test

This model is a fine-tuned version of hfl/chinese-bert-wwm-ext on the conll2003 dataset. It achieves the following results on the evaluation set:

• Loss: 0.7372
• Precision: 0.7696
• Recall: 0.8396
• F1: 0.8031
• Accuracy: 0.8847

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: 2e-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: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	2	1.9496	0.0	0.0	0.0	0.4889
No log	2.0	4	1.6137	0.0	0.0	0.0	0.4919
No log	3.0	6	1.3906	0.0	0.0	0.0	0.5650
No log	4.0	8	1.2273	0.0652	0.0481	0.0554	0.6856
No log	5.0	10	1.0565	0.2051	0.1711	0.1866	0.7125
No log	6.0	12	0.9150	0.5094	0.4332	0.4682	0.7540
No log	7.0	14	0.8051	0.5988	0.5187	0.5559	0.7679
No log	8.0	16	0.7151	0.6707	0.5989	0.6328	0.7763
No log	9.0	18	0.6334	0.6685	0.6364	0.6521	0.8086
No log	10.0	20	0.5693	0.6957	0.6845	0.6900	0.8201
No log	11.0	22	0.5192	0.7166	0.7166	0.7166	0.8363
No log	12.0	24	0.4736	0.7135	0.7326	0.7230	0.8524
No log	13.0	26	0.4448	0.6938	0.7754	0.7323	0.8555
No log	14.0	28	0.4280	0.7177	0.8021	0.7576	0.8586
No log	15.0	30	0.4179	0.7588	0.8075	0.7824	0.8663
No log	16.0	32	0.4214	0.7356	0.8182	0.7747	0.8593
No log	17.0	34	0.4070	0.7391	0.8182	0.7766	0.8616
No log	18.0	36	0.4112	0.7586	0.8235	0.7897	0.8724
No log	19.0	38	0.4530	0.7330	0.8075	0.7684	0.8693
No log	20.0	40	0.4719	0.7766	0.8182	0.7969	0.8732
No log	21.0	42	0.4886	0.7260	0.8075	0.7646	0.8632
No log	22.0	44	0.5007	0.7217	0.8182	0.7669	0.8701
No log	23.0	46	0.5169	0.7321	0.8182	0.7727	0.8762
No log	24.0	48	0.5531	0.7238	0.8128	0.7657	0.8724
No log	25.0	50	0.5895	0.7311	0.8289	0.7769	0.8655
No log	26.0	52	0.5482	0.7330	0.8075	0.7684	0.8778
No log	27.0	54	0.5361	0.7488	0.8128	0.7795	0.8832
No log	28.0	56	0.5378	0.7427	0.8182	0.7786	0.8847
No log	29.0	58	0.5543	0.7371	0.8396	0.7850	0.8824
No log	30.0	60	0.5564	0.7585	0.8396	0.7970	0.8839
No log	31.0	62	0.5829	0.7235	0.8396	0.7772	0.8724
No log	32.0	64	0.5974	0.7269	0.8396	0.7792	0.8716
No log	33.0	66	0.5750	0.7610	0.8342	0.7959	0.8839
No log	34.0	68	0.5887	0.7723	0.8342	0.8021	0.8878
No log	35.0	70	0.6219	0.7441	0.8396	0.7889	0.8747
No log	36.0	72	0.6676	0.7269	0.8396	0.7792	0.8632
No log	37.0	74	0.6517	0.7452	0.8289	0.7848	0.8693
No log	38.0	76	0.6346	0.7828	0.8289	0.8052	0.8862
No log	39.0	78	0.6239	0.7839	0.8342	0.8083	0.8855
No log	40.0	80	0.6360	0.7277	0.8289	0.775	0.8762
No log	41.0	82	0.6645	0.7336	0.8396	0.7830	0.8701
No log	42.0	84	0.6611	0.7406	0.8396	0.7870	0.8747
No log	43.0	86	0.6707	0.7488	0.8289	0.7868	0.8762
No log	44.0	88	0.6901	0.7277	0.8289	0.775	0.8709
No log	45.0	90	0.6911	0.7393	0.8342	0.7839	0.8709
No log	46.0	92	0.6540	0.7761	0.8342	0.8041	0.8878
No log	47.0	94	0.6381	0.7761	0.8342	0.8041	0.8916
No log	48.0	96	0.6285	0.7745	0.8449	0.8082	0.8885
