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---
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](https://huggingface.co/datasets/AISE-TUDelft/memtune-tuning_data)**
- The `pre-training attack`, which consists of selected samples coming from the **[TheStack-v2](https://huggingface.co/datasets/bigcode/the-stack-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 unique
- `d2` the samples inside the training set are present two times
- `d3` the samples inside the training set are present three times
- `dg3` the samples inside the training set are present more than three times
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