diff --git a/docs/intro.rst b/docs/intro.rst
index bc8510bd0..6dba84e15 100644
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+++ b/docs/intro.rst
@@ -63,7 +63,7 @@ Library Design
 LAVIS has six key modules.
 
 - ``lavis.runners`` manages the overall training and evaluation lifecycle. It is also responsible for creating required components lazily as per demand, such as optimizers, learning rate schedulers and dataloaders. Currently ``RunnerBase`` implements epoch-based training and ``RunerIters`` implements iteration-based training.
-- ``lavis.tasks`` implements concrete training and evaluation logic per task. A task could be, for example, retrieval, captioning, pre-training. The rationale to have an abstraction of task is to accomodate task-specific training and evaluation. For example, evaluating a retrieval model is different from a classification model.
+- ``lavis.tasks`` implements concrete training and evaluation logic per task. A task could be, for example, retrieval, captioning, pre-training. The rationale to have an abstraction of task is to accommodate task-specific training and evaluation. For example, evaluating a retrieval model is different from a classification model.
 - ``lavis.datasets`` is responsible for creating datasets, where ``lavis.datasets.builders`` loads dataset configurations, downloads annotations and returns a dataset object; ``lavis.datasets.datasets`` defines the supported datasets, each is a ``torch.utils.data.Dataset`` instance. We also provide `automatic dataset downloading tools` in ``datasets/download_scripts`` to help prepare common public datasets.
 - ``lavis.models`` holds definition for the supported models and shared model layers.
 - ``lavis.processors`` handles preprocessing of text and images/videos before feeding the model. For images and videos, a processor can be thought as transfroms in torchvision; for text input, this may include lowering case, truncation etc.