ner

This model is a fine-tuned version of vinai/phobert-large on the hts98/UIT dataset. It achieves the following results on the evaluation set:

Loss: 1.8209
Precision: 0.6879
Recall: 0.7164
F1: 0.7019
Accuracy: 0.8297

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: 3e-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
num_epochs: 120.0

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	487	0.6517	0.5180	0.6267	0.5672	0.7979
1.0091	2.0	974	0.6198	0.5438	0.6583	0.5956	0.8116
0.5042	3.0	1461	0.6614	0.5677	0.6745	0.6165	0.8094
0.3543	4.0	1948	0.6794	0.5792	0.6826	0.6267	0.8231
0.2476	5.0	2435	0.7132	0.6090	0.7041	0.6531	0.8249
0.1849	6.0	2922	0.7761	0.6023	0.6926	0.6443	0.8219
0.1465	7.0	3409	0.8294	0.5965	0.7007	0.6444	0.8173
0.1176	8.0	3896	0.8653	0.6150	0.6935	0.6519	0.8230
0.1023	9.0	4383	0.8614	0.6123	0.6926	0.6500	0.8226
0.0823	10.0	4870	0.9825	0.6073	0.6848	0.6437	0.8216
0.069	11.0	5357	0.9783	0.6246	0.6957	0.6582	0.8248
0.0578	12.0	5844	1.0037	0.6115	0.7030	0.6540	0.8224
0.0522	13.0	6331	1.0799	0.6177	0.6829	0.6486	0.8161
0.0461	14.0	6818	1.0693	0.6088	0.7016	0.6519	0.8203
0.0402	15.0	7305	1.0560	0.6158	0.6991	0.6548	0.8230
0.0369	16.0	7792	1.1046	0.6307	0.6910	0.6595	0.8197
0.0391	17.0	8279	1.1480	0.6228	0.6873	0.6535	0.8233
0.0537	18.0	8766	1.2141	0.6234	0.6873	0.6538	0.8204
0.0497	19.0	9253	1.2230	0.6241	0.6957	0.6580	0.8189
0.0512	20.0	9740	1.2078	0.6357	0.7016	0.6670	0.8268
0.0508	21.0	10227	1.1941	0.6153	0.6921	0.6514	0.8178
0.044	22.0	10714	1.3114	0.6377	0.6924	0.6639	0.8161
0.041	23.0	11201	1.2640	0.6191	0.6884	0.6519	0.8165
0.0216	24.0	11688	1.3127	0.6349	0.6929	0.6627	0.8240
0.0187	25.0	12175	1.3329	0.6452	0.7004	0.6717	0.8229
0.0158	26.0	12662	1.2958	0.6243	0.7004	0.6602	0.8177
0.0151	27.0	13149	1.3276	0.6204	0.6985	0.6571	0.8181
0.016	28.0	13636	1.2671	0.6481	0.6999	0.6730	0.8251
0.0157	29.0	14123	1.3374	0.6191	0.6946	0.6547	0.8204
0.0146	30.0	14610	1.3941	0.6558	0.6932	0.6740	0.8192
0.0134	31.0	15097	1.4215	0.6344	0.6854	0.6589	0.8164
0.0146	32.0	15584	1.4602	0.6510	0.6937	0.6717	0.8198
0.0105	33.0	16071	1.4085	0.6459	0.7038	0.6736	0.8240
0.0135	34.0	16558	1.3593	0.6337	0.7002	0.6653	0.8166
0.0155	35.0	17045	1.3412	0.6519	0.6943	0.6724	0.8222
0.0141	36.0	17532	1.3676	0.6385	0.7021	0.6688	0.8219
0.0145	37.0	18019	1.3878	0.6573	0.6993	0.6777	0.8251
