❯ llmcoderlab

Text statistics module

glm4-9b Pythononeshotrun 2sample 016.9s wall

← run 2 · raw JSON · challenge definitions

tests (60%)—
deliverables (20%)—
content checks (20%)—

one-shot reply is not valid JSON: Invalid \escape: line 1 column 115 (char 114)

01 what the model was asked

Create textstats.py, a small text-statistics module with exactly these three functions:

1. word_count(text: str) -> int
   Words are maximal runs of ASCII letters, digits, and apostrophes (regex [A-Za-z0-9']+). Return how many words text contains. Empty or word-free text returns 0.

2. top_words(text: str, n: int) -> list[tuple[str, int]]
   Count words case-insensitively (lowercase them). Return the n most frequent (word, count) pairs, most frequent first; break count ties alphabetically by word.

3. reading_time(text: str) -> int
   Estimated minutes to read at 200 words per minute, rounded up, with a minimum of 1 (even for empty text).

Use only the Python standard library.
required deliverables + checks
✗deliverable: textstats.py
✗textstats.py must contain “def word_count”
✗textstats.py must contain “def top_words”
✗textstats.py must contain “def reading_time”
pytest suite it was graded against
test_textstats.py · 38 lines · 873 B
from textstats import word_count, top_words, reading_time  def test_word_count_basic():    assert word_count("the cat sat on the mat") == 6  def test_word_count_empty():    assert word_count("") == 0  def test_word_count_punctuation():    assert word_count("Hi, there! Hi.") == 3  def test_word_count_apostrophes():    assert word_count("don't stop") == 2  def test_top_words_orders_by_count():    assert top_words("a b a c a b", 2) == [("a", 3), ("b", 2)]  def test_top_words_ties_alphabetical():    assert top_words("beta alpha", 2) == [("alpha", 1), ("beta", 1)]  def test_top_words_case_insensitive():    assert top_words("The the THE", 1) == [("the", 3)]  def test_reading_time_rounds_up():    assert reading_time(" ".join(["w"] * 401)) == 3  def test_reading_time_minimum_one():    assert reading_time("hello") == 1    assert reading_time("") == 1

02 what it did — full transcript

promptthe exact instruction sent to the model
view full prompt
You are completing a coding task in one shot. Write complete files that satisfy the spec below. Tests (pytest) will be run against your files; you cannot run them yourself.

Reply with ONE JSON object and nothing else:
{"files": {"<relative path>": "<complete file content>", ...}}

Required deliverable files: textstats.py

SPEC:
Create textstats.py, a small text-statistics module with exactly these three functions:

1. word_count(text: str) -> int
   Words are maximal runs of ASCII letters, digits, and apostrophes (regex [A-Za-z0-9']+). Return how many words text contains. Empty or word-free text returns 0.

2. top_words(text: str, n: int) -> list[tuple[str, int]]
   Count words case-insensitively (lowercase them). Return the n most frequent (word, count) pairs, most frequent first; break count ties alphabetically by word.

3. reading_time(text: str) -> int
   Estimated minutes to read at 200 words per minute, rounded up, with a minimum of 1 (even for empty text).

Use only the Python standard library.
reply242 tokens
view raw reply
```json
{"files": {"textstats.py": "import re\n\ndef word_count(text: str) -> int:\n    return len(re.findall(r'[A-Za-z0-9\'\']+', text))\n\ndef top_words(text: str, n: int) -> list[tuple[str, int]]:\n    words = re.findall(r'\b[A-Za-z0-9\']+\b', text.lower())\n    word_counts = {} \n    for word in words:\n        if word not in word_counts:\n            word_counts[word] = 1\n        else:\n            word_counts[word] += 1\n    sorted_words = sorted(word_counts.items(), key=lambda x: (-x[1], x[0]))\n    return sorted_words[:n]\n\ndef reading_time(text: str) -> int:\n    words = re.findall(r'[A-Za-z0-9\'\']+', text)\n    word_count = len(words)\n    if word_count == 0:\n        return 1\n    else:\n        return -(-word_count // 200)\n"}}
⚠ degenerate outputone-shot reply is not valid JSON: Invalid \escape: line 1 column 115 (char 114)

03 what it wrote

The model produced no files.

04 how it was graded

⚠ degenerate —
1
model calls
0
invalid actions
0
self test runs
242
tokens out
—
tokens in
16.9s
wall time

agent actions: —