metadata

dataset_info:
  features:
    - name: id
      dtype: int64
    - name: content
      dtype: string
    - name: language
      dtype: string
    - name: pii
      list:
        - name: context
          dtype: string
        - name: end
          dtype: int64
        - name: start
          dtype: int64
        - name: tag
          dtype: string
        - name: value
          dtype: string
    - name: assignment_id
      dtype: string
  splits:
    - name: train
      num_bytes: 17215712
      num_examples: 7878
    - name: validation
      num_bytes: 7302111
      num_examples: 4000
  download_size: 10754489
  dataset_size: 24517823
extra_gated_prompt: >-
  ## Terms of Use for the dataset


  This is an annotated dataset for Personal Identifiable Information (PII) in
  code. We ask that you read and agree to the following Terms of Use before
  using the dataset and fill this
  [form](https://docs.google.com/forms/d/e/1FAIpQLSfiWKyBB8-PxOCLo-KMsLlYNyQNJEzxJw0gcUAUHT3UY848qA/viewform):

  1. You agree that you will not use the PII dataset for any purpose other than
  training or evaluating models for PII removal from datasets.

  2. You agree that you will not share the PII dataset or any modified versions
  for whatever purpose.

  3. Unless required by applicable law or agreed to in writing, the dataset is
  provided on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,
  either express or implied, including, without limitation, any warranties or
  conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
  PARTICULAR PURPOSE. You are solely responsible for determining the
  appropriateness of using the dataset, and assume any risks associated with
  your exercise of permissions under these Terms of Use.

  4. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
  DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
  OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE DATASET OR THE USE
  OR OTHER DEALINGS IN THE DATASET.
extra_gated_fields:
  Email: text
  I have read the License and agree with its terms: checkbox

Bigcode PII Training Dataset

Dataset Description

This is the dataset used for the training of bigcode-pii-model (after training on pseudo-labeled data). It is a concatenation of an early version of bigcode-pii-dataset which had less samples, and pii-for-code (a dataset with 400 files we annotated in a previous iteration: MORE INFO TO BE ADDED).

Files with AMBIGUOUS and ID were excluded. Each PII subtype was remaped to it supertype.

Statistics

The dataset consists of 11878 files in 31 programming languages. More statistics and information about the original annotated dataset can be found at the dataset card of: bigcode-pii-dataset. We provide the training and test splits we used for the training and evaluation of the bigcode-pii-model. Below is the distribution of PII entoties in each split.

Entity type	Train	Validation
EMAIL	4721	1742
NAME	3847	1298
IP_ADDRESS	1941	521
USERNAME	1320	346
PASSWORD	390	148
KEY	171	118

How to use

from datasets import load_dataset

ds = load_dataset("bigcode/bigcode-pii-dataset-training")

DatasetDict({
    train: Dataset({
        features: ['id', 'content', 'language', 'pii', 'assignment_id'],
        num_rows: 7878
    })
    validation: Dataset({
        features: ['id', 'content', 'language', 'pii', 'assignment_id'],
        num_rows: 4000
    })
})

Considerations for Using the Data

When using this dataset, please be mindful of the data governance risks that come with handling personally identifiable information (PII). Despite sourcing the data from open, permissive GitHub repositories and having it annotated by fairly paid crowd-workers, it does contain sensitive details such as names, usernames, keys, emails, passwords, and IP addresses. To ensure responsible use for research within the open-source community, access to the dataset will be provided through a gated mechanism.

We expect researchers and developers working with the dataset to adhere to the highest ethical standards and employ robust data protection measures. To assist users in effectively detecting and masking PII, we've also released a PII model trained on this dataset. Our goal in providing access to both the dataset and the PII model is to foster the development of privacy-preserving AI technologies while minimizing potential risks related to handling PII.