learning_rate: 2e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
num_epochs: 20

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	90	0.6883	0.4376	0.4556	0.4464	0.7834
No log	2.0	180	0.4971	0.5779	0.6286	0.6022	0.8343
No log	3.0	270	0.4184	0.5892	0.7451	0.6581	0.8569
No log	4.0	360	0.3410	0.6474	0.8062	0.7182	0.8893
No log	5.0	450	0.2515	0.7554	0.8181	0.7855	0.9270
0.5383	6.0	540	0.2256	0.7738	0.8577	0.8136	0.9338
0.5383	7.0	630	0.1782	0.8270	0.8824	0.8538	0.9488
0.5383	8.0	720	0.1734	0.8271	0.8977	0.8610	0.9554
0.5383	9.0	810	0.1474	0.8702	0.9123	0.8908	0.9661
0.5383	10.0	900	0.1476	0.8806	0.9216	0.9006	0.9685
0.5383	11.0	990	0.1404	0.8913	0.9304	0.9105	0.9722
0.0733	12.0	1080	0.1354	0.9085	0.9273	0.9178	0.9741
0.0733	13.0	1170	0.1332	0.9112	0.9266	0.9188	0.9739
0.0733	14.0	1260	0.1337	0.9072	0.9396	0.9231	0.9755
0.0733	15.0	1350	0.1332	0.9283	0.9362	0.9322	0.9776
0.0733	16.0	1440	0.1293	0.9321	0.9389	0.9355	0.9783
0.0236	17.0	1530	0.1307	0.9253	0.9431	0.9341	0.9786
0.0236	18.0	1620	0.1293	0.9278	0.9439	0.9358	0.9788
0.0236	19.0	1710	0.1294	0.9306	0.9431	0.9368	0.9792
0.0236	20.0	1800	0.1293	0.9306	0.9431	0.9368	0.9792

rifailabs/medical_ner_roberta

medical-ner-roberta