metadata

license: mit
base_model: alexdg19/bert_large_cnn_daily
tags:
  - generated_from_trainer
datasets:
  - cnn_dailymail
metrics:
  - rouge
model-index:
  - name: bert_large_cnn_daily2
    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: 0.4504

bert_large_cnn_daily2

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

Loss: 1.3008
Rouge1: 0.4504
Rouge2: 0.2337
Rougel: 0.3294
Rougelsum: 0.424
Gen Len: 60.2728

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
1.1882	1.0	1021	1.1904	0.4379	0.223	0.318	0.41	61.3551
0.9513	2.0	2042	1.1891	0.4506	0.2353	0.3312	0.4239	59.6771
0.7581	3.0	3064	1.2440	0.4488	0.2317	0.3273	0.4214	59.9909
0.6364	4.0	4084	1.3008	0.4504	0.2337	0.3294	0.424	60.2728

Framework versions

Transformers 4.35.0
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1