diff --git a/mteb/tasks/classification/fra/__init__.py b/mteb/tasks/classification/fra/__init__.py
index 774668a735..d11a09e02b 100644
--- a/mteb/tasks/classification/fra/__init__.py
+++ b/mteb/tasks/classification/fra/__init__.py
@@ -1,10 +1,26 @@
+from .abg_cosqa_fr_classification import FrAbgCosQA
+from .air_dialogue_fr_classification import FrAirDialogueClassification
+from .clinc_fr_classification import FrClincIntentClassification
+from .daily_dialog_fr_classification import (
+    FrDailyDialogClassificationAct,
+    FrDailyDialogClassificationEmotion,
+)
 from .french_book_reviews import FrenchBookReviews, FrenchBookReviewsV2
+from .mantis_fr_classification import FrMantisClassification
 from .movie_review_sentiment_classification import (
     MovieReviewSentimentClassification,
     MovieReviewSentimentClassificationV2,
 )
+from .vira_intent_fr_classification import FrViraIntentClassification
 
 __all__ = [
+    "FrAbgCosQA",
+    "FrAirDialogueClassification",
+    "FrClincIntentClassification",
+    "FrDailyDialogClassificationAct",
+    "FrDailyDialogClassificationEmotion",
+    "FrMantisClassification",
+    "FrViraIntentClassification",
     "FrenchBookReviews",
     "FrenchBookReviewsV2",
     "MovieReviewSentimentClassification",
diff --git a/mteb/tasks/classification/fra/abg_cosqa_fr_classification.py b/mteb/tasks/classification/fra/abg_cosqa_fr_classification.py
new file mode 100644
index 0000000000..7258f46032
--- /dev/null
+++ b/mteb/tasks/classification/fra/abg_cosqa_fr_classification.py
@@ -0,0 +1,60 @@
+from __future__ import annotations
+
+from typing import Any
+
+from mteb.abstasks.classification import AbsTaskClassification
+from mteb.abstasks.task_metadata import TaskMetadata
+
+
+class FrAbgCosQA(AbsTaskClassification):
+    metadata = TaskMetadata(
+        name="FrAbgCosQA",
+        description="AbgCosQA",
+        dataset={
+            "path": "DeepPavlov/coqa_abg_fr",
+            "revision": "c4e23fb92e0f4a6e6d85ca0fcb35c1e96abfa024",
+        },
+        reference="https://huggingface.co/datasets/DeepPavlov/coqa_abg_fr",
+        type="Classification",
+        category="t2c",
+        modalities=["text"],
+        eval_splits=["test"],
+        eval_langs=["fra-Latn"],
+        main_score="f1",
+        date=("2021-01-01", "2021-12-31"),
+        domains=[],
+        task_subtypes=[],
+        license="not specified",
+        annotations_creators="human-annotated",
+        dialect=[],
+        sample_creation="machine-translated and verified",
+        bibtex_citation="",
+        adapted_from=["AbgCosQA"],
+    )
+
+    def dataset_transform(self, num_proc: int | None = None, **kwargs: Any) -> None:
+        def process_history(row: dict[str, Any]) -> dict[str, Any]:
+            full_text = row["story"] + " "
+            for turn in row["history_turns"]:
+                full_text += (
+                    "User: " + turn["question"] + " Assistant: " + turn["answer"] + " "
+                )
+            full_text += (
+                "User: "
+                + row["target_turn"]["question"]
+                + " Assistant: "
+                + row["target_turn"]["answer"]
+            )
+            row["text"] = full_text
+            row["label"] = row["ambiguity"] == "ambiguous"
+            return row
+
+        for subset in self.dataset:
+            self.dataset[subset] = (
+                self.dataset[subset]
+                .map(
+                    process_history,
+                    num_proc=num_proc,
+                )
+                .select_columns(["text", "label"])
+            )
diff --git a/mteb/tasks/classification/fra/air_dialogue_fr_classification.py b/mteb/tasks/classification/fra/air_dialogue_fr_classification.py
new file mode 100644
index 0000000000..d6e0c4afe3
--- /dev/null
+++ b/mteb/tasks/classification/fra/air_dialogue_fr_classification.py
@@ -0,0 +1,56 @@
+from __future__ import annotations
+
+from typing import Any
