distilbert-finetuned-ner – AI Model by mido545 | AlphaNeural AI

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mido545/
distilbert-finetuned-ner

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token-classification

generated_from_trainer

distilbert/distilbert-base-cased

autotrain_compatible

endpoints_compatible

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distilbert-finetuned-ner

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

Loss: 0.1130
Precision: 0.9327
Recall: 0.9467
F1: 0.9396
Accuracy: 0.9848

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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0915	1.0	1756	0.0935	0.8678	0.9014	0.8843	0.9731
0.0497	2.0	3512	0.0815	0.9070	0.9295	0.9181	0.9815
0.0324	3.0	5268	0.0798	0.9027	0.9320	0.9171	0.9810
0.0214	4.0	7024	0.0802	0.9252	0.9372	0.9312	0.9836
0.012	5.0	8780	0.0899	0.9275	0.9413	0.9343	0.9838
0.0088	6.0	10536	0.1003	0.9253	0.9418	0.9334	0.9840
0.0049	7.0	12292	0.1064	0.9267	0.9446	0.9356	0.9842
0.0036	8.0	14048	0.1127	0.9286	0.9438	0.9361	0.9839
0.0012	9.0	15804	0.1112	0.9307	0.9455	0.9381	0.9847
0.0007	10.0	17560	0.1130	0.9327	0.9467	0.9396	0.9848

Framework versions

Transformers 4.40.2
Pytorch 2.3.0+cu118
Datasets 2.19.1
Tokenizers 0.19.1