bert-finetuned-ner – AI Model by sickcell | AlphaNeural AI

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sickcell/
bert-finetuned-ner

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token-classification

generated_from_trainer

google-bert/bert-base-cased

autotrain_compatible

endpoints_compatible

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bert-finetuned-ner

This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.3391
Precision: 0.8826
Recall: 0.9138
F1: 0.8979
Accuracy: 0.9518

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: 1e-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
0.0318	1.0	680	0.4800	0.8075	0.8632	0.8344	0.9183
0.0206	2.0	1360	0.4822	0.8332	0.8634	0.8480	0.9233
0.0116	3.0	2040	0.5227	0.8167	0.8683	0.8417	0.9211
0.0093	4.0	2720	0.5366	0.8230	0.8749	0.8482	0.9246
0.0077	5.0	3400	0.5384	0.8370	0.8688	0.8526	0.9249
0.0061	6.0	4080	0.5450	0.8418	0.8754	0.8583	0.9275
0.0048	7.0	4760	0.5570	0.8346	0.8765	0.8550	0.9262
0.0084	8.0	5440	0.5565	0.8353	0.8765	0.8554	0.9261
0.0073	9.0	6120	0.5693	0.8353	0.8751	0.8547	0.9261
0.0058	10.0	6800	0.5688	0.8361	0.8766	0.8559	0.9265

Framework versions

Transformers 4.43.3
Pytorch 2.4.0+cu118
Datasets 2.20.0
Tokenizers 0.19.1