tabert-2k-naamapadam

AnanthZeke/tabert-2k-naamapadam

tabert-2k-naamapadam is a token classification model from AnanthZeke. 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: May 18, 2023
Updated: May 18, 2023
SHA: 2a655154

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Model card

From the Hugging Face model README

tabert-2k-naamapadam

This model is a fine-tuned version of livinNector/tabert-2k on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.2850
Precision: 0.7765
Recall: 0.8041
F1: 0.7901
Accuracy: 0.9065

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: 64
eval_batch_size: 128
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.4679	0.05	400	0.3991	0.7155	0.6561	0.6845	0.8720
0.3907	0.1	800	0.3632	0.7181	0.7233	0.7207	0.8822
0.3663	0.15	1200	0.3483	0.7271	0.7371	0.7321	0.8857
0.3557	0.21	1600	0.3457	0.7286	0.7506	0.7395	0.8874
0.3533	0.26	2000	0.3413	0.7371	0.7435	0.7403	0.8895
0.3396	0.31	2400	0.3326	0.7435	0.7546	0.7490	0.8910
0.3302	0.36	2800	0.3264	0.7528	0.7553	0.7540	0.8937
0.3344	0.41	3200	0.3231	0.7503	0.7720	0.7610	0.8951
0.3262	0.46	3600	0.3228	0.7387	0.7762	0.7570	0.8941
0.3186	0.51	4000	0.3158	0.7699	0.7666	0.7683	0.8986
0.3163	0.57	4400	0.3130	0.7453	0.7798	0.7622	0.8955
0.3143	0.62	4800	0.3150	0.7572	0.7751	0.7660	0.8961
0.3088	0.67	5200	0.3151	0.7543	0.7828	0.7683	0.8972
0.3115	0.72	5600	0.3141	0.7708	0.7706	0.7707	0.8977
0.3095	0.77	6000	0.3043	0.7657	0.7831	0.7743	0.8991
0.3044	0.82	6400	0.3087	0.7526	0.7881	0.7699	0.8972
0.2964	0.87	6800	0.3070	0.7644	0.7928	0.7783	0.8992
0.2972	0.93	7200	0.3102	0.7692	0.7738	0.7715	0.8999
0.2985	0.98	7600	0.3016	0.7731	0.7858	0.7794	0.9018
0.2822	1.03	8000	0.3049	0.7734	0.7909	0.7820	0.9031
0.2764	1.08	8400	0.3059	0.7575	0.7976	0.7770	0.9011
0.2752	1.13	8800	0.3052	0.7553	0.7996	0.7768	0.9015
0.2689	1.18	9200	0.2990	0.7642	0.7982	0.7808	0.9037
0.2738	1.23	9600	0.2985	0.7698	0.7987	0.7840	0.9035
0.2731	1.29	10000	0.2950	0.7713	0.7982	0.7845	0.9037
0.2694	1.34	10400	0.2920	0.7743	0.8017	0.7878	0.9059
0.2727	1.39	10800	0.2931	0.7693	0.7979	0.7834	0.9040
0.2622	1.44	11200	0.2946	0.7702	0.7942	0.7820	0.9032
0.2672	1.49	11600	0.2894	0.7724	0.8062	0.7890	0.9060
0.2601	1.54	12000	0.2907	0.7706	0.8010	0.7855	0.9058
0.2629	1.59	12400	0.2930	0.7628	0.8150	0.7880	0.9052
0.2635	1.65	12800	0.2907	0.7775	0.7970	0.7871	0.9047
0.2673	1.7	13200	0.2909	0.7753	0.7982	0.7866	0.9045
0.2726	1.75	13600	0.2880	0.7714	0.8048	0.7877	0.9054
0.2607	1.8	14000	0.2850	0.7760	0.8010	0.7883	0.9053
0.2684	1.85	14400	0.2847	0.7709	0.8077	0.7889	0.9059
0.2625	1.9	14800	0.2849	0.7742	0.8079	0.7907	0.9067
0.2631	1.95	15200	0.2850	0.7765	0.8041	0.7901	0.9065

Framework versions

Transformers 4.29.2
Pytorch 2.0.0
Datasets 2.12.0
Tokenizers 0.13.3

Task: Token Classification
Library: transformers
Type: distilbert
Created: May 18, 2023
Updated: May 18, 2023