bert-base-cased-finetuned-ner-cadec-no-iob

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

• Loss: 0.4487
• Precision: 0.6037
• Recall: 0.6491
• F1: 0.6256
• Accuracy: 0.9313
• Adr Precision: 0.5441
• Adr Recall: 0.6103
• Adr F1: 0.5753
• Disease Precision: 0.5
• Disease Recall: 0.375
• Disease F1: 0.4286
• Drug Precision: 0.8649
• Drug Recall: 0.8889
• Drug F1: 0.8767
• Finding Precision: 0.2903
• Finding Recall: 0.2812
• Finding F1: 0.2857
• Symptom Precision: 0.4839
• Symptom Recall: 0.5172
• Symptom F1: 0.5000
• Macro Avg F1: 0.5333
• Weighted Avg F1: 0.6256

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	Macro Avg F1	Weighted Avg F1
No log	1.0	125	0.2340	0.5044	0.6003	0.5482	0.9191	0.4397	0.5938	0.5053	0.3529	0.375	0.3636	0.7574	0.85	0.8010	0.1818	0.0625	0.0930	0.0	0.0	0.0	0.3526	0.5328
No log	2.0	250	0.2068	0.5546	0.6227	0.5867	0.9253	0.4770	0.6	0.5315	0.55	0.3438	0.4231	0.8256	0.8944	0.8587	0.3158	0.1875	0.2353	0.4286	0.1034	0.1667	0.4430	0.5782
No log	3.0	375	0.2031	0.5633	0.6161	0.5885	0.9281	0.5150	0.5670	0.5397	0.5	0.4062	0.4483	0.8093	0.8722	0.8396	0.2	0.2188	0.2090	0.375	0.5172	0.4348	0.4943	0.5891
0.209	4.0	500	0.2331	0.5483	0.6069	0.5761	0.9273	0.5009	0.5897	0.5417	0.0	0.0	0.0	0.8404	0.8778	0.8587	0.14	0.2188	0.1707	0.5	0.3103	0.3830	0.3908	0.5724
0.209	5.0	625	0.2376	0.5878	0.6491	0.6169	0.9324	0.5129	0.6165	0.5599	0.5312	0.5312	0.5312	0.8703	0.8944	0.8822	0.1429	0.0625	0.0870	0.5652	0.4483	0.5000	0.5121	0.6130
0.209	6.0	750	0.2523	0.5646	0.6346	0.5975	0.9258	0.5114	0.6021	0.5530	0.4	0.375	0.3871	0.8649	0.8889	0.8767	0.0857	0.0938	0.0896	0.4516	0.4828	0.4667	0.4746	0.6000
0.209	7.0	875	0.2753	0.5748	0.6438	0.6073	0.9249	0.5209	0.6165	0.5647	0.4762	0.3125	0.3774	0.8670	0.9056	0.8859	0.1458	0.2188	0.1750	0.5	0.3103	0.3830	0.4772	0.6096
0.0561	8.0	1000	0.2769	0.5868	0.6557	0.6193	0.9284	0.5288	0.6247	0.5728	0.6	0.375	0.4615	0.8703	0.8944	0.8822	0.2424	0.25	0.2462	0.3611	0.4483	0.4000	0.5125	0.6212
0.0561	9.0	1125	0.3161	0.5719	0.6240	0.5968	0.9281	0.5091	0.5794	0.5419	0.5263	0.3125	0.3922	0.8757	0.9	0.8877	0.1739	0.25	0.2051	0.48	0.4138	0.4444	0.4943	0.5998
0.0561	10.0	1250	0.3101	0.5867	0.6385	0.6115	0.9297	0.5343	0.5938	0.5625	0.4839	0.4688	0.4762	0.8791	0.8889	0.8840	0.1818	0.25	0.2105	0.4483	0.4483	0.4483	0.5163	0.6160
0.0561	11.0	1375	0.3321	0.5862	0.6412	0.6125	0.9295	0.5245	0.5959	0.5579	0.6	0.4688	0.5263	0.8556	0.8889	0.8719	0.2286	0.25	0.2388	0.4516	0.4828	0.4667	0.5323	0.6142
0.0206	12.0	1500	0.3459	0.5923	0.6517	0.6206	0.9303	0.5323	0.6124	0.5695	0.5517	0.5	0.5246	0.875	0.8944	0.8846	0.2581	0.25	0.2540	0.375	0.4138	0.3934	0.5252	0.6224
0.0206	13.0	1625	0.3489	0.5866	0.6214	0.6035	0.9270	0.5327	0.5876	0.5588	0.4667	0.4375	0.4516	0.8370	0.8556	0.8462	0.24	0.1875	0.2105	0.4138	0.4138	0.4138	0.4962	0.6023
0.0206	14.0	1750	0.3762	0.5709	0.6214	0.5951	0.9270	0.5047	0.5588	0.5303	0.5	0.4375	0.4667	0.8811	0.9056	0.8932	0.2143	0.2812	0.2432	0.4242	0.4828	0.4516	0.5170	0.5987
0.0206	15.0	1875	0.3729	0.5806	0.6412	0.6094	0.9280	0.5149	0.6041	0.5560	0.5652	0.4062	0.4727	0.8503	0.8833	0.8665	0.3	0.2812	0.2903	0.4286	0.4138	0.4211	0.5213	0.6098
0.0093	16.0	2000	0.3980	0.5748	0.6385	0.6050	0.9265	0.5229	0.6124	0.5641	0.4762	0.3125	0.3774	0.8525	0.8667	0.8595	0.2326	0.3125	0.2667	0.4074	0.3793	0.3929	0.4921	0.6073
