metadata

license: apache-2.0
base_model: facebook/bart-large
tags:
  - generated_from_trainer
datasets:
  - mediasum
metrics:
  - rouge
  - precision
  - recall
  - f1
model-index:
  - name: Bart_mediasum
    results:
      - task:
          name: Sequence-to-sequence Language Modeling
          type: text2text-generation
        dataset:
          name: mediasum
          type: mediasum
          config: roberta_prepended
          split: validation
          args: roberta_prepended
        metrics:
          - name: Rouge1
            type: rouge
            value: 0.3236
          - name: Precision
            type: precision
            value: 0.8858
          - name: Recall
            type: recall
            value: 0.8739
          - name: F1
            type: f1
            value: 0.8795

Bart_mediasum

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

Loss: 1.9021
Rouge1: 0.3236
Rouge2: 0.1651
Rougel: 0.2953
Rougelsum: 0.2953
Gen Len: 15.7946
Precision: 0.8858
Recall: 0.8739
F1: 0.8795

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: 2e-05
train_batch_size: 24
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 96
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 4
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len	Precision	Recall	F1
2.1171	1.0	4621	2.0135	0.3138	0.1556	0.2853	0.2853	16.4704	0.8836	0.8717	0.8773
1.9804	2.0	9242	1.9440	0.3147	0.1581	0.2864	0.2866	16.2207	0.8831	0.8725	0.8775
1.8971	3.0	13863	1.9157	0.3209	0.1638	0.2925	0.2926	15.4676	0.8857	0.8733	0.8792
1.8449	4.0	18484	1.9021	0.3236	0.1651	0.2953	0.2953	15.7946	0.8858	0.8739	0.8795

Framework versions

Transformers 4.36.0
Pytorch 2.0.1+cu117
Datasets 2.14.5
Tokenizers 0.15.0