metadata
license: mit
dataset_info:
features:
- name: num_examples
dtype: int64
- name: num_batches
dtype: int64
- name: num_epochs
dtype: int64
- name: batch_size
dtype: int64
- name: gradient_accumulation_steps
dtype: int64
- name: max_train_steps
dtype: int64
- name: label
dtype: string
- name: rank
dtype: int64
- name: step_loss
sequence: float64
- name: step_lr
sequence: float64
- name: epoch_loss
sequence: float64
- name: epoch_lr
sequence: float64
- name: lora_gradient_norm
sequence: float64
- name: lora_weight_norm
sequence: float64
splits:
- name: train
num_bytes: 73088
num_examples: 128
- name: test
num_bytes: 18272
num_examples: 32
download_size: 67880
dataset_size: 91360
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
language:
- en
tags:
- not-for-all-audiences
size_categories:
- 1K<n<10K
LORA Adapters are Good Feature Extractors Dataset
This dataset contains images of two sets of categories that are not safe for work (hentai and porn, labelled as 0 and 2 correspondingly) and one neutral category, labelled as 2. The dataset is the source data for training a zoo of LORA adapters on sample images from each category. Adapters representations will then be used as input data to a weight-space model in an experiment to verify whether WS models operating in low rank representation space are able to extract features and discriminate between harmful and non-harmful LORAs.