fix: Update datasets wich can't be loaded with datasets>=3.0

mteb/tasks/Classification/ara/HotelReviewSentimentClassification.py

@@ -8,9 +8,8 @@ class HotelReviewSentimentClassification(AbsTaskClassification):
     metadata = TaskMetadata(
         name="HotelReviewSentimentClassification",
         dataset={
-            "path": "Elnagara/hard",
-            "revision": "b108d2c32ee4e1f4176ea233e1a5ac17bceb9ef9",
-            "trust_remote_code": True,
+            "path": "mteb/HotelReviewSentimentClassification",
+            "revision": "273d5105974460d3979149e29e88c06a8214c541",
         },
         description="HARD is a dataset of Arabic hotel reviews collected from the Booking.com website.",
         reference="https://link.springer.com/chapter/10.1007/978-3-319-67056-0_3",
@@ -38,8 +37,3 @@ class HotelReviewSentimentClassification(AbsTaskClassification):
 }
 """,
     )
-
-    def dataset_transform(self):
-        self.dataset = self.stratified_subsampling(
-            self.dataset, seed=self.seed, splits=["train"]
-        )

mteb/tasks/Classification/ara/TweetEmotionClassification.py

@@ -8,9 +8,8 @@ class TweetEmotionClassification(AbsTaskClassification):
     metadata = TaskMetadata(
         name="TweetEmotionClassification",
         dataset={
-            "path": "emotone-ar-cicling2017/emotone_ar",
-            "revision": "0ded8ff72cc68cbb7bb5c01b0a9157982b73ddaf",
-            "trust_remote_code": True,
+            "path": "mteb/TweetEmotionClassification",
+            "revision": "0d803980e91953cc67c21429f74b301b7b1b3f08",
         },
         description="A dataset of 10,000 tweets that was created with the aim of covering the most frequently used emotion categories in Arabic tweets.",
         reference="https://link.springer.com/chapter/10.1007/978-3-319-77116-8_8",
@@ -38,9 +37,3 @@ class TweetEmotionClassification(AbsTaskClassification):
 }
 """,
     )
-
-    def dataset_transform(self):
-        self.dataset = self.dataset.rename_column("tweet", "text")
-        self.dataset = self.stratified_subsampling(
-            self.dataset, seed=self.seed, splits=["train"]
-        )

mteb/tasks/Classification/deu/TenKGnadClassification.py

@@ -10,9 +10,8 @@ class TenKGnadClassification(AbsTaskClassification):
         description="10k German News Articles Dataset (10kGNAD) contains news articles from the online Austrian newspaper website DER Standard with their topic classification (9 classes).",
         reference="https://tblock.github.io/10kGNAD/",
         dataset={
-            "path": "community-datasets/gnad10",
-            "revision": "0798affe9b3f88cfda4267b6fbc50fac67046ee5",
-            "trust_remote_code": True,
+            "path": "mteb/TenKGnadClassification",
+            "revision": "ae9862bbcddc27b4bd93e2a7b463b7b5d05c6c55",
         },
         type="Classification",
         category="p2p",

mteb/tasks/Classification/eng/ArxivClassification.py

@@ -9,9 +9,8 @@ class ArxivClassification(AbsTaskClassification):
         name="ArxivClassification",
         description="Classification Dataset of Arxiv Papers",
         dataset={
-            "path": "ccdv/arxiv-classification",
-            "revision": "f9bd92144ed76200d6eb3ce73a8bd4eba9ffdc85",
-            "trust_remote_code": True,
+            "path": "mteb/ArxivClassification",
+            "revision": "5e80893bf045abefbf8cbe5d713bddc91ae158d5",
         },
         reference="https://ieeexplore.ieee.org/document/8675939",
         type="Classification",

