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-  <meta name="description"
-        content="Deformable Neural Radiance Fields creates free-viewpoint portraits (nerfies) from casually captured videos.">
-  <meta name="keywords" content="Nerfies, D-NeRF, NeRF">
-  <meta name="viewport" content="width=device-width, initial-scale=1">
-  <title>Nerfies: Deformable Neural Radiance Fields</title>
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-<section class="hero">
-  <div class="hero-body">
-    <div class="container is-max-desktop">
-      <div class="columns is-centered">
-        <div class="column has-text-centered">
-          <h1 class="title is-1 publication-title">Nerfies: Deformable Neural Radiance Fields</h1>
-          <div class="is-size-5 publication-authors">
-            <span class="author-block">
-              <a href="https://keunhong.com" target="_blank">Keunhong Park</a><sup>1</sup>,</span>
-            <span class="author-block">
-              <a href="https://utkarshsinha.com" target="_blank">Utkarsh Sinha</a><sup>2</sup>,</span>
-            <span class="author-block">
-              <a href="https://jonbarron.info" target="_blank">Jonathan T. Barron</a><sup>2</sup>,
-            </span>
-            <span class="author-block">
-              <a href="http://sofienbouaziz.com" target="_blank">Sofien Bouaziz</a><sup>2</sup>,
-            </span>
-            <span class="author-block">
-              <a href="https://www.danbgoldman.com" target="_blank">Dan B Goldman</a><sup>2</sup>,
-            </span>
-            <span class="author-block">
-              <a href="https://homes.cs.washington.edu/~seitz/" target="_blank">Steven M. Seitz</a><sup>1,2</sup>,
-            </span>
-            <span class="author-block">
-              <a href="http://www.ricardomartinbrualla.com" target="_blank">Ricardo Martin-Brualla</a><sup>2</sup>
-            </span>
-          </div>
-          <div class="is-size-5 publication-authors">
-            <span class="author-block"><sup>1</sup>University of Washington,</span>
-            <span class="author-block"><sup>2</sup>Google Research</span>
-          </div>
-          <div class="column has-text-centered">
-            <div class="publication-links">
-              <!-- PDF Link. -->
-              <span class="link-block">
-                <a href="https://arxiv.org/pdf/2011.12948" target="_blank"
-                   class="external-link button is-normal is-rounded is-dark">
-                  <span class="icon">
-                      <i class="fas fa-file-pdf"></i>
-                  </span>
-                  <span>Paper</span>
-                </a>
-              </span>
-              <span class="link-block">
-                <a href="https://arxiv.org/abs/2011.12948" target="_blank"
-                   class="external-link button is-normal is-rounded is-dark">
-                  <span class="icon">
-                      <i class="ai ai-arxiv"></i>
-                  </span>
-                  <span>arXiv</span>
-                </a>
-              </span>
-              <!-- Video Link. -->
-              <span class="link-block">
-                <a href="https://www.youtube.com/watch?v=MrKrnHhk8IA" target="_blank"
-                   class="external-link button is-normal is-rounded is-dark">
-                  <span class="icon">
-                      <i class="fab fa-youtube"></i>
-                  </span>
-                  <span>Video</span>
-                </a>
-              </span>
-              <!-- Code Link. -->
-              <span class="link-block">
-                <a href="https://github.com/google/nerfies" target="_blank"
-                   class="external-link button is-normal is-rounded is-dark">
-                  <span class="icon">
-                      <i class="fab fa-github"></i>
-                  </span>
-                  <span>Code</span>
-                  </a>
-              </span>
-              <!-- Dataset Link. -->
-              <span class="link-block">
-                <a href="https://github.com/google/nerfies/releases/tag/0.1" target="_blank"
-                   class="external-link button is-normal is-rounded is-dark">
-                  <span class="icon">
-                      <i class="far fa-images"></i>
-                  </span>
-                  <span>Data</span>
-                  </a>
-            </div>
-          </div>
-        </div>
-      </div>
-    </div>
-  </div>
-</section>
-<section class="hero teaser">
-  <div class="container is-max-desktop">
-    <div class="hero-body">
-      <video id="teaser" autoplay muted loop playsinline height="100%">
-        <source src="./static/videos/teaser.mp4"
-                type="video/mp4">
-      </video>
-      <h2 class="subtitle has-text-centered">
-        <span class="dnerf">Nerfies</span> turns selfie videos from your phone into
-        free-viewpoint
-        portraits.
