metadata

base_model: google/pegasus-xsum
tags:
  - generated_from_trainer
datasets:
  - samsum
metrics:
  - rouge
  - precision
  - recall
  - f1
model-index:
  - name: Pegasus_xsum_samsum
    results:
      - task:
          name: Sequence-to-sequence Language Modeling
          type: text2text-generation
        dataset:
          name: samsum
          type: samsum
          config: samsum
          split: validation
          args: samsum
        metrics:
          - name: Rouge1
            type: rouge
            value: 0.5072
          - name: Precision
            type: precision
            value: 0.9247
          - name: Recall
            type: recall
            value: 0.9099
          - name: F1
            type: f1
            value: 0.917

Pegasus_xsum_samsum

This model is a fine-tuned version of google/pegasus-xsum on the samsum dataset. It achieves the following results on the evaluation set:

Loss: 1.4709
Rouge1: 0.5072
Rouge2: 0.2631
Rougel: 0.4243
Rougelsum: 0.4244
Gen Len: 19.1479
Precision: 0.9247
Recall: 0.9099
F1: 0.917

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: 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: 4
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len	Precision	Recall	F1
1.9542	1.0	920	1.5350	0.4928	0.2436	0.4085	0.4086	18.5672	0.9229	0.9074	0.9149
1.6331	2.0	1841	1.4914	0.5037	0.257	0.4202	0.4206	18.8154	0.9246	0.9092	0.9166
1.5694	3.0	2762	1.4761	0.5071	0.259	0.4212	0.4214	19.4487	0.9241	0.9103	0.917
1.5374	4.0	3680	1.4709	0.5072	0.2631	0.4243	0.4244	19.1479	0.9247	0.9099	0.917

Framework versions

Transformers 4.36.0
Pytorch 2.0.1+cu117
Datasets 2.14.5
Tokenizers 0.15.0