metadata

library_name: transformers
license: apache-2.0
base_model: bert-base-cased
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: fine_tune_bert-base-cased
    results: []

fine_tune_bert-base-cased

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

Loss: 0.0842
Precision: 0.9376
Recall: 0.9541
F1: 0.9458
Accuracy: 0.9866

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: 64
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.2667	1.0	220	0.0739	0.8619	0.9118	0.8862	0.9776
0.0602	2.0	440	0.0641	0.9109	0.9357	0.9231	0.9830
0.0361	3.0	660	0.0594	0.9187	0.9401	0.9293	0.9845
0.0234	4.0	880	0.0564	0.9233	0.9461	0.9346	0.9854
0.0164	5.0	1100	0.0585	0.9211	0.9465	0.9336	0.9856
0.0123	6.0	1320	0.0656	0.9212	0.9483	0.9346	0.9850
0.0084	7.0	1540	0.0639	0.9290	0.9514	0.9401	0.9864
0.0072	8.0	1760	0.0735	0.9325	0.9482	0.9403	0.9862
0.0051	9.0	1980	0.0745	0.9319	0.9488	0.9403	0.9856
0.0042	10.0	2200	0.0783	0.9308	0.9490	0.9398	0.9858
0.0034	11.0	2420	0.0782	0.9337	0.9509	0.9422	0.9862
0.0026	12.0	2640	0.0822	0.9328	0.9505	0.9416	0.9858
0.0019	13.0	2860	0.0785	0.9335	0.9525	0.9429	0.9862
0.0018	14.0	3080	0.0819	0.9382	0.9525	0.9453	0.9865
0.0015	15.0	3300	0.0846	0.9349	0.9524	0.9436	0.9863
0.0013	16.0	3520	0.0880	0.9353	0.9519	0.9435	0.9860
0.0012	17.0	3740	0.0846	0.9362	0.9527	0.9444	0.9864
0.001	18.0	3960	0.0868	0.9374	0.9532	0.9453	0.9864
0.0009	19.0	4180	0.0842	0.9381	0.9536	0.9458	0.9868
0.0009	20.0	4400	0.0842	0.9376	0.9541	0.9458	0.9866

Framework versions

Transformers 4.44.2
Pytorch 2.2.0+cu121
Datasets 2.21.0
Tokenizers 0.19.1