scott hanson bot added

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@@ -73,20 +73,23 @@
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-        <title>Huggingface&#x2F;datasets</title>
-        <published>2021-08-28T14:13:14.674+00:00</published>
-        <updated>2021-08-28T14:13:14.674+00:00</updated>
+        <title>Scott Hanson Bot</title>
+        <published>2022-09-12T00:00:00+00:00</published>
+        <updated>2022-09-12T00:00:00+00:00</updated>
         <author>
           <name>Unknown</name>
         </author>
-        <link rel="alternate" href="https://bjornelvar.dev/opensource/huggingface-datasets/" type="text/html"/>
-        <id>https://bjornelvar.dev/opensource/huggingface-datasets/</id>
-        <content type="html">&lt;p&gt;🤗 Datasets is a lightweight library providing two main features:&lt;&#x2F;p&gt;
-&lt;p&gt;one-line dataloaders for many public datasets: &lt;&#x2F;p&gt;
-&lt;ul&gt;
-&lt;li&gt;one liners to download and pre-process any of the number of datasets major public datasets (in 467 languages and dialects!) provided on the HuggingFace Datasets Hub. With a simple command like &lt;code&gt;squad_dataset = load_dataset(&amp;quot;squad&amp;quot;)&lt;&#x2F;code&gt;, get any of these datasets ready to use in a dataloader for training&#x2F;evaluating a ML model (Numpy&#x2F;Pandas&#x2F;PyTorch&#x2F;TensorFlow&#x2F;JAX),&lt;&#x2F;li&gt;
-&lt;li&gt;efficient data pre-processing: simple, fast and reproducible data pre-processing for the above public datasets as well as your own local datasets in CSV&#x2F;JSON&#x2F;text. With simple commands like &lt;code&gt;tokenized_dataset = dataset.map(tokenize_example)&lt;&#x2F;code&gt;, efficiently prepare the dataset for inspection and ML model evaluation and training.&lt;&#x2F;li&gt;
-&lt;&#x2F;ul&gt;
+        <link rel="alternate" href="https://bjornelvar.dev/projects/scott-hanson-bot/" type="text/html"/>
+        <id>https://bjornelvar.dev/projects/scott-hanson-bot/</id>
+        <content type="html">&lt;p&gt;&lt;img src=&quot;&#x2F;media&#x2F;scott_help.png&quot; alt=&quot;Scott Help&quot; &#x2F;&gt;&lt;&#x2F;p&gt;
+&lt;h3 id=&quot;description&quot;&gt;Description&lt;&#x2F;h3&gt;
+&lt;p&gt;I created this Discord bot for my Fantasy Football League. I wanted my Discord server to be more interactive and fun, as well as to archive some things, like what was the score in this particular matchup at this particular point in time. I also wanted to be able to see the current standings, current schedule and the top performers for every position and every week.&lt;&#x2F;p&gt;
+&lt;p&gt;I wrote the bot in Python and used the &lt;a href=&quot;https:&#x2F;&#x2F;discordpy.readthedocs.io&#x2F;en&#x2F;latest&#x2F;&quot;&gt;Discord.py&lt;&#x2F;a&gt; library. I also used &lt;a href=&quot;https:&#x2F;&#x2F;www.crummy.com&#x2F;software&#x2F;BeautifulSoup&#x2F;bs4&#x2F;doc&#x2F;&quot;&gt;Beautiful Soup&lt;&#x2F;a&gt; to scrape the data from the league&#x27;s website. I then used &lt;a href=&quot;https:&#x2F;&#x2F;pypi.org&#x2F;project&#x2F;table2ascii&#x2F;&quot;&gt;Table2Ascii&lt;&#x2F;a&gt; to output the data in a nice format for the Discord bot to send. The bot was hosted on my friends Raspberry Pi, to update the bot remotely I used a Discord command I wrote that runs a bash script on the Raspberry Pi.&lt;&#x2F;p&gt;
+&lt;p&gt;My future plans for next season is to refactor the code and make it more modular. Then I would like to use more of the &lt;a href=&quot;https:&#x2F;&#x2F;pandas.pydata.org&#x2F;&quot;&gt;Pandas library&lt;&#x2F;a&gt; to make the code more efficient and easier to read. After that, I would like to implement the &lt;a href=&quot;https:&#x2F;&#x2F;github.com&#x2F;uberfastman&#x2F;yfpy&quot;&gt;yfpy&lt;&#x2F;a&gt; to get historical data from our league.&lt;&#x2F;p&gt;
+&lt;h3 id=&quot;examples&quot;&gt;Examples&lt;&#x2F;h3&gt;
+&lt;p&gt;&lt;img src=&quot;&#x2F;media&#x2F;scott_top_rb.png&quot; alt=&quot;Scott Top RB&quot; &#x2F;&gt;
+&lt;img src=&quot;&#x2F;media&#x2F;scott_standings.png&quot; alt=&quot;Scott Standings&quot; &#x2F;&gt;
+&lt;img src=&quot;&#x2F;media&#x2F;scott_scores.png&quot; alt=&quot;Scott Scores&quot; &#x2F;&gt;&lt;&#x2F;p&gt;
 </content>
     </entry>
     <entry xml:lang="en">

