diff --git a/integrations/fastembed/README.md b/integrations/fastembed/README.md
index c021dec3b..f3c2bb135 100644
--- a/integrations/fastembed/README.md
+++ b/integrations/fastembed/README.md
@@ -8,6 +8,7 @@
 **Table of Contents**
 
 - [Installation](#installation)
+- [Usage](#Usage)
 - [License](#license)
 
 ## Installation
@@ -33,7 +34,7 @@ embedding = text_embedder.run(text)["embedding"]
 
 ```python
 from haystack_integrations.components.embedders.fastembed import FastembedDocumentEmbedder
-from haystack.dataclasses import Document
+from haystack import Document
 
 embedder = FastembedDocumentEmbedder(
     model="BAAI/bge-small-en-v1.5",
@@ -50,24 +51,50 @@ from haystack_integrations.components.embedders.fastembed import FastembedSparse
 
 text = "fastembed is supported by and maintained by Qdrant."
 text_embedder = FastembedSparseTextEmbedder(
-    model="prithvida/Splade_PP_en_v1"
+    model="prithivida/Splade_PP_en_v1"
 )
 text_embedder.warm_up()
-embedding = text_embedder.run(text)["embedding"]
+embedding = text_embedder.run(text)["sparse_embedding"]
 ```
 
 ```python
 from haystack_integrations.components.embedders.fastembed import FastembedSparseDocumentEmbedder
-from haystack.dataclasses import Document
+from haystack import Document
 
 embedder = FastembedSparseDocumentEmbedder(
-    model="prithvida/Splade_PP_en_v1",
+    model="prithivida/Splade_PP_en_v1",
 )
 embedder.warm_up()
 doc = Document(content="fastembed is supported by and maintained by Qdrant.", meta={"long_answer": "no",})
 result = embedder.run(documents=[doc])
 ```
 
+You can use `FastembedRanker` by importing as:
+
+```python
+from haystack import Document
+
+from haystack_integrations.components.rankers.fastembed import FastembedRanker
+
+query = "Who is maintaining Qdrant?"
+documents = [
+    Document(
+        content="This is built to be faster and lighter than other embedding libraries e.g. Transformers, Sentence-Transformers, etc."
+    ),
+    Document(content="fastembed is supported by and maintained by Qdrant."),
+]
+
+ranker = FastembedRanker(model_name="Xenova/ms-marco-MiniLM-L-6-v2")
+ranker.warm_up()
+reranked_documents = ranker.run(query=query, documents=documents)["documents"]
+
+print(reranked_documents[0])
+
+# Document(id=...,
+#  content: 'fastembed is supported by and maintained by Qdrant.',
+#  score: 5.472434997558594..)
+```
+
 ## License
 
 `fastembed-haystack` is distributed under the terms of the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license.
diff --git a/integrations/fastembed/examples/ranker_example.py b/integrations/fastembed/examples/ranker_example.py
index 7a31e4646..593334e90 100644
--- a/integrations/fastembed/examples/ranker_example.py
+++ b/integrations/fastembed/examples/ranker_example.py
@@ -15,7 +15,7 @@
 reranked_documents = ranker.run(query=query, documents=documents)["documents"]
 
 
-print(reranked_documents["documents"][0])
+print(reranked_documents[0])
 
 # Document(id=...,
 #  content: 'fastembed is supported by and maintained by Qdrant.',