diff --git a/mteb/benchmarks/benchmarks.py b/mteb/benchmarks/benchmarks.py
index a271a4de58..c350684561 100644
--- a/mteb/benchmarks/benchmarks.py
+++ b/mteb/benchmarks/benchmarks.py
@@ -1087,7 +1087,13 @@
 
 BRIGHT_LONG = Benchmark(
     name="BRIGHT (long)",
-    tasks=get_tasks(tasks=["BrightRetrieval"], eval_splits=["long"]),
+    tasks=MTEBTasks(
+        (
+            get_task(
+                "BrightLongRetrieval",
+            ),
+        )
+    ),
     description="""BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval.
 BRIGHT is the first text retrieval
 benchmark that requires intensive reasoning to retrieve relevant documents with
diff --git a/mteb/tasks/Retrieval/eng/BrightRetrieval.py b/mteb/tasks/Retrieval/eng/BrightRetrieval.py
index f970977433..4cb6cf8fbd 100644
--- a/mteb/tasks/Retrieval/eng/BrightRetrieval.py
+++ b/mteb/tasks/Retrieval/eng/BrightRetrieval.py
@@ -29,7 +29,77 @@
 DOMAINS_langs = {split: ["eng-Latn"] for split in DOMAINS}
 
 
-EVAL_SPLITS = ["standard", "long"]
+def load_bright_data(
+    self,
+    path: str,
+    domains: list,
+    eval_splits: list,
+    cache_dir: str | None = None,
+    revision: str | None = None,
+):
+    corpus = {domain: {split: None for split in eval_splits} for domain in DOMAINS}
+    queries = {domain: {split: None for split in eval_splits} for domain in DOMAINS}
+    relevant_docs = {
+        domain: {split: None for split in eval_splits} for domain in DOMAINS
+    }
+
+    for domain in domains:
+        domain_corpus = datasets.load_dataset(
+            path, "documents", split=domain, cache_dir=cache_dir, revision=revision
+        )
+        examples = datasets.load_dataset(
+            path, "examples", split=domain, cache_dir=cache_dir, revision=revision
+        )
+        if domain in DOMAINS_LONG:
+            domain_corpus_long = datasets.load_dataset(
+                path,
+                "long_documents",
+                split=domain,
+                cache_dir=cache_dir,
+                revision=revision,
+            )
+        corpus[domain]["standard"] = {
+            e["id"]: {"text": e["content"]} for e in domain_corpus
+        }
+        if domain in DOMAINS_LONG:
+            corpus[domain]["long"] = {
+                e["id"]: {"text": e["content"]} for e in domain_corpus_long
+            }
+        queries[domain]["standard"] = queries[domain]["long"] = {
+            e["id"]: e["query"] for e in examples
+        }
+        relevant_docs[domain]["standard"] = {}
+        relevant_docs[domain]["long"] = {}
+
+        for e in examples:
+            qid = e["id"]
+            gold_ids = e["gold_ids"]
+            gold_ids_long = e["gold_ids_long"]
+            relevant_docs[domain]["standard"][qid] = defaultdict(dict)
+            relevant_docs[domain]["long"][qid] = defaultdict(dict)
+            for gid in gold_ids:
+                relevant_docs[domain]["standard"][qid].update({gid: 1})
+            for gid in gold_ids_long:
+                relevant_docs[domain]["long"][qid].update({gid: 1})
+
+    corpus = datasets.DatasetDict(corpus)
+    queries = datasets.DatasetDict(queries)
+    relevant_docs = datasets.DatasetDict(relevant_docs)
+    return corpus, queries, relevant_docs
+
+
+def load_data(self, **kwargs):
+    if self.data_loaded:
+        return
+
+    self.corpus, self.queries, self.relevant_docs = self.load_bright_data(
+        path=self.metadata_dict["dataset"]["path"],
+        domains=DOMAINS,
+        eval_splits=self.metadata_dict["eval_splits"],
+        cache_dir=kwargs.get("cache_dir", None),
+        revision=self.metadata_dict["dataset"]["revision"],
+    )
+    self.data_loaded = True
 
