ner – AI Model by phi0108 | AlphaNeural AI

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phi0108/
ner

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token-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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ner

This model is a fine-tuned version of distilbert-base-uncased on the wnut_17 dataset. It achieves the following results on the evaluation set:

Loss: 0.2942
Precision: 0.5553
Recall: 0.3772
F1: 0.4492
Accuracy: 0.9469

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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	213	0.2666	0.6024	0.2808	0.3831	0.9405
No log	2.0	426	0.2605	0.5708	0.3364	0.4233	0.9456
0.1299	3.0	639	0.2827	0.5658	0.3346	0.4205	0.9452
0.1299	4.0	852	0.2836	0.5503	0.3753	0.4463	0.9469
0.051	5.0	1065	0.2942	0.5553	0.3772	0.4492	0.9469

Framework versions

Transformers 4.27.4
Pytorch 2.0.0+cu118
Datasets 2.11.0
Tokenizers 0.13.3