diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md
index ab121f2..bbaae44 100644
--- a/.github/pull_request_template.md
+++ b/.github/pull_request_template.md
@@ -20,6 +20,7 @@ base branchを`dev`にするよう、お願いいたします。
 
 ## 動作確認
 - [ ] テストが通ることを確認した
+- [ ] マージ先がdevブランチであることを確認した
 - [ ] ...
 
 <!-- 
diff --git a/poetry.lock b/poetry.lock
index 2ba271f..8937812 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
 
 [[package]]
 name = "aiohttp"
@@ -2025,7 +2025,6 @@ files = [
     {file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"},
     {file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"},
     {file = "PyYAML-6.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"},
-    {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a08c6f0fe150303c1c6b71ebcd7213c2858041a7e01975da3a99aed1e7a378ef"},
     {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"},
     {file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"},
     {file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"},
@@ -3384,4 +3383,4 @@ multidict = ">=4.0"
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<4.0"
-content-hash = "c97d2d6a18d934c8566bd48e5a5f1f8d6c0da28d7e529b7f071bf850efea5e1f"
+content-hash = "e40f842f52270eeceaf8810710c4709262b2ca68b3c38ef6117f0e50d81de8ff"
diff --git a/pyproject.toml b/pyproject.toml
index 468adc8..2cc9b97 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -12,7 +12,7 @@ description = "The evaluation scripts for JMTEB (Japanese Massive Text Embedding
 name = "JMTEB"
 packages = [{from = "src", include = "jmteb"}]
 readme = "README.md"
-version = "1.1.1"
+version = "1.2.0"
 
 [tool.poetry.dependencies]
 python = ">=3.10,<4.0"
@@ -30,6 +30,7 @@ smart-open = "^7.0.1"
 openai = "^1.16.2"
 pytest-mock = "^3.14.0"
 tiktoken = "^0.6.0"
+numpy = "^1.26"
 
 [tool.poetry.group.dev.dependencies]
 black = "^23.11.0"
diff --git a/src/jmteb/embedders/base.py b/src/jmteb/embedders/base.py
index 145f543..c74fddf 100644
--- a/src/jmteb/embedders/base.py
+++ b/src/jmteb/embedders/base.py
@@ -14,11 +14,12 @@ class TextEmbedder(ABC):
     The base class of text embedder.
     """
 
-    def encode(self, text: str | list[str]) -> np.ndarray:
+    def encode(self, text: str | list[str], prefix: str | None = None) -> np.ndarray:
         """Convert a text string or a list of texts to embedding.
 
         Args:
             text (str | list[str]): text string, or a list of texts.
+            prefix (str, optional): the prefix to use for encoding. Default to None.
         """
         raise NotImplementedError
 
@@ -31,7 +32,12 @@ def get_output_dim(self) -> int:
         raise NotImplementedError
 
     def _batch_encode_and_save_on_disk(
-        self, text_list: list[str], save_path: str | PathLike[str], batch_size: int = 64, dtype: str = "float32"
+        self,
+        text_list: list[str],
+        save_path: str | PathLike[str],
+        prefix: str | None = None,
+        batch_size: int = 64,
+        dtype: str = "float32",
     ) -> np.memmap:
         """
         Encode a list of texts and save the embeddings on disk using memmap.
@@ -39,6 +45,7 @@ def _batch_encode_and_save_on_disk(
         Args:
             text_list (list[str]): list of texts
             save_path (str): path to save the embeddings
+            prefix (str, optional): the prefix to use for encoding. Default to None.
             dtype (str, optional): data type. Defaults to "float32".
             batch_size (int): batch size. Defaults to 64.
         """
@@ -50,7 +57,7 @@ def _batch_encode_and_save_on_disk(
         with tqdm.tqdm(total=num_samples, desc="Encoding") as pbar:
             for i in range(0, num_samples, batch_size):
                 batch = text_list[i : i + batch_size]
-                batch_embeddings = self.encode(batch)
+                batch_embeddings = self.encode(batch, prefix=prefix)
                 batch_embeddings = np.asarray(batch_embeddings, dtype=dtype)
                 embeddings[i : i + batch_size] = batch_embeddings
                 pbar.update(len(batch))
@@ -61,6 +68,7 @@ def _batch_encode_and_save_on_disk(
     def batch_encode_with_cache(
         self,
         text_list: list[str],
+        prefix: str | None = None,
         cache_path: str | PathLike[str] | None = None,
         overwrite_cache: bool = False,
         batch_size: int = 64,
@@ -71,6 +79,7 @@ def batch_encode_with_cache(
 
