Python code from GitHub repo: Run & debug
Budget: $30 – $250 USD
I need assistance with running and debugging an open-source Python software from a public GitHub repo. The software is focused on NLP of scientific texts using GPT. The specific tasks are as follows:
1. This software calls Crossref API. It can work without a Crossref API key, but in this case Crossref limits the rate. Your task is to investigate whether there are built-in mechanisms in the code to handle rate limiting (e.g., internal environment variables or configurations). If such mechanisms are absent, suggest minimal code modifications to implement rate limiting.
2. I have successfully run the code and obtained reasonable results, but encountered several warnings and errors during execution, including:
* […] _asyncio.py", line 680, in __aexit__ | raise BaseExceptionGroup( | ExceptionGroup: unhandled errors in a TaskGroup (1 sub-exception) […]
* […] aiohttp\client_reqrep.py", line 1093, in raise_for_status | raise ClientResponseError( | aiohttp.client_exceptions.ClientResponseError: 429, message='', url='https://api.semanticscholar.org/ […]
* […] Unclosed client session client_session: <aiohttp.client.ClientSession object at […]
(I can provide the full error output). Your task is to either debug these errors or provide clear explanations for why certain warnings/errors can be safely ignored.
3. The documentation provided with the code (pdf file) describes a feature that allows searching for additional texts online. However, it lacks specific python code examples for this functionality. Your task will be to determine how much of this functionality is implemented in the current repository. If the code is complete and usable, provide a Python script demonstrating its usage. If the implementation is incomplete, explain what is available and what is missing.
I will prioritize candidates with demonstrable experience in:
- Python coding for NLP using LLMs,
- automated processing of advanced texts in the field of natural sciences,
- GitHub,
- asyncio, aiohttp, pydantic.
Please include verifiable proof of your relevant experience (e.g., GitHub repos, past projects related to NLP of scientific texts, etc.). No need to include polite phrases or rephrase this project description.
1. This software calls Crossref API. It can work without a Crossref API key, but in this case Crossref limits the rate. Your task is to investigate whether there are built-in mechanisms in the code to handle rate limiting (e.g., internal environment variables or configurations). If such mechanisms are absent, suggest minimal code modifications to implement rate limiting.
2. I have successfully run the code and obtained reasonable results, but encountered several warnings and errors during execution, including:
* […] _asyncio.py", line 680, in __aexit__ | raise BaseExceptionGroup( | ExceptionGroup: unhandled errors in a TaskGroup (1 sub-exception) […]
* […] aiohttp\client_reqrep.py", line 1093, in raise_for_status | raise ClientResponseError( | aiohttp.client_exceptions.ClientResponseError: 429, message='', url='https://api.semanticscholar.org/ […]
* […] Unclosed client session client_session: <aiohttp.client.ClientSession object at […]
(I can provide the full error output). Your task is to either debug these errors or provide clear explanations for why certain warnings/errors can be safely ignored.
3. The documentation provided with the code (pdf file) describes a feature that allows searching for additional texts online. However, it lacks specific python code examples for this functionality. Your task will be to determine how much of this functionality is implemented in the current repository. If the code is complete and usable, provide a Python script demonstrating its usage. If the implementation is incomplete, explain what is available and what is missing.
I will prioritize candidates with demonstrable experience in:
- Python coding for NLP using LLMs,
- automated processing of advanced texts in the field of natural sciences,
- GitHub,
- asyncio, aiohttp, pydantic.
Please include verifiable proof of your relevant experience (e.g., GitHub repos, past projects related to NLP of scientific texts, etc.). No need to include polite phrases or rephrase this project description.