metadata

license: apache-2.0
base_model: facebook/bart-large
tags:
  - generated_from_trainer
datasets:
  - reddit_tifu
metrics:
  - rouge
  - precision
  - recall
  - f1
model-index:
  - name: Bart_reddit_tifu
    results:
      - task:
          name: Sequence-to-sequence Language Modeling
          type: text2text-generation
        dataset:
          name: reddit_tifu
          type: reddit_tifu
          config: long
          split: train
          args: long
        metrics:
          - name: Rouge1
            type: rouge
            value: 0.2709
          - name: Precision
            type: precision
            value: 0.8768
          - name: Recall
            type: recall
            value: 0.8648
          - name: F1
            type: f1
            value: 0.8705

Bart_reddit_tifu

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

Loss: 2.5035
Rouge1: 0.2709
Rouge2: 0.0948
Rougel: 0.2244
Rougelsum: 0.2244
Gen Len: 19.3555
Precision: 0.8768
Recall: 0.8648
F1: 0.8705

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
2.6968	1.0	2370	2.5385	0.2634	0.0907	0.218	0.2182	19.4438	0.8766	0.8641	0.8701
2.4746	2.0	4741	2.5077	0.273	0.0941	0.2238	0.2239	19.2572	0.8774	0.8655	0.8712
2.3066	3.0	7111	2.5012	0.2671	0.0936	0.221	0.2211	19.3071	0.8756	0.864	0.8696
2.2041	4.0	9480	2.5035	0.2709	0.0948	0.2244	0.2244	19.3555	0.8768	0.8648	0.8705

Framework versions

Transformers 4.36.0
Pytorch 2.0.1+cu117
Datasets 2.14.5
Tokenizers 0.15.0