         Args:
             text_list (list[str]): list of texts
+            prefix (str, optional): the prefix to use for encoding. Default to None.
             cache_path (str, optional): path to save the embeddings. Defaults to None.
             overwrite_cache (bool, optional): whether to overwrite the cache. Defaults to False.
             batch_size (int): batch size. Defaults to 64.
@@ -79,12 +88,14 @@ def batch_encode_with_cache(
 
         if cache_path is None:
             logger.info("Encoding embeddings")
-            return self.encode(text_list).astype(dtype)
+            return self.encode(text_list, prefix=prefix).astype(dtype)
 
         if Path(cache_path).exists() and not overwrite_cache:
             logger.info(f"Loading embeddings from {cache_path}")
             return np.memmap(cache_path, dtype=dtype, mode="r", shape=(len(text_list), self.get_output_dim()))
 
         logger.info(f"Encoding and saving embeddings to {cache_path}")
-        embeddings = self._batch_encode_and_save_on_disk(text_list, cache_path, batch_size=batch_size, dtype=dtype)
+        embeddings = self._batch_encode_and_save_on_disk(
+            text_list, cache_path, prefix=prefix, batch_size=batch_size, dtype=dtype
+        )
         return embeddings
diff --git a/src/jmteb/embedders/openai_embedder.py b/src/jmteb/embedders/openai_embedder.py
index d216752..0108c83 100644
--- a/src/jmteb/embedders/openai_embedder.py
+++ b/src/jmteb/embedders/openai_embedder.py
@@ -60,13 +60,13 @@ def __init__(self, model: str = "text-embedding-3-small", dim: int | None = None
             else:
                 self.dim = dim
 
-    def encode(self, text: str | list[str]) -> np.ndarray:
+    def encode(self, text: str | list[str], prefix: str | None = None) -> np.ndarray:
         kwargs = {"dimensions": self.dim} if self.model != "text-embedding-ada-002" else {}
         # specifying `dimensions` is not allowed for "text-embedding-ada-002"
         if isinstance(text, str):
-            token_ids: list[int] = self.encode_and_truncate_text(text)
+            token_ids: list[int] = self.encode_and_truncate_text(text, prefix)
         else:
-            token_ids: list[list[int]] = [self.encode_and_truncate_text(t) for t in text]
+            token_ids: list[list[int]] = [self.encode_and_truncate_text(t, prefix) for t in text]
         result = np.asarray(
             [
                 data.embedding
@@ -84,10 +84,11 @@ def encode(self, text: str | list[str]) -> np.ndarray:
     def get_output_dim(self) -> int:
         return self.dim
 
-    def encode_and_truncate_text(self, text: str) -> list[int]:
+    def encode_and_truncate_text(self, text: str, prefix: str | None = None) -> list[int]:
         # Refer to https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken
         # return a list of token IDs
         if not text:
             text = " "
             logger.warning("Found empty string!")
+        # Ignore prefix in OpenAIEmbedder
         return self.encoding.encode(text)[: self.max_token_length]
diff --git a/src/jmteb/embedders/sbert_embedder.py b/src/jmteb/embedders/sbert_embedder.py
index 48ab984..bab3da7 100644
--- a/src/jmteb/embedders/sbert_embedder.py
+++ b/src/jmteb/embedders/sbert_embedder.py
@@ -15,20 +15,42 @@ def __init__(
         batch_size: int = 32,
         device: str | None = None,
         normalize_embeddings: bool = False,
+        max_seq_length: int | None = None,
+        add_eos: bool = False,
+        tokenizer_kwargs: dict | None = None,
     ) -> None:
-        self.model = SentenceTransformer(model_name_or_path)
+        self.model = SentenceTransformer(model_name_or_path, trust_remote_code=True, tokenizer_kwargs=tokenizer_kwargs)
+        if max_seq_length:
+            self.model.max_seq_length = max_seq_length
+
         self.batch_size = batch_size
         self.device = device
         self.normalize_embeddings = normalize_embeddings
+        self.max_seq_length = getattr(self.model, "max_seq_length", None)
+        self.add_eos = add_eos
 
-    def encode(self, text: str | list[str]) -> np.ndarray:
+    def encode(self, text: str | list[str], prefix: str | None = None) -> np.ndarray:
+        if self.add_eos:
+            text = self._add_eos_func(text)
         return self.model.encode(
             text,
+            prompt=prefix,
             convert_to_numpy=True,
             batch_size=self.batch_size,
             device=self.device,
             normalize_embeddings=self.normalize_embeddings,
         )
 
+    def _add_eos_func(self, text: str | list[str]) -> str | list[str]:
+        try:
+            eos_token = getattr(self.model.tokenizer, "eos_token")
+        except AttributeError:
+            return text
+
+        if isinstance(text, str):
+            return text + eos_token
+        elif isinstance(text, list):
+            return [t + eos_token for t in text]
+
     def get_output_dim(self) -> int:
         return self.model.get_sentence_embedding_dimension()
diff --git a/src/jmteb/evaluators/classification/evaluator.py b/src/jmteb/evaluators/classification/evaluator.py
index 5136e40..dbe2d8e 100644
--- a/src/jmteb/evaluators/classification/evaluator.py
+++ b/src/jmteb/evaluators/classification/evaluator.py
@@ -27,6 +27,7 @@ class ClassificationEvaluator(EmbeddingEvaluator):
             and delimited by comma, e.g., `macro, micro`.
             The first one is specified as the main index.
         classifiers (dict[str, Classifier]): classifiers to be evaluated.
+        prefix (str | None): prefix for sentences. Defaults to None.
     """
 
