--- 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](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:
Python code to cast to contrastive format ```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) ```
## 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. ![Per-split token length distributions of the queries](https://huggingface.co/datasets/lightonai/embeddings-fine-tuning-filtered-code-edit/resolve/main/figures/code-edit_queries.png#hf-light-mode-only) ![Per-split token length distributions of the queries](https://huggingface.co/datasets/lightonai/embeddings-fine-tuning-filtered-code-edit/resolve/main/figures/code-edit_queries_dark.png#hf-dark-mode-only) ![Per-split token length distributions of the documents](https://huggingface.co/datasets/lightonai/embeddings-fine-tuning-filtered-code-edit/resolve/main/figures/code-edit_documents.png#hf-light-mode-only) ![Per-split token length distributions of the documents](https://huggingface.co/datasets/lightonai/embeddings-fine-tuning-filtered-code-edit/resolve/main/figures/code-edit_documents_dark.png#hf-dark-mode-only) ## 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}, }```