-      </h2>
-    </div>
-  </div>
-</section>
-<section class="hero is-light is-small">
-  <div class="hero-body">
-    <div class="container">
-      <div id="results-carousel" class="carousel results-carousel">
-        <div class="item item-steve">
-          <video poster="" id="steve" autoplay controls muted loop playsinline height="100%">
-            <source src="./static/videos/steve.mp4"
-                    type="video/mp4">
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-        <div class="item item-chair-tp">
-          <video poster="" id="chair-tp" autoplay controls muted loop playsinline height="100%">
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-                    type="video/mp4">
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-        </div>
-        <div class="item item-fullbody">
-          <video poster="" id="fullbody" autoplay controls muted loop playsinline height="100%">
-            <source src="./static/videos/fullbody.mp4"
-                    type="video/mp4">
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-          <video poster="" id="blueshirt" autoplay controls muted loop playsinline height="100%">
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-                    type="video/mp4">
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-          <video poster="" id="mask" autoplay controls muted loop playsinline height="100%">
-            <source src="./static/videos/mask.mp4"
-                    type="video/mp4">
-          </video>
-        </div>
-        <div class="item item-coffee">
-          <video poster="" id="coffee" autoplay controls muted loop playsinline height="100%">
-            <source src="./static/videos/coffee.mp4"
-                    type="video/mp4">
-          </video>
-        </div>
-        <div class="item item-toby">
-          <video poster="" id="toby" autoplay controls muted loop playsinline height="100%">
-            <source src="./static/videos/toby2.mp4"
-                    type="video/mp4">
-          </video>
-        </div>
-      </div>
-    </div>
-  </div>
-</section>
-<section class="section">
-  <div class="container is-max-desktop">
-    <!-- Abstract. -->
-    <div class="columns is-centered has-text-centered">
-      <div class="column is-four-fifths">
-        <h2 class="title is-3">Abstract</h2>
-        <div class="content has-text-justified">
-          <p>
-            We present the first method capable of photorealistically reconstructing a non-rigidly
-            deforming scene using photos/videos captured casually from mobile phones.
-          </p>
-          <p>
-            Our approach augments neural radiance fields
-            (NeRF) by optimizing an
-            additional continuous volumetric deformation field that warps each observed point into a
-            canonical 5D NeRF.
-            We observe that these NeRF-like deformation fields are prone to local minima, and
-            propose a coarse-to-fine optimization method for coordinate-based models that allows for
-            more robust optimization.
-            By adapting principles from geometry processing and physical simulation to NeRF-like
-            models, we propose an elastic regularization of the deformation field that further
-            improves robustness.
-          </p>
-          <p>
-            We show that <span class="dnerf">Nerfies</span> can turn casually captured selfie
-            photos/videos into deformable NeRF
-            models that allow for photorealistic renderings of the subject from arbitrary
-            viewpoints, which we dub <i>"nerfies"</i>. We evaluate our method by collecting data
-            using a
-            rig with two mobile phones that take time-synchronized photos, yielding train/validation
-            images of the same pose at different viewpoints. We show that our method faithfully
-            reconstructs non-rigidly deforming scenes and reproduces unseen views with high
-            fidelity.
-          </p>
-        </div>
-      </div>
-    </div>
-    <!--/ Abstract. -->
-    <!-- Paper video. -->
-    <div class="columns is-centered has-text-centered">
-      <div class="column is-four-fifths">
-        <h2 class="title is-3">Video</h2>
-        <div class="publication-video">
-          <iframe src="https://www.youtube.com/embed/MrKrnHhk8IA?rel=0&amp;showinfo=0"
-                  frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
-        </div>
-      </div>
-    </div>
-    <!--/ Paper video. -->
-  </div>
-</section>
-<section class="section">
-  <div class="container is-max-desktop">
-    <div class="columns is-centered">
-      <!-- Visual Effects. -->
-      <div class="column">
-        <div class="content">
-          <h2 class="title is-3">Visual Effects</h2>
-          <p>
-            Using <i>nerfies</i> you can create fun visual effects. This Dolly zoom effect
-            would be impossible without nerfies since it would require going through a wall.
-          </p>
-          <video id="dollyzoom" autoplay controls muted loop playsinline height="100%">
-            <source src="./static/videos/dollyzoom-stacked.mp4"
-                    type="video/mp4">
-          </video>
-        </div>
-      </div>
-      <!--/ Visual Effects. -->
-      <!-- Matting. -->
-      <div class="column">
-        <h2 class="title is-3">Matting</h2>
-        <div class="columns is-centered">
-          <div class="column content">
-            <p>
-              As a byproduct of our method, we can also solve the matting problem by ignoring
-              samples that fall outside of a bounding box during rendering.
