We lifted 1 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| thunlp/OpenNRE | canonical | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| get_model | Not yet run | thunlp/OpenNRE/opennre/pretrain.py code served (permissive licence) · get_code("cbcfa8b536bd91a7") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
OpenNRE is an open-source and extensible toolkit that provides a unified framework to implement neural models for relation extraction (RE). Specifically, by implementing typical RE methods, OpenNRE not only allows developers to train custom models to extract structured relational facts from the plain text but also supports quick model validation for researchers. Besides, OpenNRE provides various functional RE modules based on both TensorFlow and PyTorch to maintain sufficient modularity and extensibility, making it becomes easy to incorporate new models into the framework. Besides the toolkit, we also release an online system to meet real-time extraction without any training and deploying. Meanwhile, the online system can extract facts in various scenarios as well as aligning the extracted facts to Wikidata, which may benefit various downstream knowledge-driven applications (e.g., information retrieval and question answering). More details of the toolkit and online system can be obtained from http://github.com/thunlp/OpenNRE.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1909.13078")
get_code_for_paper("1909.13078")
have("1909.13078")
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