+
+from mteb.abstasks.classification import AbsTaskClassification
+from mteb.abstasks.task_metadata import TaskMetadata
+
+
+class FrAirDialogueClassification(AbsTaskClassification):
+    metadata = TaskMetadata(
+        name="FrAirDialogueClassification",
+        description="AirDialogue is a dataset of goal-oriented customer-agent conversations focused on booking flights under various travel restrictions.",
+        dataset={
+            "path": "DeepPavlov/air_dialog_fr",
+            "revision": "414ee3c46fd2abc4df245f43a7ac0a33439f97f7",
+        },
+        reference="https://huggingface.co/datasets/google/air_dialogue",
+        type="Classification",
+        category="t2c",
+        modalities=["text"],
+        eval_splits=["test"],
+        eval_langs=["fra-Latn"],
+        main_score="f1",
+        date=("2018-01-01", "2022-06-07"),
+        domains=[],
+        task_subtypes=["Intent classification"],
+        license="not specified",
+        annotations_creators="human-annotated",
+        dialect=[],
+        sample_creation="machine-translated and verified",
+        bibtex_citation="",
+        adapted_from=["AirDialogueClassification"],
+    )
+
+    def dataset_transform(self, num_proc: int | None = None, **kwargs: Any) -> None:
+        def process_history(row: dict[str, Any]) -> dict[str, Any]:
+            history = row["text"]
+            text = ""
+            if len(history) > 0:
+                for entry in history:
+                    if entry["role"] == "user":
+                        text += f"User: {entry['content']}\n"
+                    else:
+                        text += f"Assistant: {entry['content']}\n"
+            row["text"] = text
+            return row
+
+        for subset in self.dataset:
+            self.dataset[subset] = (
+                self.dataset[subset]
+                .map(
+                    process_history,
+                    num_proc=num_proc,
+                )
+                .select_columns(["text", "label"])
+            )
diff --git a/mteb/tasks/classification/fra/clinc_fr_classification.py b/mteb/tasks/classification/fra/clinc_fr_classification.py
new file mode 100644
index 0000000000..f111b36782
--- /dev/null
+++ b/mteb/tasks/classification/fra/clinc_fr_classification.py
@@ -0,0 +1,33 @@
+from __future__ import annotations
+
+from mteb.abstasks.classification import AbsTaskClassification
+from mteb.abstasks.task_metadata import TaskMetadata
+
+
+class FrClincIntentClassification(AbsTaskClassification):
+    input_column_name = "utterance"
+
+    metadata = TaskMetadata(
+        name="FrClincIntentClassification",
+        description="Task-oriented dialog systems need to know when a query falls outside their range of supported intents, but current text classification corpora only define label sets that cover every example. This is the single-config ('plus'-sized) packaging of CLINC150, DeepPavlov/clinc150_fr, rather than the small/plus/imbalanced multi-config packaging used by ClincIntentClassification.",
+        dataset={
+            "path": "DeepPavlov/clinc150_fr",
+            "revision": "b5c3e44dc6d605bafb9585ce125b187dcc14e6c9",
+        },
+        reference="https://huggingface.co/datasets/clinc/clinc_oos",
+        type="Classification",
+        category="t2c",
+        modalities=["text"],
+        eval_splits=["validation", "test"],
+        eval_langs=["fra-Latn"],
+        main_score="accuracy",
+        date=("2019-01-01", "2019-01-01"),
+        domains=["Financial", "Web", "Social"],
+        task_subtypes=["Intent classification"],
+        license="cc-by-3.0",
+        annotations_creators="human-annotated",
+        dialect=[],
+        sample_creation="machine-translated and verified",
+        bibtex_citation="",
+        adapted_from=["ClincIntentClassification"],
+    )
diff --git a/mteb/tasks/classification/fra/daily_dialog_fr_classification.py b/mteb/tasks/classification/fra/daily_dialog_fr_classification.py
