metadata
dataset_info:
- config_name: documents
features:
- name: document_id
dtype: int64
- name: document
dtype: string
splits:
- name: CodeEditSearch_c
num_bytes: 2022886
num_examples: 4217
- name: CodeEditSearch_cpp
num_bytes: 924692
num_examples: 1691
- name: CodeEditSearch_go
num_bytes: 1573139
num_examples: 2870
- name: CodeEditSearch_java
num_bytes: 7768990
num_examples: 9488
- name: CodeEditSearch_javascript
num_bytes: 29411833
num_examples: 46509
- name: CodeEditSearch_php
num_bytes: 10413481
num_examples: 16429
- name: CodeEditSearch_python
num_bytes: 22801658
num_examples: 36641
- name: CodeEditSearch_ruby
num_bytes: 30676155
num_examples: 55352
- name: CodeEditSearch_rust
num_bytes: 273155
num_examples: 525
- name: CodeEditSearch_scala
num_bytes: 1175992
num_examples: 1943
- name: CodeEditSearch_shell
num_bytes: 10414614
num_examples: 21888
- name: CodeEditSearch_swift
num_bytes: 1289457
num_examples: 1851
- name: CodeEditSearch_typescript
num_bytes: 2278991
num_examples: 3332
download_size: 56331863
dataset_size: 121025043
- config_name: queries
features:
- name: query_id
dtype: int64
- name: query
dtype: string
splits:
- name: CodeEditSearch_c
num_bytes: 448349
num_examples: 3806
- name: CodeEditSearch_cpp
num_bytes: 194794
num_examples: 1748
- name: CodeEditSearch_go
num_bytes: 239010
num_examples: 2805
- name: CodeEditSearch_java
num_bytes: 652433
num_examples: 8320
- name: CodeEditSearch_javascript
num_bytes: 2712096
num_examples: 36475
- name: CodeEditSearch_php
num_bytes: 1051554
num_examples: 13993
- name: CodeEditSearch_python
num_bytes: 2299822
num_examples: 28581
- name: CodeEditSearch_ruby
num_bytes: 3737516
num_examples: 42719
- name: CodeEditSearch_rust
num_bytes: 41485
num_examples: 532
- name: CodeEditSearch_scala
num_bytes: 146745
num_examples: 1962
- name: CodeEditSearch_shell
num_bytes: 1521467
num_examples: 18008
- name: CodeEditSearch_swift
num_bytes: 133337
num_examples: 1869
- name: CodeEditSearch_typescript
num_bytes: 218320
num_examples: 3216
download_size: 8848681
dataset_size: 13396928
- config_name: scores
features:
- name: query_id
dtype: int64
- name: document_ids
list: int64
- name: scores
list: float64
- name: rerank_scores
list: float64
splits:
- name: CodeEditSearch_c
num_bytes: 1080904
num_examples: 3806
- name: CodeEditSearch_cpp
num_bytes: 496432
num_examples: 1748
- name: CodeEditSearch_go
num_bytes: 796620
num_examples: 2805
- name: CodeEditSearch_java
num_bytes: 2362880
num_examples: 8320
- name: CodeEditSearch_javascript
num_bytes: 10358900
num_examples: 36475
- name: CodeEditSearch_php
num_bytes: 3974012
num_examples: 13993
- name: CodeEditSearch_python
num_bytes: 8117004
num_examples: 28581
- name: CodeEditSearch_ruby
num_bytes: 12132196
num_examples: 42719
- name: CodeEditSearch_rust
num_bytes: 151088
num_examples: 532
- name: CodeEditSearch_scala
num_bytes: 557208
num_examples: 1962
- name: CodeEditSearch_shell
num_bytes: 5114272
num_examples: 18008
- name: CodeEditSearch_swift
num_bytes: 530796
num_examples: 1869
- name: CodeEditSearch_typescript
num_bytes: 913344
num_examples: 3216
download_size: 45294106
dataset_size: 46585656
configs:
- config_name: documents
data_files:
- split: CodeEditSearch_c
path: documents/CodeEditSearch_c-*
- split: CodeEditSearch_cpp
path: documents/CodeEditSearch_cpp-*
- split: CodeEditSearch_go
path: documents/CodeEditSearch_go-*
- split: CodeEditSearch_java
path: documents/CodeEditSearch_java-*
- split: CodeEditSearch_javascript
path: documents/CodeEditSearch_javascript-*
- split: CodeEditSearch_php
path: documents/CodeEditSearch_php-*
