add note on ablation

src/index.html

@@ -706,7 +706,7 @@
         <img src="https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/fjZQ4izIj1rx1xQnBTKKr.png" alt="Prompt for LLM annotation" style="width: 90%; max-width: 800px; height: auto;">
         <figcaption style="font-style: italic; margin-top: 10px;">Prompt used for Llama3 annotations of the educational score, also available <a href="https://huggingface.co/HuggingFaceFW/fineweb-edu-classifier/blob/main/utils/prompt.txt">here</a>.</figcaption>
     </div>    
-    <p>We also experimented with  <a href="https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1">Mixtral-8x-7B-Instruct</a> and <a href="https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1">Mixtral-8x22B-Instruct</a> and a jury of all three models<d-cite bibtex-key="verga2024replacing"></d-cite> but found that Llama3 alone gave the most reliable results.</p>
+    <p>We also experimented with  <a href="https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1">Mixtral-8x7B-Instruct</a> and <a href="https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1">Mixtral-8x22B-Instruct</a> and a jury of all three models<d-cite bibtex-key="verga2024replacing"></d-cite> but found that Llama3 alone gave the most reliable results.</p>
     <h3>Classifier Training</h3>
     <p>We added a classification head with a single regression output to <a href="https://huggingface.co/Snowflake/snowflake-arctic-embed-m">Snowflake-arctic-embed</a> and trained it on 450,000 Llama 3 annotations for 20 epochs with a learning rate of 3e-4, freezing the embedding and encoder layers.  We saved the checkpoint with the highest F1 score on our held-out validation set of 45k samples, treating Llama 3 annotations as ground-truth. After training, we rounded the scores to integers from 0 to 5.</p>
     <p>We then converted the problem to a binary classification task by using a fixed threshold to determine if a file is educational. With a threshold of 3, the model achieved an F1 score of 82% on the validation set, indicating strong performance in distinguishing high-quality educational content.</p>
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         </figure>
         <div id="plot-edu-8k"></div>
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-    <p>We then built  📚 FineWeb-Edu by filtering out samples with scores lower than 3. This removed 92% of the dataset, leaving us with 1.3 trillion educational tokens.  To evaluate the effectiveness of this filtering at a larger scale, we conducted an ablation using a 1.82B model trained on 350 billion tokens, similar to the FineWeb filtering ablation mentioned above:</p>
+    <p><strong>Note:</strong> this ablation was conducted on 8B tokens from the 2024-10 dump for both the FineWeb and FineWeb-Edu subsets, which might not be representative of the entire dataset. The next ablation shows that the findings for threshold 3 hold on a longer run of 350B tokens from all FineWeb dumps, except for HellaSwag, where we noticed a slight performance degradation.</p>
+    <p>We built  📚 FineWeb-Edu by filtering out samples with scores lower than 3. This removed 92% of the dataset, leaving us with 1.3 trillion educational tokens.  To evaluate the effectiveness of this filtering at a larger scale, we conducted an ablation using a 1.82B model trained on 350 billion tokens, similar to the FineWeb filtering ablation mentioned above:</p>
     <div class="main-plot-container">
         <figure>
             <img src="assets/images/edu-100k.png">