metadata

license: mit
base_model: alexdg19/bert_large_xsum_samsum2
tags:
  - generated_from_trainer
datasets:
  - cnn_dailymail
metrics:
  - rouge
model-index:
  - name: bert_large_cnn_daily
    results:
      - task:
          name: Sequence-to-sequence Language Modeling
          type: text2text-generation
        dataset:
          name: cnn_dailymail
          type: cnn_dailymail
          config: 3.0.0
          split: test
          args: 3.0.0
        metrics:
          - name: Rouge1
            type: rouge
            value: 0.4251

bert_large_cnn_daily

This model is a fine-tuned version of alexdg19/bert_large_xsum_samsum2 on the cnn_dailymail dataset. It achieves the following results on the evaluation set:

Loss: 1.7065
Rouge1: 0.4251
Rouge2: 0.2024
Rougel: 0.2992
Rougelsum: 0.3961
Gen Len: 60.6232

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: 3
eval_batch_size: 3
seed: 42
gradient_accumulation_steps: 3
total_train_batch_size: 9
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
1.6632	1.0	1021	1.6262	0.4191	0.1992	0.2957	0.39	60.6205
1.3734	2.0	2042	1.6078	0.4253	0.2046	0.3009	0.397	61.0692
1.1497	3.0	3064	1.6759	0.4254	0.2033	0.2998	0.3967	60.8555
1.0123	4.0	4084	1.7065	0.4251	0.2024	0.2992	0.3961	60.6232

Framework versions

Transformers 4.35.0
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1