This model is a fine-tuned version of microsoft/deberta-v3-small on the nbroad/company_names dataset. It achieves the following results on the evaluation set:

Loss: 0.0707
Precision: 0.7688
Recall: 0.7921
F1: 0.7802
Accuracy: 0.9766

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: 8e-05
train_batch_size: 48
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0746	1.0	2126	0.0657	0.7415	0.7868	0.7635	0.9753
0.0485	2.0	4252	0.0651	0.7631	0.7904	0.7765	0.9764
0.044	3.0	6378	0.0707	0.7688	0.7921	0.7802	0.9766

Framework versions

Transformers 4.34.1
Pytorch 2.0.1+cu117
Datasets 2.16.1
Tokenizers 0.14.1

nbroad/deberta-v3-small-company-names

deberta-v3-small-company-names

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

nbroad/
deberta-v3-small-company-names