index.html

@@ -361,10 +361,6 @@ <h3 class="post-title"><a href="https:&#x2F;&#x2F;bjornelvar.dev&#x2F;projects&#
 
 
 
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@@ -411,6 +407,14 @@ <h3 class="post-title"><a href="https:&#x2F;&#x2F;bjornelvar.dev&#x2F;projects&#
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   <h3><a href="https:&#x2F;&#x2F;bjornelvar.dev&#x2F;projects&#x2F;">Other projects here!</a></h3>

json/index.html

@@ -4,27 +4,27 @@
 
 
       {
-      "title": "Huggingface/datasets",
-      "url": "https://bjornelvar.dev/opensource/huggingface-datasets/",
-      "body": "🤗 Datasets is a lightweight library providing two main features:\none-line dataloaders for many public datasets: \n\none liners to download and pre-process any of the number of datasets major public datasets (in 467 languages and dialects!) provided on the HuggingFace Datasets Hub. With a simple command like squad_dataset = load_dataset("squad"), get any of these datasets ready to use in a dataloader for training/evaluating a ML model (Numpy/Pandas/PyTorch/TensorFlow/JAX),\nefficient data pre-processing: simple, fast and reproducible data pre-processing for the above public datasets as well as your own local datasets in CSV/JSON/text. With simple commands like tokenized_dataset = dataset.map(tokenize_example), efficiently prepare the dataset for inspection and ML model evaluation and training.\n\n"
+      "title": "Anti-Clockwise Rocket Science",
+      "url": "https://bjornelvar.dev/projects/anti-clockwise-rocket-science/",
+      "body": "\nPLAY HERE\nDescription\nAnti-Clockwise Rocket Science is what is best described as a 1 player ping pong game. The objective is to keep hitting the ball, bouncing it back and forth between yourself or your friendly UFO. This game was created in the first week of the 3-week course Computer Game Design and Development at Reykjavik University.\nI wanted to make this game as zen as possible and I believe I achieved that. I wanted the game to never increase in difficulty, but rather to be a relaxing experience completed by the background music and sound effects.\n"
       }
-
     ,
 
+
 
       {
-      "title": "NBA Scores: An Alfred Workflow",
-      "url": "https://bjornelvar.dev/projects/nba-scores-alfred/",
-      "body": "\n\n*Featured on Alfred Gallery!*\nDescription\nOne evening, my partner asked me what games were on tonight and I instinctively went to type nba in Alfred. I of course didn't have any workflow for it, so I decided to build one myself. I used the NBA API to get the data and then built a workflow around it using Python. And a lot of JSON parsing.\nIf you're interested in that kind of stuff, you can check out my Github repository.\nHow to use it:\n\nAn Alfred Powerpack is required. (Sorry)\nDownload the workflow from Alfred Gallery.\nDouble click it to install.\nType nba in Alfred.\nPress enter or use the built-in hotkey on a game to see the box score or summary.\n\nExamples\n\n\nCredits\n\nNBA API\nKristjana Ósk for asking me what games were on tonight.\n\n"
+      "title": "Scott Hanson Bot",
+      "url": "https://bjornelvar.dev/projects/scott-hanson-bot/",
+      "body": "\nDescription\nI created this Discord bot for my Fantasy Football League. I wanted my Discord server to be more interactive and fun, as well as to archive some things, like what was the score in this particular matchup at this particular point in time. I also wanted to be able to see the current standings, current schedule and the top performers for every position and every week.\nI wrote the bot in Python and used the Discord.py library. I also used Beautiful Soup to scrape the data from the league's website. I then used Table2Ascii to output the data in a nice format for the Discord bot to send. The bot was hosted on my friends Raspberry Pi, to update the bot remotely I used a Discord command I wrote that runs a bash script on the Raspberry Pi.\nMy future plans for next season is to refactor the code and make it more modular. Then I would like to use more of the Pandas library to make the code more efficient and easier to read. After that, I would like to implement the yfpy to get historical data from our league.\nExamples\n\n\n\n"
       }
     ,
 
