---
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.