nlpcw_bert-base-uncased-abbr – AI Model by cogniveon | AlphaNeural AI

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cogniveon/
nlpcw_bert-base-uncased-abbr

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token-classification

generated_from_trainer

google-bert/bert-base-uncased

autotrain_compatible

endpoints_compatible

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nlpcw_bert-base-uncased-abbr

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

Loss: 0.2675
Precision: 0.9390
Recall: 0.9349
F1: 0.9369
Accuracy: 0.9317

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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.6325	1.0	67	0.2629	0.9036	0.9090	0.9063	0.9043
0.3169	2.0	134	0.2297	0.9309	0.9137	0.9223	0.9182
0.1994	3.0	201	0.2282	0.9310	0.9193	0.9251	0.9223
0.17	4.0	268	0.2193	0.9366	0.9286	0.9326	0.9278
0.1457	5.0	335	0.2350	0.9395	0.9373	0.9384	0.9331
0.1086	6.0	402	0.2435	0.9418	0.9340	0.9379	0.9331
0.0908	7.0	469	0.2537	0.9357	0.9283	0.9319	0.9270
0.0791	8.0	536	0.2675	0.9390	0.9349	0.9369	0.9317

Framework versions

Transformers 4.40.0
Pytorch 2.2.1+cu121
Datasets 2.19.0
Tokenizers 0.19.1