nice synthetic_datat + aside spacing + orderiing of dropdown
Browse files- assets/data/plots/stats/lines_chars.json +1 -1
- assets/data/plots/stats/lines_ended_with_punct.json +1 -1
- assets/data/plots/stats/short_lines.json +1 -1
- assets/data/plots/synth_data_contamination/index.json +1 -0
- assets/data/plots/synth_data_contamination/words_contamination.json +1 -0
- assets/images/synth_data_contamination.png +0 -0
- dist/assets/data/plots/score_by_dump/agg_score.json +1 -2242
- dist/assets/data/plots/score_by_dump/arc_acc_norm.json +1 -1
- dist/assets/data/plots/score_by_dump/commonsense_qa_acc_norm.json +1 -1
- dist/assets/data/plots/score_by_dump/hellaswag_acc_norm.json +1 -1
- dist/assets/data/plots/score_by_dump/index.json +1 -35
- dist/assets/data/plots/score_by_dump/mmlu_acc_norm.json +1 -1
- dist/assets/data/plots/score_by_dump/openbookqa_acc_norm.json +1 -1
- dist/assets/data/plots/score_by_dump/piqa_acc_norm.json +1 -1
- dist/assets/data/plots/score_by_dump/winogrande_acc_norm.json +1 -1
- dist/assets/data/plots/synth_data_contamination/index.json +1 -0
- dist/assets/data/plots/synth_data_contamination/words_contamination.json +1 -0
- dist/assets/images/synth_data_contamination.png +0 -0
- dist/index.html +6 -1
- dist/main.bundle.js +0 -0
- dist/main.bundle.js.map +0 -0
- dist/style.css +10 -0
- notebooks/ablation_fw_edu.ipynb +150 -133
- notebooks/create_graphs_for_blog.ipynb +210 -297
- src/colors.mjs +7 -1
- src/index.html +6 -1
- src/plotting.js +68 -24
- src/style.css +10 -0
- webpack.config.js +29 -15
assets/data/plots/stats/lines_chars.json
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{"files": {"words_contamination": {"file": "words_contamination.json"}}, "settings": {"defaultMetric": "words_contamination", "slider": null, "type": "bar"}}
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dist/assets/data/plots/score_by_dump/arc_acc_norm.json
CHANGED
|
@@ -1 +1 @@
|
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| 1 |
-
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|
dist/assets/images/synth_data_contamination.png
ADDED
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dist/index.html
CHANGED
|
@@ -689,7 +689,12 @@
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| 689 |
not contain any of these phrases), but assuming that the amount of synthetic data were to not change across
|
| 690 |
crawls, one would expect these frequencies to remain approximately constant over time.</p>
|
| 691 |
<p>The results are shown in the following plot:</p>
|
| 692 |
-
<
|
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| 693 |
<p>While the frequency remained approximately constant until
|
| 694 |
2023-14 (ChatGPT was released at the end of 2022), we find a steep increase of our proxy metric
|
| 695 |
in recent crawls. While this simple test is not enough to conclude that ChatGPT completions and other synthetic data is improving the quality of the most recent crawl, it at the very least does not seem to drastically harm it.</p>
|
|
|
|
| 689 |
not contain any of these phrases), but assuming that the amount of synthetic data were to not change across
|
| 690 |
crawls, one would expect these frequencies to remain approximately constant over time.</p>
|
| 691 |
<p>The results are shown in the following plot:</p>
|
| 692 |
+
<div class="main-plot-container l-page-outset">
|
| 693 |
+
<figure>
|
| 694 |
+
<img src="assets/images/synth_data_contamination.png">
|
| 695 |
+
</figure>
|
| 696 |
+
<div id="plot-synth_data_contamination"></div>
|
| 697 |
+
</div>
|
| 698 |
<p>While the frequency remained approximately constant until
|
| 699 |
2023-14 (ChatGPT was released at the end of 2022), we find a steep increase of our proxy metric
|
| 700 |
in recent crawls. While this simple test is not enough to conclude that ChatGPT completions and other synthetic data is improving the quality of the most recent crawl, it at the very least does not seem to drastically harm it.</p>
|
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text-decoration: none;
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font-weight: 600;
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notebooks/ablation_fw_edu.ipynb
CHANGED
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@@ -9,9 +9,18 @@
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| 9 |
},
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| 10 |
{
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| 11 |
"cell_type": "code",
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| 12 |
-
"execution_count":
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| 13 |
"metadata": {},
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| 14 |
-
"outputs": [
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| 15 |
"source": [
|
| 16 |
"import os\n",
|
| 17 |
"import itertools\n",
|
|
@@ -26,7 +35,7 @@
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|
| 26 |
},
|
| 27 |
{
|
| 28 |
"cell_type": "code",
|
| 29 |
-
"execution_count":
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| 30 |
"metadata": {},
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| 31 |
"outputs": [],
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| 32 |
"source": [
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@@ -560,7 +569,7 @@
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| 560 |
},
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{
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| 562 |
"cell_type": "code",
|
| 563 |
-
"execution_count":
|
| 564 |
"metadata": {},
|
| 565 |
"outputs": [
|
| 566 |
{
|
|
@@ -585,6 +594,7 @@
|
|
| 585 |
" <tr style=\"text-align: right;\">\n",
|
| 586 |
" <th></th>\n",
|
| 587 |
" <th>runname</th>\n",
|
|
|
|
| 588 |
" <th>steps</th>\n",
|
| 589 |
" <th>agg_score</th>\n",
|
| 590 |
" <th>commonsense_qa/acc</th>\n",
|
|
@@ -593,12 +603,13 @@
|
|
| 593 |
" <th>hellaswag/acc_norm</th>\n",
|
| 594 |
" <th>openbookqa/acc</th>\n",
|
| 595 |
" <th>openbookqa/acc_norm</th>\n",
|
| 596 |
-
" <th
|
| 597 |
-
" <th>piqa/acc_norm</th>\n",
|
| 598 |
" <th>siqa/acc</th>\n",
|
| 599 |
" <th>siqa/acc_norm</th>\n",
|
| 600 |
" <th>winogrande/acc</th>\n",
|
| 601 |
" <th>winogrande/acc_norm</th>\n",
|
|
|
|
|
|
|
| 602 |
" <th>arc/acc</th>\n",
|
| 603 |
" <th>arc/acc_norm</th>\n",
|
| 604 |
" <th>mmlu/acc</th>\n",
|
|
@@ -609,6 +620,7 @@
|
|
| 609 |
" <tr>\n",
|
| 610 |
" <th>0</th>\n",
|
| 611 |
" <td>FineWeb-Edu</td>\n",
|
|
|
|
| 612 |
" <td>2000</td>\n",
|
| 613 |
" <td>0.390326</td>\n",
|
| 614 |
" <td>0.284</td>\n",
|
|
@@ -617,12 +629,13 @@
|
|
| 617 |
" <td>0.325</td>\n",
|
| 618 |
" <td>0.164</td>\n",
|
| 619 |
" <td>0.296</td>\n",
|
| 620 |
-
" <td
|
| 621 |
-
" <td>0.632</td>\n",
|
| 622 |
" <td>0.362</td>\n",
|
| 623 |
" <td>0.406</td>\n",
|
| 624 |
" <td>0.511</td>\n",
|
| 625 |
" <td>0.511</td>\n",
|
|
|
|
|
|
|
| 626 |
" <td>0.3795</td>\n",
|
| 627 |
" <td>0.3850</td>\n",
|
| 628 |
" <td>0.265997</td>\n",
|
|
@@ -631,6 +644,7 @@
|
|
| 631 |
" <tr>\n",
|
| 632 |
" <th>1</th>\n",
|
| 633 |
" <td>FineWeb-Edu</td>\n",
|
|
|
|
| 634 |
" <td>4000</td>\n",
|
| 635 |
" <td>0.414680</td>\n",
|
| 636 |
" <td>0.322</td>\n",
|
|
@@ -639,12 +653,13 @@
|
|
| 639 |
" <td>0.395</td>\n",
|
| 640 |
" <td>0.196</td>\n",
|
| 641 |
" <td>0.320</td>\n",
|
| 642 |
-
" <td
|
| 643 |
-
" <td>0.688</td>\n",
|
| 644 |
" <td>0.371</td>\n",
|
| 645 |
" <td>0.388</td>\n",
|
| 646 |
" <td>0.518</td>\n",
|
| 647 |
" <td>0.495</td>\n",
|
|
|
|
|
|
|
| 648 |
" <td>0.4215</td>\n",
|
| 649 |
" <td>0.4285</td>\n",
|
| 650 |
" <td>0.274401</td>\n",
|
|
@@ -653,6 +668,7 @@
|
|
| 653 |
" <tr>\n",
|
| 654 |
" <th>2</th>\n",
|
| 655 |
" <td>FineWeb-Edu</td>\n",
|
|
|
|
| 656 |
" <td>6000</td>\n",
|
| 657 |
" <td>0.428390</td>\n",
|
| 658 |
" <td>0.319</td>\n",
|
|
@@ -661,12 +677,13 @@
|
|
| 661 |
" <td>0.431</td>\n",
|
| 662 |
" <td>0.202</td>\n",
|
| 663 |
" <td>0.352</td>\n",
|
| 664 |
-
" <td
|
| 665 |
-
" <td>0.670</td>\n",
|
| 666 |
" <td>0.373</td>\n",
|
| 667 |
" <td>0.392</td>\n",
|
| 668 |
" <td>0.520</td>\n",
|
| 669 |
" <td>0.519</td>\n",
|
|
|
|
|
|
|
| 670 |
" <td>0.4315</td>\n",
|
| 671 |
" <td>0.4460</td>\n",
|
| 672 |
" <td>0.288591</td>\n",
|
|
@@ -675,6 +692,7 @@
|
|
| 675 |
" <tr>\n",
|
| 676 |
" <th>3</th>\n",
|
| 677 |
" <td>FineWeb-Edu</td>\n",
|
|
|
|
| 678 |
" <td>8000</td>\n",
|
| 679 |
" <td>0.443615</td>\n",
|
| 680 |
" <td>0.340</td>\n",
|
|
@@ -683,12 +701,13 @@
|
|
| 683 |
" <td>0.463</td>\n",
|
| 684 |
" <td>0.204</td>\n",
|
| 685 |