0.0106	38.0	18506	1.4314	0.6298	0.7016	0.6638	0.8239
0.0106	39.0	18993	1.3729	0.6666	0.7071	0.6863	0.8282
0.0112	40.0	19480	1.3455	0.6506	0.7032	0.6759	0.8283
0.0109	41.0	19967	1.3884	0.6429	0.7060	0.6730	0.8278
0.0084	42.0	20454	1.4240	0.6428	0.7080	0.6738	0.8255
0.0082	43.0	20941	1.4280	0.6091	0.6829	0.6439	0.8176
0.0122	44.0	21428	1.4723	0.6533	0.7032	0.6773	0.8239
0.0082	45.0	21915	1.5151	0.6189	0.6960	0.6552	0.8180
0.0068	46.0	22402	1.4441	0.6331	0.7046	0.6669	0.8211
0.0074	47.0	22889	1.4753	0.6497	0.6974	0.6727	0.8203
0.0076	48.0	23376	1.5148	0.6515	0.6957	0.6729	0.8215
0.0098	49.0	23863	1.4481	0.6319	0.6974	0.6630	0.8233
0.0104	50.0	24350	1.4814	0.6585	0.7074	0.6821	0.8235
0.0119	51.0	24837	1.4050	0.6555	0.7133	0.6832	0.8264
0.0078	52.0	25324	1.4854	0.6615	0.7049	0.6825	0.8234
0.007	53.0	25811	1.4941	0.6476	0.7013	0.6734	0.8204
0.0079	54.0	26298	1.4138	0.6529	0.7088	0.6797	0.8228
0.0092	55.0	26785	1.4301	0.6762	0.7018	0.6888	0.8218
0.0097	56.0	27272	1.5276	0.6544	0.6974	0.6752	0.8182
0.0076	57.0	27759	1.4302	0.6517	0.7032	0.6765	0.8258
0.0056	58.0	28246	1.4996	0.6675	0.7046	0.6856	0.8265
0.0047	59.0	28733	1.4309	0.6625	0.7032	0.6823	0.8241
0.0126	60.0	29220	1.4903	0.6457	0.7002	0.6718	0.8172
0.0054	61.0	29707	1.4318	0.6398	0.7035	0.6701	0.8218
0.0076	62.0	30194	1.5745	0.6660	0.6988	0.6820	0.8196
0.0043	63.0	30681	1.5102	0.6607	0.7058	0.6825	0.8268
0.0046	64.0	31168	1.5500	0.6655	0.6949	0.6799	0.8252
0.0042	65.0	31655	1.5357	0.6555	0.7138	0.6834	0.8274
0.0039	66.0	32142	1.5469	0.6650	0.7105	0.6870	0.8220
0.004	67.0	32629	1.4814	0.6542	0.7147	0.6831	0.8289
0.0031	68.0	33116	1.5210	0.6545	0.7097	0.6810	0.8250
0.0047	69.0	33603	1.5326	0.6549	0.7083	0.6805	0.8272
0.0029	70.0	34090	1.6057	0.6643	0.7027	0.6829	0.8226
0.0027	71.0	34577	1.5920	0.6594	0.7141	0.6857	0.8255
0.0016	72.0	35064	1.6220	0.6668	0.7024	0.6842	0.8255
0.0025	73.0	35551	1.6261	0.6803	0.7027	0.6913	0.8239
0.0037	74.0	36038	1.6440	0.6769	0.7049	0.6906	0.8207
0.003	75.0	36525	1.6027	0.6701	0.7071	0.6881	0.8263
0.0031	76.0	37012	1.6013	0.6670	0.7141	0.6898	0.8262
0.0031	77.0	37499	1.6714	0.6434	0.7147	0.6772	0.8185
0.002	78.0	37986	1.6293	0.6666	0.7071	0.6863	0.8267
0.0024	79.0	38473	1.6796	0.6578	0.7094	0.6826	0.8222
0.003	80.0	38960	1.6463	0.6701	0.7094	0.6892	0.8283
