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	Macro Avg F1	Weighted Avg F1
No log	1.0	125	0.2142	0.5325	0.6055	0.5667	0.9194	0.4548	0.5918	0.5143	0.4186	0.5625	0.48	0.8398	0.8444	0.8421	0.2857	0.0625	0.1026	0.0	0.0	0.0	0.3878	0.5537
No log	2.0	250	0.1798	0.6083	0.6557	0.6311	0.9339	0.5276	0.6309	0.5746	0.6842	0.4062	0.5098	0.8950	0.9	0.8975	0.32	0.25	0.2807	0.6667	0.2759	0.3902	0.5306	0.6291
No log	3.0	375	0.1910	0.5748	0.6029	0.5885	0.9282	0.5191	0.5320	0.5255	0.6	0.375	0.4615	0.8820	0.8722	0.8771	0.2927	0.375	0.3288	0.3051	0.6207	0.4091	0.5204	0.5935
0.1902	4.0	500	0.2013	0.5995	0.6398	0.6190	0.9311	0.5460	0.6	0.5717	0.25	0.0938	0.1364	0.8840	0.8889	0.8864	0.2632	0.4688	0.3371	0.6154	0.5517	0.5818	0.5027	0.6185
0.1902	5.0	625	0.2113	0.6161	0.6649	0.6396	0.9335	0.5515	0.6289	0.5877	0.5556	0.4688	0.5085	0.8852	0.9	0.8926	0.2857	0.25	0.2667	0.5185	0.4828	0.5	0.5511	0.6398
0.1902	6.0	750	0.1955	0.6223	0.6544	0.6379	0.9341	0.5541	0.6021	0.5771	0.5833	0.4375	0.5	0.8956	0.9056	0.9006	0.2857	0.375	0.3243	0.6818	0.5172	0.5882	0.5780	0.6404
0.1902	7.0	875	0.2226	0.6252	0.6491	0.6369	0.9343	0.5671	0.6186	0.5917	0.5556	0.1562	0.2439	0.8983	0.8833	0.8908	0.3061	0.4688	0.3704	0.5652	0.4483	0.5000	0.5193	0.6352
0.0648	8.0	1000	0.2345	0.6229	0.6755	0.6481	0.9363	0.5773	0.6392	0.6067	0.4138	0.375	0.3934	0.875	0.8944	0.8846	0.2973	0.3438	0.3188	0.5143	0.6207	0.5625	0.5532	0.6498
0.0648	9.0	1125	0.2316	0.6322	0.6689	0.65	0.9368	0.5851	0.6309	0.6071	0.5	0.3125	0.3846	0.8811	0.9056	0.8932	0.2766	0.4062	0.3291	0.5556	0.5172	0.5357	0.5499	0.6512
0.0648	10.0	1250	0.2944	0.6204	0.6491	0.6344	0.9320	0.5551	0.6021	0.5776	0.5789	0.3438	0.4314	0.8913	0.9111	0.9011	0.2619	0.3438	0.2973	0.6364	0.4828	0.5490	0.5513	0.6353
0.0648	11.0	1375	0.2660	0.6280	0.6794	0.6527	0.9353	0.5786	0.6454	0.6101	0.3824	0.4062	0.3939	0.8956	0.9056	0.9006	0.2812	0.2812	0.2812	0.5484	0.5862	0.5667	0.5505	0.6544
0.0284	12.0	1500	0.2819	0.6366	0.6702	0.6530	0.9355	0.5827	0.6392	0.6096	0.5	0.1875	0.2727	0.8956	0.9056	0.9006	0.3478	0.5	0.4103	0.5	0.4483	0.4727	0.5332	0.6508
0.0284	13.0	1625	0.3000	0.6326	0.6702	0.6509	0.9363	0.5736	0.6351	0.6027	0.4444	0.375	0.4068	0.8950	0.9	0.8975	0.3421	0.4062	0.3714	0.65	0.4483	0.5306	0.5618	0.6519
0.0284	14.0	1750	0.2996	0.6228	0.6491	0.6357	0.9363	0.5645	0.6041	0.5837	0.4783	0.3438	0.4	0.8743	0.8889	0.8815	0.2973	0.3438	0.3188	0.6071	0.5862	0.5965	0.5561	0.6360
0.0284	15.0	1875	0.3246	0.6311	0.6636	0.6469	0.9352	0.5788	0.6206	0.5990	0.45	0.2812	0.3462	0.9056	0.9056	0.9056	0.2683	0.3438	0.3014	0.5278	0.6552	0.5846	0.5473	0.6480
0.0136	16.0	2000	0.3305	0.6461	0.6623	0.6541	0.9377	0.5869	0.6268	0.6062	0.4545	0.3125	0.3704	0.9011	0.9111	0.9061	0.3448	0.3125	0.3279	0.5385	0.4828	0.5091	0.5439	0.6520
0.0136	17.0	2125	0.3181	0.6291	0.6781	0.6527	0.9375	0.5780	0.6495	0.6117	0.4231	0.3438	0.3793	0.9066	0.9167	0.9116	0.2857	0.3125	0.2985	0.4483	0.4483	0.4483	0.5299	0.6536
0.0136	18.0	2250	0.3414	0.6298	0.6755	0.6518	0.9362	0.5765	0.6371	0.6053	0.375	0.375	0.375	0.8962	0.9111	0.9036	0.3235	0.3438	0.3333	0.5714	0.5517	0.5614	0.5557	0.6532
