diff --git a/docs/usage/usage.md b/docs/usage/usage.md
index 06c5b9c1c3..8a71d25ff4 100644
--- a/docs/usage/usage.md
+++ b/docs/usage/usage.md
@@ -442,12 +442,15 @@ There are times you may want to cache the embeddings so you can re-use them. Thi
 
 ```python
 # define your task(s) and model above as normal
-...
+task = mteb.get_task("LccSentimentClassification")
+model = mteb.get_model("minishlab/M2V_base_glove_subword")
+evaluation = mteb.MTEB(tasks=[task])
+
 # wrap the model with the cache wrapper
 from mteb.models.cache_wrapper import CachedEmbeddingWrapper
-model_with_cached_emb = CachedEmbeddingWrapper(model, cache_path='<path_to_cache_dir>')
+model_with_cached_emb = CachedEmbeddingWrapper(model, cache_path='path_to_cache_dir')
 # run as normal
-evaluation.run(model, ...)
+evaluation.run(model_with_cached_emb)
 ```
 
 If you want to directly access the cached embeddings (e.g. for subsequent analyses) follow this example:
@@ -457,8 +460,8 @@ import numpy as np
 from mteb.models.cache_wrapper import TextVectorMap
 
 # Access the memory-mapped file and convert to array
-vector_map = TextVectorMap("<path_to_cache_dir>/AppsRetrieval")
-vector_map.load(name="AppsRetrieval")
+vector_map = TextVectorMap("path_to_cache_dir/LccSentimentClassification")
+vector_map.load(name="LccSentimentClassification")
 vectors = np.asarray(vector_map.vectors)
 
 # Remove all "placeholders" in the embedding cache
diff --git a/mteb/models/cache_wrapper.py b/mteb/models/cache_wrapper.py
index 57b8954c7b..74c05562f0 100644
--- a/mteb/models/cache_wrapper.py
+++ b/mteb/models/cache_wrapper.py
@@ -155,8 +155,8 @@ def load(self, name: str | None = None) -> None:
                     self.vectors = np.memmap(
                         self.vectors_file, dtype="float32", mode="r+"
                     )
-                    self.vectors = self.vectors.reshape(-1, self.vector_dim)
-                    logger.info(f"Loaded vectors file with shape: {self.vectors.shape}")
+                    self.vectors = self.vectors.reshape(-1, self.vector_dim)  # type: ignore
+                    logger.info(f"Loaded vectors file with shape: {self.vectors.shape}")  # type: ignore
                 else:
                     logger.warning(
                         "Vector dimension not set. Unable to load vectors file."
@@ -214,22 +214,30 @@ def __init__(self, model: Encoder, cache_path: str | Path):
         logger.info("Initialized CachedEmbeddingWrapper")
 
     def encode(
-        self, texts: list[str], batch_size: int = 32, task_name: str = None, **kwargs
+        self,
+        texts: list[str],
+        batch_size: int = 32,
+        task_name: str | None = None,
+        **kwargs,
     ) -> np.ndarray:
         """Encode texts using the wrapped model, with caching"""
+        _task_name = task_name or "no_task_name"
+
         try:
             results = []
             uncached_texts = []
             uncached_indices = []
 
             # Initialize cache
-            if task_name not in self.cache_dict:
-                self.cache_dict[task_name] = TextVectorMap(self.cache_path / task_name)
-                self.cache_dict[task_name].load(name=task_name)
+            if _task_name not in self.cache_dict:
+                self.cache_dict[_task_name] = TextVectorMap(
+                    self.cache_path / _task_name
+                )
+                self.cache_dict[_task_name].load(name=_task_name)
 
             # Check cache for each text
             for i, text in enumerate(texts):
-                vector = self.cache_dict[task_name].get_vector(text)
+                vector = self.cache_dict[_task_name].get_vector(text)
                 if vector is not None:
                     results.append(vector)
                 else:
@@ -240,16 +248,19 @@ def encode(
             if uncached_texts:
                 logger.info(f"Encoding {len(uncached_texts)} new texts")
                 new_vectors = self._model.encode(
-                    uncached_texts, batch_size=batch_size, **kwargs
+                    uncached_texts,
+                    batch_size=batch_size,
+                    task_name=task_name,  # type: ignore
+                    **kwargs,
                 )
                 if isinstance(new_vectors, torch.Tensor):
                     new_vectors = new_vectors.cpu().numpy()
 
                 # Add new vectors to cache
                 for text, vector in zip(uncached_texts, new_vectors):
-                    self.cache_dict[task_name].add(text, vector)
+                    self.cache_dict[_task_name].add(text, vector)
                 results.extend(new_vectors)
-                self.cache_dict[task_name].save()
+                self.cache_dict[_task_name].save()
             else:
                 logger.info("All texts found in cache")
 
@@ -287,7 +298,7 @@ def __getattr__(self, name: str) -> Any:
 
     def __dir__(self) -> list[str]:
         """Return all attributes from both this class and the wrapped model"""
-        return list(set(super().__dir__() + dir(self._model)))
+        return list(set(super().__dir__() + dir(self._model)))  # type: ignore
 
     def __del__(self):
         self.close()