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Kenazin/
distillbert-fine-tuned-matching

Name: distillbert-fine-tuned-matching
Availability: InStock

generated_from_trainer

distilbert/distilbert-base-uncased

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distillbert-fine-tuned-matching

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

Loss: 0.2727
Precision: 0.7356
Recall: 0.7356
F1: 0.7356

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: 1e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1
0.4031	1.0	185	0.3403	1.0	0.0268	0.0522
0.3013	2.0	370	0.2569	0.7574	0.4904	0.5953
0.2381	3.0	555	0.2347	0.6877	0.7510	0.7179
0.202	4.0	740	0.2374	0.6811	0.7854	0.7295
0.1784	5.0	925	0.2296	0.7287	0.6897	0.7087
0.1597	6.0	1110	0.2401	0.7458	0.6743	0.7082
0.1417	7.0	1295	0.2499	0.7127	0.7318	0.7221
0.1372	8.0	1480	0.2684	0.7021	0.7586	0.7293
0.1302	9.0	1665	0.2707	0.7356	0.7356	0.7356
0.1148	10.0	1850	0.2727	0.7356	0.7356	0.7356

Framework versions

Transformers 4.52.4
Pytorch 2.6.0+cu124
Datasets 2.14.4
Tokenizers 0.21.1

Kenazin/distillbert-fine-tuned-matching

distillbert-fine-tuned-matching

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Kenazin/
distillbert-fine-tuned-matching