- split: CodeEditSearch_python
path: documents/CodeEditSearch_python-*
- split: CodeEditSearch_ruby
path: documents/CodeEditSearch_ruby-*
- split: CodeEditSearch_rust
path: documents/CodeEditSearch_rust-*
- split: CodeEditSearch_scala
path: documents/CodeEditSearch_scala-*
- split: CodeEditSearch_shell
path: documents/CodeEditSearch_shell-*
- split: CodeEditSearch_swift
path: documents/CodeEditSearch_swift-*
- split: CodeEditSearch_typescript
path: documents/CodeEditSearch_typescript-*
- config_name: queries
data_files:
- split: CodeEditSearch_c
path: queries/CodeEditSearch_c-*
- split: CodeEditSearch_cpp
path: queries/CodeEditSearch_cpp-*
- split: CodeEditSearch_go
path: queries/CodeEditSearch_go-*
- split: CodeEditSearch_java
path: queries/CodeEditSearch_java-*
- split: CodeEditSearch_javascript
path: queries/CodeEditSearch_javascript-*
- split: CodeEditSearch_php
path: queries/CodeEditSearch_php-*
- split: CodeEditSearch_python
path: queries/CodeEditSearch_python-*
- split: CodeEditSearch_ruby
path: queries/CodeEditSearch_ruby-*
- split: CodeEditSearch_rust
path: queries/CodeEditSearch_rust-*
- split: CodeEditSearch_scala
path: queries/CodeEditSearch_scala-*
- split: CodeEditSearch_shell
path: queries/CodeEditSearch_shell-*
- split: CodeEditSearch_swift
path: queries/CodeEditSearch_swift-*
- split: CodeEditSearch_typescript
path: queries/CodeEditSearch_typescript-*
- config_name: scores
data_files:
- split: CodeEditSearch_c
path: scores/CodeEditSearch_c-*
- split: CodeEditSearch_cpp
path: scores/CodeEditSearch_cpp-*
- split: CodeEditSearch_go
path: scores/CodeEditSearch_go-*
- split: CodeEditSearch_java
path: scores/CodeEditSearch_java-*
- split: CodeEditSearch_javascript
path: scores/CodeEditSearch_javascript-*
- split: CodeEditSearch_php
path: scores/CodeEditSearch_php-*
- split: CodeEditSearch_python
path: scores/CodeEditSearch_python-*
- split: CodeEditSearch_ruby
path: scores/CodeEditSearch_ruby-*
- split: CodeEditSearch_rust
path: scores/CodeEditSearch_rust-*
- split: CodeEditSearch_scala
path: scores/CodeEditSearch_scala-*
- split: CodeEditSearch_shell
path: scores/CodeEditSearch_shell-*
- split: CodeEditSearch_swift
path: scores/CodeEditSearch_swift-*
- split: CodeEditSearch_typescript
path: scores/CodeEditSearch_typescript-*
Overview
This dataset is composed of high quality code-edit retrieval data with mined hard negatives annotated with bi-encoder and cross-encoder scores. It can be used to train a strong code retrieval model by itself but is better used after a large-scale contrastive pre-training, for example using the CoRNStack dataset. The negatives were mined following the NV-Retriever setup: the closest documents to each query are mined as negatives, and false negatives are filtered out if their bi-encoder similarity is higher than a percentage of the query-positive similarity score. This dataset is a ready-to-train filtered version of the CodeEditSearch splits of embeddings-fine-tuning-multilingual-unfiltered: we keep the 10 hardest negatives per sample after NV-Retriever filtering with a threshold of 0.95, and remove the samples with less than 10 valid negatives as they may contain weakly annotated pairs. The data originates from CodeEditSearchTrain, from which we keep 13 of the 47 covered programming languages. Each sample contains the query, the positive and 10 mined hard negatives. The model used for mining is gte-modernbert-base, and all the samples were annotated with the cross-encoder mxbai-rerank-large-v2, enabling knowledge distillation training on top of contrastive learning.