" <td>0.360</td>\n",
|
| 686 |
-
" <td
|
| 687 |
-
" <td>0.700</td>\n",
|
| 688 |
" <td>0.384</td>\n",
|
| 689 |
" <td>0.404</td>\n",
|
| 690 |
" <td>0.517</td>\n",
|
| 691 |
" <td>0.517</td>\n",
|
|
|
|
|
|
|
| 692 |
" <td>0.4630</td>\n",
|
| 693 |
" <td>0.4790</td>\n",
|
| 694 |
" <td>0.299186</td>\n",
|
|
@@ -697,6 +716,7 @@
|
|
| 697 |
" <tr>\n",
|
| 698 |
" <th>4</th>\n",
|
| 699 |
" <td>FineWeb-Edu</td>\n",
|
|
|
|
| 700 |
" <td>10000</td>\n",
|
| 701 |
" <td>0.441457</td>\n",
|
| 702 |
" <td>0.346</td>\n",
|
|
@@ -705,12 +725,13 @@
|
|
| 705 |
" <td>0.454</td>\n",
|
| 706 |
" <td>0.222</td>\n",
|
| 707 |
" <td>0.364</td>\n",
|
| 708 |
-
" <td
|
| 709 |
-
" <td>0.696</td>\n",
|
| 710 |
" <td>0.366</td>\n",
|
| 711 |
" <td>0.395</td>\n",
|
| 712 |
" <td>0.514</td>\n",
|
| 713 |
" <td>0.506</td>\n",
|
|
|
|
|
|
|
| 714 |
" <td>0.4890</td>\n",
|
| 715 |
" <td>0.4820</td>\n",
|
| 716 |
" <td>0.302189</td>\n",
|
|
@@ -718,39 +739,49 @@
|
|
| 718 |
" </tr>\n",
|
| 719 |
" </tbody>\n",
|
| 720 |
"</table>\n",
|
|
|
|
| 721 |
"</div>"
|
| 722 |
],
|
| 723 |
"text/plain": [
|
| 724 |
-
" runname steps agg_score commonsense_qa/acc
|
| 725 |
-
"0 FineWeb-Edu 2000 0.390326 0.284
|
| 726 |
-
"1 FineWeb-Edu 4000 0.414680 0.322
|
| 727 |
-
"2 FineWeb-Edu 6000 0.428390 0.319
|
| 728 |
-
"3 FineWeb-Edu 8000 0.443615 0.340
|
| 729 |
-
"4 FineWeb-Edu 10000 0.441457 0.346
|
| 730 |
"\n",
|
| 731 |
-
" hellaswag/acc hellaswag/acc_norm openbookqa/acc
|
| 732 |
-
"0 0.314 0.325 0.164
|
| 733 |
-
"1 0.343 0.395 0.196
|
| 734 |
-
"2 0.372 0.431 0.202
|
| 735 |
-
"3 0.379 0.463 0.204
|
| 736 |
-
"4 0.390 0.454 0.222
|
| 737 |
"\n",
|
| 738 |
-
"
|
| 739 |
-
"0
|
| 740 |
-
"1
|
| 741 |
-
"2
|
| 742 |
-
"3
|
| 743 |
-
"4
|
| 744 |
"\n",
|
| 745 |
-
" winogrande/acc_norm
|
| 746 |
-
"0 0.511 0.3795 0.3850
|
| 747 |
-
"1 0.495 0.4215 0.4285
|
| 748 |
-
"2 0.519 0.4315 0.4460
|
| 749 |
-
"3 0.517 0.4630 0.4790
|
| 750 |
-
"4 0.506 0.4890 0.4820
|
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| 751 |
]
|
| 752 |
},
|
| 753 |
-
"execution_count":
|
| 754 |
"metadata": {},
|
| 755 |
"output_type": "execute_result"
|
| 756 |
}
|
|
@@ -764,13 +795,12 @@
|
|
| 764 |
"# load FineWeb-Edu csv\n",
|
| 765 |
"df_2 = pd.read_csv(\"./loubna-edu_fw_ablations_metrics.csv\")\n",
|
| 766 |
"df_2['runname'] = df_2['runname'].replace('edu_fineweb_350b_tokens-seed-1', 'FineWeb-Edu', regex=True)\n",
|
| 767 |
-
"df_2.drop([\"seed\", \"all/acc\", \"all/acc_norm\"], axis=1, inplace=True)\n",
|
| 768 |
"df_2.head()"
|
| 769 |
]
|
| 770 |
},
|
| 771 |
{
|
| 772 |
"cell_type": "code",
|
| 773 |
-
"execution_count":
|
| 774 |
"metadata": {},
|
| 775 |
"outputs": [
|
| 776 |
{
|
|
@@ -805,9 +835,6 @@
|
|
| 805 |
" <th>openbookqa/acc_norm</th>\n",
|
| 806 |
" <th>piqa/acc</th>\n",
|
| 807 |
" <th>...</th>\n",
|
| 808 |
-
" <th>siqa/acc</th>\n",
|
| 809 |
-
" <th>siqa/acc_norm</th>\n",
|
| 810 |
-
" <th>winogrande/acc</th>\n",
|
| 811 |
" <th>winogrande/acc_norm</th>\n",
|
| 812 |
" <th>sciq/acc</th>\n",
|
| 813 |
" <th>sciq/acc_norm</th>\n",
|
|
@@ -815,6 +842,9 @@
|
|
| 815 |
" <th>arc/acc_norm</th>\n",
|
| 816 |
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| 817 |
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| 818 |
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| 833 |
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| 834 |
-
" <td>0.392</td>\n",
|
| 835 |
-
" <td>0.402</td>\n",
|
| 836 |
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" <td>0.576</td>\n",
|
| 837 |
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|
| 838 |
" <td>NaN</td>\n",
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| 839 |
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|
@@ -841,6 +868,9 @@
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| 841 |
" <td>0.5725</td>\n",
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| 842 |
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| 843 |
" <td>0.374533</td>\n",
|
|
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|
|
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|
|
|
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| 844 |
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|
| 845 |
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| 846 |
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|
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@@ -855,9 +885,6 @@
|
|
| 855 |
" <td>0.408</td>\n",
|
| 856 |
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|
| 858 |
-
" <td>0.390</td>\n",
|
| 859 |
-
" <td>0.409</td>\n",
|
| 860 |
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" <td>0.572</td>\n",
|
| 861 |
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|
| 862 |
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|
| 863 |
" <td>NaN</td>\n",
|
|
@@ -865,6 +892,9 @@
|
|
| 865 |
" <td>0.5780</td>\n",
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|
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" <td>0.372947</td>\n",
|
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|
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|
| 868 |
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|
| 869 |
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|
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|
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@@ -879,9 +909,6 @@
|
|
| 879 |
" <td>0.408</td>\n",
|
| 880 |
" <td>0.737</td>\n",
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" <td>...</td>\n",
|
| 882 |
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" <td>0.395</td>\n",
|
| 883 |
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" <td>0.406</td>\n",
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| 884 |
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" <td>0.576</td>\n",
|
| 885 |
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|
| 886 |
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|
| 887 |
" <td>NaN</td>\n",
|
|
@@ -889,6 +916,9 @@
|
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| 889 |
" <td>0.5715</td>\n",
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|
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|
|
|
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|
|
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|
| 892 |
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|
| 893 |
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|
| 894 |
" <th>1256</th>\n",
|
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@@ -903,9 +933,6 @@
|
|
| 903 |
" <td>0.414</td>\n",
|
| 904 |
" <td>0.740</td>\n",
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" <td>...</td>\n",
|
| 906 |
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" <td>0.401</td>\n",
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" <td>0.408</td>\n",
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| 908 |
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" <td>0.575</td>\n",
|
| 909 |
" <td>0.570</td>\n",
|
| 910 |
" <td>NaN</td>\n",
|
| 911 |
" <td>NaN</td>\n",
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|
@@ -913,6 +940,9 @@
|
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| 913 |
" <td>0.5760</td>\n",
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| 914 |
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|
| 915 |
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|
|
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|
|
|
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|
|
| 916 |
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|
| 917 |
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|
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|
|
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" <td>0.418</td>\n",
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" <td>0.738</td>\n",
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| 930 |
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" <td>0.395</td>\n",
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| 931 |
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" <td>0.404</td>\n",
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" <td>0.582</td>\n",
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| 933 |
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| 934 |
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| 935 |
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| 940 |
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" openbookqa/acc openbookqa/acc_norm piqa/acc ...
