| --- |
| dataset_info: |
| - config_name: documents |
| features: |
| - name: document_id |
| dtype: int64 |
| - name: document |
| dtype: string |
| splits: |
| - name: fiqa |
| num_bytes: 44966890 |
| num_examples: 57599 |
| - name: hotpotqa |
| num_bytes: 1474468794 |
| num_examples: 5220635 |
| - name: msmarco |
| num_bytes: 3089144932 |
| num_examples: 8841661 |
| - name: nq |
| num_bytes: 3105999594 |
| num_examples: 10120660 |
| - name: fever |
| num_bytes: 2880306808 |
| num_examples: 5384865 |
| - name: squadv2 |
| num_bytes: 14541224 |
| num_examples: 19029 |
| - name: trivia |
| num_bytes: 13228661481 |
| num_examples: 20970784 |
| download_size: 22328019465 |
| dataset_size: 23838089723 |
| - config_name: queries |
| features: |
| - name: query_id |
| dtype: int64 |
| - name: query |
| dtype: string |
| splits: |
| - name: fiqa |
| num_bytes: 405464 |
| num_examples: 5500 |
| - name: hotpotqa |
| num_bytes: 9999569 |
| num_examples: 85000 |
| - name: msmarco |
| num_bytes: 22742749 |
| num_examples: 502939 |
| - name: nq |
| num_bytes: 18663008 |
| num_examples: 307373 |
| - name: fever |
| num_bytes: 6541435 |
| num_examples: 109810 |
| - name: squadv2 |
| num_bytes: 9184156 |
| num_examples: 130217 |
| - name: trivia |
| num_bytes: 7297884 |
| num_examples: 78785 |
| download_size: 64492382 |
| dataset_size: 74834265 |
| - config_name: scores |
| features: |
| - name: query_id |
| dtype: int64 |
| - name: document_ids |
| list: int64 |
| - name: scores |
| list: float64 |
| splits: |
| - name: fiqa |
| num_bytes: 464644800 |
| num_examples: 14166 |
| - name: hotpotqa |
| num_bytes: 5576000000 |
| num_examples: 170000 |
| - name: nq |
| num_bytes: 4990356000 |
| num_examples: 152145 |
| - name: msmarco |
| num_bytes: 17474232800 |
| num_examples: 532751 |
| - name: fever |
| num_bytes: 4594689600 |
| num_examples: 140082 |
| - name: squadv2 |
| num_bytes: 4272364000 |
| num_examples: 130255 |
| - name: trivia |
| num_bytes: 24319100800 |
| num_examples: 741436 |
| download_size: 26855796621 |
| dataset_size: 61691388000 |
| configs: |
| - config_name: documents |
| data_files: |
| - split: fiqa |
| path: documents/fiqa-* |
| - split: nq |
| path: documents/nq-* |
| - split: hotpotqa |
| path: documents/hotpotqa-* |
| - split: msmarco |
| path: documents/msmarco-* |
| - split: fever |
| path: documents/fever-* |
| - split: squadv2 |
| path: documents/squadv2-* |
| - split: trivia |
| path: documents/trivia-* |
| - config_name: queries |
| data_files: |
| - split: fiqa |
| path: queries/fiqa-* |
| - split: nq |
| path: queries/nq-* |
| - split: hotpotqa |
| path: queries/hotpotqa-* |
| - split: msmarco |
| path: queries/msmarco-* |
| - split: fever |
| path: queries/fever-* |
| - split: squadv2 |
| path: queries/squadv2-* |
| - split: trivia |
| path: queries/trivia-* |
| - config_name: scores |
| data_files: |
| - split: fiqa |
| path: scores/fiqa-* |
| - split: hotpotqa |
| path: scores/hotpotqa-* |
| - split: nq |
| path: scores/nq-* |
| - split: msmarco |
| path: scores/msmarco-* |
| - split: fever |
| path: scores/fever-* |
| - split: squadv2 |
| path: scores/squadv2-* |
| - split: trivia |
| path: scores/trivia-* |
| --- |
| |
|
|
| ## Overview |
|
|
| This dataset is composed of high quality data sources with mined hard negatives. It can be used to train a strong retrieval model by itself but is better used after a large-scale contrastive pre-training, for example using this [dataset](https://huggingface.co/datasets/lightonai/embeddings-pre-training) or its [curated version](https://huggingface.co/datasets/lightonai/mgte-en). |