-            </p>
-            <video id="matting-video" controls playsinline height="100%">
-              <source src="./static/videos/matting.mp4"
-                      type="video/mp4">
-            </video>
-          </div>
-        </div>
-      </div>
-    </div>
-    <!--/ Matting. -->
-    <!-- Animation. -->
-    <div class="columns is-centered">
-      <div class="column is-full-width">
-        <h2 class="title is-3">Animation</h2>
-        <!-- Interpolating. -->
-        <h3 class="title is-4">Interpolating states</h3>
-        <div class="content has-text-justified">
-          <p>
-            We can also animate the scene by interpolating the deformation latent codes of two input
-            frames. Use the slider here to linearly interpolate between the left frame and the right
-            frame.
-          </p>
-        </div>
-        <div class="columns is-vcentered interpolation-panel">
-          <div class="column is-3 has-text-centered">
-            <img src="./static/images/interpolate_start.jpg"
-                 class="interpolation-image"
-                 alt="Interpolate start reference image."/>
-            <p>Start Frame</p>
-          </div>
-          <div class="column interpolation-video-column">
-            <div id="interpolation-image-wrapper">
-              Loading...
-            </div>
-            <input class="slider is-fullwidth is-large is-info"
-                   id="interpolation-slider"
-                   step="1" min="0" max="100" value="0" type="range">
-          </div>
-          <div class="column is-3 has-text-centered">
-            <img src="./static/images/interpolate_end.jpg"
-                 class="interpolation-image"
-                 alt="Interpolation end reference image."/>
-            <p class="is-bold">End Frame</p>
-          </div>
-        </div>
-        <br/>
-        <!--/ Interpolating. -->
-        <!-- Re-rendering. -->
-        <h3 class="title is-4">Re-rendering the input video</h3>
-        <div class="content has-text-justified">
-          <p>
-            Using <span class="dnerf">Nerfies</span>, you can re-render a video from a novel
-            viewpoint such as a stabilized camera by playing back the training deformations.
-          </p>
-        </div>
-        <div class="content has-text-centered">
-          <video id="replay-video"
-                 controls
-                 muted
-                 preload
-                 playsinline
-                 width="75%">
-            <source src="./static/videos/replay.mp4"
-                    type="video/mp4">
-          </video>
-        </div>
-        <!--/ Re-rendering. -->
-      </div>
-    </div>
-    <!--/ Animation. -->
-    <!-- Concurrent Work. -->
-    <div class="columns is-centered">
-      <div class="column is-full-width">
-        <h2 class="title is-3">Related Links</h2>
-        <div class="content has-text-justified">
-          <p>
-            There's a lot of excellent work that was introduced around the same time as ours.
-          </p>
-          <p>
-            <a href="https://arxiv.org/abs/2104.09125" target="_blank">Progressive Encoding for Neural Optimization</a> introduces an idea similar to our windowed position encoding for coarse-to-fine optimization.
-          </p>
-          <p>
-            <a href="https://www.albertpumarola.com/research/D-NeRF/index.html" target="_blank">D-NeRF</a> and <a href="https://gvv.mpi-inf.mpg.de/projects/nonrigid_nerf/" target="_blank">NR-NeRF</a>
-            both use deformation fields to model non-rigid scenes.
-          </p>
-          <p>
-            Some works model videos with a NeRF by directly modulating the density, such as <a href="https://video-nerf.github.io/" target="_blank">Video-NeRF</a>, <a href="https://www.cs.cornell.edu/~zl548/NSFF/" target="_blank">NSFF</a>, and <a href="https://neural-3d-video.github.io/" target="_blank">DyNeRF</a>
-          </p>
-          <p>
-            There are probably many more by the time you are reading this. Check out <a href="https://dellaert.github.io/NeRF/" target="_blank">Frank Dellart's survey on recent NeRF papers</a>, and <a href="https://github.com/yenchenlin/awesome-NeRF" target="_blank">Yen-Chen Lin's curated list of NeRF papers</a>.
