learning_rate: 2e-05
train_batch_size: 32
eval_batch_size: 64
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 5
mixed_precision_training: Native AMP

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1
0.2002	0.2670	500	0.1542	0.6783	0.6954	0.6868
0.1159	0.5339	1000	0.0952	0.8101	0.8456	0.8275
0.0887	0.8009	1500	0.0805	0.8352	0.8625	0.8486
0.0653	1.0678	2000	0.0749	0.8421	0.8807	0.8610
0.0665	1.3348	2500	0.0734	0.8545	0.8754	0.8648
0.0558	1.6017	3000	0.0715	0.8565	0.8868	0.8714
0.0583	1.8687	3500	0.0689	0.8577	0.8899	0.8735
0.0453	2.1356	4000	0.0703	0.8689	0.8827	0.8758
0.0441	2.4026	4500	0.0713	0.8671	0.8894	0.8781
0.0409	2.6695	5000	0.0731	0.8562	0.8908	0.8731
0.0419	2.9365	5500	0.0702	0.8643	0.8935	0.8787
0.0379	3.2034	6000	0.0724	0.8602	0.8936	0.8766
0.0323	3.4704	6500	0.0745	0.8631	0.8931	0.8779
0.0303	3.7373	7000	0.0740	0.8659	0.8949	0.8802
0.0309	4.0043	7500	0.0726	0.8644	0.8991	0.8815
0.0258	4.2712	8000	0.0751	0.8650	0.8961	0.8803
0.0295	4.5382	8500	0.0753	0.8651	0.8963	0.8804
0.0292	4.8051	9000	0.0748	0.8694	0.8978	0.8833

synapti/nci-ner-v1

nci-ner-v1