Direct use: quick baseline for short English text classification (binary or multiclass, per your label map). Out-of-scope: high-stakes decisions, long-context documents without chunking, non-English domains without re-training. Limitations: Augmented data is very similar original data leading to biases and lack of diversity as originally expected.

Training and evaluation data

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 3e-05
train_batch_size: 16
eval_batch_size: 8
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: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.0487	1.0	40	0.0263	1.0	1.0	1.0	1.0
0.0084	2.0	80	0.0076	1.0	1.0	1.0	1.0
0.0081	3.0	120	0.0060	1.0	1.0	1.0	1.0

Framework versions

Transformers 4.56.1
Pytorch 2.8.0+cu126
Datasets 4.0.0
Tokenizers 0.22.0

sm-riti16/text-distilbert-predictor

text-distilbert-predictor

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

sm-riti16/
text-distilbert-predictor