roberta-base-finetuned-ner-cadec

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

Loss: 0.5558
Precision: 0.6407
Recall: 0.7089
F1: 0.6731
Accuracy: 0.9169
Adr Precision: 0.6127
Adr Recall: 0.725
Adr F1: 0.6641
Disease Precision: 0.2258
Disease Recall: 0.28
Disease F1: 0.25
Drug Precision: 0.9036
Drug Recall: 0.9259
Drug F1: 0.9146
Finding Precision: 0.4878
Finding Recall: 0.2899
Finding F1: 0.3636
Symptom Precision: 0.4828
Symptom Recall: 0.5185
Symptom F1: 0.5
B-adr Precision: 0.752
B-adr Recall: 0.8319
B-adr F1: 0.7899
B-disease Precision: 0.2414
B-disease Recall: 0.28
B-disease F1: 0.2593
B-drug Precision: 0.9458
B-drug Recall: 0.9691
B-drug F1: 0.9573
B-finding Precision: 0.5526
B-finding Recall: 0.3182
B-finding F1: 0.4038
B-symptom Precision: 0.64
B-symptom Recall: 0.5926
B-symptom F1: 0.6154
I-adr Precision: 0.5810
I-adr Recall: 0.6904
I-adr F1: 0.6310
I-disease Precision: 0.2308
I-disease Recall: 0.3
I-disease F1: 0.2609
I-drug Precision: 0.9146
I-drug Recall: 0.9317
I-drug F1: 0.9231
I-finding Precision: 0.6
I-finding Recall: 0.3529
I-finding F1: 0.4444
I-symptom Precision: 0.2353
I-symptom Recall: 0.3077
I-symptom F1: 0.2667
Macro Avg F1: 0.5552
Weighted Avg F1: 0.7224

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: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 35

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy	Adr Precision	Adr Recall	Adr F1	Disease Precision	Disease Recall	Disease F1	Drug Precision	Drug Recall	Drug F1	Finding Precision	Finding Recall	Finding F1	Symptom Precision	Symptom Recall	Symptom F1	B-adr Precision	B-adr Recall	B-adr F1	B-disease Precision	B-disease Recall	B-disease F1	B-drug Precision	B-drug Recall	B-drug F1	B-finding Precision	B-finding Recall	B-finding F1	B-symptom Precision	B-symptom Recall	B-symptom F1	I-adr Precision	I-adr Recall	I-adr F1	I-disease Precision	I-disease Recall	I-disease F1	I-drug Precision	I-drug Recall	I-drug F1	I-finding Precision	I-finding Recall	I-finding F1	I-symptom Precision	I-symptom Recall	I-symptom F1	Macro Avg F1	Weighted Avg F1
No log	1.0	125	0.3063	0.4838	0.5934	0.5331	0.8971	0.4278	0.6217	0.5068	0.0	0.0	0.0	0.8207	0.9321	0.8728	0.0	0.0	0.0	0.0	0.0	0.0	0.6483	0.7929	0.7134	0.0	0.0	0.0	0.8525	0.9630	0.9043	0.0	0.0	0.0	0.0	0.0	0.0	0.4127	0.5837	0.4835	0.0385	0.05	0.0435	0.8736	0.9441	0.9075	0.0	0.0	0.0	0.0	0.0	0.0	0.3052	0.5916
