Fix typos in documentation

docs/index.rst

@@ -17,7 +17,7 @@ What's even better is that they are fully compliant with `pyrepseq <https://pyre
 
    A visual introduction to how SCEPTR works, taken from our SCEPTR preprint.
    SCEPTR is a TCR language model (a,b) pre-trained using masked-language modelling and autocontrastive learning (c,d).
-   \* (a) The default model uses the ``<cls>`` pooling method, but there is also a variant that is trained to use average-pooling (see :py:func:`sceptr.variant.average_pooling`).
+   (a) The default model uses the ``<cls>`` pooling method, but there is also a variant that is trained to use average-pooling (see :py:func:`sceptr.variant.average_pooling`).
    Please see the manuscript for more details.
 
 .. toctree::

src/sceptr/__init__.py

@@ -45,7 +45,7 @@ def calc_pdist_vector(instances: DataFrame) -> ndarray:
     Returns
     -------
     ndarray
-        A 2D numpy ndarray representing a pdist vector of distances between each pair of TCRs in `instances`.
+        A 1D numpy ndarray representing a pdist vector of distances between each pair of TCRs in `instances`.
         The returned array will have shape :math:`(\frac{1}{2}N(N-1),)`, where :math:`N` is the number of TCRs in `instances`.
     """
     return get_default_model().calc_pdist_vector(instances)

src/sceptr/model.py

@@ -115,7 +115,7 @@ def calc_pdist_vector(self, instances: DataFrame) -> ndarray:
         Returns
         -------
         ndarray
-            A 2D numpy ndarray representing a pdist vector of distances between each pair of TCRs in `instances`.
+            A 1D numpy ndarray representing a pdist vector of distances between each pair of TCRs in `instances`.
             The returned array will have shape :math:`(\frac{1}{2}N(N-1),)`, where :math:`N` is the number of TCRs in `instances`.
         """
         representations = self._calc_torch_representations(instances)