     def __init__(
@@ -36,6 +37,7 @@ def __init__(
         test_dataset: ClassificationDataset,
         average: str = "macro",
         classifiers: dict[str, Classifier] | None = None,
+        prefix: str | None = None,
     ) -> None:
         self.train_dataset = train_dataset
         self.val_dataset = val_dataset
@@ -49,6 +51,7 @@ def __init__(
             for average_name in average
             if average_name.strip().lower() in ("micro", "macro", "samples", "weighted", "binary")
         ] or ["macro"]
+        self.prefix = prefix
         self.main_metric = f"{self.average[0]}_f1"
 
     def __call__(
@@ -60,6 +63,7 @@ def __call__(
         logger.info("Encoding training and validation sentences...")
         X_train = model.batch_encode_with_cache(
             [item.text for item in self.train_dataset],
+            prefix=self.prefix,
             cache_path=Path(cache_dir) / "train_embeddings.bin" if cache_dir is not None else None,
             overwrite_cache=overwrite_cache,
         )
@@ -67,6 +71,7 @@ def __call__(
 
         X_val = model.batch_encode_with_cache(
             [item.text for item in self.val_dataset],
+            prefix=self.prefix,
             cache_path=Path(cache_dir) / "val_embeddings.bin" if cache_dir is not None else None,
             overwrite_cache=overwrite_cache,
         )
@@ -79,6 +84,7 @@ def __call__(
         else:
             X_test = model.batch_encode_with_cache(
                 [item.text for item in self.test_dataset],
+                prefix=self.prefix,
                 cache_path=Path(cache_dir) / "test_embeddings.bin" if cache_dir is not None else None,
                 overwrite_cache=overwrite_cache,
             )
diff --git a/src/jmteb/evaluators/clustering/evaluator.py b/src/jmteb/evaluators/clustering/evaluator.py
index e43fd45..d8ef443 100644
--- a/src/jmteb/evaluators/clustering/evaluator.py
+++ b/src/jmteb/evaluators/clustering/evaluator.py
@@ -30,9 +30,13 @@ def __init__(
         self,
         val_dataset: ClusteringDataset,
         test_dataset: ClusteringDataset,
+        prefix: str | None = None,
+        random_seed: int | None = None,
     ) -> None:
         self.val_dataset = val_dataset
         self.test_dataset = test_dataset
+        self.prefix = prefix
+        self.random_seed = random_seed
         self.main_metric = "v_measure_score"
 
     def __call__(
@@ -44,6 +48,7 @@ def __call__(
         logger.info("Converting validation data to embeddings...")
         val_embeddings = model.batch_encode_with_cache(
             [item.text for item in self.val_dataset],
+            prefix=self.prefix,
             cache_path=Path(cache_dir) / "val_embeddings.bin" if cache_dir is not None else None,
             overwrite_cache=overwrite_cache,
         )
@@ -56,6 +61,7 @@ def __call__(
         else:
             test_embeddings = model.batch_encode_with_cache(
                 [item.text for item in self.test_dataset],
+                prefix=self.prefix,
                 cache_path=Path(cache_dir) / "test_embeddings.bin" if cache_dir is not None else None,
                 overwrite_cache=overwrite_cache,
             )
@@ -63,9 +69,11 @@ def __call__(
 
         n_clusters = len(set(test_labels))
         model_constructors: dict[str, Callable[[], ClusterMixin]] = {
-            "MiniBatchKMeans": lambda: MiniBatchKMeans(n_clusters=n_clusters, n_init="auto"),
+            "MiniBatchKMeans": lambda: MiniBatchKMeans(
+                n_clusters=n_clusters, n_init="auto", random_state=self.random_seed
+            ),
             "AgglomerativeClustering": lambda: AgglomerativeClustering(n_clusters=n_clusters),
-            "BisectingKMeans": lambda: BisectingKMeans(n_clusters=n_clusters),
+            "BisectingKMeans": lambda: BisectingKMeans(n_clusters=n_clusters, random_state=self.random_seed),
             "Birch": lambda: Birch(n_clusters=n_clusters),
         }
 
diff --git a/src/jmteb/evaluators/pair_classification/evaluator.py b/src/jmteb/evaluators/pair_classification/evaluator.py
index d8ae1fa..6ec30d0 100644
--- a/src/jmteb/evaluators/pair_classification/evaluator.py
+++ b/src/jmteb/evaluators/pair_classification/evaluator.py
@@ -20,15 +20,21 @@ class PairClassificationEvaluator(EmbeddingEvaluator):
     Args:
         val_dataset (PairClassificationDataset): validation dataset
         test_dataset (PairClassificationDataset): test dataset
+        sentence1_prefix (str | None): prefix for sentence1. Defaults to None.
+        sentence2_prefix (str | None): prefix for sentence2. Defaults to None.
     """
 
     def __init__(
         self,
         val_dataset: PairClassificationDataset,
         test_dataset: PairClassificationDataset,
+        sentence1_prefix: str | None = None,
+        sentence2_prefix: str | None = None,
     ) -> None:
         self.test_dataset = test_dataset
         self.val_dataset = val_dataset
+        self.sentence1_prefix = sentence1_prefix
+        self.sentence2_prefix = sentence2_prefix
         self.metrics = [ThresholdAccuracyMetric(), ThresholdF1Metric()]
         self.main_metric = "binary_f1"
 
