bert-qwantz-coherent – AI Model by paul-stansifer | AlphaNeural AI

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paul-stansifer/
bert-qwantz-coherent

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text-classification

generated_from_trainer

google-bert/bert-base-uncased

autotrain_compatible

endpoints_compatible

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bert-qwantz-coherent

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

Loss: 0.6861
Accuracy: 0.8240

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: 0.0001
train_batch_size: 24
eval_batch_size: 24
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.4695	1.0	339	0.4547	0.7956
0.2521	2.0	678	0.4364	0.8131
0.0627	3.0	1017	0.6861	0.8240

Can save 90% of coherent strings by discarding 80% of dp strings (cutoff is 57.403409481048584)
Can save 95% of coherent strings by discarding 63% of dp strings (cutoff is -83.01011323928833)
Can save 98% of coherent strings by discarding 44% of dp strings (cutoff is -97.15004563331604)
Can save 99% of coherent strings by discarding 33% of dp strings (cutoff is -98.31664562225342)

Framework versions

Transformers 4.40.1
Pytorch 2.2.1+cu121
Datasets 2.19.0
Tokenizers 0.19.1