prompt-tackler_modernbert – AI Model by cgoosen | AlphaNeural AI

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cgoosen/
prompt-tackler_modernbert

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text-classification

generated_from_trainer

jhu-clsp/mmBERT-small

text-embeddings-inference

endpoints_compatible

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prompt-tackler_modernbert

This model is a fine-tuned version of jhu-clsp/mmBERT-small on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0119
Accuracy: 0.9976
Precision: 0.9976
Recall: 0.9976
F1: 0.9976

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: 16
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
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: 6
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.03	1.0	18408	0.0181	0.9964	0.9964	0.9964	0.9964
0.0151	2.0	36816	0.0119	0.9976	0.9976	0.9976	0.9976
0.0118	3.0	55224	0.0161	0.9973	0.9973	0.9973	0.9973
0.0038	4.0	73632	0.0160	0.9978	0.9978	0.9978	0.9978
0.0007	5.0	92040	0.0191	0.9981	0.9981	0.9981	0.9981
0.0	6.0	110448	0.0224	0.9981	0.9981	0.9981	0.9981

Framework versions

Transformers 4.53.3
Pytorch 2.9.1+cu128
Datasets 2.21.0
Tokenizers 0.21.4