albert-base-v2-finetuned-ner-cadec-no-iob

csNoHug/albert-base-v2-finetuned-ner-cadec-no-iob

albert-base-v2-finetuned-ner-cadec-no-iob is a token classification model from csNoHug. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Token Classification
Library: transformers
License: apache-2.0
Model type: albert
Access: Public
Created: Dec 20, 2023
Updated: Dec 21, 2023
SHA: 0a0dd10d

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Base models

Model card

From the Hugging Face model README

albert-base-v2-finetuned-ner-cadec-no-iob

This model is a fine-tuned version of albert-base-v2 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.5037
Precision: 0.5849
Recall: 0.6227
F1: 0.6032
Accuracy: 0.9311
Adr Precision: 0.5065
Adr Recall: 0.5608
Adr F1: 0.5323
Disease Precision: 0.52
Disease Recall: 0.4062
Disease F1: 0.4561
Drug Precision: 0.9121
Drug Recall: 0.9222
Drug F1: 0.9171
Finding Precision: 0.1875
Finding Recall: 0.1875
Finding F1: 0.1875
Symptom Precision: 0.4839
Symptom Recall: 0.5172
Symptom F1: 0.5000
Macro Avg F1: 0.5186
Weighted Avg F1: 0.6047

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	0.2244	0.5211	0.6029	0.5590	0.9215	0.4547	0.6103	0.5211	0.3864	0.5312	0.4474	0.8276	0.8	0.8136	0.0	0.0	0.0	0.0	0.0	0.0	0.3564	0.5455
No log	2.0	250	0.2082	0.5448	0.5937	0.5682	0.9240	0.4700	0.5649	0.5131	0.4348	0.3125	0.3636	0.8722	0.8722	0.8722	0.1892	0.2188	0.2029	0.6667	0.0690	0.125	0.4154	0.5641
No log	3.0	375	0.2113	0.5416	0.6016	0.5700	0.9273	0.4863	0.5505	0.5164	0.48	0.375	0.4211	0.8182	0.85	0.8338	0.1622	0.1875	0.1739	0.4091	0.6207	0.4932	0.4877	0.5724
0.187	4.0	500	0.2257	0.5418	0.6069	0.5725	0.9281	0.4739	0.5423	0.5058	0.3548	0.3438	0.3492	0.8944	0.8944	0.8944	0.1607	0.2812	0.2045	0.5926	0.5517	0.5714	0.5051	0.5813
0.187	5.0	625	0.2483	0.5788	0.6253	0.6011	0.9283	0.4957	0.5918	0.5395	0.6111	0.3438	0.4400	0.8883	0.8833	0.8858	0.2083	0.1562	0.1786	0.6316	0.4138	0.5	0.5088	0.6008
0.187	6.0	750	0.2584	0.5572	0.6042	0.5797	0.9242	0.4843	0.5423	0.5117	0.4	0.375	0.3871	0.8989	0.8889	0.8939	0.1951	0.25	0.2192	0.5	0.5172	0.5085	0.5041	0.5847
0.187	7.0	875	0.2676	0.5640	0.5989	0.5809	0.9261	0.4836	0.5464	0.5131	1.0	0.0938	0.1714	0.9096	0.8944	0.9020	0.1964	0.3438	0.25	0.6667	0.4828	0.56	0.4793	0.5817
0.0608	8.0	1000	0.2623	0.5797	0.6187	0.5986	0.9335	0.5121	0.5670	0.5382	0.5556	0.3125	0.4000	0.8944	0.8944	0.8944	0.1562	0.1562	0.1562	0.4286	0.6207	0.5070	0.4992	0.5996
