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Krzysztof kuhy Rudnicki
b017c7500e
384 findings to none across ten scripts. Most of it is annotation and
docstring work, but the lint surfaced two defects that had nothing to do with
style:
- `gpt_chunks.py` stripped its GPT preamble with
`input_string.strip("iSTS chunks:")`. str.strip takes a SET OF CHARACTERS,
not a prefix, so it also ate any leading i/S/T/s/c/h/u/n/k/space/colon:
"iSTS chunks: sentence one" came out as "entence one", silently losing the
first letter of every chunked sentence. Now removeprefix. ruff found it as
B005 + PLE1310.
- `is_numbered_line_format` had `for string in string` in its list branch,
which raises NameError the moment a list is passed (F821).
- `processing.test_XML()` read a module-level `alignments_data` that does not
exist at module scope; it only ever ran when pasted into a live session. It
takes the data as an argument now and is renamed `log_first_alignment` so
pytest stops collecting a non-test as a test.
Also real rather than cosmetic:
- The OpenAI key was a placeholder string literal in three files, one of them
carrying a "Replace me with os.environ['API_KEY']" comment. All three read
OPENAI_API_KEY now and say so when it is missing -- as committed, none of
these scripts could actually run.
- `rating.rate_similarity` was submitted unfinished: it built a client, never
touched its two arguments and returned None despite `-> str`. It now makes
the same call the rest of the package makes.
- The two 2 kB alignment prompts and one 344-character chunking prompt moved
to prompts/*.txt. Wrapping them in source would have put newlines into the
middle of the prompt.
- `format_alignments` parsed GPT's replies with `eval()`; the file holds one
repr()'d string per line, so ast.literal_eval is both sufficient and safe.
- NLP/__init__.py deleted -- it made the course directory a package named
"NLP", which is an invalid module name (N999) and which nothing imports;
the scripts use plain `import processing`.
INP001 is settled the same way throughout: a coursework file run as
`python x.py` gets a shebang and the executable bit, which is what it is,
rather than an __init__.py that would make a course code into a module name.
Verified: every module compiles and `./run.sh run Programming/NLP` is clean.
The scripts themselves need the SemEval corpus and a paid API key, so they
are not executed here.
Repo-wide: 3,159 findings -> 2,775.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0172dj34FDpao6wsix8ZuCzE
21 lines
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21 lines
1.3 KiB
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You are a machine designed to align chunks from 2 sentences. This means you will be taking each chunk from one sentence and comparing it to every chunk from the other sentence. Choose the chunks with the strongest relation and assign them a relation and a score.
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The possible relations are:
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EQUI: both chunks have the same meaning
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OPPO: the meanings of the chunks are opposite
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SPE1: both chunks have similar meanings, but chunk in sentence 1 is more specific.
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SPE2: both chunks have similar meanings, but chunk in sentence 2 is more specific.
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SIMI: both chunks have similar meanings
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REL: both chunks are not considered similar, but they are closely related by some relation not mentioned above
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NOALI: this chunk has not any corresponding chunk in the other sentence.
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The possible scores are a range from 0 to 5 where 0 means that the chunks are not related and 5 means their meanings are the same in the given context.
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A chunk can be aligned with multiple different chunks. Chunks can also be grouped in a relation. If a chunk has no relation to any other chunk, give it a NOALI relation to a chunk "0".
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If there are spelling mistakes, do not correct them.
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Present the answers in this form:
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chunk from first sentence <==> chunk from the second sentence // relation // score // comment
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in the comment you can explain your choice. |