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README.md

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-**SCEPTR** (**S**imple **C**ontrastive **E**mbedding of the **P**rimary sequence of **T** cell **R**eceptors) is a small, fast, and accurate TCR representation model that can be used for alignment-free TCR  analysis, including for TCR-pMHC interaction prediction and TCR clustering (metaclonotype discovery).
+| <img src="https://raw.githubusercontent.com/yutanagano/sceptr/main/docs/graphical_abstract.png" width=700> |
+|---|
+| Graphical abstract. Traditional protein language models that are trained purely on masked-language modelling underperform sequence alignment models on TCR specificity prediction. In contrast, our model SCEPTR is jointly trained on masked-language modelling and contrastive learning, allowing it to outperform other language models as well as the best sequence alignment models to achieve state-of-the-art performance. |
+
+**SCEPTR** (**S**imple **C**ontrastive **E**mbedding of the **P**rimary sequence of **T** cell **R**eceptors) is a small, fast, and informative TCR representation model that can be used for alignment-free TCR  analysis, including for TCR-pMHC interaction prediction and TCR clustering (metaclonotype discovery).
 Our [manuscript](https://www.cell.com/cell-systems/fulltext/S2405-4712(24)00369-7) demonstrates that SCEPTR can be used for few-shot TCR specificity prediction with improved accuracy over previous methods.
 
 SCEPTR is a BERT-like transformer-based neural network implemented in [Pytorch](https://pytorch.org).
 With the default model providing best-in-class performance with only 153,108 parameters (typical protein language models have tens or hundreds of millions), SCEPTR runs fast- even on a CPU!
 And if your computer does have a [CUDA-enabled GPU](https://en.wikipedia.org/wiki/CUDA), the sceptr package will automatically detect and use it, giving you blazingly fast performance without the hassle.
 
-sceptr's API exposes three intuitive functions: `calc_vector_representations`, `calc_cdist_matrix`, and `calc_pdist_vector`- and it's all you need to make full use of the SCEPTR models.
+sceptr's API exposes four intuitive functions: `calc_cdist_matrix`, `calc_pdist_vector`, `calc_vector_representations`, and `calc_residue_representations` -- and it's all you need to make full use of the SCEPTR models.
 What's even better is that they are fully compliant with [pyrepseq](https://pyrepseq.readthedocs.io)'s [tcr_metric](https://pyrepseq.readthedocs.io/en/latest/api.html#pyrepseq.metric.tcr_metric.TcrMetric) API, so sceptr will fit snugly into the rest of your repertoire analysis workflow.
 
 ## Installation

docs/index.rst

@@ -8,7 +8,7 @@ SCEPTR is a BERT-like transformer-based neural network implemented in `Pytorch <
 With the default model providing best-in-class performance with only 153,108 parameters (typical protein language models have tens or hundreds of millions), SCEPTR runs fast- even on a CPU!
 And if your computer does have a `CUDA- <https://developer.nvidia.com/cuda-zone>`_ or `MPS-enabled <https://developer.apple.com/documentation/metalperformanceshaders>`_ GPU, the sceptr package will automatically detect and use it, giving you blazingly fast performance without the hassle.
 
-sceptr's :ref:`API <api>` exposes three intuitive functions: :py:func:`~sceptr.calc_vector_representations`, :py:func:`~sceptr.calc_cdist_matrix`, and :py:func:`~sceptr.calc_pdist_vector`-- and it's all you need to make full use of the SCEPTR models.
+sceptr's :ref:`API <api>` exposes four intuitive functions: :py:func:`~sceptr.calc_cdist_matrix`, :py:func:`~sceptr.calc_pdist_vector`, :py:func:`~sceptr.calc_vector_representations`, and :py:func:`~sceptr.calc_residue_representations` -- and it's all you need to make full use of the SCEPTR models.
 What's even better is that they are fully compliant with `pyrepseq <https://pyrepseq.readthedocs.io>`_'s `tcr_metric <https://pyrepseq.readthedocs.io/en/latest/api.html#pyrepseq.metric.tcr_metric.TcrMetric>`_ API, so sceptr will fit snugly into the rest of your repertoire analysis toolkit.
 
 .. figure:: graphical_abstract.png