new file mode 100644
index 0000000000..142bce485f
--- /dev/null
+++ b/mteb/tasks/classification/fra/daily_dialog_fr_classification.py
@@ -0,0 +1,77 @@
+from __future__ import annotations
+
+from typing import Any
+
+from mteb.abstasks.classification import AbsTaskClassification
+from mteb.abstasks.task_metadata import TaskMetadata
+
+
+def combine_dialogs(row: dict) -> dict:
+    row["dialog"] = "\n".join(row["dialog"])
+    return row
+
+
+class FrDailyDialogClassificationAct(AbsTaskClassification):
+    metadata = TaskMetadata(
+        name="FrDailyDialogClassificationAct",
+        description="",
+        dataset={
+            "path": "DeepPavlov/daily_dialog_fr",
+            "revision": "4e1c4b0878f1bf5d6f41386e08aa9ad1ae787c4d",
+        },
+        reference="https://huggingface.co/datasets/li2017dailydialog/daily_dialog",
+        type="Classification",
+        category="t2c",
+        modalities=["text"],
+        eval_splits=["test", "validation"],
+        eval_langs=["fra-Latn"],
+        main_score="accuracy",
+        date=("2017-07-11", "2017-07-11"),
+        domains=["Social"],
+        task_subtypes=["Intent classification"],
+        license="cc-by-nc-sa-4.0",
+        annotations_creators="human-annotated",
+        dialect=[],
+        sample_creation="machine-translated and verified",
+        bibtex_citation="",
+        adapted_from=["DailyDialogClassificationAct"],
+    )
+
+    def dataset_transform(self, num_proc: int | None = None, **kwargs: Any):
+        self.dataset = self.dataset.map(combine_dialogs)
+        self.dataset = self.dataset.rename_columns(
+            {"act_label": "label", "dialog": "text"}
+        )
+
+
+class FrDailyDialogClassificationEmotion(AbsTaskClassification):
+    metadata = TaskMetadata(
+        name="FrDailyDialogClassificationEmotion",
+        description="",
+        dataset={
+            "path": "DeepPavlov/daily_dialog_fr",
+            "revision": "4e1c4b0878f1bf5d6f41386e08aa9ad1ae787c4d",
+        },
+        reference="https://huggingface.co/datasets/li2017dailydialog/daily_dialog",
+        type="Classification",
+        category="t2c",
+        modalities=["text"],
+        eval_splits=["test", "validation"],
+        eval_langs=["fra-Latn"],
+        main_score="accuracy",
+        date=("2017-07-11", "2017-07-11"),
+        domains=["Social"],
+        task_subtypes=["Intent classification"],
+        license="cc-by-nc-sa-4.0",
+        annotations_creators="human-annotated",
+        dialect=[],
+        sample_creation="machine-translated and verified",
+        bibtex_citation="",
+        adapted_from=["DailyDialogClassificationEmotion"],
+    )
+
+    def dataset_transform(self, num_proc: int | None = None, **kwargs: Any):
+        self.dataset = self.dataset.map(combine_dialogs)
+        self.dataset = self.dataset.rename_columns(
+            {"emotion_label": "label", "dialog": "text"}
+        )
diff --git a/mteb/tasks/classification/fra/mantis_fr_classification.py b/mteb/tasks/classification/fra/mantis_fr_classification.py
new file mode 100644
index 0000000000..eef56d561a
--- /dev/null
+++ b/mteb/tasks/classification/fra/mantis_fr_classification.py
@@ -0,0 +1,55 @@
+from __future__ import annotations
+
+from typing import Any
+
+from mteb.abstasks.classification import AbsTaskClassification
+from mteb.abstasks.task_metadata import TaskMetadata
+
+
+class FrMantisClassification(AbsTaskClassification):
+    metadata = TaskMetadata(
+        name="FrMantisClassification",
+        description="Mantis",
+        dataset={
+            "path": "DeepPavlov/mantis_fr",
+            "revision": "ca6ab868022ff5cee661d6d5f8466dd1f27d83fc",
+        },
+        reference="https://huggingface.co/datasets/DeepPavlov/mantis_fr",
+        type="Classification",
+        category="t2c",
+        modalities=["text"],
+        eval_splits=["test"],
+        eval_langs=["fra-Latn"],