| This dataset has originally been created to follow the nv-retrieve setup, that mines the closest negatives to the query in a dataset and filter false negatives if their bi-encoder similarity is higher than a percentage of the query-positive similarity score. |
| To allow the exploration of various threshold and sampling methods, we decided, as for our pre-training datasets, to be the least destructive possible. Thus, instead of giving the final filtered samples given a method/threshold, we share all of the data, including all the (2048) mined negatives alongside their scores so anyone can apply their own strategy before training easily. |
| The mined datasets are FiQa, NaturalQuestion, HotpotQA, MSMARCO, FEVER, SquadV2 and TriviaQA, for a total of 1.88M queries with 2048 mined negatives and their scores, alongside the positive. The model used for mining is [gte-modernbert](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) |
|
|
| For more information, please refer to our [blogpost.](https://huggingface.co/blog/lightonai/lateon) |
|
|
| ## How to use |
|
|
| If you want to directly use the data as contrastive data with nv-retrieve filtering in either [sentence-transformers](https://www.sbert.net) or [PyLate](https://lightonai.github.io/pylate/), you can simply map it to the `(query, positive, negative_1, negative_2, ..., negative_n)` like so: |
|
|
|
|
|
|
|
|
| <details> |
| <summary> |
| Python code to cast to contrastive format |
| </summary> |
| |
|
|
| ```python |
| import datasets |
| import os |
| class KDToContrastive: |
| """Dataset processing class for converting a KD dataset into a contrastive one. |
| |
| Parameters |
| ---------- |
| queries |
| Queries dataset. |
| documents |
| Documents dataset. |
| split |
| Split to use for the queries and documents datasets. Used only if the queries and documents are of type `datasets.DatasetDict`. |
| num_negatives |
| Number of negatives to keep. |
| nv_threshold |
| Threshold for the nv-embed filtering |
| """ |
| |
| def __init__( |
| self, |
| queries: datasets.Dataset | datasets.DatasetDict, |
| documents: datasets.Dataset | datasets.DatasetDict, |
| split: str = "train", |
| num_negatives: int = 32, |
| nv_threshold: float = 0.95, |
| ) -> None: |
| if isinstance(queries, datasets.DatasetDict): |
| self.queries = queries[split] |
| else: |
| self.queries = queries |
| |
| if isinstance(documents, datasets.DatasetDict): |
| self.documents = documents[split] |
| else: |
| self.documents = documents |
| |
| self.num_negatives = num_negatives |
| self.nv_threshold = nv_threshold |
| |
| self.queries_index = { |
| query_id: i for i, query_id in enumerate(iterable=self.queries["query_id"]) |
| } |
| |
| self.documents_index = { |
| document_id: i |
| for i, document_id in enumerate(iterable=self.documents["document_id"]) |
| } |
| |
| def has_enough_negatives(self, example): |
| """Check if example has at least 50 valid negatives""" |
| scores = example["scores"] |
| positive_score = scores[0] |
| |
| count = sum( |
| 1 for score in scores[1:] if score < self.nv_threshold * positive_score |
| ) |
| return count >= self.num_negatives |
| |
| def map_to_query_positive_negatives(self, example): |
| """ |
| Maps a scores example to the desired format: |
| query, positive, negative_0, negative_1, ..., negative_49 |
| """ |
| query_id = example["query_id"] |
| document_ids = example["document_ids"] |
| scores = example["scores"] |
| |
| # Get query text |
| query_text = self.queries[self.queries_index[query_id]] |
| |
| # First document_id is the positive |
| positive_id = document_ids[0] |
| |
| positive_text = self.documents[self.documents_index[positive_id]] |
| positive_score = scores[0] |
| |
| # Create the row |
| row = {"query": query_text, "positive": positive_text} |
| |
| # Add negatives (starting from index 1) |
| total_negatives = 0 |