No log	2.0	250	0.2575	0.6006	0.6625	0.6300	0.9134	0.5618	0.705	0.6253	0.2188	0.28	0.2456	0.8639	0.9012	0.8822	0.4211	0.1159	0.1818	1.0	0.0370	0.0714	0.7417	0.7876	0.7639	0.2917	0.28	0.2857	0.9387	0.9444	0.9415	0.7778	0.2121	0.3333	1.0	0.0370	0.0714	0.5426	0.6925	0.6085	0.1852	0.25	0.2128	0.9146	0.9317	0.9231	0.625	0.0980	0.1695	0.0	0.0	0.0	0.4310	0.6809
No log	3.0	375	0.2649	0.5909	0.6738	0.6296	0.9113	0.5631	0.7067	0.6268	0.1633	0.32	0.2162	0.8876	0.9259	0.9063	0.2381	0.0725	0.1111	0.5333	0.2963	0.3810	0.7205	0.8442	0.7775	0.2222	0.32	0.2623	0.9226	0.9568	0.9394	0.5556	0.1515	0.2381	0.6667	0.2963	0.4103	0.5680	0.6987	0.6266	0.15	0.3	0.2	0.8988	0.9379	0.9179	0.6	0.1176	0.1967	0.25	0.0769	0.1176	0.4686	0.6937
0.2914	4.0	500	0.2610	0.6256	0.6852	0.6541	0.9170	0.6071	0.7083	0.6538	0.1887	0.4	0.2564	0.8988	0.9321	0.9152	0.2903	0.1304	0.18	0.6667	0.3704	0.4762	0.7558	0.8053	0.7798	0.2439	0.4	0.3030	0.9345	0.9691	0.9515	0.6087	0.2121	0.3146	0.75	0.3333	0.4615	0.5989	0.6715	0.6331	0.1556	0.35	0.2154	0.9042	0.9379	0.9207	0.5294	0.1765	0.2647	0.75	0.2308	0.3529	0.5197	0.7071
0.2914	5.0	625	0.2837	0.6264	0.6988	0.6606	0.9130	0.5970	0.7283	0.6562	0.2424	0.32	0.2759	0.8713	0.9198	0.8949	0.375	0.1739	0.2376	0.6471	0.4074	0.5	0.7543	0.8478	0.7983	0.2727	0.36	0.3103	0.9345	0.9691	0.9515	0.5833	0.2121	0.3111	0.8462	0.4074	0.5500	0.5777	0.7155	0.6393	0.25	0.25	0.25	0.8817	0.9255	0.9030	0.4348	0.1961	0.2703	0.4444	0.3077	0.3636	0.5347	0.7161
0.2914	6.0	750	0.3233	0.6175	0.6636	0.6397	0.9097	0.6171	0.685	0.6493	0.1636	0.36	0.2250	0.8982	0.9259	0.9119	0.1739	0.0580	0.0870	0.3158	0.4444	0.3692	0.7746	0.8212	0.7973	0.2174	0.4	0.2817	0.9341	0.9630	0.9483	0.6364	0.1061	0.1818	0.5185	0.5185	0.5185	0.5888	0.6799	0.6311	0.1538	0.3	0.2034	0.9091	0.9317	0.9202	0.4118	0.1373	0.2059	0.2308	0.4615	0.3077	0.4996	0.7050
0.2914	7.0	875	0.3260	0.6226	0.6988	0.6585	0.9119	0.5917	0.71	0.6455	0.25	0.28	0.2642	0.8876	0.9259	0.9063	0.5	0.2899	0.3670	0.4118	0.5185	0.4590	0.7449	0.8372	0.7883	0.2593	0.28	0.2692	0.9290	0.9691	0.9486	0.5556	0.3030	0.3922	0.6154	0.5926	0.6038	0.5714	0.6946	0.6270	0.2381	0.25	0.2439	0.8982	0.9317	0.9146	0.5806	0.3529	0.4390	0.3158	0.4615	0.3750	0.5602	0.7188
0.093	8.0	1000	0.3299	0.6452	0.7044	0.6735	0.9172	0.6186	0.7083	0.6605	0.2308	0.24	0.2353	0.9207	0.9321	0.9264	0.4464	0.3623	0.4	0.4839	0.5556	0.5172	0.7685	0.8106	0.7890	0.2308	0.24	0.2353	0.9512	0.9630	0.9571	0.5581	0.3636	0.4404	0.68	0.6296	0.6538	0.5914	0.6904	0.6371	0.2941	0.25	0.2703	0.9207	0.9379	0.9292	0.5	0.4314	0.4632	0.3158	0.4615	0.3750	0.5750	0.7280
