Add AVMeme-Exam zero shot classification task

mteb/tasks/zeroshot_classification/eng/__init__.py

@@ -2,6 +2,10 @@
     AVEDatasetVideoZeroShotClassification,
     AVEDatasetZeroShotClassification,
 )
+from .avmeme_exam_classification import (
+    AVMemeAudioVideoZeroShotClassification,
+    AVMemeVideoZeroShotClassification,
+)
 from .birdsnap import BirdsnapZeroShotClassification
 from .breakfast_classification import BreakfastZeroShotClassification
 from .caltech101 import Caltech101ZeroShotClassification
@@ -56,6 +60,8 @@
     "CLEVR",
     "AVEDatasetVideoZeroShotClassification",
     "AVEDatasetZeroShotClassification",
+    "AVMemeAudioVideoZeroShotClassification",
+    "AVMemeVideoZeroShotClassification",
     "BirdsnapZeroShotClassification",
     "BreakfastZeroShotClassification",
     "CIFAR10ZeroShotClassification",

mteb/tasks/zeroshot_classification/eng/avmeme_exam_classification.py

@@ -0,0 +1,117 @@
+from __future__ import annotations
+
+from mteb.abstasks.task_metadata import TaskMetadata
+from mteb.abstasks.zeroshot_classification import AbsTaskZeroShotClassification
+
+CITATION = r"""
+@inproceedings{jiang2026avmeme,
+  author = {Jiang, Xilin and Wang, Qiaolin and Wu, Junkai and He, Xiaomin and Xu, Zhongweiyang and Ma, Yinghao and Piao, Minshuo and Yang, Kaiyi and Zheng, Xiuwen and Shimizu, Riki and others},
+  journal = {arXiv preprint arXiv:2601.17645},
+  title = {AVMeme Exam: A Multimodal Multilingual Multicultural Benchmark for LLMs' Contextual and Cultural Knowledge and Thinking},
+  year = {2026},
+}
+"""
+
+
+class AVMemeAudioVideoZeroShotClassification(AbsTaskZeroShotClassification):
+    metadata = TaskMetadata(
+        name="AVMemeAudioVideoZeroShot",
+        description="AVMeme Exam is a multimodal, multilingual, and multicultural benchmark of over 1,000 iconic Internet audio-visual memes spanning speech, songs, music, and sound effects. Each meme is paired with a Q&A assessing levels of understanding from surface content to context, emotion, usage, and world knowledge. This zero-shot classification task predicts the sound category of each meme clip.",
+        reference="https://arxiv.org/pdf/2601.17645",
+        dataset={
+            "path": "mteb/AVMeme-Exam",
+            "revision": "7070d1979d9a4943dd49b2e72858eb1e54f6bd5b",
+        },
+        type="VideoZeroshotClassification",
+        category="va2t",
+        eval_splits=["test"],
+        eval_langs=[
+            "bos-Latn",
+            "bre-Latn",
+            "deu-Latn",
+            "eng-Latn",
+            "fas-Arab",
+            "fin-Latn",
+            "fra-Latn",
+            "hin-Deva",
+            "ita-Latn",
+            "jpn-Jpan",
+            "kor-Hang",
+            "por-Latn",
+            "rus-Cyrl",
+            "spa-Latn",
+            "tel-Telu",
+            "zho-Hans",
+        ],
+        main_score="accuracy",
+        date=("2026-01-25", "2026-01-25"),
+        domains=["Web", "Entertainment", "Music"],
+        task_subtypes=["Topic classification"],
+        license="cc-by-4.0",
+        annotations_creators="human-annotated",
+        dialect=[],
+        modalities=["video", "audio", "text"],
+        sample_creation="found",
+        bibtex_citation=CITATION,
+        is_beta=True,
+    )
+    input_column_name = ("video", "audio")
+    label_column_name: str = "emotion"
+
+    def get_candidate_labels(self) -> list[str]:
+        return [
+            f"a video of {name}"
+            for name in self.dataset["test"].features[self.label_column_name].names
+        ]
+
+
+class AVMemeVideoZeroShotClassification(AbsTaskZeroShotClassification):
+    metadata = TaskMetadata(
+        name="AVMemeVideoZeroShot",
+        description="AVMeme Exam is a multimodal, multilingual, and multicultural benchmark of over 1,000 iconic Internet audio-visual memes spanning speech, songs, music, and sound effects. Each meme is paired with a Q&A assessing levels of understanding from surface content to context, emotion, usage, and world knowledge. This zero-shot classification task predicts the sound category of each meme clip.",
+        reference="https://arxiv.org/pdf/2601.17645",
+        dataset={
+            "path": "mteb/AVMeme-Exam",
+            "revision": "7070d1979d9a4943dd49b2e72858eb1e54f6bd5b",
+        },
+        type="VideoZeroshotClassification",
+        category="v2t",
+        eval_splits=["test"],
+        eval_langs=[
+            "bos-Latn",
+            "bre-Latn",
+            "deu-Latn",
+            "eng-Latn",
+            "fas-Arab",
+            "fin-Latn",
+            "fra-Latn",
+            "hin-Deva",
+            "ita-Latn",
+            "jpn-Jpan",
+            "kor-Hang",
+            "por-Latn",
+            "rus-Cyrl",
+            "spa-Latn",
+            "tel-Telu",
+            "zho-Hans",
+        ],
+        main_score="accuracy",
+        date=("2026-01-25", "2026-01-25"),
+        domains=["Web", "Entertainment", "Music"],
+        task_subtypes=["Topic classification"],
+        license="cc-by-4.0",
+        annotations_creators="human-annotated",
+        dialect=[],
+        modalities=["video", "text"],
+        sample_creation="found",
+        bibtex_citation=CITATION,
+        is_beta=True,
+    )
+    input_column_name = "video"
+    label_column_name: str = "emotion"
+
+    def get_candidate_labels(self) -> list[str]:
+        return [
+            f"a video of {name}"
+            for name in self.dataset["test"].features[self.label_column_name].names
+        ]