0.0608	9.0	1125	0.2968	0.5754	0.6293	0.6011	0.9314	0.5162	0.5897	0.5505	0.4062	0.4062	0.4062	0.8840	0.8889	0.8864	0.1282	0.1562	0.1408	0.5652	0.4483	0.5000	0.4968	0.6050
0.0608	10.0	1250	0.3169	0.5485	0.5897	0.5683	0.9289	0.4887	0.5361	0.5113	0.3333	0.3125	0.3226	0.8820	0.8722	0.8771	0.1389	0.1562	0.1471	0.3846	0.5172	0.4412	0.4598	0.5721
0.0608	11.0	1375	0.3367	0.5673	0.6227	0.5937	0.9261	0.5081	0.5794	0.5414	0.5625	0.2812	0.375	0.8798	0.8944	0.8871	0.175	0.2188	0.1944	0.35	0.4828	0.4058	0.4807	0.5966
0.0214	12.0	1500	0.3600	0.5917	0.6425	0.6161	0.9314	0.5325	0.5918	0.5605	0.4516	0.4375	0.4444	0.8684	0.9167	0.8919	0.2258	0.2188	0.2222	0.4375	0.4828	0.4590	0.5156	0.6162
0.0214	13.0	1625	0.3514	0.5606	0.6161	0.5871	0.9279	0.4882	0.5546	0.5193	0.4412	0.4688	0.4545	0.8967	0.9167	0.9066	0.1351	0.1562	0.1449	0.4815	0.4483	0.4643	0.4979	0.5906
0.0214	14.0	1750	0.3994	0.5654	0.5871	0.5761	0.9270	0.5090	0.5258	0.5172	0.3333	0.2812	0.3051	0.9034	0.8833	0.8933	0.125	0.1875	0.15	0.4571	0.5517	0.5	0.4731	0.5814
0.0214	15.0	1875	0.4133	0.5858	0.5989	0.5923	0.9292	0.5276	0.5526	0.5398	0.4737	0.2812	0.3529	0.8807	0.8611	0.8708	0.1538	0.1875	0.1690	0.4848	0.5517	0.5161	0.4897	0.5939
0.0089	16.0	2000	0.4126	0.5695	0.6108	0.5894	0.9301	0.4935	0.5505	0.5205	0.5	0.375	0.4286	0.9056	0.9056	0.9056	0.1951	0.25	0.2192	0.4815	0.4483	0.4643	0.5076	0.5932
0.0089	17.0	2125	0.4195	0.5856	0.6095	0.5973	0.9288	0.5057	0.5505	0.5271	0.6923	0.2812	0.4	0.9157	0.9056	0.9106	0.1765	0.1875	0.1818	0.4722	0.5862	0.5231	0.5085	0.5981
0.0089	18.0	2250	0.4177	0.5856	0.6227	0.6036	0.9300	0.5036	0.5711	0.5353	0.5	0.375	0.4286	0.9171	0.9222	0.9197	0.1667	0.1562	0.1613	0.5714	0.4138	0.4800	0.5050	0.6041
0.0089	19.0	2375	0.4675	0.5623	0.5897	0.5757	0.9257	0.5038	0.5402	0.5214	0.4118	0.2188	0.2857	0.9023	0.8722	0.8870	0.0943	0.1562	0.1176	0.5161	0.5517	0.5333	0.4690	0.5817
0.004	20.0	2500	0.4435	0.5604	0.6055	0.5821	0.9276	0.4878	0.5340	0.5098	0.4643	0.4062	0.4333	0.9066	0.9167	0.9116	0.15	0.1875	0.1667	0.4211	0.5517	0.4776	0.4998	0.5863
0.004	21.0	2625	0.4669	0.5516	0.5989	0.5743	0.9277	0.4822	0.5299	0.5049	0.4828	0.4375	0.4590	0.8962	0.9111	0.9036	0.1463	0.1875	0.1644	0.3514	0.4483	0.3939	0.4852	0.5790
0.004	22.0	2750	0.4732	0.5820	0.6042	0.5929	0.9285	0.5058	0.5381	0.5215	0.4643	0.4062	0.4333	0.9153	0.9	0.9076	0.2105	0.25	0.2286	0.5	0.4828	0.4912	0.5164	0.5959
