distilbert-base-uncased-lora-text-classification

krshahvivek/distilbert-base-uncased-lora-text-classification

distilbert-base-uncased-lora-text-classification is a machine learning model from krshahvivek. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Library: peft
License: apache-2.0
Access: Public
Created: Feb 11, 2025
Updated: Feb 11, 2025
SHA: df847ed4

Base models

Model card

From the Hugging Face model README

distilbert-base-uncased-lora-text-classification

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.1819
Accuracy: {'accuracy': 0.897}

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: 0.001
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 4
optimizer: Use 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: 15
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	250	0.5093	{'accuracy': 0.862}
0.4169	2.0	500	0.5829	{'accuracy': 0.849}
0.4169	3.0	750	0.6257	{'accuracy': 0.885}
0.1958	4.0	1000	0.7867	{'accuracy': 0.878}
0.1958	5.0	1250	0.7045	{'accuracy': 0.886}
0.0879	6.0	1500	0.8638	{'accuracy': 0.882}
0.0879	7.0	1750	0.9795	{'accuracy': 0.876}
0.024	8.0	2000	1.2233	{'accuracy': 0.886}
0.024	9.0	2250	1.2044	{'accuracy': 0.884}
0.014	10.0	2500	1.1903	{'accuracy': 0.88}
0.014	11.0	2750	1.1673	{'accuracy': 0.89}
0.0136	12.0	3000	1.1105	{'accuracy': 0.895}
0.0136	13.0	3250	1.1652	{'accuracy': 0.893}
0.0005	14.0	3500	1.1656	{'accuracy': 0.894}
0.0005	15.0	3750	1.1819	{'accuracy': 0.897}

Framework versions

PEFT 0.14.0
Transformers 4.48.3
Pytorch 2.6.0+cu124
Datasets 3.2.0
Tokenizers 0.21.0

Library: peft
License: apache-2.0
Created: Feb 11, 2025
Updated: Feb 11, 2025