ner-fine-tune-roberta-more-data

cehongw/ner-fine-tune-roberta-more-data

ner-fine-tune-roberta-more-data is a token classification model from cehongw. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.

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

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At a glance

Task: Token Classification
Library: transformers
License: mit
Model type: roberta
Access: Public
Created: Oct 27, 2023
Updated: Oct 27, 2023
SHA: 45dfe650

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Base models

Model card

From the Hugging Face model README

ner-fine-tune-roberta-more-data

This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.3443
Precision: 0.2432
Recall: 0.3373
F1: 0.2826
Accuracy: 0.9352

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: 1e-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: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	120	0.2270	0.0	0.0	0.0	0.9541
No log	2.0	240	0.1783	0.1801	0.0684	0.0991	0.9582
No log	3.0	360	0.1810	0.1023	0.0637	0.0785	0.9519
No log	4.0	480	0.1642	0.2227	0.2358	0.2291	0.9518
0.2437	5.0	600	0.1728	0.1880	0.2429	0.2119	0.9401
0.2437	6.0	720	0.1997	0.1536	0.1934	0.1712	0.9344
0.2437	7.0	840	0.2637	0.1827	0.3231	0.2334	0.9237
0.2437	8.0	960	0.2564	0.1730	0.2476	0.2037	0.9320
0.0746	9.0	1080	0.2437	0.2116	0.3184	0.2542	0.9353
0.0746	10.0	1200	0.2524	0.2340	0.3443	0.2786	0.9347
0.0746	11.0	1320	0.2636	0.2071	0.2618	0.2312	0.9373
0.0746	12.0	1440	0.2562	0.2434	0.3255	0.2785	0.9389
0.0309	13.0	1560	0.2793	0.2263	0.3042	0.2596	0.9371
0.0309	14.0	1680	0.2441	0.3455	0.3137	0.3288	0.9586
0.0309	15.0	1800	0.3174	0.2123	0.3090	0.2517	0.9324
0.0309	16.0	1920	0.2784	0.2374	0.2877	0.2601	0.9393
0.0176	17.0	2040	0.2740	0.2758	0.3090	0.2914	0.9461
0.0176	18.0	2160	0.3077	0.2319	0.3467	0.2779	0.9344
0.0176	19.0	2280	0.3088	0.2380	0.3160	0.2715	0.9388
0.0176	20.0	2400	0.2848	0.2613	0.3278	0.2908	0.9414
0.0112	21.0	2520	0.2958	0.2453	0.3420	0.2857	0.9369
0.0112	22.0	2640	0.3089	0.2295	0.3632	0.2813	0.9331
0.0112	23.0	2760	0.3435	0.2359	0.375	0.2896	0.9330
0.0112	24.0	2880	0.3303	0.2434	0.3467	0.2860	0.9366
0.0076	25.0	3000	0.3237	0.2363	0.3160	0.2704	0.9383
0.0076	26.0	3120	0.3235	0.2451	0.3278	0.2805	0.9384
0.0076	27.0	3240	0.3409	0.2491	0.3302	0.2840	0.9361
0.0076	28.0	3360	0.3446	0.2416	0.3373	0.2815	0.9351
0.0076	29.0	3480	0.3470	0.2417	0.3278	0.2783	0.9355
0.0055	30.0	3600	0.3443	0.2432	0.3373	0.2826	0.9352

Framework versions

Transformers 4.34.1
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1

Task: Token Classification
Library: transformers
Type: roberta
License: mit
Created: Oct 27, 2023
Updated: Oct 27, 2023