mteb/tasks/Classification/eng/PatentClassification.py

@@ -9,9 +9,8 @@ class PatentClassification(AbsTaskClassification):
         name="PatentClassification",
         description="Classification Dataset of Patents and Abstract",
         dataset={
-            "path": "ccdv/patent-classification",
-            "revision": "2f38a1dfdecfacee0184d74eaeafd3c0fb49d2a6",
-            "trust_remote_code": True,
+            "path": "mteb/PatentClassification",
+            "revision": "6bd77eb030ab3bfbf1e6f7a2b069979daf167311",
         },
         reference="https://aclanthology.org/P19-1212.pdf",
         type="Classification",
@@ -48,8 +47,3 @@ class PatentClassification(AbsTaskClassification):
 }
 """,
     )
-
-    def dataset_transform(self):
-        self.dataset = self.stratified_subsampling(
-            self.dataset, seed=self.seed, splits=["test"]
-        )

mteb/tasks/Classification/fil/FilipinoHateSpeechClassification.py

@@ -12,9 +12,8 @@ class FilipinoHateSpeechClassification(AbsTaskClassification):
         description="Filipino Twitter dataset for sentiment classification.",
         reference="https://pcj.csp.org.ph/index.php/pcj/issue/download/29/PCJ%20V14%20N1%20pp1-14%202019",
         dataset={
-            "path": "legacy-datasets/hate_speech_filipino",
-            "revision": "1994e9bb7f3ec07518e3f0d9e870cb293e234686",
-            "trust_remote_code": True,
+            "path": "mteb/FilipinoHateSpeechClassification",
+            "revision": "087a17c0b7f9a78901c88aea00ad2892a319fdac",
         },
         type="Classification",
         category="s2s",
@@ -41,8 +40,3 @@ class FilipinoHateSpeechClassification(AbsTaskClassification):
 }
 """,
     )
-
-    def dataset_transform(self):
-        self.dataset = self.stratified_subsampling(
-            self.dataset, seed=self.seed, splits=["validation", "test"]
-        )

mteb/tasks/Classification/mya/MyanmarNews.py

@@ -8,9 +8,8 @@ class MyanmarNews(AbsTaskClassification):
     metadata = TaskMetadata(
         name="MyanmarNews",
         dataset={
-            "path": "ayehninnkhine/myanmar_news",
-            "revision": "b899ec06227db3679b0fe3c4188a6b48cc0b65eb",
-            "trust_remote_code": True,
+            "path": "mteb/MyanmarNews",
+            "revision": "644419f24bc820bbf8af24e0b4714a069812e0a3",
         },
         description="The Myanmar News dataset on Hugging Face contains news articles in Burmese. It is designed for tasks such as text classification, sentiment analysis, and language modeling. The dataset includes a variety of news topics in 4 categorie, providing a rich resource for natural language processing applications involving Burmese which is a low resource language.",
         reference="https://huggingface.co/datasets/myanmar_news",
@@ -38,9 +37,3 @@ class MyanmarNews(AbsTaskClassification):
 }
 """,
     )
-
-    def dataset_transform(self):
-        self.dataset = self.dataset.rename_columns({"category": "label"})
-        self.dataset = self.stratified_subsampling(
-            self.dataset, seed=self.seed, splits=["train"]
-        )

mteb/tasks/Classification/nld/DutchBookReviewSentimentClassification.py

@@ -10,9 +10,8 @@ class DutchBookReviewSentimentClassification(AbsTaskClassification):
         description="A Dutch book review for sentiment classification.",
         reference="https://github.com/benjaminvdb/DBRD",
         dataset={
-            "path": "benjaminvdb/dbrd",
-            "revision": "3f756ab4572e071eb53e887ab629f19fa747d39e",
-            "trust_remote_code": True,
+            "path": "mteb/DutchBookReviewSentimentClassification",
+            "revision": "1c2815ad38cf4794eb8d678fb08f569ea79392f6",
         },
         type="Classification",
         category="s2s",

