bart-ingredients-extract

This model is a fine-tuned version of sshleifer/distilbart-xsum-12-6 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.3434
• Rouge1: 44.3464
• Rouge2: 25.67
• Rougel: 44.3032
• Rougelsum: 44.3007
• Gen Len: 16.2697

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: 2
• eval_batch_size: 2
• seed: 42
• 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
0.7151	1.0	1552	0.5275	53.7819	31.247	53.7202	53.7078	12.9069
0.5151	2.0	3104	0.4429	49.9951	28.9098	49.9357	49.9016	13.4797
0.4237	3.0	4656	0.3622	52.4925	31.4498	52.4645	52.4606	13.5396
0.3644	4.0	6208	0.3434	44.3464	25.67	44.3032	44.3007	16.2697

Framework versions

• Transformers 4.28.1
• Pytorch 2.0.0+cu118
• Datasets 2.11.0
• Tokenizers 0.13.3