+        main_score="f1",
+        date=("2019-01-01", "2019-12-31"),
+        domains=[],
+        task_subtypes=[],
+        license="not specified",
+        annotations_creators="human-annotated",
+        dialect=[],
+        sample_creation="machine-translated and verified",
+        bibtex_citation="",
+        adapted_from=["MantisClassification"],
+    )
+
+    def dataset_transform(self, num_proc: int | None = None, **kwargs: Any) -> None:
+        def process_history(row: dict[str, Any]) -> dict[str, Any]:
+            history = row["dialog"]
+            text = ""
+            if len(history) > 0:
+                for entry in history:
+                    if entry["role"] == "user":
+                        text += f"User: {entry['message']}\n"
+                    else:
+                        text += f"Assistant: {entry['message']}\n"
+            row["text"] = text
+            return row
+
+        for subset in self.dataset:
+            self.dataset[subset] = (
+                self.dataset[subset]
+                .map(
+                    process_history,
+                )
+                .rename_column("category", "label")
+            )
diff --git a/mteb/tasks/classification/fra/vira_intent_fr_classification.py b/mteb/tasks/classification/fra/vira_intent_fr_classification.py
new file mode 100644
index 0000000000..3941a2af43
--- /dev/null
+++ b/mteb/tasks/classification/fra/vira_intent_fr_classification.py
@@ -0,0 +1,31 @@
+from __future__ import annotations
+
+from mteb.abstasks.classification import AbsTaskClassification
+from mteb.abstasks.task_metadata import TaskMetadata
+
+
+class FrViraIntentClassification(AbsTaskClassification):
+    metadata = TaskMetadata(
+        name="FrViraIntentClassification",
+        description="Chatbot-delivered COVID-19 vaccine communication message preferences of young adults and public health workers in urban American communities: qualitative study",
+        dataset={
+            "path": "DeepPavlov/vira-intents-live_fr",
+            "revision": "430391e1240047b603e2e33aab4a446c25769140",
+        },
+        reference="https://huggingface.co/datasets/ibm-research/vira-intents-live",
+        type="Classification",
+        category="t2c",
+        modalities=["text"],
+        eval_splits=["val", "test"],
+        eval_langs=["fra-Latn"],
+        main_score="accuracy",
+        date=("2020-01-01", "2022-07-06"),
+        domains=["Medical"],
+        task_subtypes=["Intent classification"],
+        license="not specified",
+        annotations_creators="human-annotated",
+        dialect=[],
+        sample_creation="machine-translated and verified",
+        bibtex_citation="",
+        adapted_from=["ViraIntentClassification"],
+    )
diff --git a/mteb/tasks/pair_classification/__init__.py b/mteb/tasks/pair_classification/__init__.py
index e219bce7af..c613cc809f 100644
--- a/mteb/tasks/pair_classification/__init__.py
+++ b/mteb/tasks/pair_classification/__init__.py
@@ -4,6 +4,7 @@
 from .deu import *
 from .eng import *
 from .fas import *
+from .fra import *
 from .hye import *
 from .ind import *
 from .ita import *
diff --git a/mteb/tasks/pair_classification/fra/__init__.py b/mteb/tasks/pair_classification/fra/__init__.py
new file mode 100644
index 0000000000..4b726788ab
--- /dev/null
+++ b/mteb/tasks/pair_classification/fra/__init__.py
@@ -0,0 +1,5 @@
+from .clarqa_fr import FrClarQA
+
+__all__ = [
+    "FrClarQA",
+]
diff --git a/mteb/tasks/pair_classification/fra/clarqa_fr.py b/mteb/tasks/pair_classification/fra/clarqa_fr.py
new file mode 100644
index 0000000000..11f28d4735
--- /dev/null
+++ b/mteb/tasks/pair_classification/fra/clarqa_fr.py
@@ -0,0 +1,36 @@
+from mteb.abstasks.pair_classification import AbsTaskPairClassification
+from mteb.abstasks.task_metadata import TaskMetadata
+
+
+class FrClarQA(AbsTaskPairClassification):
+    input1_column_name = "entity1"
+    input2_column_name = "entity2"
+    label_column_name = "label"
