scideberta-cs-tdm-pretrained-finetuned-ner

sohamtiwari3120/scideberta-cs-tdm-pretrained-finetuned-ner

scideberta-cs-tdm-pretrained-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 26, 2022
Updated: Oct 31, 2022
SHA: e7ec3ce1

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Datasets

generator

Model card

From the Hugging Face model README

scideberta-cs-tdm-pretrained-finetuned-ner

This model is a fine-tuned version of sohamtiwari3120/scideberta-cs-tdm-pretrained on the generator dataset. It achieves the following results on the evaluation set:

Loss: 0.6836
Overall Precision: 0.5912
Overall Recall: 0.6850
Overall F1: 0.6347
Overall Accuracy: 0.9609
Datasetname F1: 0.5882
Hyperparametername F1: 0.6897
Hyperparametervalue F1: 0.7619
Methodname F1: 0.6525
Metricname F1: 0.7500
Metricvalue F1: 0.6452
Taskname F1: 0.5370

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	0.3507	0.3972	0.6870	0.5034	0.9410	0.4370	0.5441	0.5814	0.6124	0.5604	0.6207	0.3724
No log	2.0	264	0.3079	0.4066	0.7520	0.5278	0.9430	0.4138	0.5380	0.6222	0.5895	0.625	0.7273	0.4340
No log	3.0	396	0.3740	0.5007	0.7195	0.5905	0.9535	0.4882	0.6777	0.7500	0.6254	0.6747	0.7097	0.4962
0.4014	4.0	528	0.4072	0.5161	0.7154	0.5997	0.9540	0.5167	0.6612	0.6374	0.6337	0.6753	0.6061	0.5341
0.4014	5.0	660	0.4088	0.5590	0.7317	0.6338	0.9582	0.5660	0.6667	0.7397	0.6250	0.7226	0.75	0.5794
0.4014	6.0	792	0.4810	0.5201	0.7093	0.6002	0.9550	0.4874	0.5970	0.6506	0.6207	0.6708	0.6250	0.5756
0.4014	7.0	924	0.5288	0.5403	0.6809	0.6025	0.9576	0.4915	0.6500	0.6133	0.6255	0.7006	0.7879	0.5389
0.0912	8.0	1056	0.5281	0.5468	0.6890	0.6097	0.9574	0.5370	0.7143	0.6866	0.5854	0.6939	0.7742	0.5491
0.0912	9.0	1188	0.4744	0.5371	0.7358	0.6209	0.9560	0.5370	0.6341	0.6753	0.6554	0.6795	0.7059	0.5699
0.0912	10.0	1320	0.5498	0.5686	0.7073	0.6304	0.9586	0.5370	0.6349	0.7500	0.6553	0.7152	0.7742	0.5573
0.0912	11.0	1452	0.6424	0.5857	0.7012	0.6383	0.9597	0.56	0.6789	0.7246	0.6667	0.6974	0.6875	0.5757
0.0354	12.0	1584	0.5867	0.5641	0.6890	0.6203	0.9585	0.5185	0.6496	0.7213	0.6619	0.7152	0.7333	0.5402
0.0354	13.0	1716	0.5500	0.5667	0.6992	0.6260	0.9592	0.5524	0.6829	0.7222	0.6621	0.6466	0.7333	0.5607
0.0354	14.0	1848	0.5743	0.5780	0.7154	0.6394	0.9596	0.5283	0.6833	0.7222	0.6644	0.6716	0.7742	0.5960
0.0354	15.0	1980	0.6836	0.5912	0.6850	0.6347	0.9609	0.5882	0.6897	0.7619	0.6525	0.7500	0.6452	0.5370

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 26, 2022
Updated: Oct 31, 2022