0.004	23.0	2875	0.4922	0.5816	0.6016	0.5914	0.9258	0.5048	0.5402	0.5219	0.5	0.3438	0.4074	0.9091	0.8889	0.8989	0.2162	0.25	0.2319	0.5	0.5172	0.5085	0.5137	0.5938
0.0016	24.0	3000	0.4747	0.5789	0.6148	0.5963	0.9294	0.5038	0.5526	0.5270	0.4667	0.4375	0.4516	0.9056	0.9056	0.9056	0.2	0.1875	0.1935	0.4545	0.5172	0.4839	0.5123	0.5980
0.0016	25.0	3125	0.4849	0.5851	0.6121	0.5983	0.9300	0.5085	0.5526	0.5296	0.4783	0.3438	0.4	0.9011	0.9111	0.9061	0.2069	0.1875	0.1967	0.4688	0.5172	0.4918	0.5048	0.5981
0.0016	26.0	3250	0.4692	0.5821	0.6266	0.6036	0.9307	0.5009	0.5629	0.5301	0.48	0.375	0.4211	0.9176	0.9278	0.9227	0.2424	0.25	0.2462	0.4839	0.5172	0.5000	0.5240	0.6056
0.0016	27.0	3375	0.4785	0.5752	0.6108	0.5925	0.9299	0.5	0.5443	0.5212	0.4615	0.375	0.4138	0.9011	0.9111	0.9061	0.2105	0.25	0.2286	0.4839	0.5172	0.5000	0.5139	0.5949
0.001	28.0	3500	0.4873	0.5810	0.6201	0.5999	0.9322	0.5103	0.5629	0.5353	0.4815	0.4062	0.4407	0.8962	0.9111	0.9036	0.1613	0.1562	0.1587	0.4545	0.5172	0.4839	0.5044	0.6009
0.001	29.0	3625	0.4825	0.5813	0.6227	0.6013	0.9318	0.5028	0.5629	0.5311	0.52	0.4062	0.4561	0.8962	0.9111	0.9036	0.2333	0.2188	0.2258	0.4839	0.5172	0.5000	0.5233	0.6023
0.001	30.0	3750	0.4883	0.5769	0.6135	0.5946	0.9307	0.4944	0.5505	0.5210	0.52	0.4062	0.4561	0.9111	0.9111	0.9111	0.2069	0.1875	0.1967	0.4688	0.5172	0.4918	0.5154	0.5961
0.001	31.0	3875	0.4964	0.5734	0.6135	0.5927	0.9308	0.4963	0.5526	0.5229	0.5	0.4062	0.4483	0.9011	0.9111	0.9061	0.1613	0.1562	0.1587	0.4688	0.5172	0.4918	0.5056	0.5942
0.0005	32.0	4000	0.4977	0.5817	0.6201	0.6003	0.9309	0.5047	0.5588	0.5303	0.52	0.4062	0.4561	0.9066	0.9167	0.9116	0.1875	0.1875	0.1875	0.4688	0.5172	0.4918	0.5155	0.6018
0.0005	33.0	4125	0.5008	0.5810	0.6201	0.5999	0.9312	0.5047	0.5567	0.5294	0.5	0.4062	0.4483	0.9121	0.9222	0.9171	0.1765	0.1875	0.1818	0.4688	0.5172	0.4918	0.5137	0.6019
0.0005	34.0	4250	0.5028	0.5829	0.6214	0.6015	0.9310	0.5047	0.5588	0.5303	0.52	0.4062	0.4561	0.9121	0.9222	0.9171	0.1875	0.1875	0.1875	0.4688	0.5172	0.4918	0.5166	0.6031
0.0005	35.0	4375	0.5037	0.5849	0.6227	0.6032	0.9311	0.5065	0.5608	0.5323	0.52	0.4062	0.4561	0.9121	0.9222	0.9171	0.1875	0.1875	0.1875	0.4839	0.5172	0.5000	0.5186	0.6047

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.15.0
Tokenizers 0.15.0

Task: Token Classification
Library: transformers
Type: albert
License: apache-2.0
Created: Dec 20, 2023
Updated: Dec 21, 2023