cu118
• Datasets 2.13.1
• Tokenizers 0.13.3