0.0136	19.0	2375	0.3457	0.6302	0.6768	0.6527	0.9372	0.5877	0.6495	0.6170	0.3636	0.25	0.2963	0.8907	0.9056	0.8981	0.26	0.4062	0.3171	0.6087	0.4828	0.5385	0.5334	0.6546
0.0078	20.0	2500	0.3700	0.6367	0.6636	0.6499	0.9367	0.5805	0.6247	0.6018	0.3714	0.4062	0.3881	0.9016	0.9167	0.9091	0.3077	0.25	0.2759	0.5833	0.4828	0.5283	0.5406	0.6492
0.0078	21.0	2625	0.3772	0.6276	0.6715	0.6488	0.9325	0.5766	0.6289	0.6016	0.44	0.3438	0.3860	0.8919	0.9167	0.9041	0.2927	0.375	0.3288	0.5161	0.5517	0.5333	0.5508	0.6502
0.0078	22.0	2750	0.3622	0.6389	0.6768	0.6573	0.9345	0.5855	0.6495	0.6158	0.4333	0.4062	0.4194	0.8840	0.8889	0.8864	0.3333	0.3125	0.3226	0.625	0.5172	0.5660	0.5620	0.6575
0.0078	23.0	2875	0.3811	0.6304	0.6728	0.6509	0.9352	0.5765	0.6371	0.6053	0.4	0.375	0.3871	0.8804	0.9	0.8901	0.3438	0.3438	0.3438	0.5926	0.5517	0.5714	0.5595	0.6514
0.005	24.0	3000	0.3824	0.6322	0.6689	0.65	0.9353	0.5757	0.6351	0.6039	0.4286	0.375	0.4000	0.8901	0.9	0.8950	0.3226	0.3125	0.3175	0.5769	0.5172	0.5455	0.5524	0.6501
0.005	25.0	3125	0.3821	0.6297	0.6821	0.6548	0.9375	0.5850	0.6598	0.6202	0.4	0.375	0.3871	0.8852	0.9	0.8926	0.25	0.2812	0.2647	0.56	0.4828	0.5185	0.5366	0.6561
0.005	26.0	3250	0.4058	0.6292	0.6715	0.6496	0.9355	0.5821	0.6433	0.6112	0.3939	0.4062	0.4	0.875	0.8944	0.8846	0.2857	0.25	0.2667	0.5357	0.5172	0.5263	0.5378	0.6494
0.005	27.0	3375	0.3980	0.6262	0.6807	0.6523	0.9369	0.5743	0.6536	0.6114	0.4074	0.3438	0.3729	0.8798	0.8944	0.8871	0.3333	0.375	0.3529	0.5769	0.5172	0.5455	0.5539	0.6533
0.0031	28.0	3500	0.4100	0.6305	0.6755	0.6522	0.9351	0.5762	0.6392	0.6061	0.4074	0.3438	0.3729	0.8962	0.9111	0.9036	0.3421	0.4062	0.3714	0.5385	0.4828	0.5091	0.5526	0.6533
0.0031	29.0	3625	0.4050	0.6383	0.6939	0.6650	0.9388	0.5916	0.6660	0.6266	0.44	0.3438	0.3860	0.8913	0.9111	0.9011	0.3095	0.4062	0.3514	0.5556	0.5172	0.5357	0.5601	0.6665
0.0031	30.0	3750	0.4111	0.6348	0.6741	0.6539	0.9367	0.5819	0.6371	0.6083	0.4138	0.375	0.3934	0.8962	0.9111	0.9036	0.3243	0.375	0.3478	0.56	0.4828	0.5185	0.5543	0.6549
0.0031	31.0	3875	0.4074	0.6349	0.6768	0.6552	0.9381	0.5832	0.6433	0.6118	0.3846	0.3125	0.3448	0.8962	0.9111	0.9036	0.3333	0.4062	0.3662	0.56	0.4828	0.5185	0.5490	0.6559
0.002	32.0	4000	0.4086	0.6421	0.6794	0.6603	0.9379	0.5843	0.6433	0.6124	0.4138	0.375	0.3934	0.9016	0.9167	0.9091	0.375	0.375	0.375	0.5833	0.4828	0.5283	0.5636	0.6603
0.002	33.0	4125	0.4174	0.6378	0.6900	0.6629	0.9369	0.5847	0.6619	0.6209	0.4074	0.3438	0.3729	0.9022	0.9222	0.9121	0.3235	0.3438	0.3333	0.5385	0.4828	0.5091	0.5497	0.6632
0.002	34.0	4250	0.4131	0.6431	0.6847	0.6633	0.9379	0.5881	0.6536	0.6191	0.4138	0.375	0.3934	0.8962	0.9111	0.9036	0.375	0.375	0.375	0.5833	0.4828	0.5283	0.5639	0.6634
0.002	35.0	4375	0.4142	0.6452	0.6860	0.6650	0.9380	0.5911	0.6557	0.6217	0.4138	0.375	0.3934	0.8962	0.9111	0.9036	0.375	0.375	0.375	0.5833	0.4828	0.5283	0.5644	0.6650

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-no-iob

roberta-base-finetuned-ner-cadec-no-iob

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

csNoHug/
roberta-base-finetuned-ner-cadec-no-iob