0.093	9.0	1125	0.3947	0.6289	0.6852	0.6558	0.9132	0.5940	0.695	0.6406	0.2381	0.4	0.2985	0.9096	0.9321	0.9207	0.6154	0.2319	0.3368	0.4231	0.4074	0.4151	0.7377	0.8212	0.7772	0.2564	0.4	0.3125	0.9458	0.9691	0.9573	0.6667	0.2424	0.3556	0.6364	0.5185	0.5714	0.5692	0.6883	0.6231	0.2	0.3	0.24	0.9152	0.9379	0.9264	0.6818	0.2941	0.4110	0.2	0.2308	0.2143	0.5389	0.7120
0.093	10.0	1250	0.3795	0.6303	0.6874	0.6576	0.9164	0.5952	0.6983	0.6426	0.25	0.36	0.2951	0.9042	0.9321	0.9179	0.4667	0.2029	0.2828	0.5385	0.5185	0.5283	0.7414	0.8372	0.7864	0.2812	0.36	0.3158	0.9515	0.9691	0.9602	0.6296	0.2576	0.3656	0.7	0.5185	0.5957	0.5836	0.6862	0.6308	0.2222	0.3	0.2553	0.9096	0.9379	0.9235	0.5652	0.2549	0.3514	0.4286	0.4615	0.4444	0.5629	0.7187
0.093	11.0	1375	0.3702	0.6401	0.6908	0.6645	0.9182	0.6106	0.69	0.6479	0.25	0.28	0.2642	0.9042	0.9321	0.9179	0.5366	0.3188	0.4000	0.4103	0.5926	0.4848	0.7733	0.8212	0.7966	0.25	0.28	0.2642	0.9398	0.9630	0.9512	0.6562	0.3182	0.4286	0.5484	0.6296	0.5862	0.5862	0.6757	0.6278	0.3333	0.3	0.3158	0.9042	0.9379	0.9207	0.6286	0.4314	0.5116	0.2	0.3846	0.2632	0.5666	0.7264
0.0444	12.0	1500	0.3848	0.6375	0.6931	0.6641	0.9193	0.6009	0.695	0.6445	0.2333	0.28	0.2545	0.9152	0.9321	0.9235	0.5455	0.3478	0.4248	0.4815	0.4815	0.4815	0.7488	0.8336	0.7889	0.2333	0.28	0.2545	0.9512	0.9630	0.9571	0.6316	0.3636	0.4615	0.65	0.4815	0.5532	0.5901	0.6715	0.6282	0.2174	0.25	0.2326	0.9152	0.9379	0.9264	0.5429	0.3725	0.4419	0.4667	0.5385	0.5	0.5744	0.7243
0.0444	13.0	1625	0.3970	0.6441	0.7010	0.6714	0.9202	0.6109	0.7117	0.6574	0.2222	0.24	0.2308	0.9136	0.9136	0.9136	0.5455	0.3478	0.4248	0.4828	0.5185	0.5	0.7582	0.8159	0.7860	0.28	0.28	0.28	0.9568	0.9568	0.9568	0.6154	0.3636	0.4571	0.56	0.5185	0.5385	0.5829	0.6841	0.6295	0.2381	0.25	0.2439	0.925	0.9193	0.9221	0.6111	0.4314	0.5057	0.2667	0.3077	0.2857	0.5605	0.7236
0.0444	14.0	1750	0.3962	0.6576	0.7112	0.6834	0.9203	0.6310	0.7267	0.6754	0.2059	0.28	0.2373	0.9268	0.9383	0.9325	0.4390	0.2609	0.3273	0.6	0.5556	0.5769	0.7545	0.8212	0.7864	0.2188	0.28	0.2456	0.9512	0.9630	0.9571	0.5588	0.2879	0.3800	0.6818	0.5556	0.6122	0.6018	0.6987	0.6467	0.2143	0.3	0.25	0.9268	0.9441	0.9354	0.5312	0.3333	0.4096	0.4167	0.3846	0.4	0.5623	0.7257
0.0444	15.0	1875	0.4322	0.6351	0.6920	0.6623	0.9153	0.6173	0.7017	0.6568	0.2121	0.28	0.2414	0.9091	0.9259	0.9174	0.425	0.2464	0.3119	0.3810	0.5926	0.4638	0.7598	0.8230	0.7901	0.2258	0.28	0.25	0.9512	0.9630	0.9571	0.5625	0.2727	0.3673	0.5806	0.6667	0.6207	0.6015	0.6883	0.6420	0.24	0.3	0.2667	0.9091	0.9317	0.9202	0.5455	0.3529	0.4286	0.2222	0.4615	0.3	0.5543	0.7237