 
 class BrightRetrieval(MultilingualTask, AbsTaskRetrieval):
@@ -40,10 +110,10 @@ class BrightRetrieval(MultilingualTask, AbsTaskRetrieval):
             "revision": "a75a0eb",
         },
         reference="https://huggingface.co/datasets/xlangai/BRIGHT",
-        description=("Bright retrieval dataset."),
+        description="Bright retrieval dataset.",
         type="Retrieval",
         category="s2p",
-        eval_splits=EVAL_SPLITS,
+        eval_splits=["standard"],
         eval_langs=DOMAINS_langs,
         main_score="ndcg_at_10",
         date=("2024-03-01", "2024-06-01"),
@@ -66,74 +136,21 @@ class BrightRetrieval(MultilingualTask, AbsTaskRetrieval):
 }
         """,
     )
+    load_bright_data = load_bright_data
+    load_data = load_data
 
-    def load_bright_data(
-        self,
-        path: str,
-        domains: list,
-        eval_splits: list,
-        cache_dir: str = None,
-        revision: str = None,
-    ):
-        corpus = {domain: {split: None for split in eval_splits} for domain in DOMAINS}
-        queries = {domain: {split: None for split in eval_splits} for domain in DOMAINS}
-        relevant_docs = {
-            domain: {split: None for split in eval_splits} for domain in DOMAINS
-        }
 
-        for domain in domains:
-            domain_corpus = datasets.load_dataset(
-                path, "documents", split=domain, cache_dir=cache_dir, revision=revision
-            )
-            examples = datasets.load_dataset(
-                path, "examples", split=domain, cache_dir=cache_dir, revision=revision
-            )
-            if domain in DOMAINS_LONG:
-                domain_corpus_long = datasets.load_dataset(
-                    path,
-                    "long_documents",
-                    split=domain,
-                    cache_dir=cache_dir,
-                    revision=revision,
-                )
-            corpus[domain]["standard"] = {
-                e["id"]: {"text": e["content"]} for e in domain_corpus
-            }
-            if domain in DOMAINS_LONG:
-                corpus[domain]["long"] = {
-                    e["id"]: {"text": e["content"]} for e in domain_corpus_long
-                }
-            queries[domain]["standard"] = queries[domain]["long"] = {
-                e["id"]: e["query"] for e in examples
-            }
-            relevant_docs[domain]["standard"] = {}
-            relevant_docs[domain]["long"] = {}
-
-            for e in examples:
-                qid = e["id"]
-                gold_ids = e["gold_ids"]
-                gold_ids_long = e["gold_ids_long"]
-                relevant_docs[domain]["standard"][qid] = defaultdict(dict)
-                relevant_docs[domain]["long"][qid] = defaultdict(dict)
-                for gid in gold_ids:
-                    relevant_docs[domain]["standard"][qid].update({gid: 1})
-                for gid in gold_ids_long:
-                    relevant_docs[domain]["long"][qid].update({gid: 1})
-
-        corpus = datasets.DatasetDict(corpus)
-        queries = datasets.DatasetDict(queries)
-        relevant_docs = datasets.DatasetDict(relevant_docs)
-        return corpus, queries, relevant_docs
-
-    def load_data(self, **kwargs):
-        if self.data_loaded:
-            return
-
-        self.corpus, self.queries, self.relevant_docs = self.load_bright_data(
-            path=self.metadata_dict["dataset"]["path"],
-            domains=DOMAINS,
-            eval_splits=self.metadata_dict["eval_splits"],
-            cache_dir=kwargs.get("cache_dir", None),
-            revision=self.metadata_dict["dataset"]["revision"],
-        )
-        self.data_loaded = True
+long_metadata = BrightRetrieval.metadata.model_copy()
+long_metadata.eval_splits = ["long"]
+long_metadata.description = "Bright retrieval dataset with long documents."
+long_metadata.name = "BrightLongRetrieval"
+
+dom_langs_long = {split: ["eng-Latn"] for split in DOMAINS_LONG}
+long_metadata.eval_langs = dom_langs_long
+
+
+class BrightLongRetrieval(MultilingualTask, AbsTaskRetrieval):
+    metadata = long_metadata
+
+    load_bright_data = load_bright_data
+    load_data = load_data