This model is a fine-tuned version of answerdotai/ModernBERT-base on the ymoslem/TeleQnA-router dataset. It achieves the following results on the evaluation set:

Loss: 3.1982
Accuracy: 0.665
F1 Macro: 0.5873
F1 Weighted: 0.6479
Precision: 0.6048
Recall: 0.5852

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: 64
eval_batch_size: 64
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
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro	F1 Weighted	Precision	Recall
0.3857	1.0	704	1.0551	0.6658	0.5596	0.6328	0.5971	0.5643
0.1486	2.0	1408	1.3319	0.6342	0.5883	0.6348	0.5880	0.5887
0.0954	3.0	2112	1.8186	0.6512	0.5644	0.6302	0.5829	0.5648
0.0279	4.0	2816	1.8675	0.6262	0.6014	0.6350	0.6017	0.6119
0.0155	5.0	3520	2.6794	0.6638	0.5781	0.6425	0.6004	0.5774
0.0123	6.0	4224	2.7620	0.6458	0.5795	0.6360	0.5867	0.5776
0.0083	7.0	4928	2.7599	0.6524	0.5930	0.6456	0.5982	0.5908
0.002	8.0	5632	3.1982	0.665	0.5873	0.6479	0.6048	0.5852

Framework versions

Transformers 4.57.3
Pytorch 2.8.0+cu128
Datasets 4.1.1
Tokenizers 0.22.1

ymoslem/ModernBERT-base-TeleQnA-router-qe-classifier-binary-10ep-lr2e-05-qwen4b-5runs-5evals

Query Quality Estimation - Binary Classification

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

ymoslem/
ModernBERT-base-TeleQnA-router-qe-classifier-binary-10ep-lr2e-05-qwen4b-5runs-5evals