albert-base-v2-grammar-ner

This model is a fine-tuned version of albert/albert-base-v2 on an unknown dataset. It achieves the following results on the evaluation set:

Model description

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The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 24
eval_batch_size: 24
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: linear
num_epochs: 18

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro	F1 Micro	Precision Macro	Precision Micro	Recall Macro	Recall Micro
0.4297	1.0	93	0.2896	0.9313	0.1318	0.4462	0.1897	0.5163	0.1281	0.3928
0.2521	2.0	186	0.2192	0.9452	0.2315	0.5160	0.3282	0.6752	0.1962	0.4176
0.167	3.0	279	0.1630	0.9662	0.3546	0.7198	0.4142	0.8358	0.3295	0.6321
0.1026	4.0	372	0.1343	0.9733	0.4185	0.7769	0.5241	0.8732	0.3797	0.6998
0.0718	5.0	465	0.1231	0.9738	0.4644	0.7794	0.5584	0.8525	0.4382	0.7178
0.0483	6.0	558	0.1269	0.9778	0.4778	0.8204	0.6262	0.9415	0.4164	0.7269
0.0335	7.0	651	0.1162	0.9804	0.6028	0.8416	0.6985	0.8834	0.5846	0.8036
0.0233	8.0	744	0.1203	0.9813	0.5736	0.8475	0.7429	0.9496	0.4988	0.7652
0.0171	9.0	837	0.1052	0.9836	0.6502	0.8671	0.7023	0.8964	0.6490	0.8397
0.01	10.0	930	0.1125	0.9805	0.6681	0.8477	0.6854	0.8535	0.6875	0.8420
0.0084	11.0	1023	0.1058	0.9862	0.7195	0.8894	0.8004	0.9287	0.6870	0.8533
0.0051	12.0	1116	0.1092	0.9870	0.8015	0.9015	0.8810	0.95	0.7612	0.8578
0.0031	13.0	1209	0.1131	0.9865	0.8006	0.8983	0.8827	0.9429	0.7592	0.8578
0.0017	14.0	1302	0.1106	0.9873	0.8058	0.9039	0.8748	0.9525	0.7749	0.8600
0.0012	15.0	1395	0.1111	0.9875	0.7985	0.9058	0.8818	0.9596	0.7576	0.8578
0.0009	16.0	1488	0.1128	0.9870	0.7941	0.9008	0.8789	0.9569	0.7518	0.8510
0.0008	17.0	1581	0.1133	0.9870	0.7941	0.9008	0.8789	0.9569	0.7518	0.8510
0.0008	18.0	1674	0.1134	0.9870	0.7941	0.9008	0.8789	0.9569	0.7518	0.8510