metadata

license: mit
base_model: facebook/bart-large-xsum
tags:
  - generated_from_trainer
datasets:
  - samsum
metrics:
  - rouge
model-index:
  - name: bart-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: 0.547

bart-samsum

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

Loss: 1.3852
Rouge1: 0.547
Rouge2: 0.2837
Rougel: 0.4462
Rougelsum: 0.4454
Gen Len: 29.72

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: 2
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
1.5201	0.27	500	1.4589	0.5276	0.2694	0.4246	0.424	33.5067
1.3757	0.54	1000	1.5105	0.506	0.2566	0.415	0.4146	29.76
1.3496	0.81	1500	1.4039	0.5365	0.2759	0.4233	0.4221	29.8
1.094	1.09	2000	1.4119	0.5407	0.2827	0.4293	0.4288	29.84
1.1488	1.36	2500	1.3680	0.5275	0.2637	0.423	0.4224	26.92
1.1222	1.63	3000	1.2875	0.5369	0.2844	0.4473	0.4463	29.2267
1.1092	1.9	3500	1.3968	0.533	0.2818	0.4354	0.4363	30.0667
0.8509	2.17	4000	1.3682	0.5306	0.2874	0.4327	0.4331	29.1467
0.9565	2.44	4500	1.3450	0.5466	0.2782	0.4419	0.4409	29.2133
0.8496	2.72	5000	1.3768	0.5366	0.2807	0.4359	0.4351	30.7733
0.8397	2.99	5500	1.3852	0.547	0.2837	0.4462	0.4454	29.72

Framework versions

Transformers 4.33.1
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3