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" <th>FineWeb</th>\n",
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"/Users/hynky/.pyenv/versions/3.12.2/envs/datatrove/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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{
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" <th></th>\n",
|
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" <th>runname</th>\n",
|
| 597 |
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" <th>seed</th>\n",
|
| 598 |
" <th>steps</th>\n",
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| 599 |
" <th>agg_score</th>\n",
|
| 600 |
" <th>commonsense_qa/acc</th>\n",
|
|
|
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| 603 |
" <th>hellaswag/acc_norm</th>\n",
|
| 604 |
" <th>openbookqa/acc</th>\n",
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| 605 |
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" <th>...</th>\n",
|
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|
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| 607 |
" <th>siqa/acc</th>\n",
|
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" <th>siqa/acc_norm</th>\n",
|
| 609 |
" <th>winogrande/acc</th>\n",
|
| 610 |
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|
| 611 |
+
" <th>all/acc</th>\n",
|
| 612 |
+
" <th>all/acc_norm</th>\n",
|
| 613 |
" <th>arc/acc</th>\n",
|
| 614 |
" <th>arc/acc_norm</th>\n",
|
| 615 |
" <th>mmlu/acc</th>\n",
|
|
|
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| 620 |
" <tr>\n",
|
| 621 |
" <th>0</th>\n",
|
| 622 |
" <td>FineWeb-Edu</td>\n",
|
| 623 |
+
" <td>0</td>\n",
|
| 624 |
" <td>2000</td>\n",
|
| 625 |
" <td>0.390326</td>\n",
|
| 626 |
" <td>0.284</td>\n",
|
|
|
|
| 629 |
" <td>0.325</td>\n",
|
| 630 |
" <td>0.164</td>\n",
|
| 631 |
" <td>0.296</td>\n",
|
| 632 |
+
" <td>...</td>\n",
|
|
|
|
| 633 |
" <td>0.362</td>\n",
|
| 634 |
" <td>0.406</td>\n",
|
| 635 |
" <td>0.511</td>\n",
|
| 636 |
" <td>0.511</td>\n",
|
| 637 |
+
" <td>0.279674</td>\n",
|
| 638 |
+
" <td>0.299162</td>\n",
|
| 639 |
" <td>0.3795</td>\n",
|
| 640 |
" <td>0.3850</td>\n",
|
| 641 |
" <td>0.265997</td>\n",
|
|
|
|
| 644 |
" <tr>\n",
|
| 645 |
" <th>1</th>\n",
|
| 646 |
" <td>FineWeb-Edu</td>\n",
|
| 647 |
+
" <td>0</td>\n",
|
| 648 |
" <td>4000</td>\n",
|
| 649 |
" <td>0.414680</td>\n",
|
| 650 |
" <td>0.322</td>\n",
|
|
|
|
| 653 |
" <td>0.395</td>\n",
|
| 654 |
" <td>0.196</td>\n",
|
| 655 |
" <td>0.320</td>\n",
|
| 656 |
+
" <td>...</td>\n",
|
|
|
|
| 657 |
" <td>0.371</td>\n",
|
| 658 |
" <td>0.388</td>\n",
|
| 659 |
" <td>0.518</td>\n",
|
| 660 |
" <td>0.495</td>\n",
|
| 661 |
+
" <td>0.290613</td>\n",
|
| 662 |
+
" <td>0.312593</td>\n",
|
| 663 |
" <td>0.4215</td>\n",
|
| 664 |
" <td>0.4285</td>\n",
|
| 665 |
" <td>0.274401</td>\n",
|
|
|
|
| 668 |
" <tr>\n",
|
| 669 |
" <th>2</th>\n",
|
| 670 |
" <td>FineWeb-Edu</td>\n",
|
| 671 |
+
" <td>0</td>\n",
|
| 672 |
" <td>6000</td>\n",
|
| 673 |
" <td>0.428390</td>\n",
|
| 674 |
" <td>0.319</td>\n",
|
|
|
|
| 677 |
" <td>0.431</td>\n",
|
| 678 |
" <td>0.202</td>\n",
|
| 679 |
" <td>0.352</td>\n",
|
| 680 |
+
" <td>...</td>\n",
|
|
|
|
| 681 |
" <td>0.373</td>\n",
|
| 682 |
" <td>0.392</td>\n",
|
| 683 |
" <td>0.520</td>\n",
|
| 684 |
" <td>0.519</td>\n",
|
| 685 |
+
" <td>0.303980</td>\n",
|
| 686 |
+
" <td>0.323323</td>\n",
|
| 687 |
" <td>0.4315</td>\n",
|
| 688 |
" <td>0.4460</td>\n",
|
| 689 |
" <td>0.288591</td>\n",
|
|
|
|
| 692 |
" <tr>\n",
|
| 693 |
" <th>3</th>\n",
|
| 694 |
" <td>FineWeb-Edu</td>\n",
|
| 695 |
+
" <td>0</td>\n",
|
| 696 |
" <td>8000</td>\n",
|
| 697 |
" <td>0.443615</td>\n",
|
| 698 |
" <td>0.340</td>\n",
|
|
|
|
| 701 |
" <td>0.463</td>\n",
|
| 702 |
" <td>0.204</td>\n",
|
| 703 |
" <td>0.360</td>\n",
|
| 704 |
+
" <td>...</td>\n",
|
|
|
|
| 705 |
" <td>0.384</td>\n",
|
| 706 |
" <td>0.404</td>\n",
|
| 707 |
" <td>0.517</td>\n",
|
| 708 |
" <td>0.517</td>\n",
|
| 709 |
+
" <td>0.315148</td>\n",
|
| 710 |
+
" <td>0.333284</td>\n",
|
| 711 |
" <td>0.4630</td>\n",
|
| 712 |
" <td>0.4790</td>\n",
|
| 713 |
" <td>0.299186</td>\n",
|
|
|
|
| 716 |
" <tr>\n",
|
| 717 |
" <th>4</th>\n",
|
| 718 |
" <td>FineWeb-Edu</td>\n",
|
| 719 |
+
" <td>0</td>\n",
|
| 720 |
" <td>10000</td>\n",
|
| 721 |
" <td>0.441457</td>\n",
|
| 722 |
" <td>0.346</td>\n",
|
|
|
|
| 725 |
" <td>0.454</td>\n",
|
| 726 |
" <td>0.222</td>\n",
|
| 727 |
" <td>0.364</td>\n",
|
| 728 |
+
" <td>...</td>\n",
|
|
|
|
| 729 |
" <td>0.366</td>\n",
|
| 730 |
" <td>0.395</td>\n",
|
| 731 |
" <td>0.514</td>\n",
|
| 732 |
" <td>0.506</td>\n",
|
| 733 |
+
" <td>0.318935</td>\n",
|
| 734 |
+
" <td>0.335419</td>\n",
|
| 735 |
" <td>0.4890</td>\n",
|
| 736 |
" <td>0.4820</td>\n",
|
| 737 |
" <td>0.302189</td>\n",
|
|
|
|
| 739 |
" </tr>\n",
|
| 740 |
" </tbody>\n",
|
| 741 |
"</table>\n",
|
| 742 |
+
"<p>5 rows × 22 columns</p>\n",
|
| 743 |
"</div>"
|
| 744 |
],
|
| 745 |
"text/plain": [
|
| 746 |
+
" runname seed steps agg_score commonsense_qa/acc \\\n",
|
| 747 |
+
"0 FineWeb-Edu 0 2000 0.390326 0.284 \n",
|
| 748 |
+
"1 FineWeb-Edu 0 4000 0.414680 0.322 \n",
|
| 749 |
+
"2 FineWeb-Edu 0 6000 0.428390 0.319 \n",
|
| 750 |
+
"3 FineWeb-Edu 0 8000 0.443615 0.340 \n",
|
| 751 |
+
"4 FineWeb-Edu 0 10000 0.441457 0.346 \n",
|
| 752 |
"\n",
|
| 753 |
+
" commonsense_qa/acc_norm hellaswag/acc hellaswag/acc_norm openbookqa/acc \\\n",
|
| 754 |
+
"0 0.283 0.314 0.325 0.164 \n",
|
| 755 |
+
"1 0.307 0.343 0.395 0.196 \n",
|
| 756 |
+
"2 0.311 0.372 0.431 0.202 \n",
|
| 757 |
+
"3 0.311 0.379 0.463 0.204 \n",
|
| 758 |
+
"4 0.317 0.390 0.454 0.222 \n",
|
| 759 |
"\n",
|
| 760 |
+
" openbookqa/acc_norm ... siqa/acc siqa/acc_norm winogrande/acc \\\n",
|
| 761 |
+
"0 0.296 ... 0.362 0.406 0.511 \n",
|
| 762 |
+
"1 0.320 ... 0.371 0.388 0.518 \n",
|
| 763 |
+
"2 0.352 ... 0.373 0.392 0.520 \n",
|
| 764 |
+
"3 0.360 ... 0.384 0.404 0.517 \n",
|
| 765 |
+
"4 0.364 ... 0.366 0.395 0.514 \n",
|
| 766 |
"\n",
|
| 767 |
+
" winogrande/acc_norm all/acc all/acc_norm arc/acc arc/acc_norm \\\n",
|
| 768 |
+
"0 0.511 0.279674 0.299162 0.3795 0.3850 \n",
|
| 769 |
+
"1 0.495 0.290613 0.312593 0.4215 0.4285 \n",
|
| 770 |
+
"2 0.519 0.303980 0.323323 0.4315 0.4460 \n",
|
| 771 |
+
"3 0.517 0.315148 0.333284 0.4630 0.4790 \n",
|
| 772 |
+
"4 0.506 0.318935 0.335419 0.4890 0.4820 \n",
|
| 773 |
+
"\n",
|
| 774 |
+
" mmlu/acc mmlu/acc_norm \n",
|
| 775 |
+
"0 0.265997 0.284605 \n",
|
| 776 |
+
"1 0.274401 0.295939 \n",
|
| 777 |
+
"2 0.288591 0.306123 \n",
|
| 778 |
+
"3 0.299186 0.314921 \n",
|
| 779 |
+
"4 0.302189 0.317653 \n",
|
| 780 |
