tmvar_2e-05_ES12

This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0173
• Precision: 0.8446
• Recall: 0.8811
• F1: 0.8624
• Accuracy: 0.9969

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• training_steps: 1000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.5018	1.47	25	0.1002	0.0	0.0	0.0	0.9843
0.0852	2.94	50	0.0509	0.9286	0.0703	0.1307	0.9852
0.0373	4.41	75	0.0283	0.5485	0.6108	0.5780	0.9918
0.0256	5.88	100	0.0204	0.6429	0.7297	0.6835	0.9938
0.0123	7.35	125	0.0188	0.8063	0.8324	0.8191	0.9956
0.008	8.82	150	0.0171	0.7979	0.8324	0.8148	0.9958
0.0047	10.29	175	0.0158	0.8010	0.8919	0.8440	0.9962
0.0037	11.76	200	0.0171	0.8511	0.8649	0.8579	0.9964
0.0025	13.24	225	0.0184	0.8368	0.8595	0.848	0.9962
0.002	14.71	250	0.0180	0.8223	0.8757	0.8482	0.9961
0.0018	16.18	275	0.0176	0.8571	0.8757	0.8663	0.9966
0.0014	17.65	300	0.0170	0.8402	0.8811	0.8602	0.9968
0.0011	19.12	325	0.0180	0.8438	0.8757	0.8594	0.9968
0.001	20.59	350	0.0197	0.8482	0.8757	0.8617	0.9968
0.001	22.06	375	0.0161	0.8402	0.8811	0.8602	0.9969
0.0009	23.53	400	0.0161	0.8316	0.8811	0.8556	0.9968
0.0008	25.0	425	0.0191	0.8663	0.8757	0.8710	0.9969
0.0009	26.47	450	0.0155	0.8639	0.8919	0.8777	0.9972
0.0008	27.94	475	0.0140	0.8737	0.9351	0.9034	0.9977
0.0008	29.41	500	0.0171	0.8534	0.8811	0.8670	0.9970
0.0007	30.88	525	0.0170	0.8632	0.8865	0.8747	0.9971
0.0007	32.35	550	0.0162	0.8601	0.8973	0.8783	0.9973
0.0006	33.82	575	0.0162	0.8601	0.8973	0.8783	0.9973
0.0006	35.29	600	0.0170	0.8534	0.8811	0.8670	0.9971
0.0006	36.76	625	0.0167	0.8557	0.8973	0.8760	0.9971
0.0005	38.24	650	0.0166	0.8549	0.8919	0.8730	0.9970
0.0005	39.71	675	0.0163	0.8513	0.8973	0.8737	0.9970
0.0005	41.18	700	0.0171	0.8497	0.8865	0.8677	0.9969
0.0005	42.65	725	0.0190	0.8526	0.8757	0.8640	0.9969
0.0005	44.12	750	0.0178	0.8490	0.8811	0.8647	0.9969
0.0005	45.59	775	0.0173	0.8446	0.8811	0.8624	0.9969

Framework versions

• Transformers 4.27.4
• Pytorch 2.0.0+cu118
• Datasets 2.11.0
• Tokenizers 0.13.2