mteb/tasks/Classification/swe/SwedishSentimentClassification.py

@@ -10,9 +10,8 @@ class SwedishSentimentClassification(AbsTaskClassification):
         description="Dataset of Swedish reviews scarped from various public available websites",
         reference="https://huggingface.co/datasets/swedish_reviews",
         dataset={
-            "path": "timpal0l/swedish_reviews",
-            "revision": "105ba6b3cb99b9fd64880215be469d60ebf44a1b",
-            "trust_remote_code": True,
+            "path": "mteb/SwedishSentimentClassification",
+            "revision": "39e35f55d58338ebd602f8d83b52cfe027f5146a",
         },
         type="Classification",
         category="s2s",
@@ -29,8 +28,3 @@ class SwedishSentimentClassification(AbsTaskClassification):
         sample_creation="found",
         bibtex_citation="",
     )
-
-    def dataset_transform(self):
-        self.dataset = self.stratified_subsampling(
-            self.dataset, seed=self.seed, splits=["validation", "test"]
-        )

mteb/tasks/Classification/tha/WisesightSentimentClassification.py

@@ -10,9 +10,8 @@ class WisesightSentimentClassification(AbsTaskClassification):
         description="Wisesight Sentiment Corpus: Social media messages in Thai language with sentiment label (positive, neutral, negative, question)",
         reference="https://github.com/PyThaiNLP/wisesight-sentiment",
         dataset={
-            "path": "pythainlp/wisesight_sentiment",
-            "revision": "14aa5773afa135ba835cc5179bbc4a63657a42ae",
-            "trust_remote_code": True,
+            "path": "mteb/WisesightSentimentClassification",
+            "revision": "727ea9bd253f9eedf16aebec6ac3f07791fb3db2",
         },
         type="Classification",
         category="s2s",
@@ -43,14 +42,3 @@ class WisesightSentimentClassification(AbsTaskClassification):
 }
 """,
     )
-
-    def dataset_transform(self):
-        for split in self.dataset.keys():
-            self.dataset[split] = self.dataset[split].rename_column("texts", "text")
-            self.dataset[split] = self.dataset[split].rename_column("category", "label")
-
-        self.dataset = self.stratified_subsampling(
-            self.dataset,
-            seed=self.seed,
-            splits=["test"],
-        )

mteb/tasks/Classification/urd/UrduRomanSentimentClassification.py

@@ -10,9 +10,8 @@ class UrduRomanSentimentClassification(AbsTaskClassification):
         description="The Roman Urdu dataset is a data corpus comprising of more than 20000 records tagged for sentiment (Positive, Negative, Neutral)",
         reference="https://archive.ics.uci.edu/dataset/458/roman+urdu+data+set",
         dataset={
-            "path": "community-datasets/roman_urdu",
-            "revision": "566be6449bb30b9b9f2b59173391647fe0ca3224",
-            "trust_remote_code": True,
+            "path": "mteb/UrduRomanSentimentClassification",
+            "revision": "905c1121c002c4b9adc4ebc5faaf4d6f50d1b1ee",
         },
         type="Classification",
         category="s2s",
@@ -37,11 +36,3 @@ class UrduRomanSentimentClassification(AbsTaskClassification):
 }
 """,
     )
-
-    def dataset_transform(self):
-        self.dataset = self.dataset.rename_columns(
-            {"sentence": "text", "sentiment": "label"}
-        )
-        self.dataset = self.stratified_subsampling(
-            self.dataset, seed=self.seed, splits=["train"]
-        )

mteb/tasks/Retrieval/multilingual/IndicQARetrieval.py

@@ -1,9 +1,5 @@
 from __future__ import annotations
 
-from hashlib import sha256
-
-import datasets
-
 from mteb.abstasks.MultilingualTask import MultilingualTask
 from mteb.abstasks.TaskMetadata import TaskMetadata
 