+
"\n",
|
| 781 |
+
"[5 rows x 22 columns]"
|
| 782 |
]
|
| 783 |
},
|
| 784 |
+
"execution_count": 14,
|
| 785 |
"metadata": {},
|
| 786 |
"output_type": "execute_result"
|
| 787 |
}
|
|
|
|
| 795 |
"# load FineWeb-Edu csv\n",
|
| 796 |
"df_2 = pd.read_csv(\"./loubna-edu_fw_ablations_metrics.csv\")\n",
|
| 797 |
"df_2['runname'] = df_2['runname'].replace('edu_fineweb_350b_tokens-seed-1', 'FineWeb-Edu', regex=True)\n",
|
|
|
|
| 798 |
"df_2.head()"
|
| 799 |
]
|
| 800 |
},
|
| 801 |
{
|
| 802 |
"cell_type": "code",
|
| 803 |
+
"execution_count": 15,
|
| 804 |
"metadata": {},
|
| 805 |
"outputs": [
|
| 806 |
{
|
|
|
|
| 835 |
" <th>openbookqa/acc_norm</th>\n",
|
| 836 |
" <th>piqa/acc</th>\n",
|
| 837 |
" <th>...</th>\n",
|
|
|
|
|
|
|
|
|
|
| 838 |
" <th>winogrande/acc_norm</th>\n",
|
| 839 |
" <th>sciq/acc</th>\n",
|
| 840 |
" <th>sciq/acc_norm</th>\n",
|
|
|
|
| 842 |
" <th>arc/acc_norm</th>\n",
|
| 843 |
" <th>mmlu/acc</th>\n",
|
| 844 |
" <th>mmlu/acc_norm</th>\n",
|
| 845 |
+
" <th>seed</th>\n",
|
| 846 |
+
" <th>all/acc</th>\n",
|
| 847 |
+
" <th>all/acc_norm</th>\n",
|
| 848 |
" </tr>\n",
|
| 849 |
" </thead>\n",
|
| 850 |
" <tbody>\n",
|
|
|
|
| 861 |
" <td>0.418</td>\n",
|
| 862 |
" <td>0.744</td>\n",
|
| 863 |
" <td>...</td>\n",
|
|
|
|
|
|
|
|
|
|
| 864 |
" <td>0.575</td>\n",
|
| 865 |
" <td>NaN</td>\n",
|
| 866 |
" <td>NaN</td>\n",
|
|
|
|
| 868 |
" <td>0.5725</td>\n",
|
| 869 |
" <td>0.350226</td>\n",
|
| 870 |
" <td>0.374533</td>\n",
|
| 871 |
+
" <td>0.0</td>\n",
|
| 872 |
+
" <td>0.369137</td>\n",
|
| 873 |
+
" <td>0.393898</td>\n",
|
| 874 |
" </tr>\n",
|
| 875 |
" <tr>\n",
|
| 876 |
" <th>1254</th>\n",
|
|
|
|
| 885 |
" <td>0.408</td>\n",
|
| 886 |
" <td>0.747</td>\n",
|
| 887 |
" <td>...</td>\n",
|
|
|
|
|
|
|
|
|
|
| 888 |
" <td>0.577</td>\n",
|
| 889 |
" <td>NaN</td>\n",
|
| 890 |
" <td>NaN</td>\n",
|
|
|
|
| 892 |
" <td>0.5780</td>\n",
|
| 893 |
" <td>0.348268</td>\n",
|
| 894 |
" <td>0.372947</td>\n",
|
| 895 |
+
" <td>0.0</td>\n",
|
| 896 |
+
" <td>0.367420</td>\n",
|
| 897 |
+
" <td>0.392861</td>\n",
|
| 898 |
" </tr>\n",
|
| 899 |
" <tr>\n",
|
| 900 |
" <th>1255</th>\n",
|
|
|
|
| 909 |
" <td>0.408</td>\n",
|
| 910 |
" <td>0.737</td>\n",
|
| 911 |
" <td>...</td>\n",
|
|
|
|
|
|
|
|
|
|
| 912 |
" <td>0.580</td>\n",
|
| 913 |
" <td>NaN</td>\n",
|
| 914 |
" <td>NaN</td>\n",
|
|
|
|
| 916 |
" <td>0.5715</td>\n",
|
| 917 |
" <td>0.349943</td>\n",
|
| 918 |
" <td>0.372246</td>\n",
|
| 919 |
+
" <td>0.0</td>\n",
|
| 920 |
+
" <td>0.368319</td>\n",
|
| 921 |
+
" <td>0.392000</td>\n",
|
| 922 |
" </tr>\n",
|
| 923 |
" <tr>\n",
|
| 924 |
" <th>1256</th>\n",
|
|
|
|
| 933 |
" <td>0.414</td>\n",
|
| 934 |
" <td>0.740</td>\n",
|
| 935 |
" <td>...</td>\n",
|
|
|
|
|
|
|
|
|
|
| 936 |
" <td>0.570</td>\n",
|
| 937 |
" <td>NaN</td>\n",
|
| 938 |
" <td>NaN</td>\n",
|
|
|
|
| 940 |
" <td>0.5760</td>\n",
|
| 941 |
" <td>0.352203</td>\n",
|
| 942 |
" <td>0.373463</td>\n",
|
| 943 |
+
" <td>0.0</td>\n",
|
| 944 |
+
" <td>0.370593</td>\n",
|
| 945 |
+
" <td>0.393176</td>\n",
|
| 946 |
" </tr>\n",
|
| 947 |
" <tr>\n",
|
| 948 |
" <th>1257</th>\n",
|
|
|
|
| 957 |
" <td>0.418</td>\n",
|
| 958 |
" <td>0.738</td>\n",
|
| 959 |
" <td>...</td>\n",
|
|
|
|
|
|
|
|
|
|
| 960 |
" <td>0.578</td>\n",
|
| 961 |
" <td>NaN</td>\n",
|
| 962 |
" <td>NaN</td>\n",
|
|
|
|
| 964 |
" <td>0.5735</td>\n",
|
| 965 |
" <td>0.350671</td>\n",
|
| 966 |
" <td>0.374453</td>\n",
|
| 967 |
+
" <td>0.0</td>\n",
|
| 968 |
+
" <td>0.369666</td>\n",
|
| 969 |
+
" <td>0.394136</td>\n",
|
| 970 |
" </tr>\n",
|
| 971 |
" </tbody>\n",
|
| 972 |
"</table>\n",
|
| 973 |
+
"<p>5 rows × 24 columns</p>\n",
|
| 974 |
"</div>"
|
| 975 |
],
|
| 976 |
"text/plain": [
|
|
|
|
| 988 |
"1256 0.364 0.472 0.593 \n",
|
| 989 |
"1257 0.362 0.472 0.597 \n",
|
| 990 |
"\n",
|
| 991 |
+
" openbookqa/acc openbookqa/acc_norm piqa/acc ... winogrande/acc_norm \\\n",
|
| 992 |
+
"1253 0.282 0.418 0.744 ... 0.575 \n",
|
| 993 |
+
"1254 0.288 0.408 0.747 ... 0.577 \n",
|
| 994 |
+
"1255 0.276 0.408 0.737 ... 0.580 \n",
|
| 995 |
+
"1256 0.282 0.414 0.740 ... 0.570 \n",
|
| 996 |
+
"1257 0.290 0.418 0.738 ... 0.578 \n",
|
| 997 |
"\n",
|
| 998 |
+
" sciq/acc sciq/acc_norm arc/acc arc/acc_norm mmlu/acc mmlu/acc_norm \\\n",
|
| 999 |
+
"1253 NaN NaN 0.5670 0.5725 0.350226 0.374533 \n",
|
| 1000 |
+
"1254 NaN NaN 0.5720 0.5780 0.348268 0.372947 \n",
|
| 1001 |
+
"1255 NaN NaN 0.5635 0.5715 0.349943 0.372246 \n",
|
| 1002 |
+
"1256 NaN NaN 0.5640 0.5760 0.352203 0.373463 \n",
|
| 1003 |
+
"1257 NaN NaN 0.5670 0.5735 0.350671 0.374453 \n",
|
| 1004 |
"\n",
|
| 1005 |
+
" seed all/acc all/acc_norm \n",
|
| 1006 |
+
"1253 0.0 0.369137 0.393898 \n",
|
| 1007 |
+
"1254 0.0 0.367420 0.392861 \n",
|
| 1008 |
+
"1255 0.0 0.368319 0.392000 \n",
|
| 1009 |
+
"1256 0.0 0.370593 0.393176 \n",
|
| 1010 |
+
"1257 0.0 0.369666 0.394136 \n",
|
| 1011 |
"\n",
|
| 1012 |
+
"[5 rows x 24 columns]"
|
| 1013 |
]
|
| 1014 |
},
|
| 1015 |
+
"execution_count": 15,
|
| 1016 |
"metadata": {},
|
| 1017 |
"output_type": "execute_result"
|
| 1018 |
}
|
|
|
|
| 1031 |
},
|
| 1032 |
{
|
| 1033 |
"cell_type": "code",
|
| 1034 |
+
"execution_count": 5,
|
| 1035 |
"metadata": {},
|
| 1036 |
"outputs": [
|
| 1037 |
{
|
|
|
|
| 1072 |
" <td>168</td>\n",
|
| 1073 |
" </tr>\n",
|
| 1074 |
" <tr>\n",
|
| 1075 |
+
" <th>FineWeb (ours)</th>\n",
|
| 1076 |
" <td>168</td>\n",
|
| 1077 |
" </tr>\n",
|
| 1078 |
" <tr>\n",
|
|
|
|
| 1100 |
"</div>"
|
| 1101 |
],
|
| 1102 |
"text/plain": [
|
| 1103 |
+
" steps\n",
|
| 1104 |
+
"runname \n",
|
| 1105 |
+
"C4 168\n",
|
| 1106 |
+
"Dolma 168\n",
|
| 1107 |
+
"FineWeb (ours) 168\n",
|
| 1108 |
+
"FineWeb-Edu 82\n",
|
| 1109 |
+
"RedPajama2 168\n",
|
| 1110 |
+
"RefinedWeb 168\n",
|
| 1111 |
+
"SlimPajama 168\n",
|
| 1112 |
+
"The Pile 168"
|
| 1113 |
]
|
| 1114 |
},
|
| 1115 |
+
"execution_count": 5,
|
| 1116 |
"metadata": {},
|
| 1117 |
"output_type": "execute_result"
|
| 1118 |
}
|
|
|
|
| 1123 |
},
|
| 1124 |
{
|
| 1125 |
"cell_type": "code",
|
| 1126 |
+
"execution_count": 6,
|
| 1127 |
"metadata": {},
|
| 1128 |
"outputs": [],
|
| 1129 |
"source": [
|
|
|
|
| 1134 |
},
|
| 1135 |
{
|
| 1136 |
"cell_type": "code",
|
| 1137 |
+
"execution_count": 13,
|
| 1138 |
"metadata": {},
|
| 1139 |
"outputs": [],
|
| 1140 |
"source": [
|
|
|
|
| 1233 |
},
|
| 1234 |
{
|
| 1235 |
"cell_type": "code",
|
| 1236 |
+
"execution_count": 10,
|
| 1237 |
"metadata": {},
|
| 1238 |
"outputs": [
|
| 1239 |
{
|
|
|
|
| 1344 |
"\n",
|
| 1345 |
"\n",
|
| 1346 |
"metrics = [\n",
|
| 1347 |
+
" \"agg_score\",\n",
|
| 1348 |