-          </p>
-        </div>
-      </div>
-    </div>
-    <!--/ Concurrent Work. -->
-  </div>
-</section>
-<section class="section" id="BibTeX">
-  <div class="container is-max-desktop content">
-    <h2 class="title">BibTeX</h2>
-    <pre><code>@article{park2021nerfies,
-  author    = {Park, Keunhong and Sinha, Utkarsh and Barron, Jonathan T. and Bouaziz, Sofien and Goldman, Dan B and Seitz, Steven M. and Martin-Brualla, Ricardo},
-  title     = {Nerfies: Deformable Neural Radiance Fields},
-  journal   = {ICCV},
-  year      = {2021},
-}</code></pre>
-  </div>
-</section>
-<footer class="footer">
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-          <p>
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+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=5.0, minimum-scale=1.0, user-scalable=yes">
+    <title>Practical Applications of Hugging Face Transformers in NLP</title>
+    <link rel="icon" type="image/svg+xml" href="data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24' fill='none' stroke='%232D3748' stroke-width='2' stroke-linecap='round' stroke-linejoin='round'%3E%3Cpath d='M12 2L2 7L12 12L22 7L12 2Z'/%3E%3Cpath d='M2 17L12 22L22 17'/%3E%3Cpath d='M2 12L12 17L22 12'/%3E%3C/svg%3E">
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+        }
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+            background-color: #EAECEF;
+            padding: 2px 4px;
+            border-radius: 3px;
+            font-family: monospace;
+        }
+    </style>
 </head>
 <body>
+    <header>
+        <h1>Practical Applications of Hugging Face Transformers in Natural Language Processing</h1>
+        <p><strong>Author:</strong> [Your Name]</p>
+        <p><strong>Date:</strong> [Publication Date]</p>
+    </header>
+    <section>
+        <h2>Introduction</h2>
+        <p>Hugging Face Transformers have revolutionized Natural Language Processing (NLP) by providing versatile models capable of understanding and generating human-like text.
+        Beyond traditional applications, these models are increasingly influential in specialized domains, including <strong>code generation</strong>, where they assist in tasks like code completion and synthesis.</p>
+    </section>
+    <section>
+        <h2>Performance Enhancements Through Fine-Tuning</h2>
+        <p>Fine-tuning pre-trained Transformer models on domain-specific datasets significantly enhances their performance. For instance, in code-related tasks such as
+        <strong>code summarization</strong> and <strong>bug detection</strong>, fine-tuning on specialized code datasets has led to notable improvements.</p>
+        <p>Models like CodeGen, trained on extensive code repositories, have demonstrated remarkable proficiency in generating accurate and efficient code snippets.</p>
+        <p>Source: <a href="https://huggingface.co/docs/transformers/en/model_doc/codegen">Hugging Face CodeGen</a></p>
+    </section>
+    <section>
+        <h2>Hybrid Model Advantages</h2>
+        <p>Integrating Transformer-based embeddings with traditional programming analysis methods offers substantial benefits in <strong>code analysis</strong> and <strong>generation</strong>.
+        This hybrid approach leverages the contextual understanding of Transformers alongside established static analysis techniques, resulting in more robust and reliable code generation systems.</p>
+    </section>
+    <section>
+        <h2>Industry-Specific Applications</h2>
+        <h3>Customer Service</h3>
+        <p>In customer service, Transformers have been utilized to enhance automated support systems. Notably, they can generate <strong>code snippets</strong> for technical queries,
+        enabling chatbots to provide precise solutions to programming-related questions.</p>
+        <h3>Software Development</h3>
+        <p>Transformers are transforming software development by automating code generation tasks. Models like <strong>CodeGen</strong>, developed through collaborations within the
+        Hugging Face community, can generate code across multiple programming languages, streamlining the development process.</p>
+        <p>Source: <a href="https://huggingface.co/docs/transformers/en/model_doc/codegen">Hugging Face CodeGen</a></p>
+    </section>
+    <section>
+        <h2>Optimization Techniques</h2>
+        <p>Deploying large Transformer models in code-related applications necessitates efficient optimization strategies. Techniques such as <strong>quantization</strong> and
+        <strong>pruning</strong> are essential to reduce latency, ensuring real-time code generation without compromising accuracy.</p>
+    </section>
+    <section>
+        <h2>Ethical Considerations and Bias Mitigation</h2>
+        <p>While code-generating Transformers offer significant advantages, they may inadvertently introduce <strong>security vulnerabilities</strong> or propagate
+        <strong>inefficient coding practices</strong>. Ongoing research focuses on mitigating these risks by implementing robust bias detection and correction mechanisms,
+        ensuring the generated code adheres to best practices and security standards.</p>
+    </section>
+    <section>
+        <h2>Community Contributions</h2>
+        <p>The Hugging Face community plays a pivotal role in advancing code-related Transformer models. Collaborative efforts have led to the development of specialized
+        models and datasets, which are openly accessible for further research and application.</p>
+    </section>
+    <section>
+        <h2>Conclusion</h2>
+        <p>Hugging Face Transformers continue to reshape the NLP landscape, extending their capabilities to domains like <strong>code generation</strong>. Their adaptability
+        and performance enhancements hold the potential to revolutionize software development, making coding more efficient and accessible.</p>
+    </section>
+    <footer>
+        <p>Published under <a href="https://opensource.org/licenses/MIT">MIT License</a></p>
+    </footer>
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