 
 
       {
-      "title": "Anti-Clockwise Rocket Science",
-      "url": "https://bjornelvar.dev/projects/anti-clockwise-rocket-science/",
-      "body": "\nPLAY HERE\nDescription\nAnti-Clockwise Rocket Science is what is best described as a 1 player ping pong game. The objective is to keep hitting the ball, bouncing it back and forth between yourself or your friendly UFO. This game was created in the first week of the 3-week course Computer Game Design and Development at Reykjavik University.\nI wanted to make this game as zen as possible and I believe I achieved that. I wanted the game to never increase in difficulty, but rather to be a relaxing experience completed by the background music and sound effects.\n"
+      "title": "NBA Scores: An Alfred Workflow",
+      "url": "https://bjornelvar.dev/projects/nba-scores-alfred/",
+      "body": "\n\n*Featured on Alfred Gallery!*\nDescription\nOne evening, my partner asked me what games were on tonight and I instinctively went to type nba in Alfred. I of course didn't have any workflow for it, so I decided to build one myself. I used the NBA API to get the data and then built a workflow around it using Python. And a lot of JSON parsing.\nIf you're interested in that kind of stuff, you can check out my Github repository.\nHow to use it:\n\nAn Alfred Powerpack is required. (Sorry)\nDownload the workflow from Alfred Gallery.\nDouble click it to install.\nType nba in Alfred.\nPress enter or use the built-in hotkey on a game to see the box score or summary.\n\nExamples\n\n\nCredits\n\nNBA API\nKristjana Ósk for asking me what games were on tonight.\n\n"
       }
     ,
 
@@ -39,17 +39,17 @@
     ,
 
       {
-      "title": "N-LTP: A Open-source Neural Chinese Language Technology Platform with Pretrained Models",
-      "url": "https://bjornelvar.dev/publications/n-ltp-a-open-source-neural-chinese-language-technology-platform-with-pretrained-models/",
-      "body": "An open-source neural language technology platform supporting six fundamental Chinese NLP tasks: \n\nlexical analysis (Chinese word segmentation, part-of-speech tagging, and named entity recognition)\nsyntactic parsing (dependency parsing)\nsemantic parsing (semantic dependency parsing and semantic role labeling). \n\nUnlike the existing state-of-the-art toolkits, such as Stanza, that adopt an independent model for each task, N-LTP adopts the multi-task framework by using a shared pre-trained model, which has the advantage of capturing the shared knowledge across relevant Chinese tasks. \nIn addition, knowledge distillation where the single-task model teaches the multi-task model is further introduced to encourage the multi-task model to surpass its single-task teacher.\nFinally, we provide a collection of easy-to-use APIs and a visualization tool to make users easier to use and view the processing results directly. To the best of our knowledge, this is the first toolkit to support six Chinese NLP fundamental tasks. \n"
+      "title": "HIT-SCIR at MRP 2020: Transition-based Parser and Iterative Inference Parser",
+      "url": "https://bjornelvar.dev/publications/hit-scir-at-mrp-2020-transition-based-parser-and-iterative-inference-parser/",
+      "body": "This paper describes our submission system (HIT-SCIR) for the CoNLL 2020 shared task: Cross-Framework and Cross-Lingual Meaning Representation Parsing. \nThe task includes five frameworks for graph-based meaning representations, i.e., UCCA, EDS, PTG, AMR, and DRG. \nOur solution consists of two sub-systems: \n\ntransition-based parser for Flavor (1) frameworks (UCCA, EDS, PTG)\niterative inference parser for Flavor (2) frameworks (DRG, AMR). \n\nIn the final evaluation, our system is ranked 3rd among the seven team both in Cross-Framework Track and Cross-Lingual Track, with the macro-averaged MRP F1 score of 0.81/0.69.\n"
       }
     ,
 