| for i in range(1, len(document_ids)): |
| if scores[i] < self.nv_threshold * positive_score: |
| negative_id = document_ids[i] |
| row[f"negative_{total_negatives}"] = self.documents[ |
| self.documents_index[negative_id] |
| ] |
| total_negatives += 1 |
| if total_negatives >= self.num_negatives: |
| break |
| |
| return row |
| |
| |
| def load_train_datasets(): |
| """Load all available splits from raphael data, with caching""" |
| cache_dir = "nv_retrieve_99_50_cached" |
| os.makedirs(cache_dir, exist_ok=True) |
| train_dataset = datasets.DatasetDict() |
| splits = ["trivia", "hotpotqa", "nq", "msmarco", "fever", "squadv2", "fiqa"] |
| |
| for split in splits: |
| try: |
| dataset = datasets.Dataset.load_from_disk(f"{cache_dir}/{split}") |
| print("Loaded dataset from disk") |
| except FileNotFoundError: |
| print("Creating dataset") |
| dataset = datasets.load_dataset( |
| "lightonai/nv-embed-supervised-distill-dedup", |
| name="scores", |
| num_proc=144, |
| split=split, |
| ) |
| queries = datasets.load_dataset( |
| "lightonai/nv-embed-supervised-distill-dedup", |
| name="queries", |
| num_proc=144, |
| split=split, |
| ) |
| documents = datasets.load_dataset( |
| "lightonai/nv-embed-supervised-distill-dedup", |
| name="documents", |
| num_proc=144, |
| split=split, |
| ) |
| processor = KDToContrastive( |
| queries, documents, num_negatives=50, nv_threshold=0.99 |
| ) |
| dataset = dataset.filter( |
| processor.has_enough_negatives, |
| desc="Filtering examples with <50 negatives", |
| ).map( |
| processor.map_to_query_positive_negatives, |
| remove_columns=dataset.column_names, |
| desc="Creating query-positive-negatives dataset", |
| ) |
| dataset.save_to_disk(f"{cache_dir}/{split}") |
| |
| train_dataset[split] = dataset |
| return train_dataset |
| ``` |
| </details> |
|
|
|
|
| ## Dataset structure |
|
|
| The dataset is composed of 7 high quality datasets, 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/passage. | |
|
|
| | Split | Rows | |
| |----------|-------:| |
| | fiqa | 57.6k | |
| | nq | 10.1M | |
| | hotpotqa | 5.22M | |
| | msmarco | 8.84M | |
| | fever | 5.38M | |
| | squadv2 | 19k | |
| | trivia | 21M | |
| | **Total**| **50.64M** | |
|
|
| ### Queries |
|
|
| | Column | Type | Description | |
| |------------|--------|------------------------------------------------------| |
| | `query_id` | int64 | Unique identifier of the query within the split. | |
| | `query` | string | Raw text of the query. | |
|
|
|
|
| | Split | Rows | |
| |----------|-------:| |
| | fiqa | 5.5k | |
| | nq | 307k | |
| | hotpotqa | 85k | |
| | msmarco | 503k | |
| | fever | 110k | |
| | squadv2 | 130k | |
| | trivia | 78.8k | |
| | **Total**| **1.22M** | |
|
|
| ### 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 top-2048 mined for the query. | |
| | `scores` | list[float] | Relevance scores for each document w.r.t the query. The first element is the positive document, followed by the top-2048 mined for the query. Can be used for nv-retrieve filtering or knowledge distillation. | |
|
|
| | Split | Rows | |
| |----------|-------:| |
| | fiqa | 14.2k | |
| | hotpotqa | 170k | |
| | nq | 152k | |
| | msmarco | 533k | |
| | fever | 140k | |
| | squadv2 | 130k | |
| | trivia | 741k | |
| | **Total**| **1.88M** | |
|
|
|
|
| ## Citation |
| If you are using this dataset, please consider citing our work |
| ```bibtex |
| @misc{sourty2025denseonlateon, |
| title={DenseOn with LateOn: Open State-of-the-Art Single and Multi-Vector Models}, |
| author={Sourty, Raphael and Chaffin, Antoine and Weller, Orion and Demoura, Paulo and Chatelain, Amelie}, |
| year={2026}, |
| howpublished={\url{https://huggingface.co/blog/lightonai/denseon-lateon}}, |
| }``` |
|
|
