electra-base-discriminator-finetuned-ner-cadec-no-iob

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

Loss: 0.3907
Precision: 0.6459
Recall: 0.6715
F1: 0.6585
Accuracy: 0.9355
Adr Precision: 0.5961
Adr Recall: 0.6330
Adr F1: 0.614
Disease Precision: 0.4615
Disease Recall: 0.375
Disease F1: 0.4138
Drug Precision: 0.9022
Drug Recall: 0.9222
Drug F1: 0.9121
Finding Precision: 0.2571
Finding Recall: 0.2812
Finding F1: 0.2687
Symptom Precision: 0.5357
Symptom Recall: 0.5172
Symptom F1: 0.5263
Macro Avg F1: 0.5470
Weighted Avg F1: 0.6584

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: 40

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	Macro Avg F1	Weighted Avg F1
No log	1.0	125	0.2178	0.5120	0.5646	0.5370	0.9209	0.4311	0.5608	0.4875	0.1	0.0312	0.0476	0.8158	0.8611	0.8378	0.0	0.0	0.0	0.0	0.0	0.0	0.2746	0.5129
No log	2.0	250	0.1864	0.5799	0.6227	0.6005	0.9296	0.5017	0.6	0.5465	0.4146	0.5312	0.4658	0.9011	0.9111	0.9061	0.0	0.0	0.0	0.0	0.0	0.0	0.3837	0.5845
No log	3.0	375	0.1845	0.5894	0.6306	0.6093	0.9310	0.5520	0.6021	0.5759	0.2353	0.375	0.2892	0.9016	0.9167	0.9091	0.0857	0.0938	0.0896	0.4615	0.2069	0.2857	0.4299	0.6113
0.2159	4.0	500	0.1968	0.6002	0.6359	0.6176	0.9318	0.5570	0.6041	0.5796	0.0667	0.0312	0.0426	0.9121	0.9222	0.9171	0.1967	0.375	0.2581	0.5263	0.3448	0.4167	0.4428	0.6173
0.2159	5.0	625	0.2026	0.6357	0.6583	0.6468	0.9338	0.5817	0.6165	0.5986	0.52	0.4062	0.4561	0.9071	0.9222	0.9146	0.1951	0.25	0.2192	0.5909	0.4483	0.5098	0.5397	0.6482
0.2159	6.0	750	0.2142	0.6055	0.6359	0.6203	0.9319	0.5697	0.5814	0.5755	0.375	0.4688	0.4167	0.9066	0.9167	0.9116	0.1163	0.1562	0.1333	0.4167	0.5172	0.4615	0.4997	0.6256
0.2159	7.0	875	0.2283	0.6420	0.6649	0.6533	0.9328	0.5867	0.6206	0.6032	0.5	0.3125	0.3846	0.9076	0.9278	0.9176	0.2444	0.3438	0.2857	0.6522	0.5172	0.5769	0.5536	0.6542
0.0651	8.0	1000	0.2430	0.6103	0.6570	0.6328	0.9312	0.5632	0.6062	0.5839	0.5625	0.2812	0.375	0.9121	0.9222	0.9171	0.2241	0.4062	0.2889	0.4211	0.5517	0.4776	0.5285	0.6377
0.0651	9.0	1125	0.2367	0.6308	0.6583	0.6443	0.9338	0.5800	0.6206	0.5996	0.48	0.375	0.4211	0.8811	0.9056	0.8932	0.2286	0.25	0.2388	0.5556	0.5172	0.5357	0.5377	0.6441
0.0651	10.0	1250	0.2694	0.6241	0.6570	0.6401	0.9294	0.5639	0.6186	0.5900	0.4286	0.2812	0.3396	0.9171	0.9222	0.9197	0.2222	0.3125	0.2597	0.6842	0.4483	0.5417	0.5301	0.6419
0.0651	11.0	1375	0.2731	0.61	0.6438	0.6264	0.9325	0.5626	0.6021	0.5817	0.4286	0.375	0.4000	0.8511	0.8889	0.8696	0.2812	0.2812	0.2812	0.4545	0.5172	0.4839	0.5233	0.6259