@@ -101,8 +107,8 @@ def __call__(
             },
         )
 
-    @staticmethod
     def _convert_to_embeddings(
+        self,
         model: TextEmbedder,
         dataset: PairClassificationDataset,
         split: str = "test",
@@ -111,11 +117,13 @@ def _convert_to_embeddings(
     ) -> tuple[np.ndarray, np.ndarray, list[float]]:
         embeddings1 = model.batch_encode_with_cache(
             [item.sentence1 for item in dataset],
+            prefix=self.sentence1_prefix,
             cache_path=Path(cache_dir) / f"{split}_embeddings1.bin" if cache_dir is not None else None,
             overwrite_cache=overwrite_cache,
         )
         embeddings2 = model.batch_encode_with_cache(
             [item.sentence2 for item in dataset],
+            prefix=self.sentence2_prefix,
             cache_path=Path(cache_dir) / f"{split}_embeddings2.bin" if cache_dir is not None else None,
             overwrite_cache=overwrite_cache,
         )
diff --git a/src/jmteb/evaluators/reranking/evaluator.py b/src/jmteb/evaluators/reranking/evaluator.py
index 4b71dfe..1029aab 100644
--- a/src/jmteb/evaluators/reranking/evaluator.py
+++ b/src/jmteb/evaluators/reranking/evaluator.py
@@ -28,6 +28,8 @@ class RerankingEvaluator(EmbeddingEvaluator):
         test_query_dataset (RerankingQueryDataset): test query dataset used for computing the scores
         doc_dataset (RerankingDocDataset): document dataset
         ndcg_at_k (list[int] | None): top k documents to consider in NDCG (Normalized Documented Cumulative Gain).
+        query_prefix (str | None): prefix for queries. Defaults to None.
+        doc_prefix (str | None): prefix for documents. Defaults to None.
     """
 
     def __init__(
@@ -36,12 +38,16 @@ def __init__(
         test_query_dataset: RerankingQueryDataset,
         doc_dataset: RerankingDocDataset,
         ndcg_at_k: list[int] | None = None,
+        query_prefix: str | None = None,
+        doc_prefix: str | None = None,
     ) -> None:
         self.test_query_dataset = test_query_dataset
         self.val_query_dataset = val_query_dataset
         self.doc_dataset = doc_dataset
         self.ndcg_at_k = ndcg_at_k or [10, 20, 40]
         self.main_metric = f"ndcg@{self.ndcg_at_k[0]}"
+        self.query_prefix = query_prefix
+        self.doc_prefix = doc_prefix
 
     def __call__(
         self,
@@ -54,6 +60,7 @@ def __call__(
 
         val_query_embeddings = model.batch_encode_with_cache(
             text_list=[item.query for item in self.val_query_dataset],
+            prefix=self.query_prefix,
             cache_path=Path(cache_dir) / "val_query.bin" if cache_dir is not None else None,
             overwrite_cache=overwrite_cache,
         )
@@ -62,11 +69,13 @@ def __call__(
         else:
             test_query_embeddings = model.batch_encode_with_cache(
                 text_list=[item.query for item in self.test_query_dataset],
+                prefix=self.query_prefix,
                 cache_path=Path(cache_dir) / "test_query.bin" if cache_dir is not None else None,
                 overwrite_cache=overwrite_cache,
             )
         doc_embeddings = model.batch_encode_with_cache(
             text_list=[item.text for item in self.doc_dataset],
+            prefix=self.doc_prefix,
             cache_path=Path(cache_dir) / "corpus.bin" if cache_dir is not None else None,
             overwrite_cache=overwrite_cache,
         )
diff --git a/src/jmteb/evaluators/retrieval/evaluator.py b/src/jmteb/evaluators/retrieval/evaluator.py
index bc97e33..64d48d9 100644
--- a/src/jmteb/evaluators/retrieval/evaluator.py
+++ b/src/jmteb/evaluators/retrieval/evaluator.py
@@ -31,6 +31,8 @@ class RetrievalEvaluator(EmbeddingEvaluator):
         doc_chunk_size (int): The maximum size of corpus chunk. Smaller chunk requires less memory but lowers speed.
         ndcg_at_k (list[int] | None): top k documents to consider in NDCG (Normalized Documented Cumulative Gain).
         accuracy_at_k (list[int] | None): accuracy in top k hits.
+        query_prefix (str | None): prefix for queries. Defaults to None.
+        doc_prefix (str | None): prefix for documents. Defaults to None.
     """
 
     def __init__(
@@ -41,6 +43,8 @@ def __init__(
         doc_chunk_size: int = 1000000,
         accuracy_at_k: list[int] | None = None,
         ndcg_at_k: list[int] | None = None,
+        query_prefix: str | None = None,
+        doc_prefix: str | None = None,
     ) -> None:
         self.val_query_dataset = val_query_dataset
         self.test_query_dataset = test_query_dataset
@@ -53,6 +57,9 @@ def __init__(
         self.max_top_k = max(sum([self.accuracy_at_k, self.ndcg_at_k], []))
         self.main_metric = f"ndcg@{self.ndcg_at_k[0]}"
 
