HealthScienceBARTMainSections

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

• Loss: 3.8753
• Rouge1: 57.7808
• Rouge2: 23.8942
• Rougel: 42.2385
• Rougelsum: 54.2817
• Bertscore Precision: 83.4992
• Bertscore Recall: 85.0665
• Bertscore F1: 84.2729
• Bleu: 0.1888
• Gen Len: 234.2871

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: 5e-05
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• gradient_accumulation_steps: 16
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Bertscore Precision	Bertscore Recall	Bertscore F1	Bleu	Gen Len
5.7855	0.0835	100	5.6539	48.5675	17.0972	33.6039	45.6212	80.1089	82.0896	81.0838	0.1318	234.2871
5.2884	0.1671	200	5.1333	51.1112	18.5297	35.438	47.6657	80.5123	82.7897	81.6311	0.1454	234.2871
4.9931	0.2506	300	4.8074	52.1485	19.5231	36.7572	48.8489	81.0844	83.2739	82.1612	0.1534	234.2871
4.6538	0.3342	400	4.5657	52.6901	20.3073	37.8168	49.4253	81.3851	83.5259	82.438	0.1580	234.2871
4.435	0.4177	500	4.3963	54.6727	20.9559	39.1501	51.7226	82.5534	83.8521	83.1954	0.1618	234.2871
4.4327	0.5013	600	4.2367	55.3497	21.849	39.8267	51.9765	82.6498	84.2237	83.4265	0.1698	234.2871
4.2704	0.5848	700	4.1312	56.2031	22.5317	40.6962	52.8736	82.966	84.4887	83.7178	0.1762	234.2871
4.2211	0.6684	800	4.0373	56.1405	22.9558	41.2482	52.772	82.9397	84.6224	83.7695	0.1800	234.2871
4.0727	0.7519	900	3.9672	57.5881	23.6311	41.712	53.9676	83.2595	84.8583	84.0486	0.1850	234.2871
4.0741	0.8355	1000	3.9182	57.2156	23.6916	42.074	53.7327	83.3537	84.9605	84.1466	0.1868	234.2871
3.8563	0.9190	1100	3.8753	57.7808	23.8942	42.2385	54.2817	83.4992	85.0665	84.2729	0.1888	234.2871

Framework versions

• Transformers 4.41.2
• Pytorch 2.3.1+cu121
• Datasets 2.20.0
• Tokenizers 0.19.1