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| title: ChEMU NER Demo | |
| emoji: ⚗️ | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: gradio | |
| sdk_version: 5.49.1 | |
| python_version: "3.12" | |
| app_file: app.py | |
| pinned: false | |
| license: cc-by-nc-3.0 | |
| short_description: BioBERT-based NER for chemical patents (ChEMU 2020) | |
| # ChEMU NER Demo | |
| Interactive demo for a BioBERT-based named entity recognition model | |
| fine-tuned on the [ChEMU 2020 Task 1](https://chemu-patent-ie.github.io/) | |
| chemical patent corpus. | |
| The model ([`mpkato/chemu-biobert-ner`](https://huggingface.co/mpkato/chemu-biobert-ner)) | |
| extracts 10 reaction-step entity types from patent text: | |
| - Compounds: `STARTING_MATERIAL`, `REAGENT_CATALYST`, `REACTION_PRODUCT`, `SOLVENT`, `OTHER_COMPOUND` | |
| - Conditions: `TEMPERATURE`, `TIME` | |
| - Yields: `YIELD_PERCENT`, `YIELD_OTHER` | |
| - Labels: `EXAMPLE_LABEL` | |
| Held-out dev exact-match micro-F1 ≈ 0.95. | |
| ## Usage | |
| Paste any reaction description into the text box and click **Extract entities**. | |
| Three quick examples are provided at the bottom. | |
| ## License | |
| Training data (ChEMU 2020) is licensed under CC BY-NC 3.0; this demo and | |
| the underlying model are therefore for **non-commercial research use only**. | |