epsilon-compositionality

Code for our paper Measuring Idiomaticity in Text Embedding Models with 𝜀-compositionality
git clone https://esimon.eu/repos/epsilon-compositionality.git
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commit 446e8c0ef8dd8162fa76826893e43c4db52ffe42
parent a02677026c33d217b07506ec99bfea99f20c1d63
Author: Sondre Wold <sondrewold1@gmail.com>
Date:   Tue, 26 Aug 2025 09:55:27 +0200

fast error handling

Diffstat:
Mexperiment.py | 12+++++++++---
1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/experiment.py b/experiment.py @@ -247,8 +247,14 @@ def find_diff(a: str, b: str) -> list[str] | None: if a == b: # Should not happen for SICK or NCIP, but you never know. return None - words_a = a.split() - words_b = b.split() + #print(f"A={a}\nB={b}\n##############") + + try: + words_a = a.split() + words_b = b.split() + except: + raise Exception(f"Parse error. A={a}, B={b}") + prefix_len = 0 min_len = min(len(words_a), len(words_b)) @@ -400,7 +406,7 @@ def main() -> None: elif args.experiment_type == "ncip-non": # Here we measure the difference between two non-idiomatic phrases in context. sentence_A = df["synonym both"].values.tolist() - sentence_B = df["both synonym alt2_1"].values.tolist() + sentence_B = df["both synonym alt1_1"].values.tolist() elif args.experiment_type == "sick": lexA = df.loc[df["sentence_A_expRule"] == "S2_lex"]