" \"mmlu/acc_norm\",\n",
|
| 1349 |
" \"arc/acc_norm\",\n",
|
| 1350 |
" \"openbookqa/acc_norm\",\n",
|
|
|
|
| 1765 |
},
|
| 1766 |
{
|
| 1767 |
"cell_type": "code",
|
| 1768 |
+
"execution_count": 18,
|
| 1769 |
"metadata": {},
|
| 1770 |
"outputs": [
|
| 1771 |
{
|
| 1772 |
"name": "stderr",
|
| 1773 |
"output_type": "stream",
|
| 1774 |
"text": [
|
| 1775 |
+
"Fetching datafiles...: 100%|██████████| 4/4 [00:00<00:00, 5.09it/s]\n",
|
| 1776 |
+
"Loading evals data...: 100%|██████████| 26/26 [00:04<00:00, 5.71it/s]"
|
| 1777 |
]
|
| 1778 |
},
|
| 1779 |
{
|
|
|
|
| 1810 |
},
|
| 1811 |
{
|
| 1812 |
"cell_type": "code",
|
| 1813 |
+
"execution_count": 19,
|
| 1814 |
"metadata": {},
|
| 1815 |
"outputs": [
|
| 1816 |
{
|
|
|
|
| 2022 |
"[5 rows x 22 columns]"
|
| 2023 |
]
|
| 2024 |
},
|
| 2025 |
+
"execution_count": 19,
|
| 2026 |
"metadata": {},
|
| 2027 |
"output_type": "execute_result"
|
| 2028 |
}
|
|
|
|
| 2037 |
},
|
| 2038 |
{
|
| 2039 |
"cell_type": "code",
|
| 2040 |
+
"execution_count": 17,
|
| 2041 |
"metadata": {},
|
| 2042 |
"outputs": [
|
| 2043 |
{
|
| 2044 |
"data": {
|
| 2045 |
"text/plain": [
|
| 2046 |
+
"0 C4\n",
|
| 2047 |
+
"1 C4\n",
|
| 2048 |
+
"2 C4\n",
|
| 2049 |
+
"3 C4\n",
|
| 2050 |
+
"4 C4\n",
|
| 2051 |
+
" ... \n",
|
| 2052 |
+
"1171 The Pile\n",
|
| 2053 |
+
"1172 The Pile\n",
|
| 2054 |
+
"1173 The Pile\n",
|
| 2055 |
+
"1174 The Pile\n",
|
| 2056 |
+
"1175 The Pile\n",
|
| 2057 |
+
"Name: runname, Length: 1176, dtype: object"
|
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| 2058 |
]
|
| 2059 |
},
|
| 2060 |
+
"execution_count": 17,
|
| 2061 |
"metadata": {},
|
| 2062 |
"output_type": "execute_result"
|
| 2063 |
}
|
|
|
|
| 2075 |
},
|
| 2076 |
{
|
| 2077 |
"cell_type": "code",
|
| 2078 |
+
"execution_count": 20,
|
| 2079 |
"metadata": {},
|
| 2080 |
"outputs": [
|
| 2081 |
{
|
|
|
|
| 2089 |
"source": [
|
| 2090 |
"\n",
|
| 2091 |
"metrics = [\n",
|
| 2092 |
+
" \"agg_score\",\n",
|
| 2093 |
" \"mmlu/acc_norm\",\n",
|
| 2094 |
" \"arc/acc_norm\",\n",
|
| 2095 |
" \"openbookqa/acc_norm\",\n",
|
notebooks/create_graphs_for_blog.ipynb
CHANGED
|
@@ -2,13 +2,13 @@
|
|
| 2 |
"cells": [
|
| 3 |
{
|
| 4 |
"cell_type": "code",
|
| 5 |
-
"execution_count":
|
| 6 |
"metadata": {},
|
| 7 |
"outputs": [],
|
| 8 |
"source": [
|
| 9 |
"TITLE=\"Histograms of selected metrics\"\n",
|
| 10 |
-
"RED_OP=\"
|
| 11 |
-
"RED_FULL=\"
|
| 12 |
"DEFAULT_LEGEND = {\n",
|
| 13 |
" \"xanchor\": \"right\",\n",
|
| 14 |
" \"yanchor\": \"top\",\n",
|
|
@@ -59,7 +59,7 @@
|
|
| 59 |
" \"font\": {\n",
|
| 60 |
" \"size\": 10\n",
|
| 61 |
" },\n",
|
| 62 |
-
" \"
|
| 63 |
" }\n",
|
| 64 |
" ],\n",
|
| 65 |
" \"legend\": DEFAULT_LEGEND\n",
|
|
@@ -160,7 +160,7 @@
|
|
| 160 |
},
|
| 161 |
{
|
| 162 |
"cell_type": "code",
|
| 163 |
-
"execution_count":
|
| 164 |
"metadata": {},
|
| 165 |
"outputs": [],
|
| 166 |
"source": [
|
|
@@ -205,7 +205,7 @@
|
|
| 205 |
},
|
| 206 |
{
|
| 207 |
"cell_type": "code",
|
| 208 |
-
"execution_count":
|
| 209 |
"metadata": {},
|
| 210 |
"outputs": [
|
| 211 |
{
|
|
@@ -230,30 +230,30 @@
|
|
| 230 |
" return name.replace(\" \", \"-\").lower()\n",
|
| 231 |
"TABBED_PLOT = {}\n",
|
| 232 |
"\n",
|
| 233 |
-
"OUTPUT_DIR = \"data/plots/stats\"\n",
|
| 234 |
"\n",
|
| 235 |
-
"with open(\"
|
| 236 |
" data = json.load(f)\n",
|
| 237 |
" print(data)\n",
|
| 238 |
-
"
|
| 239 |
" TABBED_PLOT[\"lines_ended_with_punct\"] = {\n",
|
| 240 |
-
" \"data\":
|
| 241 |
" \"layout\": LINES_ENDED_WITH_PUNC_LAYOUT\n",
|
| 242 |
" }\n",
|
| 243 |
"\n",
|
| 244 |
-
"with open(\"
|
| 245 |
" data = json.load(f)\n",
|
| 246 |
-
"
|
| 247 |
" TABBED_PLOT[\"lines_chars\"] = {\n",
|
| 248 |
-
" \"data\":
|
| 249 |
" \"layout\": LINES_CHARS\n",
|
| 250 |
" }\n",
|
| 251 |
"\n",
|
| 252 |
-
"with open(\"
|
| 253 |
" data = json.load(f)\n",
|
| 254 |
-
"
|
| 255 |
" TABBED_PLOT[\"short_lines\"] = {\n",
|
| 256 |
-
" \"data\":
|
| 257 |
" \"layout\": SHORT_LINES\n",
|
| 258 |
" }\n",
|
| 259 |
"\n",
|
|
@@ -283,317 +283,230 @@
|
|
| 283 |
},
|
| 284 |
{
|
| 285 |
"cell_type": "code",
|
| 286 |
-
"execution_count":
|
| 287 |
"metadata": {},
|
| 288 |
"outputs": [
|
| 289 |
{
|
| 290 |
-
"
|
| 291 |
-
|
| 292 |
-
|
| 293 |
-
|
| 294 |
-
|
| 295 |
-
|
| 296 |
-
" 365.61199999999997,\n",
|
| 297 |
-
" 446.148,\n",
|
| 298 |
-
" 526.684,\n",
|
| 299 |
-
" 607.22,\n",
|
| 300 |
-
" 687.7560000000001,\n",
|
| 301 |
-
" 768.2919999999999,\n",
|
| 302 |
-
" 848.828,\n",
|
| 303 |
-
" 929.364,\n",
|
| 304 |
-
" 1009.8999999999999,\n",
|
| 305 |
-
" 1090.436,\n",
|
| 306 |
-
" 1170.972,\n",
|
| 307 |
-
" 1251.508,\n",
|
| 308 |
-
" 1332.0439999999999,\n",
|
| 309 |
-
" 1412.58,\n",
|
| 310 |
-
" 1493.116,\n",
|
| 311 |
-
" 1573.652,\n",
|
| 312 |
-
" 1654.188,\n",
|
| 313 |
-
" 1734.724,\n",
|
| 314 |
-
" 1815.26,\n",
|
| 315 |
-
" 1895.796,\n",
|
| 316 |
-
" 1976.3319999999999,\n",
|
| 317 |
-
" 2056.868,\n",
|
| 318 |
-
" 2137.404,\n",
|
| 319 |
-
" 2217.94,\n",
|
| 320 |
-
" 2298.476,\n",
|
| 321 |
-
" 2379.012,\n",
|
| 322 |
-
" 2459.548,\n",
|
| 323 |
-
" 2540.084,\n",
|
| 324 |
-
" 2620.62,\n",
|
| 325 |
-
" 2701.156,\n",
|
| 326 |
-
" 2781.692,\n",
|
| 327 |
-
" 2862.228,\n",
|
| 328 |
-
" 2942.764,\n",
|
| 329 |
-
" 3023.3,\n",
|
| 330 |
-
" 3103.8360000000002,\n",
|
| 331 |
-
" 3184.372,\n",
|
| 332 |
-
" 3264.908,\n",
|
| 333 |
-
" 3345.444,\n",
|
| 334 |
-
" 3425.98,\n",
|
| 335 |
-
" 3506.516,\n",
|
| 336 |
-
" 3587.052,\n",
|
| 337 |
-
" 3667.588,\n",
|
| 338 |
-
" 3748.124,\n",
|
| 339 |
-
" 3828.66,\n",
|
| 340 |
-
" 3909.196,\n",
|
| 341 |
-
" 3989.732,\n",
|
| 342 |
-
" 4070.268,\n",
|
| 343 |
-
" 4150.804,\n",
|
| 344 |
-
" 4231.34,\n",
|
| 345 |
-
" 4311.876,\n",
|
| 346 |
-
" 4392.412,\n",
|
| 347 |
-
" 4472.948,\n",
|
| 348 |
-
" 4553.484,\n",
|
| 349 |
-
" 4634.07,\n",
|
| 350 |
-
" 4714.656,\n",
|
| 351 |
-
" 4795.192,\n",
|
| 352 |
-
" 4875.778,\n",
|
| 353 |
-
" 4956.414,\n",
|
| 354 |
-
" 5037.2,\n",
|
| 355 |
-
" 5118.436,\n",
|
| 356 |
-
" 5199.6720000000005,\n",
|
| 357 |
-
" 5280.908,\n",
|
| 358 |
-
" 5362.694,\n",
|
| 359 |
-
" 5444.780000000001,\n",
|
| 360 |
-
" 5527.166,\n",
|
| 361 |
-
" 5610.352000000001,\n",
|
| 362 |
-
" 5694.238,\n",
|
| 363 |
-
" 5779.474,\n",
|
| 364 |
-
" 5865.66,\n",
|
| 365 |
-
" 5951.796,\n",
|
| 366 |
-
" 6039.532,\n",
|
| 367 |
-
" 6129.968000000001,\n",
|
| 368 |
-
" 6224.154,\n",
|
| 369 |
-
" 6319.6900000000005,\n",
|
| 370 |
-
" 6416.226000000001,\n",
|
| 371 |
-
" 6517.162,\n",
|
| 372 |
-
" 6621.998,\n",
|
| 373 |
-
" 6729.684,\n",
|
| 374 |
-
" 6841.514,\n",
|
| 375 |
-
" 6956.25,\n",
|
| 376 |
