LLM_Teached_Bart_100k

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

• Loss: 1.4623
• Rouge1: 0.4678
• Rouge2: 0.2472
• Rougel: 0.4081
• Rougelsum: 0.4082
• Gen Len: 19.8816
• Precision: 0.9185
• Recall: 0.8957
• F1: 0.9068

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: 24
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 96
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 16
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len	Precision	Recall	F1
1.6328	1.0	1041	1.4801	0.448	0.2243	0.385	0.385	19.8782	0.9134	0.893	0.9029
1.4598	2.0	2083	1.4051	0.4428	0.2273	0.3851	0.385	19.9344	0.9147	0.8903	0.9022
1.3402	3.0	3125	1.3840	0.4498	0.2318	0.3921	0.392	19.95	0.9158	0.8918	0.9034
1.2446	4.0	4167	1.3682	0.4604	0.2405	0.4014	0.4014	19.884	0.9169	0.8944	0.9054
1.1651	5.0	5208	1.3695	0.4594	0.2401	0.3995	0.3995	19.894	0.9173	0.8942	0.9055
1.1002	6.0	6250	1.3783	0.4607	0.2423	0.4014	0.4014	19.9118	0.9166	0.8945	0.9053
1.0427	7.0	7292	1.3851	0.462	0.2432	0.4028	0.4028	19.9075	0.9172	0.8946	0.9056
0.9881	8.0	8334	1.3911	0.4635	0.2442	0.4038	0.4037	19.9071	0.9177	0.8947	0.9059
0.9435	9.0	9375	1.4075	0.468	0.2471	0.4085	0.4084	19.8805	0.918	0.8959	0.9067
0.9035	10.0	10417	1.4125	0.4675	0.248	0.4085	0.4086	19.8811	0.9178	0.8957	0.9064
0.8702	11.0	11459	1.4219	0.4646	0.2455	0.405	0.4051	19.8947	0.9181	0.895	0.9063
0.8458	12.0	12501	1.4339	0.4643	0.2447	0.4055	0.4055	19.8985	0.9177	0.8952	0.9061
0.8207	13.0	13542	1.4430	0.4671	0.2463	0.4068	0.4069	19.9053	0.9182	0.8952	0.9064
0.7987	14.0	14584	1.4495	0.4633	0.2455	0.4046	0.4047	19.918	0.9179	0.8944	0.9059
0.787	15.0	15626	1.4560	0.4666	0.2471	0.407	0.4072	19.8956	0.9182	0.8953	0.9064
0.772	15.99	16656	1.4623	0.4678	0.2472	0.4081	0.4082	19.8816	0.9185	0.8957	0.9068

Framework versions

• Transformers 4.36.0
• Pytorch 2.0.1+cu117
• Datasets 2.14.5
• Tokenizers 0.15.0