bart-large

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: 2.0027
• Accuracy: 0.7916
• Precision: 0.7858
• Recall: 0.7916
• Precision Macro: 0.7201
• Recall Macro: 0.7056
• Macro Fpr: 0.0201
• Weighted Fpr: 0.0195
• Weighted Specificity: 0.9714
• Macro Specificity: 0.9836
• Weighted Sensitivity: 0.7823
• Macro Sensitivity: 0.7056
• F1 Micro: 0.7823
• F1 Macro: 0.7080
• F1 Weighted: 0.7801

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: 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: 30
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	Precision Macro	Recall Macro	Macro Fpr	Weighted Fpr	Weighted Specificity	Macro Specificity	Weighted Sensitivity	Macro Sensitivity	F1 Micro	F1 Macro	F1 Weighted
1.1685	1.0	2569	1.2587	0.6847	0.6360	0.6847	0.4176	0.4720	0.0331	0.0318	0.9550	0.9760	0.6847	0.4720	0.6847	0.4296	0.6471
1.1965	2.0	5138	1.1623	0.6638	0.6943	0.6638	0.4564	0.4261	0.0342	0.0349	0.9654	0.9753	0.6638	0.4261	0.6638	0.3955	0.6468
1.189	3.0	7707	1.3574	0.7235	0.7220	0.7235	0.5413	0.5528	0.0271	0.0266	0.9628	0.9791	0.7235	0.5528	0.7235	0.5196	0.7031
1.0127	4.0	10276	1.4685	0.7668	0.7584	0.7668	0.6671	0.6202	0.0224	0.0213	0.9653	0.9821	0.7668	0.6202	0.7668	0.6233	0.7569
1.0205	5.0	12845	1.4232	0.7668	0.7711	0.7668	0.6765	0.6872	0.0215	0.0213	0.9737	0.9827	0.7668	0.6872	0.7668	0.6732	0.7643
0.7927	6.0	15414	1.5678	0.7428	0.7451	0.7428	0.6489	0.6333	0.0248	0.0241	0.9690	0.9808	0.7428	0.6333	0.7428	0.6108	0.7292
0.7701	7.0	17983	1.7337	0.7467	0.7600	0.7467	0.6863	0.6536	0.0240	0.0237	0.9680	0.9810	0.7467	0.6536	0.7467	0.6584	0.7399
0.584	8.0	20552	1.6188	0.7692	0.7766	0.7692	0.6979	0.7065	0.0214	0.0210	0.9706	0.9827	0.7692	0.7065	0.7692	0.6980	0.7683
0.5659	9.0	23121	1.6983	0.7599	0.7665	0.7599	0.7000	0.6804	0.0227	0.0221	0.9695	0.9820	0.7599	0.6804	0.7599	0.6728	0.7542
0.7021	10.0	25690	1.6445	0.7699	0.7656	0.7699	0.7144	0.6857	0.0223	0.0209	0.9608	0.9821	0.7699	0.6857	0.7699	0.6954	0.7634
0.6216	11.0	28259	1.6562	0.7676	0.7634	0.7676	0.6856	0.6776	0.0223	0.0212	0.9640	0.9821	0.7676	0.6776	0.7676	0.6786	0.7624
0.6408	12.0	30828	1.6682	0.7668	0.7629	0.7668	0.6706	0.6719	0.0223	0.0213	0.9666	0.9822	0.7668	0.6719	0.7668	0.6666	0.7608
0.523	13.0	33397	1.7727	0.7653	0.7674	0.7653	0.8238	0.6934	0.0226	0.0214	0.9659	0.9821	0.7653	0.6934	0.7653	0.7066	0.7534
0.3688	14.0	35966	1.8404	0.7792	0.7788	0.7792	0.7229	0.6921	0.0209	0.0198	0.9675	0.9831	0.7792	0.6921	0.7792	0.6960	0.7731
0.2394	15.0	38535	1.7885	0.7816	0.7809	0.7816	0.7441	0.7115	0.0210	0.0196	0.9628	0.9830	0.7816	0.7115	0.7816	0.7230	0.7765
0.2734	16.0	41104	1.8944	0.7777	0.7870	0.7777	0.7539	0.7265	0.0203	0.0200	0.9724	0.9833	0.7777	0.7265	0.7777	0.7295	0.7777
0.4319	17.0	43673	1.7744	0.7885	0.7847	0.7885	0.7247	0.7320	0.0195	0.0188	0.9718	0.9840	0.7885	0.7320	0.7885	0.7269	0.7855
0.2347	18.0	46242	2.0036	0.7413	0.7352	0.7413	0.6934	0.6799	0.0255	0.0243	0.9597	0.9801	0.7413	0.6799	0.7413	0.6825	0.7354
0.1882	19.0	48811	1.9298	0.7816	0.7804	0.7816	0.7243	0.7262	0.0202	0.0196	0.9708	0.9835	0.7816	0.7262	0.7816	0.7225	0.7792
0.1799	20.0	51380	1.9688	0.7792	0.7892	0.7792	0.7312	0.7343	0.0205	0.0198	0.9714	0.9834	0.7792	0.7343	0.7792	0.7242	0.7779
0.1366	21.0	53949	1.9910	0.7847	0.7846	0.7847	0.7148	0.7455	0.0198	0.0192	0.9730	0.9838	0.7847	0.7455	0.7847	0.7265	0.7833
0.1793	22.0	56518	2.2548	0.7630	0.7648	0.7630	0.7150	0.7273	0.0230	0.0217	0.9633	0.9818	0.7630	0.7273	0.7630	0.7150	0.7582
0.1749	23.0	59087	2.1109	0.7816	0.7768	0.7816	0.7466	0.7230	0.0205	0.0196	0.9690	0.9834	0.7816	0.7230	0.7816	0.7289	0.7774
0.1154	24.0	61656	2.0637	0.7878	0.7837	0.7878	0.7590	0.7269	0.0196	0.0189	0.9718	0.9840	0.7878	0.7269	0.7878	0.7331	0.7828
0.1447	25.0	64225	2.0027	0.7916	0.7858	0.7916	0.7750	0.7299	0.0194	0.0185	0.9697	0.9841	0.7916	0.7299	0.7916	0.7408	0.7861
0.0806	26.0	66794	2.0777	0.7885	0.7831	0.7885	0.7162	0.7134	0.0196	0.0188	0.9715	0.9840	0.7885	0.7134	0.7885	0.7118	0.7840
0.0407	27.0	69363	2.1754	0.7885	0.7863	0.7885	0.7192	0.7080	0.0194	0.0188	0.9725	0.9841	0.7885	0.7080	0.7885	0.7105	0.7866
0.0701	28.0	71932	2.1578	0.7823	0.7817	0.7823	0.7130	0.7097	0.0201	0.0195	0.9714	0.9836	0.7823	0.7097	0.7823	0.7066	0.7810
0.1034	29.0	74501	2.2132	0.7800	0.7789	0.7800	0.7163	0.7044	0.0203	0.0197	0.9713	0.9834	0.7800	0.7044	0.7800	0.7064	0.7785
0.0388	30.0	77070	2.1833	0.7823	0.7806	0.7823	0.7201	0.7056	0.0201	0.0195	0.9714	0.9836	0.7823	0.7056	0.7823	0.7080	0.7801

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu121
• Datasets 2.19.0
• Tokenizers 0.15.1