-
" 7083.992,\n",
|
| 377 |
-
" 7223.878,\n",
|
| 378 |
-
" 7367.364,\n",
|
| 379 |
-
" 7521.8,\n",
|
| 380 |
-
" 7694.136,\n",
|
| 381 |
-
" 7887.222,\n",
|
| 382 |
-
" 8107.012000000001,\n",
|
| 383 |
-
" 8351.09,\n",
|
| 384 |
-
" 8628.02,\n",
|
| 385 |
-
" 8955.047999999999,\n",
|
| 386 |
-
" 9344.636,\n",
|
| 387 |
-
" 9842.778,\n",
|
| 388 |
-
" 10525.202000000001,\n",
|
| 389 |
-
" 11521.948,\n",
|
| 390 |
-
" 13390.356,\n",
|
| 391 |
-
" 79941.29199999999]"
|
| 392 |
-
]
|
| 393 |
-
},
|
| 394 |
-
"execution_count": 157,
|
| 395 |
-
"metadata": {},
|
| 396 |
-
"output_type": "execute_result"
|
| 397 |
}
|
| 398 |
],
|
| 399 |
-
"source": [
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 400 |
},
|
| 401 |
{
|
| 402 |
"cell_type": "code",
|
| 403 |
-
"execution_count":
|
| 404 |
"metadata": {},
|
| 405 |
"outputs": [
|
| 406 |
{
|
| 407 |
-
"
|
| 408 |
-
|
| 409 |
-
|
| 410 |
-
|
| 411 |
-
|
| 412 |
-
|
| 413 |
-
|
| 414 |
-
" (808.56, 889.096], (889.096, 969.632],\n",
|
| 415 |
-
" (969.632, 1050.168], (1050.168, 1130.704],\n",
|
| 416 |
-
" (1130.704, 1211.24], (1211.24, 1291.776],\n",
|
| 417 |
-
" (1291.776, 1372.312], (1372.312, 1452.848],\n",
|
| 418 |
-
" (1452.848, 1533.384], (1533.384, 1613.92],\n",
|
| 419 |
-
" (1613.92, 1694.456], (1694.456, 1774.992],\n",
|
| 420 |
-
" (1774.992, 1855.528], (1855.528, 1936.064],\n",
|
| 421 |
-
" (1936.064, 2016.6], (2016.6, 2097.136],\n",
|
| 422 |
-
" (2097.136, 2177.672], (2177.672, 2258.208],\n",
|
| 423 |
-
" (2258.208, 2338.744], (2338.744, 2419.28],\n",
|
| 424 |
-
" (2419.28, 2499.816], (2499.816, 2580.352],\n",
|
| 425 |
-
" (2580.352, 2660.888], (2660.888, 2741.424],\n",
|
| 426 |
-
" (2741.424, 2821.96], (2821.96, 2902.496],\n",
|
| 427 |
-
" (2902.496, 2983.032], (2983.032, 3063.568],\n",
|
| 428 |
-
" (3063.568, 3144.104], (3144.104, 3224.64],\n",
|
| 429 |
-
" (3224.64, 3305.176], (3305.176, 3385.712],\n",
|
| 430 |
-
" (3385.712, 3466.248], (3466.248, 3546.784],\n",
|
| 431 |
-
" (3546.784, 3627.32], (3627.32, 3707.856],\n",
|
| 432 |
-
" (3707.856, 3788.392], (3788.392, 3868.928],\n",
|
| 433 |
-
" (3868.928, 3949.464], (3949.464, 4030.0],\n",
|
| 434 |
-
" (4030.0, 4110.536], (4110.536, 4191.072],\n",
|
| 435 |
-
" (4191.072, 4271.608], (4271.608, 4352.144],\n",
|
| 436 |
-
" (4352.144, 4432.68], (4432.68, 4513.216],\n",
|
| 437 |
-
" (4513.216, 4593.752], (4593.752, 4674.388],\n",
|
| 438 |
-
" (4674.388, 4754.924], (4754.924, 4835.46],\n",
|
| 439 |
-
" (4835.46, 4916.096], (4916.096, 4996.732],\n",
|
| 440 |
-
" (4996.732, 5077.668], (5077.668, 5159.204],\n",
|
| 441 |
-
" (5159.204, 5240.14], (5240.14, 5321.676],\n",
|
| 442 |
-
" (5321.676, 5403.712], (5403.712, 5485.848],\n",
|
| 443 |
-
" (5485.848, 5568.484], (5568.484, 5652.22],\n",
|
| 444 |
-
" (5652.22, 5736.256], (5736.256, 5822.692],\n",
|
| 445 |
-
" (5822.692, 5908.628], (5908.628, 5994.964],\n",
|
| 446 |
-
" (5994.964, 6084.1], (6084.1, 6175.836],\n",
|
| 447 |
-
" (6175.836, 6272.472], (6272.472, 6366.908],\n",
|
| 448 |
-
" (6366.908, 6465.544], (6465.544, 6568.78],\n",
|
| 449 |
-
" (6568.78, 6675.216], (6675.216, 6784.152],\n",
|
| 450 |
-
" (6784.152, 6898.876], (6898.876, 7013.624],\n",
|
| 451 |
-
" (7013.624, 7154.36], (7154.36, 7293.396],\n",
|
| 452 |
-
" (7293.396, 7441.332], (7441.332, 7602.268],\n",
|
| 453 |
-
" (7602.268, 7786.004], (7786.004, 7988.44],\n",
|
| 454 |
-
" (7988.44, 8225.584], (8225.584, 8476.596],\n",
|
| 455 |
-
" (8476.596, 8779.444], (8779.444, 9130.652],\n",
|
| 456 |
-
" (9130.652, 9558.62], (9558.62, 10126.936],\n",
|
| 457 |
-
" (10126.936, 10923.468], (10923.468, 12120.428],\n",
|
| 458 |
-
" (12120.428, 14660.284], (14660.284, 145222.3]],\n",
|
| 459 |
-
" categories=[(2.099, 83.736], (83.736, 164.272], (164.272, 244.808], (244.808, 325.344], ..., (10126.936, 10923.468], (10923.468, 12120.428], (12120.428, 14660.284], (14660.284, 145222.3]], ordered=True, dtype='category', name='bin')"
|
| 460 |
-
]
|
| 461 |
-
},
|
| 462 |
-
"execution_count": 148,
|
| 463 |
-
"metadata": {},
|
| 464 |
-
"output_type": "execute_result"
|
| 465 |
}
|
| 466 |
],
|
| 467 |
"source": [
|
| 468 |
-
"
|
| 469 |
-
|
| 470 |
-
|
| 471 |
-
|
| 472 |
-
|
| 473 |
-
|
| 474 |
-
|
| 475 |
-
|
| 476 |
-
|
| 477 |
-
"
|
|
|
|
|
|
|
|
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|
|
|
|
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|
| 478 |
]
|
| 479 |
},
|
| 480 |
{
|
| 481 |
"cell_type": "code",
|
| 482 |
-
"execution_count":
|
| 483 |
"metadata": {},
|
| 484 |
"outputs": [
|
| 485 |
{
|
| 486 |
"data": {
|
| 487 |
"text/plain": [
|
| 488 |
-
"
|
| 489 |
-
" (14522, 1454.3019999999997),\n",
|
| 490 |
-
" (14522, 2906.5039999999995),\n",
|
| 491 |
-
" (14321, 4358.706),\n",
|
| 492 |
-
" (11209, 5810.907999999999),\n",
|
| 493 |
-
" (5650, 7263.109999999999),\n",
|
| 494 |
-
" (2620, 8715.312),\n",
|
| 495 |
-
" (1273, 10167.514),\n",
|
| 496 |
-
" (659, 11619.715999999999),\n",
|
| 497 |
-
" (404, 13071.917999999998),\n",
|
| 498 |
-
" (247, 14524.119999999997),\n",
|
| 499 |
-
" (156, 15976.321999999998),\n",
|
| 500 |
-
" (89, 17428.523999999998),\n",
|
| 501 |
-
" (57, 18880.725999999995),\n",
|
| 502 |
-
" (42, 20332.927999999996),\n",
|
| 503 |
-
" (34, 21785.129999999994),\n",
|
| 504 |
-
" (32, 23237.331999999995),\n",
|
| 505 |
-
" (12, 24689.533999999996),\n",
|
| 506 |
-
" (21, 26141.735999999994),\n",
|
| 507 |
-
" (17, 27593.937999999995),\n",
|
| 508 |
-
" (9, 29046.139999999992),\n",
|
| 509 |
-
" (7, 30498.341999999993),\n",
|
| 510 |
-
" (12, 31950.543999999994),\n",
|
| 511 |
-
" (13, 33402.74599999999),\n",
|
| 512 |
-
" (12, 34854.948),\n",
|
| 513 |
-
" (8, 36307.149999999994),\n",
|
| 514 |
-
" (7, 37759.35199999999),\n",
|
| 515 |
-
" (9, 39211.55399999999),\n",
|
| 516 |
-
" (8, 40663.755999999994),\n",
|
| 517 |
-
" (7, 42115.95799999999),\n",
|
| 518 |
-
" (1, 43568.15999999999),\n",
|
| 519 |
-
" (4, 45020.361999999994),\n",
|
| 520 |
-
" (5, 46472.56399999999),\n",
|
| 521 |
-
" (5, 47924.76599999999),\n",
|
| 522 |
-
" (1, 49376.96799999999),\n",
|
| 523 |
-
" (1, 50829.16999999999),\n",
|
| 524 |
-
" (2, 52281.37199999999),\n",
|
| 525 |
-
" (4, 53733.57399999999),\n",
|
| 526 |
-
" (3, 55185.77599999999),\n",
|
| 527 |
-
" (1, 56637.97799999999),\n",
|
| 528 |
-
" (2, 58090.179999999986),\n",
|
| 529 |
-
" (2, 59542.38199999999),\n",
|
| 530 |
-
" (2, 60994.58399999999),\n",
|
| 531 |
-
" (1, 62446.785999999986),\n",
|
| 532 |
-
" (1, 63898.98799999999),\n",
|
| 533 |
-
" (0, 65351.18999999999),\n",
|
| 534 |
-
" (0, 66803.39199999999),\n",
|
| 535 |
