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--- |
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dataset_info: |
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features: |
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- name: messages |
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list: |
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- name: content |
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dtype: string |
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- name: role |
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dtype: string |
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- name: source |
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dtype: string |
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- name: split |
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dtype: string |
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- name: mode |
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dtype: string |
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- name: category |
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dtype: string |
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- name: file_id |
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dtype: string |
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- name: ref_count |
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dtype: int64 |
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- name: language |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 3012211 |
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num_examples: 1708 |
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- name: validation |
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num_bytes: 199719 |
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num_examples: 115 |
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download_size: 887241 |
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dataset_size: 3211930 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: validation |
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path: data/validation-* |
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--- |
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# Reference Parsing Finetuning Dataset |
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A fine-tuning dataset for bibliographic reference extraction and parsing, combining LinkedBooks, CEX, and EXCITE datasets into conversation-style examples for LLM SFT. |
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## Dataset Description |
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This dataset teaches models to extract and parse bibliographic references from text into structured JSON format. Examples follow a conversational format with system/user/assistant messages, using various prompt variants for diversity. |
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**Data Sources:** |
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- **LinkedBooks**: Multi-language reference strings with structured metadata |
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- **CEX**: English academic papers with TEI XML parsed references |
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- **EXCITE**: Multi-language academic papers with parsed references |
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## Data Fields |
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| Field | Type | Description | |
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|-------|------|-------------| |
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| `messages` | list | Conversation messages with `role` (system/user/assistant) and `content` | |
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| `source` | string | Data source: `linkedbook`, `cex`, or `excite` | |
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| `split` | string | Dataset split: `train` or `valid` | |
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| `mode` | string | Example type: `single` (1 reference) or `group` (multiple references) | |
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| `language` | string | Language code (e.g., `en`, `de`, `fr`) | |
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| `ref_count` | int | Number of references in the example | |
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| `file_id` | string\|null | Source document ID (for CEX/EXCITE) | |
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| `category` | string\|null | Document category (for CEX) | |
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## Splits |
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| Split | Examples | Description | |
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|-------|----------|-------------| |
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| `train` | ~1,708 | Main training data | |
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| `valid` | ~115 | Validation set | |
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**Distribution:** |
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- ~70% single-reference examples, ~30% multi-reference groups |
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- ~10-15% LinkedBook, ~30-35% CEX, ~50-55% EXCITE |
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## Data Creation and Processing |
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1. **Data Loading**: Loads references from LinkedBooks (Training and Validation JSONL), CEX (JSON + TEI XML), and EXCITE (JSON + XML) |
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2. **Validation**: Filters invalid references (missing titles/authors, unparsed authors, mismatched counts) |
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3. **Sampling**: Stratified sampling by category/class (30% train rate for CEX/EXCITE) |
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4. **Grouping**: Groups references into batches (3-20 refs per group with weighted probabilities) |
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5. **Prompt Variants**: Applies 5 prompt variants with weighted distribution (40% detailed, 25% minimal, 25% task-based, 5% ultra-minimal, 5% no prompt) |
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6. **Format Conversion**: Converts to conversation-style format with structured JSON output |
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## Credits |
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The dataset is being developed by [Yurui Zhu](https://github.com/RuiaRui) ([Odoma](https://github.com/odoma-ch)). This work is carried out in the context of the EU-funded [GRAPHIA project](https://graphia-ssh.eu/) (grant ID: [101188018](https://cordis.europa.eu/project/id/101188018)). |
