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-    <h2 class="title is-3">🧩 Main Pipeline Steps</h2>
-    <figure>
-      <img src="https://cdn-uploads.huggingface.co/production/uploads/64bfc4d55ce3d382c05c0f9a/1zPQcwqt9Li_gCvd04_2_.png" alt="JQL Pipeline Overview">
-      <figcaption><em>Figure 1: Overview of the JQL pipeline</em></figcaption>
-    </figure>
-
-    <ol>
-      <li><strong>📋 Ground Truth Creation:</strong> Human annotators label monolingual documents based on a structured instruction prompt. These documents are translated into all target languages to create a multilingual gold-standard dataset. (See Figure 1)</li>
-      <li><strong>🤖 LLM-as-a-Judge Selection & Data Annotation:</strong> Strong multilingual LLMs (e.g., Gemma, Mistral, LLaMA) are evaluated against the ground truth, and top-performing models are used to produce synthetic annotations. (See Figure 1)</li>
-      <li><strong>🪶 Lightweight Annotator Training:</strong> Train compact regression heads on frozen multilingual embeddings to create efficient, high-throughput annotators. (See Figure 1)</li>
-      <li><strong>🚀 Scalable Data Filtering:</strong> Use trained annotators to filter large-scale pretraining corpora using quantile thresholds. (See Figure 1)</li>
-    </ol>
+    <h2 class="title is-3">📁 Available Artifacts</h2>
+    <ul>
+      <li><a href="https://huggingface.co/datasets/Jackal-AI/JQL-Human-Edu-Annotations" target="_blank">📄 Ground truth annotations in 35 languages</a></li>
+      <li><a href="https://huggingface.co/datasets/Jackal-AI/JQL-LLM-Edu-Annotations" target="_blank">🧠 Synthetic LLM-annotated dataset (14M+ documents)</a></li>
+      <li><a href="https://huggingface.co/Jackal-AI/JQL-Edu-Heads" target="_blank">🪶 Lightweight annotation models</a>:
+        <ul>
+          <li>JQL-Gemma</li>
+          <li>JQL-Mistral</li>
+          <li>JQL-Llama</li>
+        </ul>
+      </li>
+      <li>🛠️ Training & inference scripts: Coming soon</li>
+    </ul>
   </div>
 </section>