output_dir_nlpA – AI Model by MaryDatascientist | AlphaNeural AI

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MaryDatascientist/
output_dir_nlpA

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token-classification

generated_from_trainer

google-bert/bert-base-uncased

autotrain_compatible

endpoints_compatible

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output_dir_nlpA

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

Loss: 0.0719
Precision: 0.8688
Recall: 0.8863
F1: 0.8775
Accuracy: 0.9747

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: 0.00023562901531222485
train_batch_size: 64
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.114	0.12	500	0.1153	0.8189	0.7594	0.7880	0.9607
0.0973	0.24	1000	0.1092	0.7920	0.8177	0.8047	0.9614
0.088	0.37	1500	0.0954	0.8387	0.8284	0.8335	0.9674
0.0744	0.49	2000	0.0883	0.8630	0.8248	0.8434	0.9694
0.06	0.61	2500	0.0853	0.8307	0.8833	0.8562	0.9703
0.0509	0.73	3000	0.0783	0.8657	0.8724	0.8690	0.9729
0.0422	0.85	3500	0.0753	0.8598	0.8875	0.8734	0.9738
0.0392	0.97	4000	0.0719	0.8688	0.8863	0.8775	0.9747

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0