For more information, please read our multilingual models blog post, our English models blog post and our paper.
How to use
The negatives are already mined and filtered, so using the data as contrastive data in either sentence-transformers or PyLate only requires joining the three subsets into the (query, positive, negative_0, negative_1, ..., negative_n) format. The cross-encoder rerank_scores of the kept documents are carried along in the same order as the columns, so they can be used as teacher scores by a knowledge distillation loss (a KL-divergence between the student and teacher relevance distributions, for instance) on top of the contrastive loss:
Python code to cast to contrastive format
import datasets
class KDToContrastive:
"""Maps the scores table of a split to the contrastive knowledge distillation format.
Parameters
----------
queries
Queries subset of the split.
documents
Documents subset of the split.
num_negatives
Number of hard negatives to keep per query, out of the 10 stored ones.
"""
def __init__(
self,
queries: datasets.Dataset,
documents: datasets.Dataset,
num_negatives: int = 10,
) -> None:
self.queries = dict(zip(queries["query_id"], queries["query"]))
self.documents = dict(zip(documents["document_id"], documents["document"]))
self.num_negatives = num_negatives
def map_to_query_positive_negatives(self, example) -> dict:
# document_ids, scores and rerank_scores are all ordered [positive, negative_0, ..., negative_9]
document_ids = example["document_ids"][: self.num_negatives + 1]
return {
"query": self.queries[example["query_id"]],
"positive": self.documents[document_ids[0]],
"teacher_scores": example["rerank_scores"][: self.num_negatives + 1],
**{
f"negative_{negative}": self.documents[document_id]
for negative, document_id in enumerate(document_ids[1:])
},
}
def load_train_datasets(num_negatives: int = 10) -> datasets.DatasetDict:
"""Load every split as a (query, positive, negatives, teacher_scores) dataset."""
repo = "lightonai/embeddings-fine-tuning-filtered-code-edit"
splits = [
"CodeEditSearch_c", "CodeEditSearch_cpp", "CodeEditSearch_go", "CodeEditSearch_java",
"CodeEditSearch_javascript", "CodeEditSearch_php", "CodeEditSearch_python",
"CodeEditSearch_ruby", "CodeEditSearch_rust", "CodeEditSearch_scala",
"CodeEditSearch_shell", "CodeEditSearch_swift", "CodeEditSearch_typescript",
]
train_dataset = datasets.DatasetDict()
for split in splits:
# data_files restricts the download to the split being processed, hence skipping the checks on the other splits
load = lambda config: datasets.load_dataset(
repo,
name=config,
data_files=f"{config}/{split}-*",
split="train",
verification_mode="no_checks",
)
scores = load("scores")
processor = KDToContrastive(
queries=load("queries"), documents=load("documents"), num_negatives=num_negatives
)
train_dataset[split] = scores.map(
processor.map_to_query_positive_negatives,
remove_columns=scores.column_names,
desc=f"Creating the contrastive dataset ({split})",
)
return train_dataset
train_dataset = load_train_datasets()
print(train_dataset)
Dataset structure
The dataset is composed of the 13 per-programming-language splits of CodeEditSearch (C, C++, Go, Java, JavaScript, PHP, Python, Ruby, Rust, Scala, Shell, Swift, TypeScript), defined by the splits parameters.