-
" (1, 68255.594),\n",
|
| 536 |
-
" (1, 69707.796),\n",
|
| 537 |
-
" (0, 71159.99799999999),\n",
|
| 538 |
-
" (1, 72612.2),\n",
|
| 539 |
-
" (1, 74064.40199999999),\n",
|
| 540 |
-
" (0, 75516.60399999999),\n",
|
| 541 |
-
" (0, 76968.806),\n",
|
| 542 |
-
" (1, 78421.00799999999),\n",
|
| 543 |
-
" (0, 79873.20999999999),\n",
|
| 544 |
-
" (1, 81325.412),\n",
|
| 545 |
-
" (0, 82777.61399999999),\n",
|
| 546 |
-
" (0, 84229.81599999999),\n",
|
| 547 |
-
" (2, 85682.018),\n",
|
| 548 |
-
" (0, 87134.21999999999),\n",
|
| 549 |
-
" (0, 88586.42199999999),\n",
|
| 550 |
-
" (0, 90038.624),\n",
|
| 551 |
-
" (0, 91490.82599999999),\n",
|
| 552 |
-
" (0, 92943.02799999999),\n",
|
| 553 |
-
" (0, 94395.23),\n",
|
| 554 |
-
" (0, 95847.43199999999),\n",
|
| 555 |
-
" (0, 97299.63399999999),\n",
|
| 556 |
-
" (0, 98751.836),\n",
|
| 557 |
-
" (0, 100204.03799999999),\n",
|
| 558 |
-
" (0, 101656.23999999999),\n",
|
| 559 |
-
" (0, 103108.442),\n",
|
| 560 |
-
" (0, 104560.64399999999),\n",
|
| 561 |
-
" (0, 106012.84599999999),\n",
|
| 562 |
-
" (0, 107465.048),\n",
|
| 563 |
-
" (0, 108917.24999999999),\n",
|
| 564 |
-
" (0, 110369.45199999999),\n",
|
| 565 |
-
" (1, 111821.654),\n",
|
| 566 |
-
" (0, 113273.85599999999),\n",
|
| 567 |
-
" (0, 114726.05799999999),\n",
|
| 568 |
-
" (0, 116178.25999999998),\n",
|
| 569 |
-
" (0, 117630.46199999998),\n",
|
| 570 |
-
" (1, 119082.66399999999),\n",
|
| 571 |
-
" (0, 120534.86599999998),\n",
|
| 572 |
-
" (0, 121987.06799999998),\n",
|
| 573 |
-
" (0, 123439.26999999999),\n",
|
| 574 |
-
" (0, 124891.47199999998),\n",
|
| 575 |
-
" (0, 126343.67399999998),\n",
|
| 576 |
-
" (0, 127795.87599999999),\n",
|
| 577 |
-
" (0, 129248.07799999998),\n",
|
| 578 |
-
" (0, 130700.27999999998),\n",
|
| 579 |
-
" (0, 132152.482),\n",
|
| 580 |
-
" (0, 133604.68399999998),\n",
|
| 581 |
-
" (0, 135056.886),\n",
|
| 582 |
-
" (0, 136509.088),\n",
|
| 583 |
-
" (0, 137961.28999999998),\n",
|
| 584 |
-
" (0, 139413.492),\n",
|
| 585 |
-
" (0, 140865.694),\n",
|
| 586 |
-
" (0, 142317.89599999998),\n",
|
| 587 |
-
" (1, 143770.09799999997)]"
|
| 588 |
]
|
| 589 |
},
|
| 590 |
-
"execution_count":
|
| 591 |
"metadata": {},
|
| 592 |
"output_type": "execute_result"
|
| 593 |
}
|
| 594 |
],
|
| 595 |
"source": [
|
| 596 |
-
"
|
| 597 |
]
|
| 598 |
},
|
| 599 |
{
|
|
|
|
| 2 |
"cells": [
|
| 3 |
{
|
| 4 |
"cell_type": "code",
|
| 5 |
+
"execution_count": 62,
|
| 6 |
"metadata": {},
|
| 7 |
"outputs": [],
|
| 8 |
"source": [
|
| 9 |
"TITLE=\"Histograms of selected metrics\"\n",
|
| 10 |
+
"RED_OP=\"red 0.2\"\n",
|
| 11 |
+
"RED_FULL=\"red 1.0\"\n",
|
| 12 |
"DEFAULT_LEGEND = {\n",
|
| 13 |
" \"xanchor\": \"right\",\n",
|
| 14 |
" \"yanchor\": \"top\",\n",
|
|
|
|
| 59 |
" \"font\": {\n",
|
| 60 |
" \"size\": 10\n",
|
| 61 |
" },\n",
|
| 62 |
+
" \"showarrow\": False\n",
|
| 63 |
" }\n",
|
| 64 |
" ],\n",
|
| 65 |
" \"legend\": DEFAULT_LEGEND\n",
|
|
|
|
| 160 |
},
|
| 161 |
{
|
| 162 |
"cell_type": "code",
|
| 163 |
+
"execution_count": 63,
|
| 164 |
"metadata": {},
|
| 165 |
"outputs": [],
|
| 166 |
"source": [
|
|
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|
| 205 |
},
|
| 206 |
{
|
| 207 |
"cell_type": "code",
|
| 208 |
+
"execution_count": 124,
|
| 209 |
"metadata": {},
|
| 210 |
"outputs": [
|
| 211 |
{
|
|
|
|
| 230 |
" return name.replace(\" \", \"-\").lower()\n",
|
| 231 |
"TABBED_PLOT = {}\n",
|
| 232 |
"\n",
|
| 233 |
+
"OUTPUT_DIR = \"../assets/data/plots/stats\"\n",
|
| 234 |
"\n",
|
| 235 |
+
"with open(\"../src_data/lines_ending_with_terminal_mark_ratio.json\", \"r\") as f:\n",
|
| 236 |
" data = json.load(f)\n",
|
| 237 |
" print(data)\n",
|
| 238 |
+
" bars = {k: {**prepare_histogram(v, digits=2)} for k, v in data.items()}\n",
|
| 239 |
" TABBED_PLOT[\"lines_ended_with_punct\"] = {\n",
|
| 240 |
+
" \"data\": bars,\n",
|
| 241 |
" \"layout\": LINES_ENDED_WITH_PUNC_LAYOUT\n",
|
| 242 |
" }\n",
|
| 243 |
"\n",
|
| 244 |
+
"with open(\"../src_data/line_char_duplicates_v2.json\", \"r\") as f:\n",
|
| 245 |
" data = json.load(f)\n",
|
| 246 |
+
" bars = {k: {**prepare_histogram(v, digits=3)} for k, v in data.items()}\n",
|
| 247 |
" TABBED_PLOT[\"lines_chars\"] = {\n",
|
| 248 |
+
" \"data\": bars,\n",
|
| 249 |
" \"layout\": LINES_CHARS\n",
|
| 250 |
" }\n",
|
| 251 |
"\n",
|
| 252 |
+
"with open(\"../src_data/short_line_ratio_chars_30.json\", \"r\") as f:\n",
|
| 253 |
" data = json.load(f)\n",
|
| 254 |
+
" bars = {k: {**prepare_histogram(v, digits=2)} for k, v in data.items()}\n",
|
| 255 |
" TABBED_PLOT[\"short_lines\"] = {\n",
|
| 256 |
+
" \"data\": bars,\n",
|
| 257 |
" \"layout\": SHORT_LINES\n",
|
| 258 |
" }\n",
|
| 259 |
"\n",
|
|
|
|
| 283 |
},
|
| 284 |
{
|
| 285 |
"cell_type": "code",
|
| 286 |
+
"execution_count": 285,
|
| 287 |
"metadata": {},
|
| 288 |
"outputs": [
|
| 289 |
{
|
| 290 |
+
"name": "stderr",
|
| 291 |
+
"output_type": "stream",
|
| 292 |
+
"text": [
|
| 293 |
+
"/var/folders/cj/cyw80lln0mz_9hlly0x3qvf80000gn/T/ipykernel_78990/2352012771.py:26: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
|
| 294 |
+
" result = grouped.apply(top_6_stats).reset_index()\n"
|
| 295 |
+
]
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|
| 296 |
}
|
| 297 |
],
|
| 298 |
+
"source": [
|
| 299 |
+
"df = pd.read_csv(\"../src_data/commoncrawl_new_fixed_dumps.csv\")\n",
|
| 300 |
+
"grouped = df.groupby('runname')\n",
|
| 301 |
+
"\n",
|
| 302 |
+
"# Define a function to take the top 6 rows of each group\n",
|
| 303 |
+
"def top_6_avg(group):\n",
|
| 304 |
+
" # Sort the group by \"steps\" in descending order\n",
|
| 305 |
+
" sorted_group = group.sort_values(by='steps', ascending=False)\n",
|
| 306 |
+
" # Take the top 6 rows\n",
|
| 307 |
+
" top_6 = sorted_group.head(6)\n",
|
| 308 |
+
" # Calculate the average of \"agg_score\"\n",
|
| 309 |
+
" avg_score = top_6['agg_score'].mean()\n",
|
| 310 |
+
" return avg_score\n",
|
| 311 |
+
"\n",
|
| 312 |
+
"def top_6_stats(group):\n",
|
| 313 |
+
" # Sort the group by \"steps\" in descending order\n",
|
| 314 |
+
" sorted_group = group.sort_values(by='steps', ascending=False)\n",
|
| 315 |
+
" # Take the top 6 rows\n",
|
| 316 |
+
" top_6 = sorted_group.head(6)\n",
|
| 317 |
+
" # Calculate the average of \"agg_score\"\n",
|
| 318 |
+
" avg_score = top_6['agg_score'].mean()\n",
|
| 319 |
+
" # Calculate the standard deviation of \"agg_score\"\n",
|
| 320 |
+
" std_dev = top_6['agg_score'].std()\n",
|
| 321 |
+
" return pd.Series({'avg': avg_score, 'std_dev': std_dev})\n",
|
| 322 |
+
"\n",
|
| 323 |
+
"# Apply the function to each group and aggregate the results\n",