0.023	16.0	2000	0.4250	0.6621	0.7055	0.6831	0.9211	0.6243	0.7117	0.6651	0.2414	0.28	0.2593	0.9264	0.9321	0.9292	0.5526	0.3043	0.3925	0.6296	0.6296	0.6296	0.7659	0.8106	0.7876	0.25	0.28	0.2642	0.9571	0.9630	0.9600	0.625	0.3030	0.4082	0.64	0.5926	0.6154	0.5861	0.6904	0.6340	0.2609	0.3	0.2791	0.9264	0.9379	0.9321	0.6562	0.4118	0.5060	0.4167	0.3846	0.4	0.5787	0.7273
0.023	17.0	2125	0.4284	0.6499	0.7044	0.6761	0.9195	0.6210	0.71	0.6625	0.2	0.24	0.2182	0.9152	0.9321	0.9235	0.5476	0.3333	0.4144	0.4706	0.5926	0.5246	0.7553	0.8248	0.7885	0.2414	0.28	0.2593	0.9512	0.9630	0.9571	0.6216	0.3485	0.4466	0.5333	0.5926	0.5614	0.6059	0.6820	0.6417	0.2174	0.25	0.2326	0.9207	0.9379	0.9292	0.6364	0.4118	0.5	0.2632	0.3846	0.3125	0.5629	0.7285
0.023	18.0	2250	0.4274	0.6494	0.6942	0.6710	0.9200	0.6129	0.6967	0.6521	0.2727	0.24	0.2553	0.9146	0.9259	0.9202	0.4808	0.3623	0.4132	0.5833	0.5185	0.5490	0.7615	0.8195	0.7894	0.2727	0.24	0.2553	0.9565	0.9506	0.9536	0.5435	0.3788	0.4464	0.6667	0.5185	0.5833	0.5845	0.6799	0.6286	0.2778	0.25	0.2632	0.9259	0.9317	0.9288	0.5	0.4314	0.4632	0.3571	0.3846	0.3704	0.5682	0.7244
0.023	19.0	2375	0.4651	0.6393	0.6965	0.6667	0.9166	0.5969	0.6983	0.6436	0.25	0.24	0.2449	0.9152	0.9321	0.9235	0.5111	0.3333	0.4035	0.6154	0.5926	0.6038	0.7545	0.8106	0.7816	0.2727	0.24	0.2553	0.9573	0.9691	0.9632	0.6216	0.3485	0.4466	0.6818	0.5556	0.6122	0.5767	0.6925	0.6293	0.2632	0.25	0.2564	0.9207	0.9379	0.9292	0.5833	0.4118	0.4828	0.3333	0.3846	0.3571	0.5714	0.7237
0.0136	20.0	2500	0.4697	0.6439	0.7044	0.6728	0.9171	0.61	0.7117	0.6569	0.2143	0.24	0.2264	0.9146	0.9259	0.9202	0.5106	0.3478	0.4138	0.5556	0.5556	0.5556	0.7404	0.8230	0.7795	0.25	0.24	0.2449	0.9512	0.9630	0.9571	0.6429	0.4091	0.5000	0.6522	0.5556	0.6	0.5833	0.6883	0.6315	0.2273	0.25	0.2381	0.9146	0.9317	0.9231	0.5882	0.3922	0.4706	0.3333	0.3846	0.3571	0.5702	0.7236
0.0136	21.0	2625	0.5053	0.6301	0.7022	0.6642	0.9132	0.6037	0.7133	0.6539	0.2	0.24	0.2182	0.8830	0.9321	0.9069	0.4565	0.3043	0.3652	0.5	0.5185	0.5091	0.7424	0.8265	0.7822	0.2222	0.24	0.2308	0.9240	0.9753	0.9489	0.5	0.3333	0.4	0.625	0.5556	0.5882	0.5814	0.6946	0.6330	0.2	0.25	0.2222	0.8988	0.9379	0.9179	0.5758	0.3725	0.4524	0.2353	0.3077	0.2667	0.5442	0.7175
0.0136	22.0	2750	0.4975	0.6310	0.6954	0.6616	0.9114	0.5980	0.7067	0.6478	0.2	0.24	0.2182	0.9085	0.9198	0.9141	0.4565	0.3043	0.3652	0.5833	0.5185	0.5490	0.7389	0.8212	0.7779	0.2308	0.24	0.2353	0.9512	0.9630	0.9571	0.5610	0.3485	0.4299	0.6818	0.5556	0.6122	0.5679	0.6820	0.6198	0.2174	0.25	0.2326	0.9141	0.9255	0.9198	0.5588	0.3725	0.4471	0.2857	0.3077	0.2963	0.5528	0.7149