+
+    metadata = TaskMetadata(
+        name="FrClarQA",
+        description="ClarQA.",
+        reference="https://huggingface.co/datasets/DeepPavlov/clarqa_fr",
+        dataset={
+            "path": "DeepPavlov/clarqa_fr",
+            "revision": "fe5d77ba4762df41c8004619c9af7a7a5d2926ad",
+        },
+        type="PairClassification",
+        category="t2t",
+        modalities=["text"],
+        eval_splits=["test"],
+        eval_langs={
+            "single_turn": ["fra-Latn"],
+            "multi_turn": ["fra-Latn"],
+        },
+        main_score="max_ap",
+        date=("2019-01-01", "2019-12-31"),
+        domains=[],
+        task_subtypes=[],
+        license="not specified",
+        annotations_creators="derived",
+        dialect=[],
+        sample_creation="machine-translated and verified",
+        bibtex_citation="",
+        adapted_from=["ClarQA"],
+    )
diff --git a/mteb/tasks/reranking/fra/__init__.py b/mteb/tasks/reranking/fra/__init__.py
index 378dcc8730..ef98adbef3 100644
--- a/mteb/tasks/reranking/fra/__init__.py
+++ b/mteb/tasks/reranking/fra/__init__.py
@@ -1,4 +1,9 @@
 from .alloprof_reranking import AlloprofReranking
 from .syntec_reranking import SyntecReranking
+from .web_linx_fr_candidates_reranking import FrWebLINXCandidatesReranking
 
-__all__ = ["AlloprofReranking", "SyntecReranking"]
+__all__ = [
+    "AlloprofReranking",
+    "FrWebLINXCandidatesReranking",
+    "SyntecReranking",
+]
diff --git a/mteb/tasks/reranking/fra/web_linx_fr_candidates_reranking.py b/mteb/tasks/reranking/fra/web_linx_fr_candidates_reranking.py
new file mode 100644
index 0000000000..758fd4bf6c
--- /dev/null
+++ b/mteb/tasks/reranking/fra/web_linx_fr_candidates_reranking.py
@@ -0,0 +1,36 @@
+from mteb.abstasks import AbsTaskReranking
+from mteb.abstasks.task_metadata import TaskMetadata
+
+
+class FrWebLINXCandidatesReranking(AbsTaskReranking):
+    metadata = TaskMetadata(
+        name="FrWebLINXCandidatesReranking",
+        description="WebLINX is a large-scale benchmark of 100K interactions across 2300 expert demonstrations of conversational web navigation. The reranking task focuses on finding relevant elements at every given step in the trajectory.",
+        reference="https://huggingface.co/datasets/DeepPavlov/weblinx_fr",
+        dataset={
+            "path": "DeepPavlov/weblinx_fr",
+            "revision": "352cf5e4cd129145d0b6a3c0188947647ff6da2c",
+        },
+        type="Reranking",
+        category="t2t",
+        modalities=["text"],
+        eval_splits=[
+            "validation",
+            "test_iid",
+            "test_cat",
+            "test_geo",
+            "test_vis",
+            "test_web",
+        ],
+        eval_langs=["fra-Latn"],
+        main_score="mrr_at_10",
+        date=("2023-03-01", "2023-10-30"),
+        domains=["Academic", "Web", "Written"],
+        task_subtypes=["Code retrieval", "Conversational retrieval"],
+        license="cc-by-nc-sa-4.0",
+        annotations_creators="expert-annotated",
+        dialect=[],
+        sample_creation="machine-translated and verified",
+        bibtex_citation="",
+        adapted_from=["WebLINXCandidatesReranking"],
+    )
diff --git a/mteb/tasks/retrieval/fra/__init__.py b/mteb/tasks/retrieval/fra/__init__.py
index 007c13be88..b77821020d 100644
--- a/mteb/tasks/retrieval/fra/__init__.py
+++ b/mteb/tasks/retrieval/fra/__init__.py
@@ -1,15 +1,25 @@
 from .alloprof_retrieval import AlloprofRetrieval
 from .bsard_retrieval import BSARDRetrieval, BSARDRetrievalv2
+from .canard_fr_retrieval import FrCanard
+from .coral_fr_retrieval import FrCoral
 from .f_qu_ad_retrieval import FQuADRetrieval
+from .faith_dial_fr_retrieval import FrFaithDialRetrieval
 from .french1_retrieval import French1Retrieval
 from .french_legal1_retrieval import FrenchLegal1Retrieval
 from .syntec_retrieval import SyntecRetrieval
+from .topiocqa_fr_retrieval import FrTopiOCQARetrieval