0.0015	81.0	39447	1.6634	0.6765	0.7074	0.6916	0.8266
0.003	82.0	39934	1.6947	0.6636	0.7055	0.6839	0.8255
0.0036	83.0	40421	1.6515	0.6554	0.7046	0.6791	0.8227
0.0018	84.0	40908	1.6855	0.6641	0.7102	0.6864	0.8266
0.0012	85.0	41395	1.6966	0.6545	0.7108	0.6815	0.8241
0.0019	86.0	41882	1.6564	0.6623	0.7058	0.6833	0.8255
0.0015	87.0	42369	1.6363	0.6501	0.7080	0.6778	0.8239
0.0022	88.0	42856	1.6879	0.6813	0.7055	0.6932	0.8260
0.0011	89.0	43343	1.6870	0.6660	0.7113	0.6879	0.8294
0.0017	90.0	43830	1.7018	0.6707	0.7041	0.6870	0.8276
0.0016	91.0	44317	1.6699	0.6701	0.7133	0.6910	0.8281
0.0015	92.0	44804	1.6737	0.6773	0.7125	0.6944	0.8320
0.0017	93.0	45291	1.7271	0.6769	0.7189	0.6973	0.8280
0.0005	94.0	45778	1.7245	0.6654	0.7127	0.6882	0.8261
0.0013	95.0	46265	1.8143	0.6772	0.7052	0.6909	0.8235
0.0012	96.0	46752	1.7299	0.6736	0.7091	0.6909	0.8262
0.002	97.0	47239	1.7251	0.6758	0.7125	0.6937	0.8273
0.0009	98.0	47726	1.7183	0.6565	0.7183	0.6860	0.8262
0.0009	99.0	48213	1.7801	0.6759	0.7116	0.6933	0.8279
0.0008	100.0	48700	1.7749	0.6817	0.7108	0.6959	0.8263
0.0006	101.0	49187	1.7413	0.6732	0.7113	0.6917	0.8272
0.0005	102.0	49674	1.7939	0.6648	0.7144	0.6887	0.8270
0.0008	103.0	50161	1.7955	0.6602	0.7111	0.6847	0.8237
0.0007	104.0	50648	1.7844	0.6686	0.7130	0.6901	0.8266
0.0005	105.0	51135	1.7983	0.6808	0.7127	0.6964	0.8279
0.0004	106.0	51622	1.7945	0.6798	0.7130	0.6960	0.8256
0.0005	107.0	52109	1.8209	0.6879	0.7164	0.7019	0.8297
0.0004	108.0	52596	1.8150	0.6839	0.7085	0.6960	0.8281
0.0006	109.0	53083	1.7784	0.6778	0.7166	0.6967	0.8287
0.0009	110.0	53570	1.7941	0.6761	0.7180	0.6965	0.8293
0.0006	111.0	54057	1.8079	0.6762	0.7200	0.6974	0.8280
0.0006	112.0	54544	1.7968	0.6752	0.7166	0.6953	0.8277
0.0003	113.0	55031	1.7972	0.6753	0.7166	0.6954	0.8285
0.0003	114.0	55518	1.7985	0.6764	0.7172	0.6962	0.8298
0.0006	115.0	56005	1.8048	0.6759	0.7172	0.6959	0.8287
0.0006	116.0	56492	1.7985	0.6758	0.7152	0.6950	0.8298
0.0004	117.0	56979	1.7883	0.6835	0.7164	0.6996	0.8314
0.0009	118.0	57466	1.7852	0.6830	0.7180	0.7001	0.8311
0.0002	119.0	57953	1.7869	0.6853	0.7180	0.7013	0.8309
0.0003	120.0	58440	1.7865	0.6846	0.7180	0.7009	0.8312

Framework versions

Transformers 4.32.0.dev0
Pytorch 2.1.0+cu121
Datasets 3.1.0
Tokenizers 0.13.3

hts98/ner

ner

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

为此模型做种