0.0136	23.0	2875	0.4871	0.6399	0.6942	0.6659	0.9175	0.6089	0.7083	0.6549	0.2143	0.24	0.2264	0.9030	0.9198	0.9113	0.5	0.2899	0.3670	0.4815	0.4815	0.4815	0.7607	0.8159	0.7874	0.2692	0.28	0.2745	0.9512	0.9630	0.9571	0.6471	0.3333	0.44	0.6087	0.5185	0.5600	0.5825	0.6946	0.6336	0.2273	0.25	0.2381	0.9030	0.9255	0.9141	0.5938	0.3725	0.4578	0.25	0.3077	0.2759	0.5538	0.7224
0.0085	24.0	3000	0.4869	0.6528	0.7112	0.6808	0.9202	0.6211	0.7267	0.6697	0.2069	0.24	0.2222	0.9259	0.9259	0.9259	0.5238	0.3188	0.3964	0.5185	0.5185	0.5185	0.7565	0.8301	0.7916	0.2222	0.24	0.2308	0.9630	0.9630	0.9630	0.6053	0.3485	0.4423	0.6667	0.5926	0.6275	0.5972	0.7071	0.6475	0.2083	0.25	0.2273	0.9259	0.9317	0.9288	0.6129	0.3725	0.4634	0.2667	0.3077	0.2857	0.5608	0.7309
0.0085	25.0	3125	0.4876	0.6488	0.6988	0.6728	0.9183	0.6106	0.7083	0.6559	0.2143	0.24	0.2264	0.9202	0.9259	0.9231	0.5405	0.2899	0.3774	0.5926	0.5926	0.5926	0.7667	0.8142	0.7897	0.2308	0.24	0.2353	0.9689	0.9630	0.9659	0.6	0.3182	0.4158	0.64	0.5926	0.6154	0.5813	0.7029	0.6364	0.2174	0.25	0.2326	0.9202	0.9317	0.9259	0.6071	0.3333	0.4304	0.3571	0.3846	0.3704	0.5618	0.7253
0.0085	26.0	3250	0.4933	0.6365	0.6999	0.6667	0.9174	0.6065	0.7167	0.6570	0.2069	0.24	0.2222	0.9202	0.9259	0.9231	0.4865	0.2609	0.3396	0.4242	0.5185	0.4667	0.7528	0.8248	0.7872	0.2222	0.24	0.2308	0.9571	0.9630	0.9600	0.5806	0.2727	0.3711	0.5714	0.5926	0.5818	0.5846	0.7008	0.6375	0.2083	0.25	0.2273	0.9202	0.9317	0.9259	0.6	0.3529	0.4444	0.3158	0.4615	0.3750	0.5541	0.7220
0.0085	27.0	3375	0.5263	0.6470	0.7078	0.6760	0.9200	0.6090	0.7217	0.6606	0.2692	0.28	0.2745	0.9141	0.9198	0.9169	0.5128	0.2899	0.3704	0.5926	0.5926	0.5926	0.7465	0.8389	0.7900	0.28	0.28	0.28	0.9571	0.9630	0.9600	0.6286	0.3333	0.4356	0.625	0.5556	0.5882	0.5982	0.6946	0.6428	0.2857	0.3	0.2927	0.9141	0.9255	0.9198	0.6333	0.3725	0.4691	0.3846	0.3846	0.3846	0.5763	0.7293
0.0053	28.0	3500	0.5326	0.6454	0.7112	0.6767	0.9194	0.6124	0.7267	0.6646	0.2333	0.28	0.2545	0.9085	0.9198	0.9141	0.5405	0.2899	0.3774	0.5333	0.5926	0.5614	0.7504	0.8354	0.7906	0.2414	0.28	0.2593	0.9512	0.9630	0.9571	0.6061	0.3030	0.4040	0.6154	0.5926	0.6038	0.5922	0.6987	0.6411	0.24	0.3	0.2667	0.9085	0.9255	0.9169	0.6429	0.3529	0.4557	0.3333	0.3846	0.3571	0.5652	0.7261
0.0053	29.0	3625	0.5403	0.6517	0.7055	0.6775	0.9169	0.6189	0.72	0.6656	0.2333	0.28	0.2545	0.9085	0.9198	0.9141	0.5263	0.2899	0.3738	0.5769	0.5556	0.5660	0.7569	0.8265	0.7902	0.25	0.28	0.2642	0.9512	0.9630	0.9571	0.6	0.3182	0.4158	0.6522	0.5556	0.6	0.5921	0.6925	0.6384	0.2308	0.3	0.2609	0.9141	0.9255	0.9198	0.6	0.3529	0.4444	0.3333	0.3846	0.3571	0.5648	0.7254
