deberta-v3-base-finetuned-ner

NeverLearn/deberta-v3-base-finetuned-ner

deberta-v3-base-finetuned-ner is a token classification model from NeverLearn. 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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Hugging Face

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At a glance

Task: Token Classification
Library: transformers
License: mit
Model type: deberta-v2
Access: Public
Created: Apr 5, 2024
Updated: Apr 10, 2024
SHA: f0784714

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Base models

microsoft/deberta-v3-base

Datasets

maccrobat_biomedical_ner

Model card

From the Hugging Face model README

deberta-v3-base-finetuned-ner

This model is a fine-tuned version of microsoft/deberta-v3-base on the maccrobat_biomedical_ner dataset. It achieves the following results on the evaluation set:

Loss: 0.9704
Precision: 0.7844
Recall: 0.7816
F1: 0.7830
Accuracy: 0.8584

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: 4.555607052152088e-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	20	0.9499	0.7670	0.7685	0.7678	0.8477
No log	2.0	40	0.9042	0.7721	0.7629	0.7675	0.8484
No log	3.0	60	0.9360	0.7674	0.7573	0.7623	0.8475
No log	4.0	80	0.8984	0.7630	0.7589	0.7609	0.8442
No log	5.0	100	0.8159	0.7695	0.7701	0.7698	0.8495
No log	6.0	120	0.8086	0.7557	0.7730	0.7643	0.8454
No log	7.0	140	0.7937	0.7766	0.7712	0.7739	0.8509
No log	8.0	160	0.8430	0.7703	0.7707	0.7705	0.8513
No log	9.0	180	0.8711	0.7715	0.7710	0.7712	0.8517
No log	10.0	200	0.8649	0.7687	0.7626	0.7656	0.8485
No log	11.0	220	0.8686	0.7817	0.7635	0.7725	0.8516
No log	12.0	240	0.8644	0.7765	0.7802	0.7784	0.8546
No log	13.0	260	0.8680	0.7771	0.7796	0.7783	0.8550
No log	14.0	280	0.8845	0.7728	0.7748	0.7738	0.8528
No log	15.0	300	0.9084	0.7774	0.7713	0.7743	0.8537
No log	16.0	320	0.9396	0.7782	0.7659	0.7720	0.8509
No log	17.0	340	0.9338	0.7776	0.7781	0.7778	0.8547
No log	18.0	360	0.9205	0.7749	0.7770	0.7759	0.8537
No log	19.0	380	0.9426	0.7781	0.7724	0.7752	0.8523
No log	20.0	400	0.9403	0.7769	0.7827	0.7798	0.8550
No log	21.0	420	0.9393	0.7795	0.7713	0.7754	0.8536
No log	22.0	440	0.9618	0.7771	0.7790	0.7780	0.8547
No log	23.0	460	0.9420	0.7814	0.7836	0.7825	0.8582
No log	24.0	480	0.9455	0.7842	0.7808	0.7825	0.8583
0.0412	25.0	500	0.9599	0.7821	0.7801	0.7811	0.8571
0.0412	26.0	520	0.9518	0.7815	0.7833	0.7824	0.8578
0.0412	27.0	540	0.9570	0.7800	0.7818	0.7809	0.8567
0.0412	28.0	560	0.9634	0.7819	0.7801	0.7810	0.8573
0.0412	29.0	580	0.9685	0.7818	0.7831	0.7825	0.8579
0.0412	30.0	600	0.9704	0.7844	0.7816	0.7830	0.8584

Framework versions

Transformers 4.39.3
Pytorch 2.2.1
Datasets 2.18.0
Tokenizers 0.15.2

Task: Token Classification
Library: transformers
Type: deberta-v2
License: mit
Created: Apr 5, 2024
Updated: Apr 10, 2024