README.md

metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - multi_news
metrics:
  - rouge
model-index:
  - name: bart-large-cnn-finetuned-multi-news
    results:
      - task:
          name: Sequence-to-sequence Language Modeling
          type: text2text-generation
        dataset:
          name: multi_news
          type: multi_news
          args: default
        metrics:
          - name: Rouge1
            type: rouge
            value: 42.0423

bart-large-cnn-finetuned-multi-news

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

• Loss: 2.0950
• Rouge1: 42.0423
• Rouge2: 14.8812
• Rougel: 23.3412
• Rougelsum: 36.2613

Model description

bart-large-cnn fine tuned on sample of multi-news dataset

Intended uses & limitations

The intended use of the model is for downstream summarization tasks but it's limited to input text 1024 words. Any text longer than that would be truncated.

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
2.2037	1.0	750	2.0950	42.0423	14.8812	23.3412	36.2613

Framework versions

• Transformers 4.18.0
• Pytorch 1.10.0+cu111
• Datasets 2.0.0
• Tokenizers 0.11.6