taNER-1k-V2

livinNector/taNER-1k-V2

taNER-1k-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: 270b825e

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Model card

From the Hugging Face model README

taNER-1k-V2

This model is a fine-tuned version of livinNector/tabert-1k on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.4253
Precision: 0.7866
Recall: 0.8029
F1: 0.7947
Accuracy: 0.9049

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
0.3724	0.49	1000	0.3865	0.7280	0.7372	0.7326	0.8758
0.3199	0.99	2000	0.3516	0.7524	0.7561	0.7543	0.8858
0.2911	1.48	3000	0.3436	0.7543	0.7765	0.7653	0.8906
0.2867	1.98	4000	0.3391	0.7522	0.7908	0.7710	0.8909
0.2654	2.47	5000	0.3262	0.7696	0.7845	0.7770	0.8961
0.2616	2.96	6000	0.3294	0.7784	0.7800	0.7792	0.8954
0.2422	3.46	7000	0.3191	0.7779	0.7934	0.7856	0.8999
0.2422	3.95	8000	0.3272	0.7735	0.7962	0.7847	0.8985
0.2208	4.44	9000	0.3252	0.7811	0.7952	0.7881	0.9012
0.2227	4.94	10000	0.3220	0.7789	0.7993	0.7890	0.9026
0.204	5.43	11000	0.3413	0.7904	0.7894	0.7899	0.9007
0.2036	5.93	12000	0.3329	0.7810	0.7984	0.7896	0.9009
0.1874	6.42	13000	0.3362	0.7872	0.7986	0.7929	0.9033
0.1877	6.91	14000	0.3414	0.7764	0.8029	0.7894	0.9013
0.172	7.41	15000	0.3463	0.7871	0.7997	0.7933	0.9032
0.1729	7.9	16000	0.3441	0.7863	0.8001	0.7931	0.9034
0.159	8.4	17000	0.3625	0.7856	0.7970	0.7912	0.9019
0.1585	8.89	18000	0.3575	0.7867	0.7980	0.7923	0.9030
0.1485	9.38	19000	0.3761	0.7850	0.7965	0.7907	0.9029
0.1468	9.88	20000	0.3658	0.7874	0.8019	0.7946	0.9037
0.1378	10.37	21000	0.3835	0.7851	0.8039	0.7944	0.9042
0.1364	10.86	22000	0.3852	0.7861	0.8019	0.7940	0.9043
0.1294	11.36	23000	0.3906	0.7854	0.7973	0.7913	0.9038
0.1277	11.85	24000	0.3947	0.7875	0.7988	0.7931	0.9030
0.1207	12.35	25000	0.4082	0.7841	0.7997	0.7918	0.9035
0.1199	12.84	26000	0.4137	0.7888	0.7993	0.7940	0.9049
0.1144	13.33	27000	0.4155	0.7875	0.7996	0.7935	0.9046
0.113	13.83	28000	0.4177	0.7840	0.8053	0.7945	0.9046
0.1103	14.32	29000	0.4280	0.7867	0.8021	0.7943	0.9042
0.1078	14.81	30000	0.4253	0.7866	0.8029	0.7947	0.9049

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