bert-finetuned-requirements – AI Model by RodP | AlphaNeural AI

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RodP/
bert-finetuned-requirements

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token-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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bert-finetuned-requirements

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: 0.0538
Precision: 0.9609
Recall: 0.9609
F1: 0.9609
Accuracy: 0.9822

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: 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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	33	0.1992	0.8903	0.8880	0.8892	0.9476
No log	2.0	66	0.1351	0.9039	0.9062	0.9051	0.9554
No log	3.0	99	0.0949	0.9368	0.9271	0.9319	0.9677
No log	4.0	132	0.0613	0.9556	0.9531	0.9544	0.9788
No log	5.0	165	0.0538	0.9609	0.9609	0.9609	0.9822

Framework versions

Transformers 4.25.1
Pytorch 1.13.1+cu116
Datasets 2.8.0
Tokenizers 0.13.2