taNER-500-V2

livinNector/taNER-500-V2

taNER-500-V2 is a token classification model from livinNector. Use it when you need labels on individual words, such as names. It is set up for transformers.

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

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At a glance

Task: Token Classification
Library: transformers
Model type: distilbert
Access: Public
Created: Apr 28, 2023
Updated: Apr 28, 2023
SHA: 0d909feb

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Model card

From the Hugging Face model README

taNER-500-V2

This model is a fine-tuned version of livinNector/tabert-500 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.4057
Precision: 0.7870
Recall: 0.8040
F1: 0.7954
Accuracy: 0.9056

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: 5e-05
train_batch_size: 256
eval_batch_size: 512
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.3751	0.49	1000	0.3876	0.7294	0.7381	0.7337	0.8758
0.3211	0.99	2000	0.3530	0.7603	0.7427	0.7514	0.8851
0.2932	1.48	3000	0.3443	0.7501	0.7757	0.7627	0.8882
0.2884	1.98	4000	0.3404	0.7553	0.7878	0.7712	0.8907
0.268	2.47	5000	0.3241	0.7705	0.7888	0.7795	0.8959
0.2638	2.96	6000	0.3246	0.7823	0.7850	0.7836	0.8954
0.246	3.46	7000	0.3175	0.7769	0.7989	0.7878	0.8999
0.2457	3.95	8000	0.3216	0.7732	0.7934	0.7832	0.8999
0.2253	4.44	9000	0.3180	0.7792	0.7983	0.7887	0.8995
0.2271	4.94	10000	0.3250	0.7868	0.7895	0.7882	0.8996
0.2085	5.43	11000	0.3435	0.7838	0.7967	0.7902	0.8995
0.2091	5.93	12000	0.3300	0.7855	0.7958	0.7906	0.9009
0.1927	6.42	13000	0.3272	0.7771	0.7983	0.7876	0.9017
0.1932	6.91	14000	0.3310	0.7836	0.8060	0.7946	0.9047
0.1777	7.41	15000	0.3377	0.7882	0.8045	0.7963	0.9052
0.1785	7.9	16000	0.3406	0.7812	0.8042	0.7925	0.9036
0.1658	8.4	17000	0.3528	0.7892	0.7992	0.7942	0.9043
0.1651	8.89	18000	0.3419	0.7914	0.8072	0.7992	0.9068
0.1549	9.38	19000	0.3600	0.7931	0.7964	0.7948	0.9045
0.1539	9.88	20000	0.3525	0.7851	0.8091	0.7970	0.9052
0.1449	10.37	21000	0.3634	0.7881	0.7998	0.7939	0.9046
0.1436	10.86	22000	0.3736	0.7916	0.8058	0.7986	0.9069
0.1368	11.36	23000	0.3771	0.7892	0.8020	0.7955	0.9053
0.1347	11.85	24000	0.3800	0.7861	0.8060	0.7959	0.9045
0.1281	12.35	25000	0.3911	0.7852	0.8055	0.7952	0.9059
0.1272	12.84	26000	0.3919	0.7880	0.8005	0.7942	0.9052
0.1217	13.33	27000	0.4021	0.7887	0.7981	0.7934	0.9050
0.1202	13.83	28000	0.3959	0.7845	0.8057	0.7950	0.9056
0.1175	14.32	29000	0.4066	0.7864	0.8031	0.7947	0.9052
0.115	14.81	30000	0.4057	0.7870	0.8040	0.7954	0.9056

Framework versions

Transformers 4.27.4
Pytorch 2.0.0+cu117
Datasets 2.11.0
Tokenizers 0.13.3

Task: Token Classification
Library: transformers
Type: distilbert
Created: Apr 28, 2023
Updated: Apr 28, 2023