+        self.query_prefix = query_prefix
+        self.doc_prefix = doc_prefix
+
     def __call__(
         self,
         model: TextEmbedder,
@@ -64,6 +71,7 @@ def __call__(
 
         val_query_embeddings = model.batch_encode_with_cache(
             text_list=[item.query for item in self.val_query_dataset],
+            prefix=self.query_prefix,
             cache_path=Path(cache_dir) / "val_query.bin" if cache_dir is not None else None,
             overwrite_cache=overwrite_cache,
         )
@@ -72,12 +80,14 @@ def __call__(
         else:
             test_query_embeddings = model.batch_encode_with_cache(
                 text_list=[item.query for item in self.test_query_dataset],
+                prefix=self.query_prefix,
                 cache_path=Path(cache_dir) / "test_query.bin" if cache_dir is not None else None,
                 overwrite_cache=overwrite_cache,
             )
 
         doc_embeddings = model.batch_encode_with_cache(
             text_list=[item.text for item in self.doc_dataset],
+            prefix=self.doc_prefix,
             cache_path=Path(cache_dir) / "corpus.bin" if cache_dir is not None else None,
             overwrite_cache=overwrite_cache,
         )
diff --git a/src/jmteb/evaluators/sts/evaluator.py b/src/jmteb/evaluators/sts/evaluator.py
index 33f2ffb..8a20b3d 100644
--- a/src/jmteb/evaluators/sts/evaluator.py
+++ b/src/jmteb/evaluators/sts/evaluator.py
@@ -23,11 +23,21 @@ class STSEvaluator(EmbeddingEvaluator):
     Args:
         val_dataset (STSDataset): dev dataset for hyperparameter tuning
         test_dataset (STSDataset): test dataset
+        sentence1_prefix (str | None): prefix for sentence1. Defaults to None.
+        sentence2_prefix (str | None): prefix for sentence2. Defaults to None.
     """
 
-    def __init__(self, val_dataset: STSDataset, test_dataset: STSDataset) -> None:
+    def __init__(
+        self,
+        val_dataset: STSDataset,
+        test_dataset: STSDataset,
+        sentence1_prefix: str | None = None,
+        sentence2_prefix: str | None = None,
+    ) -> None:
         self.val_dataset = val_dataset
         self.test_dataset = test_dataset
+        self.sentence1_prefix = sentence1_prefix
+        self.sentence2_prefix = sentence2_prefix
         self.main_metric = "spearman"
 
     def __call__(
@@ -98,8 +108,8 @@ def _compute_similarity(
             "spearman": spearmanr(golden_scores, test_sim_score)[0],
         }
 
-    @staticmethod
     def _convert_to_embeddings(
+        self,
         model: TextEmbedder,
         dataset: STSDataset,
         split: str = "test",
@@ -108,11 +118,13 @@ def _convert_to_embeddings(
     ) -> tuple[Tensor, Tensor, list[float]]:
         embeddings1 = model.batch_encode_with_cache(
             [item.sentence1 for item in dataset],
+            prefix=self.sentence1_prefix,
             cache_path=Path(cache_dir) / f"{split}_embeddings1.bin" if cache_dir is not None else None,
             overwrite_cache=overwrite_cache,
         )
         embeddings2 = model.batch_encode_with_cache(
             [item.sentence2 for item in dataset],
+            prefix=self.sentence2_prefix,
             cache_path=Path(cache_dir) / f"{split}_embeddings2.bin" if cache_dir is not None else None,
             overwrite_cache=overwrite_cache,
         )
diff --git a/tests/conftest.py b/tests/conftest.py
index 9a104d9..b6d95ff 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -25,7 +25,7 @@ def pytest_collection_modifyitems(config: pytest.Config, items: pytest.Parser):
 
 
 class DummyTextEmbedder(TextEmbedder):
-    def encode(self, text: str | list[str]) -> np.ndarray:
+    def encode(self, text: str | list[str], prefix: str | None = None) -> np.ndarray:
         if isinstance(text, str):
             batch_size = 1
         else:
diff --git a/tests/embedders/test_sbert.py b/tests/embedders/test_sbert.py
index 48d7e4f..77f0585 100644
--- a/tests/embedders/test_sbert.py
+++ b/tests/embedders/test_sbert.py
@@ -17,3 +17,8 @@ def test_encode(self):
 
     def test_get_output_dim(self):
         assert self.model.get_output_dim() == OUTPUT_DIM
+
+    def test_tokenizer_kwargs(self):
+        assert self.model.model.tokenizer.sep_token == "[SEP]"
+        model = SentenceBertEmbedder(MODEL_NAME_OR_PATH, tokenizer_kwargs={"sep_token": "<sep>"})
+        assert model.model.tokenizer.sep_token == "<sep>"
diff --git a/tests/evaluator/test_classification_evaluator.py b/tests/evaluator/test_classification_evaluator.py
index 60e911c..bce9964 100644
--- a/tests/evaluator/test_classification_evaluator.py
+++ b/tests/evaluator/test_classification_evaluator.py
@@ -8,11 +8,12 @@
 from jmteb.evaluators.classification.data import JsonlClassificationDataset
 
