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| # Example input for: researchlink ingest --config examples/sample_paper_input.yml | |
| # | |
| # Runs fully offline (RESEARCHLINK_OFFLINE=true) with no PDF, keys, or network: | |
| # manual_metadata seeds the module; arxiv_id/github_url become strong identifiers | |
| # used for provenance and (when online) live metadata resolution + discovery. | |
| arxiv_id: "1706.03762" | |
| github_url: "https://github.com/tensorflow/tensor2tensor" | |
| target_output_dir: "examples/sample_outputs" | |
| manual_metadata: | |
| title: "Attention Is All You Need" | |
| year: 2017 | |
| venue: "NeurIPS" | |
| authors: | |
| - "Ashish Vaswani" | |
| - "Noam Shazeer" | |
| - "Niki Parmar" | |
| abstract: "Introduces the Transformer, a sequence model based solely on attention, dispensing with recurrence and convolutions." | |
| areas: | |
| - "deep learning" | |
| - "natural language processing" | |
| tags: | |
| - "transformer" | |
| - "attention" | |
| - "sequence modeling" | |
| author_notes: | | |
| Seminal architecture paper. Metadata needs verification against the primary source. | |