Scientific_data_summarisation_model

This model is a fine-tuned version of sambydlo/bart-large-scientific-lay-summarisation on the scientific_lay_summarisation dataset. It achieves the following results on the evaluation set:

• Loss: 2.0270
• Rouge1: 0.1355
• Rouge2: 0.0486
• Rougel: 0.1082
• Rougelsum: 0.1082
• Gen Len: 21.0

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: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 5
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
2.1526	1.0	1549	2.0667	0.1336	0.0483	0.1075	0.1075	21.0
2.0567	2.0	3098	2.0417	0.136	0.0486	0.1082	0.1082	21.0
1.975	3.0	4647	2.0308	0.1352	0.0488	0.1083	0.1082	21.0
1.9154	4.0	6196	2.0268	0.1356	0.048	0.1079	0.1078	21.0
1.8691	5.0	7745	2.0270	0.1355	0.0486	0.1082	0.1082	21.0

Framework versions

• Transformers 4.49.0.dev0
• Pytorch 2.5.1+cu121
• Datasets 3.2.0
• Tokenizers 0.21.0