scideberta-cs-finetuned-ner

sohamtiwari3120/scideberta-cs-finetuned-ner

scideberta-cs-finetuned-ner is a token classification model from sohamtiwari3120. 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: deberta
Access: Public
Created: Oct 25, 2022
Updated: Oct 31, 2022
SHA: b30b0d2f

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Datasets

generator

Model card

From the Hugging Face model README

scideberta-cs-finetuned-ner

This model is a fine-tuned version of KISTI-AI/scideberta-cs on the generator dataset. It achieves the following results on the evaluation set:

Loss: 0.8848
Overall Precision: 0.5492
Overall Recall: 0.6240
Overall F1: 0.5842
Overall Accuracy: 0.9552
Datasetname F1: 0.4590
Hyperparametername F1: 0.7273
Hyperparametervalue F1: 0.7937
Methodname F1: 0.6227
Metricname F1: 0.7597
Metricvalue F1: 0.6250
Taskname F1: 0.4348

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: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Overall Precision	Overall Recall	Overall F1	Overall Accuracy	Datasetname F1	Hyperparametername F1	Hyperparametervalue F1	Methodname F1	Metricname F1	Metricvalue F1	Taskname F1
No log	1.0	132	0.4127	0.3852	0.6646	0.4877	0.9411	0.3875	0.4690	0.6	0.6338	0.6438	0.5806	0.3670
No log	2.0	264	0.3424	0.3447	0.6972	0.4613	0.9353	0.3204	0.4103	0.5600	0.5691	0.5848	0.7027	0.3594
No log	3.0	396	0.3942	0.4767	0.6850	0.5621	0.9534	0.5385	0.6500	0.7429	0.6583	0.6437	0.6111	0.3830
0.4541	4.0	528	0.3542	0.4516	0.7012	0.5494	0.9503	0.4127	0.6552	0.5417	0.6068	0.6243	0.4762	0.4895
0.4541	5.0	660	0.4092	0.5076	0.6829	0.5823	0.9560	0.3857	0.5827	0.6933	0.6866	0.7465	0.6875	0.4865
0.4541	6.0	792	0.4450	0.4465	0.6870	0.5412	0.9491	0.3613	0.5985	0.6506	0.6278	0.6667	0.6857	0.4332
0.4541	7.0	924	0.4487	0.4985	0.6707	0.5719	0.9552	0.4407	0.6400	0.5789	0.6590	0.6980	0.7429	0.4667
0.1083	8.0	1056	0.4361	0.5068	0.6850	0.5825	0.9569	0.4553	0.6457	0.7429	0.6667	0.6887	0.6875	0.4536
0.1083	9.0	1188	0.5592	0.4954	0.6504	0.5624	0.9549	0.4538	0.6552	0.6753	0.6397	0.6581	0.7647	0.4118
0.1083	10.0	1320	0.5272	0.4686	0.6667	0.5503	0.9497	0.3816	0.6074	0.7	0.6340	0.7347	0.7429	0.3917
0.1083	11.0	1452	0.6108	0.5412	0.6809	0.6031	0.9562	0.4727	0.6724	0.7222	0.6615	0.7097	0.6857	0.5027
0.0491	12.0	1584	0.7836	0.5481	0.6138	0.5791	0.9546	0.5043	0.6446	0.7246	0.6286	0.7347	0.7273	0.4217
0.0491	13.0	1716	0.5258	0.4838	0.6667	0.5607	0.9527	0.4580	0.6299	0.6944	0.6234	0.7089	0.6667	0.4060
0.0491	14.0	1848	0.6477	0.5487	0.6301	0.5866	0.9576	0.4685	0.6909	0.7692	0.6312	0.6528	0.7273	0.4773
0.0491	15.0	1980	0.5891	0.5359	0.6972	0.6060	0.9577	0.4865	0.6777	0.7123	0.6667	0.7114	0.6875	0.4986
0.0288	16.0	2112	0.6913	0.5510	0.6809	0.6091	0.9575	0.5053	0.6783	0.7463	0.7063	0.6853	0.6842	0.4602
0.0288	17.0	2244	0.7530	0.5425	0.6484	0.5907	0.9572	0.5149	0.6446	0.8065	0.6796	0.6993	0.75	0.3974
0.0288	18.0	2376	0.7542	0.5815	0.6524	0.6149	0.9594	0.5306	0.6667	0.7353	0.6918	0.7077	0.7273	0.4706
0.0137	19.0	2508	0.7550	0.5529	0.6585	0.6011	0.9561	0.5333	0.6957	0.6765	0.6508	0.7746	0.7059	0.4389
0.0137	20.0	2640	0.6984	0.5335	0.6789	0.5975	0.9538	0.4828	0.6721	0.7353	0.6382	0.7518	0.6667	0.4731
0.0137	21.0	2772	0.6706	0.5221	0.7215	0.6058	0.9511	0.4640	0.6780	0.72	0.6389	0.7355	0.6667	0.5215
0.0137	22.0	2904	0.7129	0.5533	0.6646	0.6039	0.9561	0.5091	0.7	0.6667	0.6553	0.7761	0.6111	0.4673
0.0096	23.0	3036	0.7137	0.5601	0.6728	0.6113	0.9583	0.5185	0.6780	0.7879	0.6621	0.7328	0.6	0.4926
0.0096	24.0	3168	0.6871	0.5235	0.6789	0.5912	0.9534	0.4828	0.6891	0.6667	0.6414	0.7310	0.7273	0.4676
0.0096	25.0	3300	0.7823	0.5641	0.6524	0.6051	0.9567	0.4628	0.7009	0.7576	0.6716	0.7183	0.6875	0.4762
0.0096	26.0	3432	0.7905	0.5512	0.6565	0.5993	0.9556	0.5143	0.7368	0.7463	0.6332	0.7121	0.6875	0.4531
0.0061	27.0	3564	0.8666	0.5557	0.6585	0.6028	0.9553	0.4779	0.7130	0.7692	0.6689	0.7391	0.6667	0.4465
0.0061	28.0	3696	0.8848	0.5492	0.6240	0.5842	0.9552	0.4590	0.7273	0.7937	0.6227	0.7597	0.6250	0.4348

Framework versions

Transformers 4.23.1
Pytorch 1.12.1+cu102
Datasets 2.6.1
Tokenizers 0.13.1

Task: Token Classification
Library: transformers
Type: deberta
Created: Oct 25, 2022
Updated: Oct 31, 2022