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dataset_info:
- config_name: documents
features:
- name: document_id
dtype: int64
- name: document
dtype: string
splits:
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download_size: 56331863
dataset_size: 121025043
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- name: query_id
dtype: int64
- name: query
dtype: string
splits:
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- name: CodeEditSearch_typescript
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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
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- name: CodeEditSearch_swift
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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](https://huggingface.co/datasets/lightonai/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](https://huggingface.co/datasets/lightonai/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](https://huggingface.co/datasets/lightonai/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](https://huggingface.co/Alibaba-NLP/gte-modernbert-base), and all the samples were annotated with the cross-encoder [mxbai-rerank-large-v2](https://huggingface.co/mixedbread-ai/mxbai-rerank-large-v2), enabling knowledge distillation training on top of contrastive learning.
For more information, please read our [multilingual models blog post](https://huggingface.co/blog/lightonai/mdenseon-mlateon), our [English models blog post](https://huggingface.co/blog/lightonai/denseon-lateon) and our [paper](https://arxiv.org/abs/2607.27178).
## How to use
The negatives are already mined and filtered, so using the data as contrastive data in either [sentence-transformers](https://www.sbert.net) or [PyLate](https://lightonai.github.io/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:
<details>
<summary>
Python code to cast to contrastive format
</summary>
```python
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)
```
</details>
## 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](https://huggingface.co/mixedbread-ai/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](https://huggingface.co/jhu-clsp/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
```bibtex
@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},
}```
|