 EXPECTED_OUTPUT_DICT_KEYS = {"val_scores", "test_scores", "optimal_classifier_name"}
+PREFIX = "以下の文を分類する: "
 
 
 class DummyClassificationDataset(ClassificationDataset):
-    def __init__(self):
-        self._items = [ClassificationInstance(text=f"dummy text {i}", label=i // 2) for i in range(10)]
+    def __init__(self, prefix: str = ""):
+        self._items = [ClassificationInstance(text=f"{prefix}dummy text {i}", label=i // 2) for i in range(10)]
 
     def __len__(self):
         return len(self._items)
@@ -43,6 +44,29 @@ def test_classification_evaluator(embedder):
             assert set(value.keys()) == expected_metrics
 
 
+def test_classification_evaluator_with_prefix(embedder):
+    evaluator_with_prefix = ClassificationEvaluator(
+        train_dataset=DummyClassificationDataset(),
+        val_dataset=DummyClassificationDataset(),
+        test_dataset=DummyClassificationDataset(),
+        prefix=PREFIX,
+        classifiers={
+            "logreg": LogRegClassifier(),
+            "knn": KnnClassifier(k=2, distance_metric="cosine"),
+        },
+    )
+    evaluator_with_manual_prefix = ClassificationEvaluator(
+        train_dataset=DummyClassificationDataset(prefix=PREFIX),
+        val_dataset=DummyClassificationDataset(prefix=PREFIX),
+        test_dataset=DummyClassificationDataset(prefix=PREFIX),
+        classifiers={
+            "logreg": LogRegClassifier(),
+            "knn": KnnClassifier(k=2, distance_metric="cosine"),
+        },
+    )
+    assert evaluator_with_prefix(embedder) == evaluator_with_manual_prefix(embedder)
+
+
 def test_classification_jsonl_dataset():
     dummy_jsonl_dataset = JsonlClassificationDataset(
         filename="tests/test_data/dummy_classification/val.jsonl",
diff --git a/tests/evaluator/test_clustering_evaluator.py b/tests/evaluator/test_clustering_evaluator.py
index 9272cfd..217d850 100644
--- a/tests/evaluator/test_clustering_evaluator.py
+++ b/tests/evaluator/test_clustering_evaluator.py
@@ -8,11 +8,13 @@
 EXPECTED_OUTPUT_DICT_KEYS = {"val_scores", "test_scores", "optimal_clustering_model_name"}
 EXPECTED_METRIC_NAMES = {"v_measure_score", "completeness_score", "homogeneity_score"}
 EXPECTED_MODEL_NAMES = {"MiniBatchKMeans", "AgglomerativeClustering", "BisectingKMeans", "Birch"}
+PREFIX = "以下の文を主題により分類する: "
+RANDOM_SEED = 42
 
 
 class DummyClusteringDataset(ClusteringDataset):
-    def __init__(self):
-        self._items = [ClusteringInstance(text=f"dummy text {i}", label=i // 2) for i in range(10)]
+    def __init__(self, prefix: str = ""):
+        self._items = [ClusteringInstance(text=f"{prefix}dummy text {i}", label=i // 2) for i in range(10)]
 
     def __len__(self):
         return len(self._items)
@@ -37,6 +39,21 @@ def test_kmeans_clustering(embedder):
                 assert set(results.details[score_splitname][clustering_model].keys()) == expected_metrics
 
 
+def test_clustering_with_prefix(embedder):
+    evaluator_with_prefix = ClusteringEvaluator(
+        val_dataset=DummyClusteringDataset(),
+        test_dataset=DummyClusteringDataset(),
+        prefix=PREFIX,
+        random_seed=RANDOM_SEED,
+    )
+    evaluator_with_manual_prefix = ClusteringEvaluator(
+        val_dataset=DummyClusteringDataset(prefix=PREFIX),
+        test_dataset=DummyClusteringDataset(prefix=PREFIX),
+        random_seed=RANDOM_SEED,
+    )
+    assert evaluator_with_prefix(embedder) == evaluator_with_manual_prefix(embedder)
+
+
 def test_clustering_jsonl_dataset():
     dataset = JsonlClusteringDataset(
         filename="tests/test_data/dummy_clustering/val.jsonl",
diff --git a/tests/evaluator/test_pair_classification_evaluator.py b/tests/evaluator/test_pair_classification_evaluator.py
index 443568d..c2ea8c4 100644
--- a/tests/evaluator/test_pair_classification_evaluator.py
+++ b/tests/evaluator/test_pair_classification_evaluator.py
@@ -8,12 +8,17 @@
 EXPECTED_OUTPUT_DICT_KEYS = {"val_scores", "test_scores", "optimal_distance_metric"}
 EXPECTED_METRIC_NAMES = {"accuracy", "binary_f1", "accuracy_threshold", "binary_f1_threshold"}
 EXPECTED_DIST_FUNC_NAMES = {"cosine_distances", "dot_similarities", "manhatten_distances", "euclidean_distances"}
+SENT1_PREFIX = "文1: "
+SENT2_PREFIX = "文2: "
 