No log	49.0	98	0.6449	0.7692	0.8556	0.8101	0.8862
No log	50.0	100	0.6809	0.7442	0.8556	0.7960	0.8732
No log	51.0	102	0.6898	0.7395	0.8503	0.7910	0.8716
No log	52.0	104	0.6897	0.75	0.8503	0.7970	0.8762
No log	53.0	106	0.6714	0.7656	0.8556	0.8081	0.8855
No log	54.0	108	0.6612	0.7692	0.8556	0.8101	0.8855
No log	55.0	110	0.6583	0.7692	0.8556	0.8101	0.8855
No log	56.0	112	0.6648	0.7692	0.8556	0.8101	0.8855
No log	57.0	114	0.6757	0.7656	0.8556	0.8081	0.8832
No log	58.0	116	0.6803	0.7656	0.8556	0.8081	0.8839
No log	59.0	118	0.6834	0.7692	0.8556	0.8101	0.8862
No log	60.0	120	0.6889	0.7833	0.8503	0.8154	0.8878
No log	61.0	122	0.6963	0.7772	0.8396	0.8072	0.8862
No log	62.0	124	0.7057	0.7772	0.8396	0.8072	0.8862
No log	63.0	126	0.7212	0.7910	0.8503	0.8196	0.8862
No log	64.0	128	0.7334	0.7833	0.8503	0.8154	0.8824
No log	65.0	130	0.7398	0.7833	0.8503	0.8154	0.8801
No log	66.0	132	0.7400	0.7833	0.8503	0.8154	0.8809
No log	67.0	134	0.7345	0.7783	0.8449	0.8103	0.8855
No log	68.0	136	0.7270	0.79	0.8449	0.8165	0.8870
No log	69.0	138	0.7245	0.7839	0.8342	0.8083	0.8862
No log	70.0	140	0.7260	0.7868	0.8289	0.8073	0.8847
No log	71.0	142	0.7275	0.7817	0.8235	0.8021	0.8839
No log	72.0	144	0.7283	0.7778	0.8235	0.8000	0.8832
No log	73.0	146	0.7296	0.78	0.8342	0.8062	0.8847
No log	74.0	148	0.7344	0.7734	0.8396	0.8051	0.8832
No log	75.0	150	0.7314	0.7745	0.8449	0.8082	0.8824
No log	76.0	152	0.7299	0.7794	0.8503	0.8133	0.8832
No log	77.0	154	0.7282	0.7794	0.8503	0.8133	0.8839
No log	78.0	156	0.7252	0.7783	0.8449	0.8103	0.8839
No log	79.0	158	0.7216	0.7756	0.8503	0.8112	0.8855
No log	80.0	160	0.7194	0.7756	0.8503	0.8112	0.8870
No log	81.0	162	0.7191	0.7756	0.8503	0.8112	0.8878
No log	82.0	164	0.7201	0.7696	0.8396	0.8031	0.8862
No log	83.0	166	0.7211	0.7696	0.8396	0.8031	0.8862
No log	84.0	168	0.7222	0.7696	0.8396	0.8031	0.8862
No log	85.0	170	0.7220	0.7696	0.8396	0.8031	0.8862
No log	86.0	172	0.7239	0.7734	0.8396	0.8051	0.8870
No log	87.0	174	0.7291	0.7772	0.8396	0.8072	0.8847
No log	88.0	176	0.7344	0.7745	0.8449	0.8082	0.8824
No log	89.0	178	0.7373	0.7745	0.8449	0.8082	0.8824
No log	90.0	180	0.7391	0.7707	0.8449	0.8061	0.8832
No log	91.0	182	0.7403	0.7745	0.8449	0.8082	0.8824
No log	92.0	184	0.7412	0.7745	0.8449	0.8082	0.8832
No log	93.0	186	0.7417	0.7707	0.8449	0.8061	0.8832
No log	94.0	188	0.7402	0.7745	0.8449	0.8082	0.8839
No log	95.0	190	0.7389	0.7745	0.8449	0.8082	0.8847
No log	96.0	192	0.7381	0.7696	0.8396	0.8031	0.8839
No log	97.0	194	0.7377	0.7696	0.8396	0.8031	0.8847
No log	98.0	196	0.7374	0.7696	0.8396	0.8031	0.8847
No log	99.0	198	0.7372	0.7696	0.8396	0.8031	0.8847
No log	100.0	200	0.7372	0.7696	0.8396	0.8031	0.8847

Framework versions

• Transformers 4.18.0
• Pytorch 1.11.0+cu113
• Datasets 2.1.0
• Tokenizers 0.12.1