fix formatting

docs/source/index.rst

@@ -50,7 +50,7 @@ Quickstart
 2. Find common issues in your data
 ----------------------------------
 
-cleanlab automatically detects various issues in *any dataset that a classifier can be trained on*. The cleanlab package *works with any ML model* by operating on model outputs (predicted class probabilities or feature embeddings) -- it doesn't require that a particular model created those outputs. For any classification dataset, use your trained model to produce `pred_probs` (predicted class probabilities) and/or `feature_embeddings` (numeric vector representations of each datapoint). Then, these few lines of code can detect common real-world issues in your dataset like label errors, outliers, near duplicates, etc:
+cleanlab automatically detects various issues in *any dataset that a classifier can be trained on*. The cleanlab package *works with any ML model* by operating on model outputs (predicted class probabilities or feature embeddings) -- it doesn't require that a particular model created those outputs. For any classification dataset, use your trained model to produce `pred_probs` (predicted class probabilities) and/or `feature_embeddings` (numeric vector representations of each datapoint). To quickly check your dataset for common real-world issues like label errors, outliers, near duplicates, ..., simply run these few lines of code:
 
 .. code-block:: python
 
@@ -58,7 +58,7 @@ cleanlab automatically detects various issues in *any dataset that a classifier
 
     lab = Datalab(data=your_dataset, label_name="column_name_of_labels")
     lab.find_issues(features=feature_embeddings, pred_probs=pred_probs)
-    lab.report()  # summarize issues in dataset, how severe they are, ...
+    lab.report()  # summarize issues in dataset, how severe they are in each data point, ...
 
 
 3. Handle label errors and train robust models with noisy labels
@@ -73,7 +73,7 @@ Mislabeled data is a particularly concerning issue plaguing real-world datasets.
     # This works with any sklearn-compatible model - just input data + labels and cleanlab will detect label issues ツ
     label_issues_info = CleanLearning(clf=sklearn_compatible_model).find_label_issues(data, labels)
 
-:py:class:`CleanLearning <cleanlab.classification.CleanLearning>` also works with models from most standard ML frameworks by wrapping the model for scikit-learn compliance, e.g. pytorch (can use `skorch <https://github.com/skorch-dev/skorch>`_ package), tensorflow/keras (can use our :py:class:`KerasWrapperModel <cleanlab/models/keras>`_), etc.
+:py:class:`CleanLearning <cleanlab.classification.CleanLearning>` also works with models from most standard ML frameworks by wrapping the model for scikit-learn compliance, e.g. pytorch (can use `skorch <https://github.com/skorch-dev/skorch>`_ package), tensorflow/keras (can use our :py:class:`KerasWrapperModel <cleanlab.models.keras>`_), etc.
 
 :py:meth:`find_label_issues <cleanlab.classification.CleanLearning.find_label_issues>` returns a boolean mask flagging which examples have label issues and a numeric label quality score for each example quantifying our confidence that its label is correct.
 
@@ -138,7 +138,7 @@ While this open-source library **finds** data issues, its utility depends on you
    :width: 800
    :alt: Stages of modern AI pipeline that can now be automated with Cleanlab Studio
 
-`There is no easier way <https://cleanlab.ai/blog/data-centric-ai/>`_ to turn *unreliable* raw data into *reliable* models/analytics. `Try it for free! <https://cleanlab.ai/signup/>`_
+There is no faster way to turn *unreliable* raw data into *reliable* models/analytics. `Try it for free! <https://cleanlab.ai/signup/>`_
 
 Link to Cleanlab Studio docs: `help.cleanlab.ai <https://help.cleanlab.ai/>`_