fixed for twitter thread

3 files changed, 17 insertions, 8 deletions

diff --git a/Content/posts/2022-05-21-Similar-Movies-Recommender.md b/Content/posts/2022-05-21-Similar-Movies-Recommender.md
index b889002..66dd54a 100644
--- a/Content/posts/2022-05-21-Similar-Movies-Recommender.md
+++ b/Content/posts/2022-05-21-Similar-Movies-Recommender.md
@@ -208,7 +208,9 @@ I did not want to put my poor Mac through the estimated 23 hours it would have t
 
 Because of the small size of the database file, I was able to just upload the file.
 
-For the encoding model, I decided to use the pretrained `paraphrase-multilingual-MiniLM-L12-v2` model for SentenceTransformers, a Python framework for SOTA sentence, text and image embeddings. I wanted to use a multilingual model as I personally consume content in various languages (natively, no dubs or subs) and some of the sources for their information do not translate to English. As of writing this post, I did not include any other database except Trakt. 
+For the encoding model, I decided to use the pretrained `paraphrase-multilingual-MiniLM-L12-v2` model for SentenceTransformers, a Python framework for SOTA sentence, text and image embeddings. 
+I wanted to use a multilingual model as I personally consume content in various languages and some of the sources for their information do not translate to English. 
+As of writing this post, I did not include any other database except Trakt. 
 
 While deciding how I was going to process the embeddings, I came across multiple solutions:
 
@@ -269,7 +271,8 @@ That's it!
 
 We use the `trakt_id` for the movie as the ID for the vectors and upsert it into the index. 
 
-To find similar items, we will first have to map the name of the movie to its trakt_id, get the embeddings we have for that id and then perform a similarity search. It is possible that this additional step of mapping could be avoided by storing information as metadata in the index.
+To find similar items, we will first have to map the name of the movie to its trakt_id, get the embeddings we have for that id and then perform a similarity search. 
+It is possible that this additional step of mapping could be avoided by storing information as metadata in the index.
 
 ```python
 def get_trakt_id(df, title: str):
diff --git a/docs/feed.rss b/docs/feed.rss
index 11e6861..85c0a02 100644
--- a/docs/feed.rss
+++ b/docs/feed.rss
@@ -4,8 +4,8 @@
 		<title>Navan's Archive</title>
 		<description>Rare Tips, Tricks and Posts</description>
 		<link>https://web.navan.dev/</link><language>en</language>
-		<lastBuildDate>Sun, 22 May 2022 12:18:20 -0000</lastBuildDate>
-		<pubDate>Sun, 22 May 2022 12:18:20 -0000</pubDate>
+		<lastBuildDate>Sun, 22 May 2022 12:30:06 -0000</lastBuildDate>
+		<pubDate>Sun, 22 May 2022 12:30:06 -0000</pubDate>
 		<ttl>250</ttl>
 		<atom:link href="https://web.navan.dev/feed.rss" rel="self" type="application/rss+xml"/>
 		
@@ -776,7 +776,9 @@ export BABEL_LIBDIR="/usr/lib/openbabel/3.1.0"
 
 <p>Because of the small size of the database file, I was able to just upload the file.</p>
 
-<p>For the encoding model, I decided to use the pretrained <code>paraphrase-multilingual-MiniLM-L12-v2</code> model for SentenceTransformers, a Python framework for SOTA sentence, text and image embeddings. I wanted to use a multilingual model as I personally consume content in various languages (natively, no dubs or subs) and some of the sources for their information do not translate to English. As of writing this post, I did not include any other database except Trakt. </p>
+<p>For the encoding model, I decided to use the pretrained <code>paraphrase-multilingual-MiniLM-L12-v2</code> model for SentenceTransformers, a Python framework for SOTA sentence, text and image embeddings. 
+I wanted to use a multilingual model as I personally consume content in various languages and some of the sources for their information do not translate to English. 
+As of writing this post, I did not include any other database except Trakt. </p>
 
 <p>While deciding how I was going to process the embeddings, I came across multiple solutions:</p>
 
@@ -835,7 +837,8 @@ export BABEL_LIBDIR="/usr/lib/openbabel/3.1.0"
 
 <p>We use the <code>trakt_id</code> for the movie as the ID for the vectors and upsert it into the index. </p>
 
-<p>To find similar items, we will first have to map the name of the movie to its trakt_id, get the embeddings we have for that id and then perform a similarity search. It is possible that this additional step of mapping could be avoided by storing information as metadata in the index.</p>
+<p>To find similar items, we will first have to map the name of the movie to its trakt_id, get the embeddings we have for that id and then perform a similarity search. 
+It is possible that this additional step of mapping could be avoided by storing information as metadata in the index.</p>
 
 <div class="codehilite"><pre><span></span><code><span class="k">def</span> <span class="nf">get_trakt_id</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">title</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
   <span class="n">rec</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;title&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span><span class="o">==</span><span class="n">movie_name</span><span class="o">.</span><span class="n">lower</span><span class="p">()]</span>
diff --git a/docs/posts/2022-05-21-Similar-Movies-Recommender.html b/docs/posts/2022-05-21-Similar-Movies-Recommender.html
index 2c0b488..2e2fb6b 100644
--- a/docs/posts/2022-05-21-Similar-Movies-Recommender.html
+++ b/docs/posts/2022-05-21-Similar-Movies-Recommender.html
@@ -240,7 +240,9 @@
 
 <p>Because of the small size of the database file, I was able to just upload the file.</p>
 
-<p>For the encoding model, I decided to use the pretrained <code>paraphrase-multilingual-MiniLM-L12-v2</code> model for SentenceTransformers, a Python framework for SOTA sentence, text and image embeddings. I wanted to use a multilingual model as I personally consume content in various languages (natively, no dubs or subs) and some of the sources for their information do not translate to English. As of writing this post, I did not include any other database except Trakt. </p>
+<p>For the encoding model, I decided to use the pretrained <code>paraphrase-multilingual-MiniLM-L12-v2</code> model for SentenceTransformers, a Python framework for SOTA sentence, text and image embeddings. 
+I wanted to use a multilingual model as I personally consume content in various languages and some of the sources for their information do not translate to English. 
+As of writing this post, I did not include any other database except Trakt. </p>
 
 <p>While deciding how I was going to process the embeddings, I came across multiple solutions:</p>
 
@@ -299,7 +301,8 @@
 
 <p>We use the <code>trakt_id</code> for the movie as the ID for the vectors and upsert it into the index. </p>
 
-<p>To find similar items, we will first have to map the name of the movie to its trakt_id, get the embeddings we have for that id and then perform a similarity search. It is possible that this additional step of mapping could be avoided by storing information as metadata in the index.</p>
+<p>To find similar items, we will first have to map the name of the movie to its trakt_id, get the embeddings we have for that id and then perform a similarity search. 
+It is possible that this additional step of mapping could be avoided by storing information as metadata in the index.</p>
 
 <div class="codehilite"><pre><span></span><code><span class="k">def</span> <span class="nf">get_trakt_id</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">title</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
   <span class="n">rec</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;title&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span><span class="o">==</span><span class="n">movie_name</span><span class="o">.</span><span class="n">lower</span><span class="p">()]</span>