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

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pariakashani/
en-multinerd-ner

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token-classification

generated_from_trainer

distilbert/distilbert-base-uncased

autotrain_compatible

endpoints_compatible

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en-multinerd-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: 0.0490
Precision: 0.8904
Recall: 0.8960
F1: 0.8932
Accuracy: 0.9829
Per-precision: 0.9943
Per-recall: 0.9964
Per-f1: 0.9953
Org-precision: 0.9347
Org-recall: 0.9463
Org-f1: 0.9405
Loc-precision: 0.9663
Loc-recall: 0.9721
Loc-f1: 0.9692
Dis-precision: 0.6965
Dis-recall: 0.7215
Dis-f1: 0.7088
Anim-precision: 0.6922
Anim-recall: 0.7257
Anim-f1: 0.7086

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
0.0445	1.0	8205	0.0523	0.8584	0.8999	0.8786	0.9808	0.9932	0.9964	0.9948	0.8947	0.9529	0.9229	0.9595	0.9708	0.9651	0.6459	0.7392	0.6894	0.6534	0.7513	0.6989
0.0308	2.0	16410	0.0490	0.8904	0.8960	0.8932	0.9829	0.9943	0.9964	0.9953	0.9347	0.9463	0.9405	0.9663	0.9721	0.9692	0.6965	0.7215	0.7088	0.6922	0.7257	0.7086

Framework versions

Transformers 4.36.1
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0