bert-base-cased-tajik-ner

muhtasham/bert-base-cased-tajik-ner

bert-base-cased-tajik-ner is a token classification model from muhtasham. 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: bert
Access: Public
Created: Aug 26, 2022
Updated: Aug 26, 2022
SHA: 0aa08dd2

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Datasets

wikiann

Model card

From the Hugging Face model README

bert-base-cased-tajik-ner

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

Loss: 1.1137
Precision: 0.5124
Recall: 0.5962
F1: 0.5511
Accuracy: 0.8521

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: 200

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	2.0	50	0.8416	0.0739	0.125	0.0929	0.6948
No log	4.0	100	0.7061	0.2229	0.3558	0.2741	0.7415
No log	6.0	150	0.6467	0.3057	0.4615	0.3678	0.8167
No log	8.0	200	0.7923	0.3968	0.4808	0.4348	0.8073
No log	10.0	250	0.7003	0.4656	0.5865	0.5191	0.8653
No log	12.0	300	0.7723	0.4380	0.5769	0.4979	0.8560
No log	14.0	350	0.9088	0.4762	0.5769	0.5217	0.8470
No log	16.0	400	0.9756	0.472	0.5673	0.5153	0.8424
No log	18.0	450	1.1114	0.4576	0.5192	0.4865	0.8151
0.2358	20.0	500	1.0887	0.48	0.5769	0.5240	0.8330
0.2358	22.0	550	1.0968	0.4419	0.5481	0.4893	0.8268
0.2358	24.0	600	1.3330	0.5140	0.5288	0.5213	0.8042
0.2358	26.0	650	1.0911	0.6019	0.5962	0.5990	0.8521
0.2358	28.0	700	1.1949	0.4586	0.5865	0.5148	0.8388
0.2358	30.0	750	1.1208	0.4444	0.5769	0.5021	0.8470
0.2358	32.0	800	1.0968	0.5413	0.5673	0.5540	0.8661
0.2358	34.0	850	1.1618	0.5	0.5769	0.5357	0.8575
0.2358	36.0	900	1.1018	0.5169	0.5865	0.5495	0.8505
0.2358	38.0	950	1.1948	0.4797	0.5673	0.5198	0.8431
0.0039	40.0	1000	1.1063	0.4511	0.5769	0.5063	0.8533
0.0039	42.0	1050	1.0651	0.5702	0.625	0.5963	0.8723
0.0039	44.0	1100	1.1475	0.472	0.5673	0.5153	0.8466
0.0039	46.0	1150	1.3080	0.4590	0.5385	0.4956	0.8353
0.0039	48.0	1200	1.1165	0.5741	0.5962	0.5849	0.8610
0.0039	50.0	1250	1.2525	0.4724	0.5769	0.5195	0.8431
0.0039	52.0	1300	1.2443	0.5161	0.6154	0.5614	0.8521
0.0039	54.0	1350	1.5720	0.4597	0.5481	0.5	0.8054
0.0039	56.0	1400	1.2487	0.5446	0.5865	0.5648	0.8513
0.0039	58.0	1450	1.3936	0.4754	0.5577	0.5133	0.8365
0.0051	60.0	1500	1.2980	0.5636	0.5962	0.5794	0.8544
0.0051	62.0	1550	1.3284	0.5175	0.5673	0.5413	0.8490
0.0051	64.0	1600	1.3345	0.5268	0.5673	0.5463	0.8447
0.0051	66.0	1650	1.1006	0.5872	0.6154	0.6009	0.8641
0.0051	68.0	1700	1.0886	0.4580	0.5769	0.5106	0.8525
0.0051	70.0	1750	1.1017	0.4959	0.5865	0.5374	0.8525
0.0051	72.0	1800	1.1137	0.5124	0.5962	0.5511	0.8521

Framework versions

Transformers 4.21.2
Pytorch 1.12.1+cu113
Datasets 2.4.0
Tokenizers 0.12.1

Task: Token Classification
Library: transformers
Type: bert
License: apache-2.0
Created: Aug 26, 2022
Updated: Aug 26, 2022