 
       {
-      "title": "HIT-SCIR at MRP 2020: Transition-based Parser and Iterative Inference Parser",
-      "url": "https://bjornelvar.dev/publications/hit-scir-at-mrp-2020-transition-based-parser-and-iterative-inference-parser/",
-      "body": "This paper describes our submission system (HIT-SCIR) for the CoNLL 2020 shared task: Cross-Framework and Cross-Lingual Meaning Representation Parsing. \nThe task includes five frameworks for graph-based meaning representations, i.e., UCCA, EDS, PTG, AMR, and DRG. \nOur solution consists of two sub-systems: \n\ntransition-based parser for Flavor (1) frameworks (UCCA, EDS, PTG)\niterative inference parser for Flavor (2) frameworks (DRG, AMR). \n\nIn the final evaluation, our system is ranked 3rd among the seven team both in Cross-Framework Track and Cross-Lingual Track, with the macro-averaged MRP F1 score of 0.81/0.69.\n"
+      "title": "N-LTP: A Open-source Neural Chinese Language Technology Platform with Pretrained Models",
+      "url": "https://bjornelvar.dev/publications/n-ltp-a-open-source-neural-chinese-language-technology-platform-with-pretrained-models/",
+      "body": "An open-source neural language technology platform supporting six fundamental Chinese NLP tasks: \n\nlexical analysis (Chinese word segmentation, part-of-speech tagging, and named entity recognition)\nsyntactic parsing (dependency parsing)\nsemantic parsing (semantic dependency parsing and semantic role labeling). \n\nUnlike the existing state-of-the-art toolkits, such as Stanza, that adopt an independent model for each task, N-LTP adopts the multi-task framework by using a shared pre-trained model, which has the advantage of capturing the shared knowledge across relevant Chinese tasks. \nIn addition, knowledge distillation where the single-task model teaches the multi-task model is further introduced to encourage the multi-task model to surpass its single-task teacher.\nFinally, we provide a collection of easy-to-use APIs and a visualization tool to make users easier to use and view the processing results directly. To the best of our knowledge, this is the first toolkit to support six Chinese NLP fundamental tasks. \n"
       }
 
     ]

opensource/index.html

@@ -90,52 +90,11 @@
 <div class="container is-fluid" style="min-height: 100vh;">
   <div class="is-flex is-flex-direction-column is-justify-content-center" style="min-height: 100vh;">
     <div>
-      <h1>Open Source Contributions</h1>
-      <div class="content"><p>A collection of efforts to which I contributed, but did not create. Contributing back to Open Source projects is a strong passion of mine, and requires a considerate approach to learn norms, standards and approach for each community for a successful merge!</p>
+      <h1>Open Source Placeholder</h1>
+      <div class="content"><p>Open source placeholder</p>
 </div>
     </div>
 
-    <div class="mb-5">
-      <nav class="level mb-0">
-        <div class="level-left">
-          <div class="level-item">
-            <h3 class="post-title"><a href="https:&#x2F;&#x2F;bjornelvar.dev&#x2F;opensource&#x2F;huggingface-datasets&#x2F;">Huggingface&#x2F;datasets</a></h3>
-          </div>
-
-        </div>
-        <div class="level-right">
-          <div class="level-item">
-            <span class="post-date">2021-08-28</span>
-          </div>
-        </div>
-      </nav>
-      <div class="is-flex is-flex-direction-row">
-
-        <div class="content" style="width: 100%;">
-
-          <figure class="image is-128x128" style="float: left;">
-
-            <img src="https://bjornelvar.dev/media/huggingface_logo.svg">
-
-          </figure>
-
-          <p>🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools</p>
-
-          <a href="https:&#x2F;&#x2F;bjornelvar.dev&#x2F;opensource&#x2F;huggingface-datasets&#x2F;">Read More</a>
-        </div>
-
-      </div>
-
-      <div class="tags">
-
-        <span class="tag"><a href="/tags/nlp">NLP</a></span>
-
-        <span class="tag"><a href="/tags/datasets">Datasets</a></span>
-
-      </div>
-
-    </div>
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