We lifted 5 functions out of this paper's own repositories and ran 5 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 |
|---|---|---|
| nd-ball/py-irt | canonical | 5 of 5 |
| Function | Status | Where it lives |
|---|---|---|
| create_anchor_gradient_zeroer | Ran | nd-ball/py-irt/py_irt/anchor_utils.py code served (permissive licence) · get_code("8b831d008aa4e817") |
| read_json | Ran | nd-ball/py-irt/py_irt/io.py code served (permissive licence) · get_code("b8fe10303ae293f8") |
| read_jsonlines | Ran | nd-ball/py-irt/py_irt/io.py code served (permissive licence) · get_code("497e934f0ffd8827") |
| register | Ran | nd-ball/py-irt/py_irt/initializers.py code served (permissive licence) · get_code("06eee8f34be53db4") |
| safe_file | Ran | nd-ball/py-irt/py_irt/io.py code served (permissive licence) · get_code("c34bf8c144fbba30") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
py-irt is a Python library for fitting Bayesian Item Response Theory (IRT) models. py-irt estimates latent traits of subjects and items, making it appropriate for use in IRT tasks as well as ideal-point models. py-irt is built on top of the Pyro and PyTorch frameworks and uses GPU-accelerated training to scale to large data sets. Code, documentation, and examples can be found at https://github.com/nd-ball/py-irt. py-irt can be installed from the GitHub page or the Python Package Index (PyPI).
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2203.01282")
get_code_for_paper("2203.01282")
have("2203.01282")
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