@@ -24,13 +20,12 @@
 }
 
 
-class IndicQARetrieval(MultilingualTask, AbsTaskRetrieval):
+class IndicQARetrieval(AbsTaskRetrieval, MultilingualTask):
     metadata = TaskMetadata(
         name="IndicQARetrieval",
         dataset={
-            "path": "ai4bharat/IndicQA",
-            "revision": "570d90ae4f7b64fe4fdd5f42fc9f9279b8c9fd9d",
-            "trust_remote_code": True,
+            "path": "mteb/IndicQARetrieval",
+            "revision": "51e8b328988795d658f6f34acd34044e9346e2ee",
         },
         description="IndicQA is a manually curated cloze-style reading comprehension dataset that can be used for evaluating question-answering models in 11 Indic languages. It is repurposed retrieving relevant context for each question.",
         reference="https://arxiv.org/abs/2212.05409",
@@ -57,45 +52,3 @@ class IndicQARetrieval(MultilingualTask, AbsTaskRetrieval):
 }
 """,
     )
-
-    def load_data(self, **kwargs):
-        if self.data_loaded:
-            return
-
-        split = "test"
-        queries = {lang: {split: {}} for lang in self.hf_subsets}
-        corpus = {lang: {split: {}} for lang in self.hf_subsets}
-        relevant_docs = {lang: {split: {}} for lang in self.hf_subsets}
-
-        for lang in self.hf_subsets:
-            data = datasets.load_dataset(
-                name=f"indicqa.{lang}", **self.metadata_dict["dataset"]
-            )[split]
-            data = data.filter(lambda x: x["answers"]["text"] != "")
-
-            question_ids = {
-                question: sha256(question.encode("utf-8")).hexdigest()
-                for question in set(data["question"])
-            }
-            context_ids = {
-                context: sha256(context.encode("utf-8")).hexdigest()
-                for context in set(data["context"])
-            }
-
-            for row in data:
-                question = row["question"]
-                context = row["context"]
-                query_id = question_ids[question]
-                queries[lang][split][query_id] = question
-
-                doc_id = context_ids[context]
-                corpus[lang][split][doc_id] = {"text": context}
-                if query_id not in relevant_docs[lang][split]:
-                    relevant_docs[lang][split][query_id] = {}
-                relevant_docs[lang][split][query_id][doc_id] = 1
-
-        self.corpus = datasets.DatasetDict(corpus)
-        self.queries = datasets.DatasetDict(queries)
-        self.relevant_docs = datasets.DatasetDict(relevant_docs)
-
-        self.data_loaded = True

mteb/tasks/STS/jpn/JSTS.py

@@ -9,10 +9,8 @@ class JSTS(AbsTaskSTS):
     metadata = TaskMetadata(
         name="JSTS",
         dataset={
-            "path": "shunk031/JGLUE",
-            "revision": "50e79c314a7603ebc92236b66a0973d51a00ed8c",
-            "name": "JSTS",
-            "trust_remote_code": True,
+            "path": "mteb/JSTS",
+            "revision": "5bac629e25799df4c9c80a6a5db983d6cba9e77d",
         },
         description="Japanese Semantic Textual Similarity Benchmark dataset construct from YJ Image Captions Dataset "
         + "(Miyazaki and Shimizu, 2016) and annotated by crowdsource annotators.",
@@ -67,6 +65,3 @@ def metadata_dict(self) -> dict[str, str]:
         metadata_dict["min_score"] = 0
         metadata_dict["max_score"] = 5
         return metadata_dict
-
-    def dataset_transform(self) -> None:
-        self.dataset = self.dataset.rename_column("label", "score")

pyproject.toml

@@ -25,7 +25,7 @@ classifiers = [
 ]
 requires-python = ">=3.9,<3.13"
 dependencies = [
-    "datasets>=2.19.0,<3.0.0",
+    "datasets>=2.19.0",
     "numpy>=1.0.0,<3.0.0",
     "requests>=2.26.0",
     "scikit_learn>=1.0.2",