| --- |
| language: |
| - en |
| - ru |
| - pt |
| - fr |
| license: cc-by-4.0 |
| task_categories: |
| - text-classification |
| task_ids: |
| - semantic-similarity-classification |
| tags: |
| - duplicate-detection |
| - news |
| - trade |
| - URNO |
| - semantic-duplicates |
| pretty_name: Trade News Semantic Duplicates (URNO) |
| size_categories: |
| - 1K<n<10K |
| configs: |
| - config_name: goldset |
| data_files: |
| - split: test |
| path: goldset/test-* |
| - config_name: silverset |
| data_files: |
| - split: train |
| path: silverset/train-* |
| - split: validation |
| path: silverset/validation-* |
| dataset_info: |
| - config_name: goldset |
| features: |
| - name: id |
| dtype: int64 |
| - name: pair_id |
| dtype: large_string |
| - name: document_id_1 |
| dtype: int64 |
| - name: document_id_2 |
| dtype: int64 |
| - name: text_1 |
| dtype: large_string |
| - name: text_2 |
| dtype: large_string |
| - name: model_label |
| dtype: large_string |
| - name: human_label |
| dtype: large_string |
| - name: human_difficulty_tag |
| dtype: large_string |
| - name: binary_label |
| dtype: int64 |
| splits: |
| - name: test |
| num_bytes: 3050353 |
| num_examples: 2250 |
| download_size: 1561967 |
| dataset_size: 3050353 |
| - config_name: silverset |
| features: |
| - name: id |
| dtype: int64 |
| - name: pair_id |
| dtype: large_string |
| - name: document_id_1 |
| dtype: int64 |
| - name: document_id_2 |
| dtype: int64 |
| - name: text_1 |
| dtype: large_string |
| - name: text_2 |
| dtype: large_string |
| - name: label |
| dtype: large_string |
| - name: difficulty_tag |
| dtype: large_string |
| - name: confidence_score |
| dtype: float64 |
| - name: binary_label |
| dtype: int64 |
| splits: |
| - name: train |
| num_bytes: 4968946 |
| num_examples: 3672 |
| - name: validation |
| num_bytes: 1236513 |
| num_examples: 919 |
| download_size: 3287214 |
| dataset_size: 6205459 |
| --- |
| |
| # Trade News Semantic Duplicates (URNO) |
|
|
| Dataset for detecting **semantic duplicates** in international trade/economic news, built as part of a Master's thesis (ВКР, ИТМО). |
|
|
| Pairs of news articles are labeled using the **URNO scheme** — a four-way diagnostic taxonomy that distinguishes structurally different types of negatives: |
|
|
| | Label | Meaning | |
| |-------|---------| |
| | **D** | Duplicate — same trade event, same information | |
| | **U** | Update — same event, new data or development (hard negative) | |
| | **R** | Related — different but similar events, e.g. same sector (hard negative) | |
| | **N** | Not related — different topics/events | |
| | **O** | Out of scope — non-trade content | |
|
|
| `binary_label = 1` for **D**, `0` for all others. |
|
|
| ## Configurations |
|
|
| ### `goldset` — Gold Standard (2,250 pairs) |
|
|
| 100% human-annotated. Used as the evaluation benchmark. |
| Label distribution: D=54%, U=24%, N=19%, R=2%, O=1%. |
| LLM ↔ Human agreement: **98.98%**. |
|
|
| ### `silverset` — Silver Set (4,591 pairs) |
|
|
| LLM-annotated (DeepSeek) with train/validation splits. |
| Used for fine-tuning duplicate detection models. |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Gold standard (evaluation) |
| gold = load_dataset("timerlanmukhtarov/trade-news-datasets", "goldset") |
| |
| # Silver set (fine-tuning) |
| silver = load_dataset("timerlanmukhtarov/trade-news-datasets", "silverset") |
| silver["train"][0] |
| ``` |
|
|
| ## Fields |
|
|
| ### goldset |
|
|
| | Field | Type | Description | |
| |-------|------|-------------| |
| | `id` | int | Row index | |
| | `pair_id` | string | Unique pair identifier | |
| | `document_id_1` | string | ID of article A | |
| | `document_id_2` | string | ID of article B | |
| | `text_1` | string | English summary of article A | |
| | `text_2` | string | English summary of article B | |
| | `model_label` | string | LLM annotation (D/U/R/N/O) | |
| | `human_label` | string | Human annotation (D/U/R/N/O) — **ground truth** | |
| | `human_difficulty_tag` | string | Annotator difficulty tag | |
| | `binary_label` | int | 1=Duplicate (D), 0=Non-duplicate | |
|
|
| ### silverset |
|
|
| | Field | Type | Description | |
| |-------|------|-------------| |
| | `id` | int | Row index | |
| | `pair_id` | string | Unique pair identifier | |
| | `document_id_1` | string | ID of article A | |
| | `document_id_2` | string | ID of article B | |
| | `text_1` | string | English summary of article A | |
| | `text_2` | string | English summary of article B | |
| | `label` | string | LLM annotation (D/U/R/N/O) | |
| | `difficulty_tag` | string | LLM-assigned difficulty | |
| | `confidence_score` | float | LLM confidence (0–1) | |
| | `binary_label` | int | 1=Duplicate (D), 0=Non-duplicate | |
|
|
| ## Source corpus |
|
|
| 86,913 articles from 250+ multilingual news sources (EN/RU/PT/FR). |
| Articles were embedded with `BAAI/bge-m3`, clustered via agglomerative clustering, |
| then pairs were sampled per URNO category and annotated. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @mastersthesis{mukhtarov2026trade, |
| author = {Mukhtarov, Timerlan}, |
| title = {Анализ и выявление семантических дублей в торговых новостях}, |
| school = {ITMO University}, |
| year = {2026} |
| } |
| ``` |
|
|