+from .wizard_of_wikipedia_fr_retrieval import FrWizardOfWikipedia
 
 __all__ = [
     "AlloprofRetrieval",
     "BSARDRetrieval",
     "BSARDRetrievalv2",
     "FQuADRetrieval",
+    "FrCanard",
+    "FrCoral",
+    "FrFaithDialRetrieval",
+    "FrTopiOCQARetrieval",
+    "FrWizardOfWikipedia",
     "French1Retrieval",
     "FrenchLegal1Retrieval",
     "SyntecRetrieval",
diff --git a/mteb/tasks/retrieval/fra/canard_fr_retrieval.py b/mteb/tasks/retrieval/fra/canard_fr_retrieval.py
new file mode 100644
index 0000000000..41db57f045
--- /dev/null
+++ b/mteb/tasks/retrieval/fra/canard_fr_retrieval.py
@@ -0,0 +1,29 @@
+from mteb.abstasks.retrieval import AbsTaskRetrieval
+from mteb.abstasks.task_metadata import TaskMetadata
+
+
+class FrCanard(AbsTaskRetrieval):
+    metadata = TaskMetadata(
+        name="FrCanard",
+        description="canard",
+        reference="https://huggingface.co/datasets/DeepPavlov/canard_fr",
+        dataset={
+            "path": "DeepPavlov/canard_fr",
+            "revision": "e1ea7582a3e62c18262a28935a6a579f45a303e1",
+        },
+        type="Retrieval",
+        category="t2t",
+        modalities=["text"],
+        eval_splits=["test"],
+        eval_langs=["fra-Latn"],
+        main_score="ndcg_at_10",
+        date=("2019-01-01", "2019-12-31"),
+        domains=[],
+        task_subtypes=[],
+        license="not specified",
+        annotations_creators="derived",
+        dialect=[],
+        sample_creation="machine-translated and verified",
+        bibtex_citation="",
+        adapted_from=["Canard"],
+    )
diff --git a/mteb/tasks/retrieval/fra/coral_fr_retrieval.py b/mteb/tasks/retrieval/fra/coral_fr_retrieval.py
new file mode 100644
index 0000000000..768f78a459
--- /dev/null
+++ b/mteb/tasks/retrieval/fra/coral_fr_retrieval.py
@@ -0,0 +1,40 @@
+from typing import Any
+
+from datasets import Value
+
+from mteb.abstasks.retrieval import AbsTaskRetrieval
+from mteb.abstasks.task_metadata import TaskMetadata
+
+
+class FrCoral(AbsTaskRetrieval):
+    metadata = TaskMetadata(
+        name="FrCoral",
+        description="coral",
+        reference="https://huggingface.co/datasets/DeepPavlov/coral_fr",
+        dataset={
+            "path": "DeepPavlov/coral_fr",
+            "revision": "ba6a23690ebf49b5509601d9ff6a0955aa09fc90",
+        },
+        type="Retrieval",
+        category="t2t",
+        modalities=["text"],
+        eval_splits=["test", "train"],
+        eval_langs=["fra-Latn"],
+        main_score="ndcg_at_10",
+        date=("2024-01-01", "2024-12-31"),
+        domains=[],
+        task_subtypes=[],
+        license="mit",
+        annotations_creators="derived",
+        dialect=[],
+        sample_creation="machine-translated and verified",
+        bibtex_citation="",
+        adapted_from=["Coral"],
+    )
+
+    def dataset_transform(self, num_proc: int | None = None, **kwargs: Any) -> None:
+        for subset in self.dataset:
+            for split in self.dataset[subset]:
+                self.dataset[subset][split]["corpus"] = self.dataset[subset][split][
+                    "corpus"
+                ].cast_column("id", Value("string"))
diff --git a/mteb/tasks/retrieval/fra/faith_dial_fr_retrieval.py b/mteb/tasks/retrieval/fra/faith_dial_fr_retrieval.py
new file mode 100644
index 0000000000..cb910b53de
--- /dev/null
+++ b/mteb/tasks/retrieval/fra/faith_dial_fr_retrieval.py
@@ -0,0 +1,72 @@
+from datasets import load_dataset
+
+from mteb.abstasks import AbsTaskRetrieval
+from mteb.abstasks.task_metadata import TaskMetadata
+
+
+class FrFaithDialRetrieval(AbsTaskRetrieval):
+    metadata = TaskMetadata(
+        name="FrFaithDialRetrieval",
+        dataset={
+            "path": "DeepPavlov/faithdial_fr",
+            "revision": "172f3344378327b5798c7902526a2412bad5e8a4",
+        },
+        reference="https://mcgill-nlp.github.io/FaithDial",
+        description=(
+            "FaithDial is a faithful knowledge-grounded dialogue benchmark."
