We lifted 1 functions out of this paper's own repositories and ran 1 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 |
|---|---|---|
| hugailab/hugnlp | pwc_unofficial | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| masked_mean | Ran | hugailab/hugnlp/loss/rl_loss.py code served (permissive licence) · get_code("58d722395c4aab62") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In this paper, we introduce HugNLP, a unified and comprehensive library for natural language processing (NLP) with the prevalent backend of HuggingFace Transformers, which is designed for NLP researchers to easily utilize off-the-shelf algorithms and develop novel methods with user-defined models and tasks in real-world scenarios. HugNLP consists of a hierarchical structure including models, processors and applications that unifies the learning process of pre-trained language models (PLMs) on different NLP tasks. Additionally, we present some featured NLP applications to show the effectiveness of HugNLP, such as knowledge-enhanced PLMs, universal information extraction, low-resource mining, and code understanding and generation, etc. The source code will be released on GitHub (https://github.com/wjn1996/HugNLP).
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2302.14286")
get_code_for_paper("2302.14286")
have("2302.14286")
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