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Suppress NLTK download logs for meteor and word_length #726

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Suppress NLTK download logs for meteor and word_length #726

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8967c3a
Suppress NLTK download logs
52147 Jan 5, 2026
710aa93
Update docs example to silence NLTK download logs
52147 Jan 5, 2026
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6 changes: 5 additions & 1 deletion docs/source/creating_and_sharing.mdx
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Original file line number Diff line number Diff line change
Expand Up @@ -58,8 +58,12 @@ Or if you need to download the NLTK `"punkt_tab"` resources:

```py
def _download_and_prepare(self, dl_manager):
import io
from contextlib import redirect_stdout
import nltk
nltk.download("punkt_tab")

with redirect_stdout(io.StringIO()):
nltk.download("punkt_tab", quiet=True)
```

Next, we need to define how the computation of the evaluation module works.
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11 changes: 9 additions & 2 deletions measurements/word_length/word_length.py
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Expand Up @@ -82,12 +82,19 @@ def _info(self):
)

def _download_and_prepare(self, dl_manager):
import io
from contextlib import redirect_stdout

import nltk

def _silent_download(pkg: str) -> None:
with redirect_stdout(io.StringIO()):
nltk.download(pkg, quiet=True)

if NLTK_VERSION >= version.Version("3.9.0"):
nltk.download("punkt_tab")
_silent_download("punkt_tab")
else:
nltk.download("punkt")
_silent_download("punkt")

def _compute(self, data, tokenizer=word_tokenize):
"""Returns the average word length of the input data"""
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15 changes: 11 additions & 4 deletions metrics/meteor/meteor.py
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Expand Up @@ -117,15 +117,22 @@ def _info(self):
)

def _download_and_prepare(self, dl_manager):
import io
from contextlib import redirect_stdout

import nltk

nltk.download("wordnet")
def _silent_download(pkg: str) -> None:
with redirect_stdout(io.StringIO()):
nltk.download(pkg, quiet=True)

_silent_download("wordnet")
if NLTK_VERSION >= version.Version("3.9.0"):
nltk.download("punkt_tab")
_silent_download("punkt_tab")
elif NLTK_VERSION >= version.Version("3.6.5"):
nltk.download("punkt")
_silent_download("punkt")
if NLTK_VERSION >= version.Version("3.6.6"):
nltk.download("omw-1.4")
_silent_download("omw-1.4")

def _compute(self, predictions, references, alpha=0.9, beta=3, gamma=0.5):
multiple_refs = isinstance(references[0], list)
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