Clean up some parsing issues with the README

README.md

@@ -141,61 +141,66 @@ This dataset contains 1,881,712 sequences collected from 600 different bacterial
 
 ### Install HuggingFace Datasets package
 
-Each subset can be loaded into python using the HuggingFace [datasets] (https://huggingface.co/docs/datasets/index) library.  First, from the command line install the 'datsets' library
+Each subset can be loaded into python using the HuggingFace [datasets](https://huggingface.co/docs/datasets/index) library.  First, from the command line install the `datasets` library
  
-  $ pip install datasets
+    $ pip install datasets
 
 Optionally set the cache directory, e.g.
- 
-  $ HF\_HOME=${HOME}/.cache/huggingface/
-  $ export HF\_HOME
+
+    $ HF_HOME=${HOME}/.cache/huggingface/
+    $ export HF_HOME
 
 then, from within python load the datasets library
  
-  \>\>\> import datasets
+    >>> import datasets
 
 ### Load model datasets
 
 To load one of the IsItABarrel model datasets, use 'datasets.load_datase(...)':
 
 
- \>\>\> dataset \= datasets.load\_dataset(f"rmauder/IsItABarrel", "TMBB_information")
+    >>> dataset = datasets.load_dataset("rmauder/IsItABarrel", "TMBB_information")
 
-and the dataset is loaded as a 'datasets.arrow_dataset.Dataset'
+and the dataset is loaded as a `datasets.arrow_dataset.Dataset`
 
- \>\>\> dataset
- \<RESULT OF LOADING DATASET MODEL\>
+    >>> dataset
+    <RESULT OF LOADING DATASET MODEL>
 
-Which is a column oriented format that can be accessed directly, converted in to a 'pandas.DataFrame', or 'parquet' format, e.g.
+Which is a column oriented format that can be accessed directly, converted in to a `pandas.DataFrame`, or `parquet` format, e.g.
 
- \>\>\> dataset.data.column('\<colum_name\>')
- \>\>\> dataset.to\_pandas()
- \>\>\> dataset.to\_parquet("dataset.parquet")
+    >>> dataset.data.column('\<colum_name\>')
+    >>> dataset.to_pandas()
+    >>> dataset.to_parquet("dataset.parquet")
 
 ## Dataset Details
-Dataset 1: TMBB_information
+Dataset 1: `TMBB_information`
 This dataset contains a csv file split into test/train/validate sets.  Each set contains 13 columns of information about each protein.
 
-Dataset 2: fasta_files
+Dataset 2: `fasta_files`
 This dataset contains pointers to fasta files that are already split into test/train/validate sets just like the csv files.  Each fasta file contains protein ID, organism, and the fasta sequence of the protein.
 
-Dataset 3: unsplit_TMBB_information
+Dataset 3: `unsplit_TMBB_information`
 This dataset contains a full unsplit csv file with information about every TMBB protein.  This can be used if anyone would like to test different splits or to filter data further based on organism or TMBB type.
 
-### Dataset Description <This dataset contains information like protein_ID and protein type for almost two million transmembrane beta barrel proteins.  It contains both known proteins and predicted proteins based on prokaryotic genomes.>
+### Dataset Description 
+This dataset contains information like protein_ID and protein type for almost two million transmembrane beta barrel proteins.  It contains both known proteins and predicted proteins based on prokaryotic genomes.
 
 - **Acknowledgements:** Daniel Montezano, Rebecca Bernstein, Matthew M. Copeland, and Joanna S. G. Slusky
 
 - **License:** agpl-3.0
 
-### Dataset Sources - **Repository:** <https://isitabarrel.ku.edu/search> - **Paper:** 
-  Montezano, D., Bernstein, R., Copeland, M. M., & Slusky, J. S. G. (2023).
-  General features of transmembrane beta barrels from a large database. Proceedings
-  of the National Academy of Sciences of the United States of America, 120(29), e2220762120.
-  doi:10.1073/pnas.2220762120>
+### Dataset Sources
+- **Repository:** [https://isitabarrel.ku.edu/search](https://isitabarrel.ku.edu/search)
+- **Paper:**
+  
+    Montezano, D., Bernstein, R., Copeland, M. M., & Slusky, J. S. G. (2023).
+    General features of transmembrane beta barrels from a large database. Proceedings
+    of the National Academy of Sciences of the United States of America, 120(29), e2220762120.
+    doi:10.1073/pnas.2220762120
 
 
-### Source Data <The source data is set up as a large downloadable file that is not clustered or split into train test validate splits.>
+### Source Data 
+The source data is set up as a large downloadable file that is not clustered or split into train test validate splits.
 
 ## Citation 
 @article{Montezano2023, title = {General features of transmembrane
@@ -206,4 +211,4 @@ This dataset contains a full unsplit csv file with information about every TMBB
   Slusky,  Joanna S. G.}, year = {2023}, month = jul}
 
 ## Dataset Card Authors
-Ryan Mauder/rmauder@butler.edu
+Ryan Mauder rmauder@butler.edu