en-multinerd-masked-ner – AI Model by pariakashani | AlphaNeural AI

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pariakashani/
en-multinerd-masked-ner

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token-classification

generated_from_trainer

distilbert/distilbert-base-uncased

autotrain_compatible

endpoints_compatible

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en-multinerd-masked-ner

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0251
Precision: 0.9353
Recall: 0.9420
F1: 0.9386
Accuracy: 0.9917
Per-precision: 0.9945
Per-recall: 0.9953
Per-f1: 0.9949
Org-precision: 0.9261
Org-recall: 0.9434
Org-f1: 0.9347
Loc-precision: 0.9679
Loc-recall: 0.9718
Loc-f1: 0.9699
Dis-precision: 0.7003
Dis-recall: 0.7156
Dis-f1: 0.7079
Anim-precision: 0.7021
Anim-recall: 0.7169
Anim-f1: 0.7094

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: 16
eval_batch_size: 16
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	Per-precision	Per-recall	Per-f1	Org-precision	Org-recall	Org-f1	Loc-precision	Loc-recall	Loc-f1	Dis-precision	Dis-recall	Dis-f1	Anim-precision	Anim-recall	Anim-f1
0.0235	1.0	8205	0.0268	0.9171	0.9466	0.9316	0.9907	0.9943	0.9960	0.9951	0.8943	0.9521	0.9223	0.9595	0.9713	0.9654	0.6531	0.7298	0.6893	0.6518	0.7593	0.7014
0.0151	2.0	16410	0.0251	0.9353	0.9420	0.9386	0.9917	0.9945	0.9953	0.9949	0.9261	0.9434	0.9347	0.9679	0.9718	0.9699	0.7003	0.7156	0.7079	0.7021	0.7169	0.7094

Framework versions

Transformers 4.36.1
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0