+            + "It was curated by asking annotators to amend hallucinated utterances in Wizard of Wikipedia (WoW). "
+            + "It consists of conversation histories along with manually labelled relevant passage. "
+            + "For the purpose of retrieval, we only consider the instances marked as 'Edification' in the VRM field, "
+            + "as the gold passage associated with these instances is non-ambiguous."
+        ),
+        type="Retrieval",
+        category="t2t",
+        modalities=["text"],
+        eval_splits=["test"],
+        eval_langs=["fra-Latn"],
+        main_score="ndcg_at_10",
+        date=("2022-01-01", "2022-03-31"),
+        domains=["Encyclopaedic", "Written"],
+        task_subtypes=["Conversational retrieval"],
+        license="cc-by-nc-sa-4.0",
+        annotations_creators="human-annotated",
+        dialect=[],
+        sample_creation="machine-translated and verified",
+        bibtex_citation="",
+        adapted_from=["FaithDial"],
+    )
+
+    # Mirrors RuFaithDialRetrieval's construction: DeepPavlov/faithdial_fr is the raw
+    # dialogue format (not a pre-built corpus/queries/qrels retrieval dataset), so we
+    # build the retrieval triples ourselves here, using the "_fr" (translated) columns.
+    def load_data(self, **kwargs):
+        if self.data_loaded:
+            return
+        self.corpus, self.queries, self.relevant_docs = {}, {}, {}
+        for split in kwargs.get("eval_splits", self.metadata.eval_splits):
+            corpus, queries, qrels = self._load_data_for_split(split)
+            self.corpus[split], self.queries[split], self.relevant_docs[split] = (
+                corpus,
+                queries,
+                qrels,
+            )
+
+        self.data_loaded = True
+
+    def _load_data_for_split(self, split):
+        ds = load_dataset(split=split, **self.metadata.dataset)
+        queries, corpus, qrels = {}, {}, {}
+        for i, sample in enumerate(ds):
+            # document is added to corpus for all samples
+            doc_id = "doc:" + str(i)
+            corpus[doc_id] = {
+                "title": "",  # title is not available
+                "text": sample["knowledge_fr"],
+            }
+            if "Edification" in sample["VRM"]:
+                query_id = "query:" + str(i)
+                query = sample["history_fr"]
+                queries[query_id] = query
+                qrels[query_id] = {doc_id: 1}
+
+        return corpus, queries, qrels
diff --git a/mteb/tasks/retrieval/fra/topiocqa_fr_retrieval.py b/mteb/tasks/retrieval/fra/topiocqa_fr_retrieval.py
new file mode 100644
index 0000000000..5aabdc90e8
--- /dev/null
+++ b/mteb/tasks/retrieval/fra/topiocqa_fr_retrieval.py
@@ -0,0 +1,86 @@
+from datasets import load_dataset
+
+from mteb.abstasks import AbsTaskRetrieval
+from mteb.abstasks.task_metadata import TaskMetadata
+
+
+class FrTopiOCQARetrieval(AbsTaskRetrieval):
+    metadata = TaskMetadata(
+        name="FrTopiOCQARetrieval",
+        dataset={
+            "path": "DeepPavlov/topiocqa_fr",
+            "revision": "3cb6238bcd5876f79a25ef482ac61c63c3fbaf09",
+        },
+        reference="https://mcgill-nlp.github.io/topiocqa",
+        description=(
+            "TopiOCQA (Human-in-the-loop Attributable Generative Retrieval for Information-seeking Dataset) "
+            + "is information-seeking conversational dataset with challenging topic switching phenomena. "
+            + "It consists of conversation histories along with manually labelled relevant/gold passage. "
+            + "Unlike the pre-built mteb/TopiOCQA corpus/queries/qrels split, DeepPavlov/topiocqa_fr ships the "
+            + "raw per-turn QA rows (Conversation_no, Turn_no, Question, Answer, Context, ...), so this task "
+            + "builds the retrieval triples itself: for each turn, the query is the conversation history up to "
+            + "and including that turn's Question, and the corpus document is that turn's gold Context "
+            + "passage(s), one document per turn, following the same construction pattern used for "
+            + "FrFaithDialRetrieval."
