text-distilbert-predictor

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

• Loss: 0.0260
• Accuracy: 1.0
• F1: 1.0
• Precision: 1.0
• Recall: 1.0

Model description

This model is a fine-tuned version of distilbert-base-uncased for text classification. DistilBERT encoder with a linear classification head

Intended uses & limitations

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