| | --- |
| | license: apache-2.0 |
| | --- |
| | --- |
| | license: apache-2.0 |
| | task_categories: |
| | - document-understanding |
| | - text-generation |
| | language: |
| | - en |
| | --- |
| | |
| | README.md |
| | train.jsonl |
| | val.jsonl |
| | test.jsonl |
| | {"instruction":"Extract payment amount and due date from the document","input":"Invoice #1029. Total payable amount is $5,000 due by January 30, 2025.","output":"{\"amount\":\"$5,000\",\"due_date\":\"2025-01-30\"}"} |
| | {"instruction":"Identify potential risks in the contract","input":"Payment shall be made within 90 days. No penalty clause is specified.","output":"{\"risk\":\"Delayed payment risk\"}"} |
| |
|
| | {"instruction":"Extract key entities from the agreement","input":"The client agrees to pay USD 12,000 upon project completion.","output":"{\"amount\":\"$12,000\"}"} |
| |
|
| | {"instruction":"Classify the document section","input":"Termination clause: Either party may terminate with 30 days notice.","output":"{\"section\":\"Termination\"}"} |
| |
|
| |
|
| |
|
| | # DocuMind ERNIE 4.5 Document Reasoning Dataset |
| |
|
| | Instruction-style dataset for fine-tuning ERNIE 4.5 on OCR-based document |
| | understanding and reasoning tasks. |
| |
|
| | ## Data Source |
| | Documents are processed using PaddleOCR-VL to extract text and layout. |
| | Data is anonymized and partially synthetic. |
| |
|
| | ## Schema |
| | Each record: |
| | { |
| | "instruction": "...", |
| | "input": "<OCR extracted document text>", |
| | "output": "<structured JSON>" |
| | } |
| |
|
| | ## Tasks |
| | - Entity extraction |
| | - Risk identification |
| | - Section classification |
| | - Contract reasoning |
| |
|
| | ## Splits |
| | - train.jsonl |
| | - val.jsonl |
| | - test.jsonl |
| |
|
| | ## License |
| | Apache-2.0 |
| |
|
