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dataset release
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awards.html

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<div class="banner" style="background: url('img/bari.jpg') no-repeat center; background-size: cover; height: 500px;">
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<div class="banner-table flex-column" style="background-color: rgba(0, 0, 0, 0.75);">
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<div class="flex-item flex-column">s
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<h1 class="add-top-margin-small strokeme">
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Task - Paper Awards
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</h1>

index.html

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<h3>News</h3>
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<hr>
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<ul>
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<li>
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<p class="text">
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<b>Registration is open!</b></br>
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You can now register for the challenge by filling out the form
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<a href="https://docs.google.com/forms/d/1Vm3GD6NoyGbwq6vSS6oEFKTFbdp2j-thQSUox0sptXo/edit" target="_blank">here</a>.
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</p>
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</li>
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<li>
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<p class="text">
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<b>SVELA task announced!</b></br>
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The SVELA task has been officially announced as part of EVALITA 2026 Evaluation Campaign.
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</p>
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</li>
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<li>
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<p class="text">
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<b>Official task website is live!</b></br>
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The official website for SVELA @ EVALITA 2026 is now live.
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</p>
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</li>
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</ul>
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<hr>
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<ul>
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<li>
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<p class="text">
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<b>New dataset splits released!</b></br>
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The SVELA <a href="https://huggingface.co/datasets/ClaudioSavelli/SVELA-train-split" target="_blank">train split</a>
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(with retain, forget, and test partitions) and the
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<a href="https://huggingface.co/datasets/ClaudioSavelli/SVELA-val-split" target="_blank">validation split</a>
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(unlabeled) are now available on Hugging Face.
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</p>
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</li>
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<li>
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<p class="text">
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<b>Registration is open!</b></br>
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You can now register for the challenge by filling out the form
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<a href="https://docs.google.com/forms/d/1Vm3GD6NoyGbwq6vSS6oEFKTFbdp2j-thQSUox0sptXo/edit" target="_blank">here</a>.
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</p>
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</li>
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<li>
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<p class="text">
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<b>SVELA task announced!</b></br>
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The SVELA task has been officially announced as part of EVALITA 2026 Evaluation Campaign.
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</p>
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</li>
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<li>
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<p class="text">
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<b>Official task website is live!</b></br>
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The official website for SVELA @ EVALITA 2026 is now live.
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</p>
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</li>
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</ul>
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<!-- <h3>Co-located with ICASSP 2025</h3>

workshop.html

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<p class="text">
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SVELA introduces the first multilingual synthetic dataset of fictional identities, ensuring that any knowledge the model shows comes solely from controlled fine-tuning, making forgetting measurable and reliable.
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</p>
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<!-- NEW SECTION: Dataset Splits Release -->
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<h3>Dataset Splits Release</h3>
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<p class="text">
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We have released official dataset splits on Hugging Face:
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</p>
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<ul>
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<li>
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<strong>SVELA – Train Split:</strong>
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<a href="https://huggingface.co/datasets/ClaudioSavelli/SVELA-train-split" target="_blank">view here</a>.<br>
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Contains identities, topic IDs, and questions with labels for <em>retain</em>, <em>forget</em>, and <em>test</em>.
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</li>
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<li>
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<strong>SVELA – Validation Split:</strong>
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<a href="https://huggingface.co/datasets/ClaudioSavelli/SVELA-val-split" target="_blank">view here</a>.<br>
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Larger set with the same structure but <em>unlabeled</em>, provided for validation.
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</li>
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</ul>
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<p class="text">
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You can easily load the data using the 🤗 <code>datasets</code> library:
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</p>
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<pre><code class="language-python">from datasets import load_dataset
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# --- Train split with labeled partitions ---
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retain = load_dataset("ClaudioSavelli/SVELA-train-split", split="retain")
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forget = load_dataset("ClaudioSavelli/SVELA-train-split", split="forget")
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test = load_dataset("ClaudioSavelli/SVELA-train-split", split="test")
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# --- Unlabeled validation split ---
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val = load_dataset("ClaudioSavelli/SVELA-val-split", split="train")
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print(retain.column_names)
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# ['identity_id', 'name', 'language', 'topic_id', 'question']</code></pre>
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<!-- END NEW SECTION -->
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<h3>Dataset Construction</h3>
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<p class="text">

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