Each split contains 3 subsets, one containing the queries, one containing the documents and one joining tables also containing the corresponding pairwise query-documents scores.
Documents
| Column | Type | Description |
|---|---|---|
document_id |
int64 | Unique identifier of the document within the split. |
document |
string | Raw text of the document/code snippet. |
| Split | Rows |
|---|---|
| CodeEditSearch_c | 4.2k |
| CodeEditSearch_cpp | 1.7k |
| CodeEditSearch_go | 2.9k |
| CodeEditSearch_java | 9.5k |
| CodeEditSearch_javascript | 46.5k |
| CodeEditSearch_php | 16.4k |
| CodeEditSearch_python | 36.6k |
| CodeEditSearch_ruby | 55.4k |
| CodeEditSearch_rust | 525 |
| CodeEditSearch_scala | 1.9k |
| CodeEditSearch_shell | 21.9k |
| CodeEditSearch_swift | 1.9k |
| CodeEditSearch_typescript | 3.3k |
| Total | 203k |
Queries
| Column | Type | Description |
|---|---|---|
query_id |
int64 | Unique identifier of the query within the split. |
query |
string | Raw text of the query. |
| Split | Rows |
|---|---|
| CodeEditSearch_c | 3.8k |
| CodeEditSearch_cpp | 1.7k |
| CodeEditSearch_go | 2.8k |
| CodeEditSearch_java | 8.3k |
| CodeEditSearch_javascript | 36.5k |
| CodeEditSearch_php | 14k |
| CodeEditSearch_python | 28.6k |
| CodeEditSearch_ruby | 42.7k |
| CodeEditSearch_rust | 532 |
| CodeEditSearch_scala | 2.0k |
| CodeEditSearch_shell | 18k |
| CodeEditSearch_swift | 1.9k |
| CodeEditSearch_typescript | 3.2k |
| Total | 164k |
Scores
| Column | Type | Description |
|---|---|---|
query_id |
int64 | Identifier joining back to the corresponding row in queries. |
document_ids |
list[int64] | List of document IDs (joining back to documents). The first element is the positive document, followed by the 10 hardest negatives kept after NV-Retriever filtering. |
scores |
list[float] | Bi-encoder relevance scores for each document w.r.t the query, in the same order as document_ids. Can be used for knowledge distillation. |
rerank_scores |
list[float] | Cross-encoder scores from mxbai-rerank-large-v2 for each document w.r.t the query, in the same order as document_ids. Can be used for knowledge distillation. |
| Split | Rows |
|---|---|
| CodeEditSearch_c | 3.8k |
| CodeEditSearch_cpp | 1.7k |
| CodeEditSearch_go | 2.8k |
| CodeEditSearch_java | 8.3k |
| CodeEditSearch_javascript | 36.5k |
| CodeEditSearch_php | 14k |
| CodeEditSearch_python | 28.6k |
| CodeEditSearch_ruby | 42.7k |
| CodeEditSearch_rust | 532 |
| CodeEditSearch_scala | 2.0k |
| CodeEditSearch_shell | 18k |
| CodeEditSearch_swift | 1.9k |
| CodeEditSearch_typescript | 3.2k |
| Total | 164k |
Token length distributions
Token counts are computed with the mmBERT-base tokenizer. For readability, each histogram is truncated after the last bin containing at least 5 samples; the statistics reported in the boxes (including the maximum) are computed on the full data.
Citation
If you are using this dataset, please consider citing our work
@misc{sourty2026denseonlateonfullyopen,
title = {DenseOn with the LateOn: Fully Open Dense and Late-Interaction Models for Multilingual, Long-Context, and Code Search},
author = {Raphaël Sourty and Antoine Chaffin and Paulo Roberto Moura Junior and Amélie Chatelain},
year = {2026},
eprint = {2607.27178},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2607.27178},
}```