reddit_summarization_model

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

• Loss: 1.9410
• Rouge1: 0.4169
• Rouge2: 0.163
• Rougel: 0.276
• Rougelsum: 0.3001
• Gen Len: 61.6276

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: 5
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
1.9905	1.0	972	1.8412	0.412	0.1593	0.2725	0.2965	61.7025
1.5293	2.0	1944	1.8022	0.4162	0.1634	0.2766	0.2998	61.6673
1.2934	3.0	2916	1.8352	0.4194	0.1641	0.2789	0.3019	61.548
1.1481	4.0	3888	1.8898	0.415	0.1623	0.2753	0.2985	61.5825
1.04	5.0	4860	1.9410	0.4169	0.163	0.276	0.3001	61.6276

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu121
• Datasets 2.15.0
• Tokenizers 0.15.0