0.0265	12.0	1500	0.2842	0.6221	0.6451	0.6334	0.9323	0.5697	0.5979	0.5835	0.3929	0.3438	0.3667	0.9027	0.9278	0.9151	0.2121	0.2188	0.2154	0.4516	0.4828	0.4667	0.5095	0.6331
0.0265	13.0	1625	0.2861	0.6438	0.6675	0.6554	0.9348	0.5802	0.6268	0.6026	0.5	0.4375	0.4667	0.9278	0.9278	0.9278	0.3	0.2812	0.2903	0.5	0.4138	0.4528	0.5480	0.6552
0.0265	14.0	1750	0.3202	0.6413	0.6557	0.6484	0.9323	0.5742	0.6144	0.5936	0.5263	0.3125	0.3922	0.9071	0.9222	0.9146	0.2647	0.2812	0.2727	0.7	0.4828	0.5714	0.5489	0.6469
0.0265	15.0	1875	0.3118	0.6284	0.6491	0.6385	0.9336	0.5731	0.6062	0.5892	0.4348	0.3125	0.3636	0.9066	0.9167	0.9116	0.25	0.3125	0.2778	0.52	0.4483	0.4815	0.5247	0.6390
0.0115	16.0	2000	0.3144	0.6293	0.6583	0.6435	0.9335	0.5747	0.6268	0.5996	0.3846	0.3125	0.3448	0.9061	0.9111	0.9086	0.2286	0.25	0.2388	0.5909	0.4483	0.5098	0.5203	0.6436
0.0115	17.0	2125	0.3235	0.6411	0.6623	0.6515	0.9346	0.5918	0.6247	0.6078	0.4	0.375	0.3871	0.9171	0.9222	0.9197	0.2121	0.2188	0.2154	0.5185	0.4828	0.5	0.5260	0.6519
0.0115	18.0	2250	0.3258	0.6230	0.6583	0.6402	0.9344	0.5948	0.6144	0.6045	0.3429	0.375	0.3582	0.8913	0.9111	0.9011	0.1957	0.2812	0.2308	0.4571	0.5517	0.5	0.5189	0.6447
0.0115	19.0	2375	0.3347	0.6312	0.6728	0.6513	0.9361	0.5794	0.6392	0.6078	0.4348	0.3125	0.3636	0.9022	0.9222	0.9121	0.2564	0.3125	0.2817	0.5185	0.4828	0.5	0.5331	0.6519
0.0076	20.0	2500	0.3334	0.6508	0.6662	0.6584	0.9328	0.6031	0.6330	0.6177	0.375	0.375	0.375	0.9231	0.9333	0.9282	0.1765	0.1875	0.1818	0.6316	0.4138	0.5	0.5205	0.6583
0.0076	21.0	2625	0.3426	0.6385	0.6781	0.6577	0.9344	0.5943	0.6495	0.6207	0.4231	0.3438	0.3793	0.9071	0.9222	0.9146	0.1765	0.1875	0.1818	0.5	0.5517	0.5246	0.5242	0.6581
0.0076	22.0	2750	0.3431	0.6269	0.6649	0.6453	0.9344	0.5862	0.6309	0.6077	0.3793	0.3438	0.3607	0.9071	0.9222	0.9146	0.1707	0.2188	0.1918	0.4828	0.4828	0.4828	0.5115	0.6478
0.0076	23.0	2875	0.3374	0.6410	0.6689	0.6546	0.9348	0.5913	0.6412	0.6152	0.3913	0.2812	0.3273	0.9180	0.9333	0.9256	0.2368	0.2812	0.2571	0.4762	0.3448	0.4000	0.5051	0.6534
0.0049	24.0	3000	0.3484	0.6338	0.6530	0.6433	0.9352	0.5819	0.6082	0.5948	0.4	0.375	0.3871	0.9126	0.9278	0.9201	0.2258	0.2188	0.2222	0.4667	0.4828	0.4746	0.5198	0.6429
0.0049	25.0	3125	0.3441	0.6444	0.6741	0.6589	0.9362	0.6015	0.6412	0.6208	0.4194	0.4062	0.4127	0.9076	0.9278	0.9176	0.1935	0.1875	0.1905	0.4667	0.4828	0.4746	0.5232	0.6587
0.0049	26.0	3250	0.3573	0.6330	0.6689	0.6504	0.9347	0.5802	0.6268	0.6026	0.4138	0.375	0.3934	0.9235	0.9389	0.9311	0.2286	0.25	0.2388	0.4667	0.4828	0.4746	0.5281	0.6515
