diff --git a/docs/source/main_classes/pipelines.mdx b/docs/source/main_classes/pipelines.mdx
index 145f17f448..5b2509c70d 100644
--- a/docs/source/main_classes/pipelines.mdx
+++ b/docs/source/main_classes/pipelines.mdx
@@ -254,7 +254,7 @@ For users, a rule of thumb is:
 ## Pipeline chunk batching
 
 `zero-shot-classification` and `question-answering` are slightly specific in the sense, that a single input might yield
-mutliple forward pass of a model. Under normal circumstances, this would yield issues with `batch_size` argument.
+multiple forward pass of a model. Under normal circumstances, this would yield issues with `batch_size` argument.
 
 In order to circumvent this issue, both of these pipelines are a bit specific, they are `ChunkPipeline` instead of
 regular `Pipeline`. In short:
@@ -263,7 +263,7 @@ regular `Pipeline`. In short:
 ```python
 preprocessed = pipe.preprocess(inputs)
 model_outputs = pipe.forward(preprocessed)
-outputs = pipe.postprocess(model_ouputs)
+outputs = pipe.postprocess(model_outputs)
 ```
 
 Now becomes:
@@ -274,7 +274,7 @@ all_model_outputs = []
 for preprocessed in pipe.preprocess(inputs):
     model_outputs = pipe.forward(preprocessed)
     all_model_outputs.append(model_outputs)
-outputs = pipe.postprocess(all_model_ouputs)
+outputs = pipe.postprocess(all_model_outputs)
 ```
 
 This should be very transparent to your code because the pipelines are used in
@@ -282,7 +282,7 @@ the same way.
 
 This is a simplified view, since the pipeline can handle automatically the batch to ! Meaning you don't have to care
 about how many forward passes you inputs are actually going to trigger, you can optimize the `batch_size`
-independantly of the inputs. The caveats from the previous section still apply.
+independently of the inputs. The caveats from the previous section still apply.
 
 ## Pipeline custom code