+        ),
+        type="Retrieval",
+        category="t2t",
+        modalities=["text"],
+        eval_splits=["validation"],
+        eval_langs=["fra-Latn"],
+        main_score="ndcg_at_10",
+        date=("2021-03-01", "2021-07-31"),
+        domains=["Encyclopaedic", "Written"],
+        task_subtypes=["Conversational retrieval"],
+        license="cc-by-nc-sa-4.0",
+        annotations_creators="human-annotated",
+        dialect=[],
+        sample_creation="machine-translated and verified",
+        bibtex_citation="",
+        adapted_from=["TopiOCQA"],
+    )
+
+    def load_data(self, **kwargs):
+        if self.data_loaded:
+            return
+        self.corpus, self.queries, self.relevant_docs = {}, {}, {}
+        for split in kwargs.get("eval_splits", self.metadata.eval_splits):
+            corpus, queries, qrels = self._load_data_for_split(split)
+            self.corpus[split], self.queries[split], self.relevant_docs[split] = (
+                corpus,
+                queries,
+                qrels,
+            )
+
+        self.data_loaded = True
+
+    def _load_data_for_split(self, split):
+        ds = load_dataset(split=split, **self.metadata.dataset)
+        ds = ds.sort(["Conversation_no", "Turn_no"])
+
+        queries, corpus, qrels = {}, {}, {}
+        history_by_conversation: dict[int, str] = {}
+        for i, sample in enumerate(ds):
+            conv_no = sample["Conversation_no"]
+            history = history_by_conversation.get(conv_no, "")
+            query_text = (
+                f"{history}\nUser: {sample['Question']}"
+                if history
+                else f"User: {sample['Question']}"
+            )
+
+            doc_id = "doc:" + str(i)
+            corpus[doc_id] = {
+                "title": sample["Topic"] or "",
+                "text": " ".join(sample["Context"]),
+            }
+
+            query_id = "query:" + str(i)
+            queries[query_id] = query_text
+            qrels[query_id] = {doc_id: 1}
+
+            history_by_conversation[conv_no] = (
+                f"{query_text}\nAssistant: {sample['Answer']}"
+            )
+
+        return corpus, queries, qrels
diff --git a/mteb/tasks/retrieval/fra/wizard_of_wikipedia_fr_retrieval.py b/mteb/tasks/retrieval/fra/wizard_of_wikipedia_fr_retrieval.py
new file mode 100644
index 0000000000..ee3bee0626
--- /dev/null
+++ b/mteb/tasks/retrieval/fra/wizard_of_wikipedia_fr_retrieval.py
@@ -0,0 +1,29 @@
+from mteb.abstasks.retrieval import AbsTaskRetrieval
+from mteb.abstasks.task_metadata import TaskMetadata
+
+
+class FrWizardOfWikipedia(AbsTaskRetrieval):
+    metadata = TaskMetadata(
+        name="FrWizardOfWikipedia",
+        description="WizardOfWikipedia",
+        reference="https://huggingface.co/datasets/DeepPavlov/wizard_of_wikipedia_fr",
+        dataset={
+            "path": "DeepPavlov/wizard_of_wikipedia_fr",
+            "revision": "0c37af2e1d0e776a8d63d86b42fc4f90b19a2811",
+        },
+        type="Retrieval",
+        category="t2t",
+        modalities=["text"],
+        eval_splits=["test"],
+        eval_langs=["fra-Latn"],
+        main_score="ndcg_at_10",
+        date=("2019-01-01", "2019-12-31"),
+        domains=[],
+        task_subtypes=[],
+        license="not specified",
+        annotations_creators="derived",
+        dialect=[],
+        sample_creation="machine-translated and verified",
+        bibtex_citation="",
+        adapted_from=["WiardOfWikipedia"],
+    )