Revert "Fix benchmark documentation (#931)"

This reverts commit 6d0998b.

docs/source/benchmarks/text_summarization.rst

@@ -1,11 +1,11 @@
 Text summarization benchmark 
 ====
-In this benchmark, we compare the performance of text summarization between EvaDB and MindsDB on `CNN-DailyMail News <https://www.kaggle.com/datasets/gowrishankarp/newspaper-text-summarization-cnn-dailymail>`_.
+In this benchmark, we compare the performance of text summarization between EvaDB and MindsDB on `CNN-DailyMail News <https://www.kaggle.com/datasets/gowrishankarp/newspaper-text-summarization-cnn-dailymail>`.
 
 1. Prepare dataset
 ----
 
-.. code-block:: bash
+.. code-block: bash
 
    cd benchmark/text_summarization
    bash download_dataset.sh
@@ -17,7 +17,7 @@ In this benchmark, we compare the performance of text summarization between EvaD
 
    Install ray in your EvaDB virtual environment. ``pip install "ray>=1.13.0,<2.5.0"``
 
-.. code-block:: bash
+.. code-block: bash
 
    cd benchmark/text_summarization
    python text_summarization_with_evadb.py
@@ -26,10 +26,12 @@ In this benchmark, we compare the performance of text summarization between EvaD
 3. Using MindsDB to summarize the CNN DailyMail News
 ----
 
+.. _sqlite database:
+
 Prepare sqlite database for MindsDB
 ****
 
-.. code-block:: bash
+.. code-block: bash
 
    sqlite3 cnn_news_test.db
    > .mode csv
@@ -39,24 +41,24 @@ Prepare sqlite database for MindsDB
 
 Install MindsDB
 ****
-Follow the `Setup for Source Code via pip <https://docs.mindsdb.com/setup/self-hosted/pip/source>`_ to install mindsdb.
+Follow the `Setup for Source Code via pip <https://docs.mindsdb.com/setup/self-hosted/pip/source>` to install mindsdb.
 
 .. note::
 
    At the time of this documentation, we need to manully ``pip install evaluate`` for huggingface model to work in MindsDB.
 
 After the installation, we use mysql cli to connect to MindsDB. Replace the port number as needed.
 
-.. code-block:: bash
+.. code-block: bash
 
    mysql -h 127.0.0.1 --port 47335 -u mindsdb -p
 
 Run Experiment
 ****
 
-Connect the sqlite database we created before.
+Connect the sqlite database we created before: :ref:`sqlite database`.
 
-.. code-block:: sql
+.. code-block: sql
 
    CREATE DATABASE sqlite_datasource
    WITH ENGINE = 'sqlite',
@@ -66,7 +68,7 @@ Connect the sqlite database we created before.
 
 Create text summarization model and wait for its readiness.
 
-.. code-block:: sql
+.. code-block: sql
 
    CREATE MODEL mindsdb.hf_bart_sum_20
    PREDICT PRED
@@ -80,9 +82,9 @@ Create text summarization model and wait for its readiness.
 
    DESCRIBE mindsdb.hf_bart_sum_20;
 
-Use the model to summarize the CNN DailyMail news.
+Use the model to summarize the CNN DailyMail news
 
-.. code-block:: sql
+.. code-block: sql
 
    CREATE OR REPLACE TABLE sqlite_datasource.cnn_news_summary (
      SELECT PRED
@@ -93,7 +95,7 @@ Use the model to summarize the CNN DailyMail news.
 
 4. Experiment results
 ----
-Below are nubmers from a server with 56 Intel(R) Xeon(R) CPU E5-2690 v4 @ 2.60GHz and two Quadro P6000 GPU.
+Below are nubmers from a server with 56 Intel(R) Xeon(R) CPU E5-2690 v4 @ 2.60GHz and two Quadro P6000 GPU
 
 .. list-table:: Text summarization with ``sshleifer/distilbart-cnn-12-6`` on CNN-DailyMail News