 
 class DummyBinaryDataset(PairClassificationDataset):
-    def __init__(self):
+    def __init__(self, sent1_prefix: str = "", sent2_prefix: str = ""):
         self._items = [
-            PairClassificationInstance(f"dummy sentence 1 {i}", f"dummy sentence 2 {i}", i % 2) for i in range(10)
+            PairClassificationInstance(
+                f"{sent1_prefix}dummy sentence 1 {i}", f"{sent2_prefix}dummy sentence 2 {i}", i % 2
+            )
+            for i in range(10)
         ]
 
     def __len__(self):
@@ -39,6 +44,20 @@ def test_pair_classification_binary(embedder):
         assert set(value.keys()) == EXPECTED_METRIC_NAMES
 
 
+def test_pair_classification_binary_with_prefix(embedder):
+    evaluator_with_prefix = PairClassificationEvaluator(
+        val_dataset=DummyBinaryDataset(),
+        test_dataset=DummyBinaryDataset(),
+        sentence1_prefix=SENT1_PREFIX,
+        sentence2_prefix=SENT2_PREFIX,
+    )
+    evaluator_with_manual_prefix = PairClassificationEvaluator(
+        val_dataset=DummyBinaryDataset(sent1_prefix=SENT1_PREFIX, sent2_prefix=SENT2_PREFIX),
+        test_dataset=DummyBinaryDataset(sent1_prefix=SENT1_PREFIX, sent2_prefix=SENT2_PREFIX),
+    )
+    assert evaluator_with_prefix(embedder) == evaluator_with_manual_prefix(embedder)
+
+
 def test_pair_classification_jsonl_dataset():
     dataset = JsonlPairClassificationDataset(
         filename="tests/test_data/dummy_pair_classification/binary.jsonl",
diff --git a/tests/evaluator/test_reranking_evaluator.py b/tests/evaluator/test_reranking_evaluator.py
index 0d903cb..ef847a9 100644
--- a/tests/evaluator/test_reranking_evaluator.py
+++ b/tests/evaluator/test_reranking_evaluator.py
@@ -12,11 +12,13 @@
 
 EXPECTED_OUTPUT_DICT_KEYS = {"val_scores", "test_scores", "optimal_distance_metric"}
 EXPECTED_DIST_FUNC_NAMES = {"cosine_similarity", "euclidean_distance", "dot_score"}
+QUERY_PREFIX = "クエリ: "
+DOC_PREFIX = "ドキュメント: "
 
 
 class DummyDocDataset(RerankingDocDataset):
-    def __init__(self):
-        self._items = [RerankingDoc(id=str(i), text=f"dummy document {i}") for i in range(30)]
+    def __init__(self, prefix: str = ""):
+        self._items = [RerankingDoc(id=str(i), text=f"{prefix}dummy document {i}") for i in range(30)]
 
     def __len__(self):
         return len(self._items)
@@ -26,9 +28,10 @@ def __getitem__(self, idx):
 
 
 class DummyQueryDataset(RerankingQueryDataset):
-    def __init__(self):
+    def __init__(self, prefix: str = ""):
         self._items = [
-            RerankingQuery(query=f"dummy query {i}", retrieved_docs=[str(i)], relevance_scores=[1]) for i in range(10)
+            RerankingQuery(query=f"{prefix}dummy query {i}", retrieved_docs=[str(i)], relevance_scores=[1])
+            for i in range(10)
         ]
 
     def __len__(self):
@@ -57,6 +60,22 @@ def test_reranking_evaluator(embedder):
                 assert any(score.startswith(metric) for metric in ["ndcg"])
 
 
+def test_reranking_evaluator_with_prefix(embedder):
+    evaluator_with_prefix = RerankingEvaluator(
+        val_query_dataset=DummyQueryDataset(),
+        test_query_dataset=DummyQueryDataset(),
+        doc_dataset=DummyDocDataset(),
+        query_prefix=QUERY_PREFIX,
+        doc_prefix=DOC_PREFIX,
+    )
+    evaluator_with_manual_prefix = RerankingEvaluator(
+        val_query_dataset=DummyQueryDataset(prefix=QUERY_PREFIX),
+        test_query_dataset=DummyQueryDataset(prefix=QUERY_PREFIX),
+        doc_dataset=DummyDocDataset(prefix=DOC_PREFIX),
+    )
+    assert evaluator_with_prefix(model=embedder) == evaluator_with_manual_prefix(model=embedder)
+
+
 def test_jsonl_reranking_datasets():
     query = JsonlRerankingQueryDataset(
         filename="tests/test_data/dummy_reranking/val.jsonl",
diff --git a/tests/evaluator/test_retrieval_evaluator.py b/tests/evaluator/test_retrieval_evaluator.py
index 82b7944..fa52c52 100644
--- a/tests/evaluator/test_retrieval_evaluator.py
+++ b/tests/evaluator/test_retrieval_evaluator.py
@@ -12,11 +12,13 @@
 