0.0093	17.0	2125	0.3885	0.5951	0.6359	0.6148	0.9285	0.5343	0.5938	0.5625	0.6087	0.4375	0.5091	0.8587	0.8778	0.8681	0.25	0.25	0.25	0.4375	0.4828	0.4590	0.5297	0.6157
0.0093	18.0	2250	0.4024	0.6015	0.6491	0.6244	0.9310	0.5368	0.6021	0.5675	0.5	0.4375	0.4667	0.8811	0.9056	0.8932	0.2857	0.25	0.2667	0.4545	0.5172	0.4839	0.5356	0.6247
0.0093	19.0	2375	0.4019	0.6025	0.6478	0.6243	0.9302	0.5399	0.6	0.5684	0.5714	0.5	0.5333	0.8703	0.8944	0.8822	0.2667	0.25	0.2581	0.4545	0.5172	0.4839	0.5452	0.6251
0.0053	20.0	2500	0.4061	0.5847	0.6332	0.6080	0.9291	0.5268	0.5876	0.5556	0.5652	0.4062	0.4727	0.8595	0.8833	0.8712	0.2286	0.25	0.2388	0.4054	0.5172	0.4545	0.5186	0.6098
0.0053	21.0	2625	0.4219	0.5903	0.6425	0.6153	0.9288	0.5213	0.6062	0.5605	0.55	0.3438	0.4231	0.8587	0.8778	0.8681	0.3103	0.2812	0.2951	0.5357	0.5172	0.5263	0.5346	0.6153
0.0053	22.0	2750	0.4190	0.6024	0.6557	0.6279	0.9309	0.5420	0.6247	0.5805	0.5185	0.4375	0.4746	0.8548	0.8833	0.8689	0.32	0.25	0.2807	0.4643	0.4483	0.4561	0.5321	0.6271
0.0053	23.0	2875	0.4272	0.5870	0.6412	0.6129	0.9287	0.5192	0.5856	0.5504	0.6154	0.5	0.5517	0.8610	0.8944	0.8774	0.2564	0.3125	0.2817	0.5172	0.5172	0.5172	0.5557	0.6155
0.0034	24.0	3000	0.4206	0.5887	0.6438	0.6150	0.9308	0.5160	0.6	0.5548	0.5769	0.4688	0.5172	0.8602	0.8889	0.8743	0.2963	0.25	0.2712	0.5385	0.4828	0.5091	0.5453	0.6154
0.0034	25.0	3125	0.4260	0.6037	0.6491	0.6256	0.9309	0.5365	0.6062	0.5692	0.52	0.4062	0.4561	0.8859	0.9056	0.8956	0.2692	0.2188	0.2414	0.4688	0.5172	0.4918	0.5308	0.6251
0.0034	26.0	3250	0.4341	0.5995	0.6478	0.6227	0.9310	0.5307	0.6062	0.5659	0.5417	0.4062	0.4643	0.8710	0.9	0.8852	0.2857	0.25	0.2667	0.5185	0.4828	0.5	0.5364	0.6223
0.0034	27.0	3375	0.4476	0.6010	0.6438	0.6217	0.9300	0.5314	0.5938	0.5609	0.56	0.4375	0.4912	0.8710	0.9	0.8852	0.3	0.2812	0.2903	0.5172	0.5172	0.5172	0.5490	0.6219
0.0025	28.0	3500	0.4281	0.6010	0.6478	0.6235	0.9299	0.5328	0.6021	0.5653	0.56	0.4375	0.4912	0.8663	0.9	0.8828	0.2667	0.25	0.2581	0.5556	0.5172	0.5357	0.5466	0.6235
0.0025	29.0	3625	0.4339	0.5988	0.6438	0.6205	0.9299	0.5378	0.6021	0.5681	0.52	0.4062	0.4561	0.8595	0.8833	0.8712	0.2903	0.2812	0.2857	0.4839	0.5172	0.5000	0.5362	0.6208
0.0025	30.0	3750	0.4408	0.6105	0.6596	0.6341	0.9311	0.5404	0.6206	0.5777	0.5909	0.4062	0.4815	0.8663	0.9	0.8828	0.36	0.2812	0.3158	0.5357	0.5172	0.5263	0.5568	0.6331
0.0025	31.0	3875	0.4450	0.6079	0.6504	0.6284	0.9309	0.5410	0.6124	0.5745	0.5417	0.4062	0.4643	0.8656	0.8944	0.8798	0.2917	0.2188	0.25	0.5357	0.5172	0.5263	0.5390	0.6268
0.0016	32.0	4000	0.4435	0.5988	0.6359	0.6168	0.9305	0.5345	0.5918	0.5616	0.52	0.4062	0.4561	0.8641	0.8833	0.8736	0.2857	0.25	0.2667	0.4839	0.5172	0.5000	0.5316	0.6165
0.0016	33.0	4125	0.4448	0.6017	0.6438	0.6221	0.9308	0.5369	0.6	0.5667	0.5417	0.4062	0.4643	0.8696	0.8889	0.8791	0.3103	0.2812	0.2951	0.4688	0.5172	0.4918	0.5394	0.6222
0.0016	34.0	4250	0.4459	0.6030	0.6451	0.6233	0.9304	0.5436	0.6041	0.5723	0.5	0.375	0.4286	0.8649	0.8889	0.8767	0.2812	0.2812	0.2812	0.4839	0.5172	0.5000	0.5318	0.6234
0.0016	35.0	4375	0.4487	0.6037	0.6491	0.6256	0.9313	0.5441	0.6103	0.5753	0.5	0.375	0.4286	0.8649	0.8889	0.8767	0.2903	0.2812	0.2857	0.4839	0.5172	0.5000	0.5333	0.6256

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu121
• Datasets 2.15.0
• Tokenizers 0.15.0