metadata
dataset_info:
- config_name: fine-tuning
features:
- name: file_name
dtype: string
- name: file_path
dtype: string
- name: content
dtype: string
- name: file_size
dtype: int64
- name: language
dtype: string
- name: extension
dtype: string
- name: repo_name
dtype: string
- name: repo_stars
dtype: int64
- name: repo_forks
dtype: int64
- name: repo_open_issues
dtype: int64
- name: repo_created_at
dtype: string
- name: repo_pushed_at
dtype: string
- name: sha
dtype: string
- name: near_dups_stkv2_idx
sequence: int64
- name: input_ids
sequence: int32
- name: attention_mask
sequence:
sequence: int8
- name: n_tok
dtype: int64
- name: sample
sequence: int32
- name: hash
dtype: int64
- name: uniques_1
dtype: bool
- name: uniques_2
dtype: bool
- name: uniques_3
dtype: bool
- name: uniques_g3
dtype: bool
- name: sample_query
sequence: int32
- name: hash_sq
dtype: int64
- name: uniques
dtype: bool
- name: prefix_250
dtype: string
- name: prefix_200
dtype: string
- name: prefix_150
dtype: string
- name: prefix_100
dtype: string
- name: suffix
dtype: string
splits:
- name: d1
num_bytes: 19115683
num_examples: 1000
- name: d2
num_bytes: 27505874
num_examples: 1000
- name: d3
num_bytes: 23302451
num_examples: 1000
- name: dg3
num_bytes: 17600353
num_examples: 1000
download_size: 13691547
dataset_size: 87524361
- config_name: pre-train
features:
- name: blob_id
dtype: string
- name: directory_id
dtype: string
- name: path
dtype: string
- name: content_id
dtype: string
- name: detected_licenses
sequence: string
- name: license_type
dtype: string
- name: repo_name
dtype: string
- name: snapshot_id
dtype: string
- name: revision_id
dtype: string
- name: branch_name
dtype: string
- name: visit_date
dtype: timestamp[ns]
- name: revision_date
dtype: timestamp[ns]
- name: committer_date
dtype: timestamp[ns]
- name: github_id
dtype: float64
- name: star_events_count
dtype: int64
- name: fork_events_count
dtype: int64
- name: gha_license_id
dtype: string
- name: gha_event_created_at
dtype: timestamp[ns]
- name: gha_created_at
dtype: timestamp[ns]
- name: gha_language
dtype: string
- name: src_encoding
dtype: string
- name: language
dtype: string
- name: is_vendor
dtype: bool
- name: is_generated
dtype: bool
- name: length_bytes
dtype: int64
- name: extension
dtype: string
- name: content
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: n_tok
dtype: int64
- name: sample
sequence: int32
- name: hash
dtype: int64
- name: uniques_1
dtype: bool
- name: uniques_2
dtype: bool
- name: uniques_3
dtype: bool
- name: uniques_g3
dtype: bool
- name: sample_query
sequence: int32
- name: hash_sq
dtype: int64
- name: uniques
dtype: bool
- name: prefix_250
dtype: string
- name: prefix_200
dtype: string
- name: prefix_150
dtype: string
- name: prefix_100
dtype: string
- name: suffix
dtype: string
splits:
- name: d1
num_bytes: 36390284
num_examples: 1000
- name: d2
num_bytes: 82410057
num_examples: 1000
- name: d3
num_bytes: 100435041
num_examples: 1000
- name: dg3
num_bytes: 110727894
num_examples: 1000
download_size: 30652421
dataset_size: 329963276
configs:
- config_name: fine-tuning
data_files:
- split: d1
path: fine-tuning/d1-*
- split: d2
path: fine-tuning/d2-*
- split: d3
path: fine-tuning/d3-*
- split: dg3
path: fine-tuning/dg3-*
- config_name: pre-train
data_files:
- split: d1
path: pre-train/d1-*
- split: d2
path: pre-train/d2-*
- split: d3
path: pre-train/d3-*
- split: dg3
path: pre-train/dg3-*
tags:
- code
size_categories:
- 1K<n<10K
This dataset consists of the attack samples used for the paper "How Much Do Code Language Models Remember? An Investigation on Data Extraction Attacks before and after Fine-tuning"
We have two splits:
- The
fine-tuning attack
, which consists of selected samples coming from the fine-tuning set - The
pre-training attack
, which consists of selected samples coming from the TheStack-v2 on the Java section
We have different splits depending on the duplication rate of the samples:
d1
the samples inside the training set are uniqued2
the samples inside the training set are present two timesd3
the samples inside the training set are present three timesdg3
the samples inside the training set are present more than three times