D5v2 – AI Model by nickbull | AlphaNeural AI

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nickbull/
D5v2

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token-classification

generated_from_trainer

distilbert/distilbert-base-uncased

endpoints_compatible

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D5v2

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

Loss: 0.1752
Precision: 0.9407
Recall: 0.8599
F1: 0.8985

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: 32
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 0.1
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1
0.0658	1.0	4598	0.1741	0.9199	0.8561	0.8868
0.0461	2.0	9196	0.1943	0.9553	0.8372	0.8923
0.0422	3.0	13794	0.1752	0.9408	0.8599	0.8985
0.0295	4.0	18392	0.1796	0.9405	0.8587	0.8977
0.0256	5.0	22990	0.1838	0.9347	0.8636	0.8977

Framework versions

Transformers 5.3.0
Pytorch 2.11.0+cu130
Datasets 4.8.4
Tokenizers 0.22.2