learning_rate: 2e-05
train_batch_size: 32
eval_batch_size: 32
seed: 5226
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: cosine_with_restarts
lr_scheduler_warmup_ratio: 0.1
num_epochs: 13
mixed_precision_training: Native AMP
label_smoothing_factor: 0.1

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.7373	1.0	1000	0.7242	0.7652	0.7968	0.7807	0.9523
0.6986	2.0	2000	0.6944	0.7920	0.8249	0.8081	0.9581
0.6785	3.0	3000	0.6893	0.8026	0.8291	0.8156	0.9598
0.6749	4.0	4000	0.6847	0.7984	0.8352	0.8164	0.9601
0.6626	5.0	5000	0.6826	0.8121	0.8373	0.8245	0.9617
0.6637	6.0	6000	0.6807	0.8176	0.8415	0.8294	0.9623
0.6528	7.0	7000	0.6812	0.8134	0.8425	0.8277	0.9626
0.6532	8.0	8000	0.6816	0.8209	0.8453	0.8329	0.9629
0.6431	9.0	9000	0.6814	0.8178	0.8447	0.8311	0.9631
0.6444	10.0	10000	0.6801	0.8176	0.8466	0.8318	0.9629
0.6347	11.0	11000	0.6826	0.8214	0.8470	0.8340	0.9631
0.6377	12.0	12000	0.6849	0.8214	0.8468	0.8339	0.9630
0.628	13.0	13000	0.6861	0.8244	0.8485	0.8363	0.9634

shellypeng/distillbert-base-cased-finetuned-ner6

distillbert-base-cased-finetuned-ner6