 EXPECTED_OUTPUT_DICT_KEYS = {"val_scores", "test_scores", "optimal_distance_metric"}
 EXPECTED_DIST_FUNC_NAMES = {"cosine_similarity", "euclidean_distance", "dot_score"}
+QUERY_PREFIX = "クエリ: "
+DOC_PREFIX = "ドキュメント: "
 
 
 class DummyDocDataset(RetrievalDocDataset):
-    def __init__(self):
-        self._items = [RetrievalDoc(id=str(i), text=f"dummy document {i}") for i in range(30)]
+    def __init__(self, prefix: str = ""):
+        self._items = [RetrievalDoc(id=str(i), text=f"{prefix}dummy document {i}") for i in range(30)]
 
     def __len__(self):
         return len(self._items)
@@ -26,8 +28,8 @@ def __getitem__(self, idx):
 
 
 class DummyQueryDataset(RetrievalQueryDataset):
-    def __init__(self):
-        self._items = [RetrievalQuery(f"dummy query {i}", relevant_docs=[str(i)]) for i in range(10)]
+    def __init__(self, prefix: str = ""):
+        self._items = [RetrievalQuery(f"{prefix}dummy query {i}", relevant_docs=[str(i)]) for i in range(10)]
 
     def __len__(self):
         return len(self._items)
@@ -58,6 +60,28 @@ def test_retrieval_evaluator(embedder):
                 assert any(score.startswith(metric) for metric in ["accuracy", "mrr", "ndcg"])
 
 
+def test_retrieval_evaluator_with_prefix(embedder):
+    evaluator_with_prefix = RetrievalEvaluator(
+        val_query_dataset=DummyQueryDataset(),
+        test_query_dataset=DummyQueryDataset(),
+        doc_dataset=DummyDocDataset(),
+        query_prefix=QUERY_PREFIX,
+        doc_prefix=DOC_PREFIX,
+        accuracy_at_k=[1, 3, 5, 10],
+        ndcg_at_k=[1, 3, 5],
+        doc_chunk_size=3,
+    )
+    evaluator_with_manual_prefix = RetrievalEvaluator(
+        val_query_dataset=DummyQueryDataset(prefix=QUERY_PREFIX),
+        test_query_dataset=DummyQueryDataset(prefix=QUERY_PREFIX),
+        doc_dataset=DummyDocDataset(prefix=DOC_PREFIX),
+        accuracy_at_k=[1, 3, 5, 10],
+        ndcg_at_k=[1, 3, 5],
+        doc_chunk_size=3,
+    )
+    assert evaluator_with_prefix(model=embedder) == evaluator_with_manual_prefix(model=embedder)
+
+
 def test_if_chunking_does_not_change_result(embedder):
     evaluator1 = RetrievalEvaluator(
         val_query_dataset=DummyQueryDataset(),
diff --git a/tests/evaluator/test_sts_evaluator.py b/tests/evaluator/test_sts_evaluator.py
index a151814..69469cc 100644
--- a/tests/evaluator/test_sts_evaluator.py
+++ b/tests/evaluator/test_sts_evaluator.py
@@ -4,11 +4,16 @@
 EXPECTED_OUTPUT_DICT_KEYS = {"val_scores", "test_scores", "optimal_similarity_metric"}
 EXPECTED_SIM_FUNC_NAMES = {"cosine_similarity", "manhatten_distance", "euclidean_distance", "dot_score"}
 EXPECTED_METRIC_NAMES = {"pearson", "spearman"}
+SENT1_PREFIX = "文1: "
+SENT2_PREFIX = "文2: "
 
 
 class DummySTSDataset(STSDataset):
-    def __init__(self):
-        self._items = [STSInstance("dummy sentence 1", "dummy sentence 2", i * 0.3) for i in range(10)]
+    def __init__(self, sent1_prefix: str = "", sent2_prefix: str = ""):
+        self._items = [
+            STSInstance(f"{sent1_prefix}dummy sentence 1", f"{sent2_prefix}dummy sentence 2", i * 0.3)
+            for i in range(10)
+        ]
 
     def __len__(self):
         return len(self._items)
@@ -32,6 +37,20 @@ def test_sts(embedder):
                 assert set(results.details[score_splitname][dist].keys()) == EXPECTED_METRIC_NAMES
 
 
+def test_sts_with_prefix(embedder):
+    evaluator_with_prefix = STSEvaluator(
+        val_dataset=DummySTSDataset(),
+        test_dataset=DummySTSDataset(),
+        sentence1_prefix=SENT1_PREFIX,
+        sentence2_prefix=SENT2_PREFIX,
+    )
+    evaluator_with_manual_prefix = STSEvaluator(
+        val_dataset=DummySTSDataset(sent1_prefix=SENT1_PREFIX, sent2_prefix=SENT2_PREFIX),
+        test_dataset=DummySTSDataset(sent1_prefix=SENT1_PREFIX, sent2_prefix=SENT2_PREFIX),
+    )
+    assert evaluator_with_prefix(embedder) == evaluator_with_manual_prefix(embedder)
+
+
 def test_sts_jsonl_dataset():
     dataset = JsonlSTSDataset(
         filename="tests/test_data/dummy_sts/val.jsonl",