0.0049	27.0	3375	0.3589	0.6477	0.6596	0.6536	0.9336	0.5909	0.6165	0.6034	0.48	0.375	0.4211	0.9126	0.9278	0.9201	0.25	0.2812	0.2647	0.5909	0.4483	0.5098	0.5438	0.6531
0.0037	28.0	3500	0.3629	0.6307	0.6715	0.6505	0.9354	0.5900	0.6351	0.6117	0.4444	0.375	0.4068	0.9016	0.9167	0.9091	0.2326	0.3125	0.2667	0.4375	0.4828	0.4590	0.5307	0.6533
0.0037	29.0	3625	0.3694	0.6490	0.6781	0.6632	0.9358	0.6035	0.6433	0.6228	0.4286	0.375	0.4000	0.9071	0.9222	0.9146	0.2105	0.25	0.2286	0.6154	0.5517	0.5818	0.5495	0.6644
0.0037	30.0	3750	0.3779	0.6456	0.6728	0.6589	0.9350	0.5946	0.6351	0.6142	0.5	0.375	0.4286	0.9071	0.9222	0.9146	0.2647	0.2812	0.2727	0.4839	0.5172	0.5000	0.5460	0.6589
0.0037	31.0	3875	0.3776	0.6465	0.6636	0.6549	0.9360	0.5992	0.6227	0.6107	0.4615	0.375	0.4138	0.9022	0.9222	0.9121	0.2424	0.25	0.2462	0.4839	0.5172	0.5000	0.5366	0.6543
0.0027	32.0	4000	0.3775	0.6429	0.6675	0.6550	0.9355	0.5984	0.6268	0.6123	0.4444	0.375	0.4068	0.9022	0.9222	0.9121	0.2368	0.2812	0.2571	0.5	0.5172	0.5085	0.5394	0.6558
0.0027	33.0	4125	0.3784	0.6378	0.6715	0.6542	0.9363	0.5881	0.6330	0.6097	0.4783	0.3438	0.4	0.9022	0.9222	0.9121	0.2381	0.3125	0.2703	0.5556	0.5172	0.5357	0.5456	0.6555
0.0027	34.0	4250	0.3824	0.6332	0.6741	0.6530	0.9356	0.5830	0.6371	0.6089	0.4286	0.375	0.4000	0.9022	0.9222	0.9121	0.2432	0.2812	0.2609	0.5357	0.5172	0.5263	0.5416	0.6542
0.0027	35.0	4375	0.3825	0.6415	0.6728	0.6568	0.9358	0.5954	0.6371	0.6155	0.4444	0.375	0.4068	0.9022	0.9222	0.9121	0.225	0.2812	0.25	0.56	0.4828	0.5185	0.5406	0.6580
0.0018	36.0	4500	0.3841	0.6390	0.6794	0.6586	0.9364	0.5928	0.6454	0.6180	0.4444	0.375	0.4068	0.9022	0.9222	0.9121	0.2368	0.2812	0.2571	0.5172	0.5172	0.5172	0.5422	0.6598
0.0018	37.0	4625	0.3892	0.6477	0.6741	0.6606	0.9361	0.6008	0.6392	0.6194	0.4286	0.375	0.4000	0.9022	0.9222	0.9121	0.2571	0.2812	0.2687	0.5385	0.4828	0.5091	0.5418	0.6606
0.0018	38.0	4750	0.3893	0.6480	0.6702	0.6589	0.9363	0.5988	0.6309	0.6145	0.4444	0.375	0.4068	0.9022	0.9222	0.9121	0.25	0.2812	0.2647	0.5769	0.5172	0.5455	0.5487	0.6590
0.0018	39.0	4875	0.3911	0.6422	0.6702	0.6559	0.9354	0.5907	0.6309	0.6102	0.4615	0.375	0.4138	0.9022	0.9222	0.9121	0.2571	0.2812	0.2687	0.5357	0.5172	0.5263	0.5462	0.6559
0.0016	40.0	5000	0.3907	0.6459	0.6715	0.6585	0.9355	0.5961	0.6330	0.614	0.4615	0.375	0.4138	0.9022	0.9222	0.9121	0.2571	0.2812	0.2687	0.5357	0.5172	0.5263	0.5470	0.6584

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.15.0
Tokenizers 0.15.0

csNoHug/electra-base-discriminator-finetuned-ner-cadec-no-iob

electra-base-discriminator-finetuned-ner-cadec-no-iob

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Seed this model