0.0053	30.0	3750	0.5468	0.6520	0.7214	0.6849	0.9163	0.6227	0.74	0.6763	0.2258	0.28	0.25	0.9146	0.9259	0.9202	0.4878	0.2899	0.3636	0.5714	0.5926	0.5818	0.7544	0.8372	0.7936	0.2414	0.28	0.2593	0.9512	0.9630	0.9571	0.5385	0.3182	0.4000	0.6667	0.5926	0.6275	0.5937	0.7092	0.6463	0.2308	0.3	0.2609	0.9202	0.9317	0.9259	0.6207	0.3529	0.4500	0.3125	0.3846	0.3448	0.5665	0.7295
0.0053	31.0	3875	0.5451	0.6409	0.7135	0.6752	0.9175	0.6078	0.7283	0.6626	0.2258	0.28	0.25	0.9146	0.9259	0.9202	0.4878	0.2899	0.3636	0.5714	0.5926	0.5818	0.7480	0.8354	0.7893	0.25	0.28	0.2642	0.9512	0.9630	0.9571	0.5676	0.3182	0.4078	0.64	0.5926	0.6154	0.5807	0.6925	0.6317	0.2222	0.3	0.2553	0.9202	0.9317	0.9259	0.5625	0.3529	0.4337	0.3333	0.3846	0.3571	0.5637	0.7232
0.0035	32.0	4000	0.5502	0.6471	0.7123	0.6782	0.9182	0.6172	0.7283	0.6682	0.2258	0.28	0.25	0.9141	0.9198	0.9169	0.4878	0.2899	0.3636	0.5517	0.5926	0.5714	0.7540	0.8301	0.7902	0.25	0.28	0.2642	0.9571	0.9630	0.9600	0.6	0.3182	0.4158	0.64	0.5926	0.6154	0.5901	0.6987	0.6398	0.2308	0.3	0.2609	0.9141	0.9255	0.9198	0.5938	0.3725	0.4578	0.3125	0.3846	0.3448	0.5669	0.7268
0.0035	33.0	4125	0.5468	0.6451	0.7101	0.6760	0.9183	0.6147	0.7233	0.6646	0.2333	0.28	0.2545	0.9036	0.9259	0.9146	0.5128	0.2899	0.3704	0.5161	0.5926	0.5517	0.7565	0.8301	0.7916	0.25	0.28	0.2642	0.9458	0.9691	0.9573	0.6	0.3182	0.4158	0.64	0.5926	0.6154	0.5861	0.6904	0.6340	0.2308	0.3	0.2609	0.9146	0.9317	0.9231	0.6	0.3529	0.4444	0.3684	0.5385	0.4375	0.5744	0.7259
0.0035	34.0	4250	0.5503	0.6379	0.7044	0.6695	0.9174	0.6070	0.7183	0.6580	0.2258	0.28	0.25	0.9036	0.9259	0.9146	0.5128	0.2899	0.3704	0.4828	0.5185	0.5	0.7528	0.8301	0.7896	0.2414	0.28	0.2593	0.9458	0.9691	0.9573	0.5833	0.3182	0.4118	0.64	0.5926	0.6154	0.5792	0.6883	0.6291	0.2308	0.3	0.2609	0.9146	0.9317	0.9231	0.6	0.3529	0.4444	0.2353	0.3077	0.2667	0.5557	0.7220
0.0035	35.0	4375	0.5558	0.6407	0.7089	0.6731	0.9169	0.6127	0.725	0.6641	0.2258	0.28	0.25	0.9036	0.9259	0.9146	0.4878	0.2899	0.3636	0.4828	0.5185	0.5	0.752	0.8319	0.7899	0.2414	0.28	0.2593	0.9458	0.9691	0.9573	0.5526	0.3182	0.4038	0.64	0.5926	0.6154	0.5810	0.6904	0.6310	0.2308	0.3	0.2609	0.9146	0.9317	0.9231	0.6	0.3529	0.4444	0.2353	0.3077	0.2667	0.5552	0.7224

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.15.0
Tokenizers 0.15.0

csNoHug/roberta-base-finetuned-ner-cadec

roberta-base-finetuned-ner-cadec

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

为此模型做种