|
| 324 |
+
"result = grouped.apply(top_6_stats).reset_index()\n",
|
| 325 |
+
"\n",
|
| 326 |
+
"def filter_old(x):\n",
|
| 327 |
+
" value = int(str(x[\"runname\"]).split(\"-\")[0])\n",
|
| 328 |
+
" return value >= 2021\n",
|
| 329 |
+
"result_2021_plus = result[result.apply(filter_old, axis=1)]\n",
|
| 330 |
+
"\n",
|
| 331 |
+
"cc_traces = {\n",
|
| 332 |
+
" \"x\": result_2021_plus[\"runname\"].tolist(),\n",
|
| 333 |
+
" \"y\": result_2021_plus[\"avg\"].tolist(),\n",
|
| 334 |
+
" \"type\": \"scattergl\",\n",
|
| 335 |
+
" \"mode\": \"lines+markers\",\n",
|
| 336 |
+
" \"name\": \"Dump Score\",\n",
|
| 337 |
+
" \"yaxis\": \"y2\",\n",
|
| 338 |
+
" \"line\": {\n",
|
| 339 |
+
" \"color\": \"black 1.0\"\n",
|
| 340 |
+
" },\n",
|
| 341 |
+
" \"marker\": {\n",
|
| 342 |
+
" \"size\": 10,\n",
|
| 343 |
+
" \"color\": \"black 0.5\",\n",
|
| 344 |
+
" \"line\": {\n",
|
| 345 |
+
" \"color\": \"black 1.0\",\n",
|
| 346 |
+
" \"width\": 2\n",
|
| 347 |
+
" }\n",
|
| 348 |
+
" }\n",
|
| 349 |
+
"}"
|
| 350 |
+
]
|
| 351 |
},
|
| 352 |
{
|
| 353 |
"cell_type": "code",
|
| 354 |
+
"execution_count": 286,
|
| 355 |
"metadata": {},
|
| 356 |
"outputs": [
|
| 357 |
{
|
| 358 |
+
"name": "stdout",
|
| 359 |
+
"output_type": "stream",
|
| 360 |
+
"text": [
|
| 361 |
+
"22\n",
|
| 362 |
+
"22\n",
|
| 363 |
+
"{'data': {'dedup_minhash_independent_output_CC-MAIN-2021-04': {'x': ['2021-04'], 'y': [4.49282993950334e-06], 'label': '2021-04', 'color': 'blue 1.0'}, 'dedup_minhash_independent_output_CC-MAIN-2021-10': {'x': ['2021-10'], 'y': [4.4320506429770015e-06], 'label': '2021-10', 'color': 'blue 1.0'}, 'dedup_minhash_independent_output_CC-MAIN-2021-17': {'x': ['2021-17'], 'y': [4.525422769007163e-06], 'label': '2021-17', 'color': 'blue 1.0'}, 'dedup_minhash_independent_output_CC-MAIN-2021-21': {'x': ['2021-21'], 'y': [4.337506002649248e-06], 'label': '2021-21', 'color': 'blue 1.0'}, 'dedup_minhash_independent_output_CC-MAIN-2021-25': {'x': ['2021-25'], 'y': [4.31209874426111e-06], 'label': '2021-25', 'color': 'blue 1.0'}, 'dedup_minhash_independent_output_CC-MAIN-2021-31': {'x': ['2021-31'], 'y': [4.549984757361091e-06], 'label': '2021-31', 'color': 'blue 1.0'}, 'dedup_minhash_independent_output_CC-MAIN-2021-39': {'x': ['2021-39'], 'y': [4.476627474039729e-06], 'label': '2021-39', 'color': 'blue 1.0'}, 'dedup_minhash_independent_output_CC-MAIN-2021-43': {'x': ['2021-43'], 'y': [4.515299780479149e-06], 'label': '2021-43', 'color': 'blue 1.0'}, 'dedup_minhash_independent_output_CC-MAIN-2021-49': {'x': ['2021-49'], 'y': [3.711316662237467e-06], 'label': '2021-49', 'color': 'blue 1.0'}, 'dedup_minhash_independent_output_CC-MAIN-2022-05': {'x': ['2022-05'], 'y': [3.833222545464518e-06], 'label': '2022-05', 'color': 'blue 1.0'}, 'dedup_minhash_independent_output_CC-MAIN-2022-21': {'x': ['2022-21'], 'y': [4.033461274185702e-06], 'label': '2022-21', 'color': 'blue 1.0'}, 'dedup_minhash_independent_output_CC-MAIN-2022-27': {'x': ['2022-27'], 'y': [3.977774743517952e-06], 'label': '2022-27', 'color': 'blue 1.0'}, 'dedup_minhash_independent_output_CC-MAIN-2022-33': {'x': ['2022-33'], 'y': [3.735864798219777e-06], 'label': '2022-33', 'color': 'blue 1.0'}, 'dedup_minhash_independent_output_CC-MAIN-2022-40': {'x': ['2022-40'], 'y': [4.000056932617072e-06], 'label': '2022-40', 'color': 'blue 1.0'}, 'dedup_minhash_independent_output_CC-MAIN-2022-49': {'x': ['2022-49'], 'y': [4.073748029584546e-06], 'label': '2022-49', 'color': 'blue 1.0'}, 'dedup_minhash_independent_output_CC-MAIN-2023-06': {'x': ['2023-06'], 'y': [4.084332699102002e-06], 'label': '2023-06', 'color': 'blue 1.0'}, 'dedup_minhash_independent_output_CC-MAIN-2023-14': {'x': ['2023-14'], 'y': [4.092708480795626e-06], 'label': '2023-14', 'color': 'blue 1.0'}, 'dedup_minhash_independent_output_CC-MAIN-2023-23': {'x': ['2023-23'], 'y': [4.986546453643025e-06], 'label': '2023-23', 'color': 'blue 1.0'}, 'dedup_minhash_independent_output_CC-MAIN-2023-40': {'x': ['2023-40'], 'y': [8.10525888274716e-06], 'label': '2023-40', 'color': 'blue 1.0'}, 'dedup_minhash_independent_output_CC-MAIN-2023-50': {'x': ['2023-50'], 'y': [1.1324709237817275e-05], 'label': '2023-50', 'color': 'blue 1.0'}, 'dedup_minhash_independent_output_CC-MAIN-2024-10': {'x': ['2024-10'], 'y': [1.645258450134167e-05], 'label': '2024-10', 'color': 'blue 1.0'}, 'dedup_minhash_independent_output_new_CC-MAIN-2024-18': {'x': ['2024-18'], 'y': [2.1801896395395062e-05], 'label': '2024-18', 'color': 'blue 1.0'}}, 'traces': [{'x': ['2021-04', '2021-10', '2021-17', '2021-21', '2021-25', '2021-31', '2021-39', '2021-43', '2021-49', '2022-05', '2022-21', '2022-27', '2022-33', '2022-40', '2022-49', '2023-06', '2023-14', '2023-23', '2023-40', '2023-50', '2024-10', '2024-18'], 'y': [0.4303327426314354, 0.4285315126180649, 0.43032742974658805, 0.42827240626017254, 0.42655438867708045, 0.43065756373107433, 0.42699132797618705, 0.432743809496363, 0.426107710848252, 0.4314096675564845, 0.4288001439223687, 0.43330975994467735, 0.4283793264379104, 0.43007233180105686, 0.43284351378679276, 0.43421785968045395, 0.4327918446312348, 0.43310930766165257, 0.4327431519826253, 0.4331989834705989, 0.43582472391426563, 0.4374971886475881], 'type': 'scattergl', 'mode': 'lines+markers', 'name': 'Dump Score', 'yaxis': 'y2', 'line': {'color': 'black 1.0'}, 'marker': {'size': 10, 'color': 'black 0.5', 'line': {'color': 'black 1.0', 'width': 2}}}], 'layout': {'showlegend': False, 'title': {'text': 'Synthetic Data Contamination'}, 'margin': {'b': 80}, 'xaxis': {'title': {'text': 'Year', 'standoff': 20}, 'type': 'category'}, 'yaxis': {'title': {'text': 'Synthetic proxy Words Ratio'}, 'side': 'right'}, 'shapes': [{'type': 'line', 'x0': 13.92, 'y0': 0.424, 'x1': 13.92, 'y1': 0.439, 'xref': 'x', 'yref': 'y2', 'line': {'color': 'red 1.0', 'width': 2, 'dash': 'dashdot'}}], 'annotations': [{'text': 'Chat-GPT Release', 'x': 12.3, 'y': 0.4357, 'xref': 'x', 'yref': 'y2', 'showarrow': False}], 'yaxis2': {'title': {'text': 'Aggregate Score'}, 'overlaying': 'y', 'side': 'left'}}}\n"
|
| 364 |
+
]
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|
| 365 |
}
|
| 366 |
],
|
| 367 |
"source": [
|
| 368 |
+
"import json\n",
|
| 369 |
+
"from collections import defaultdict\n",
|
| 370 |
+
"import os\n",
|
| 371 |
+
"import json\n",
|
| 372 |
+
"import re\n",
|
| 373 |
+
"\n",
|
| 374 |
+
"import numpy as np\n",
|
| 375 |
+
"MAX_DATA_POINTS = 1000\n",
|
| 376 |
+
"\n",
|
| 377 |
+
"def rename_ind_dedups(name):\n",
|
| 378 |
+
" # Grep for \\d{4}-\\d{2}\n",
|
| 379 |
+
" match = re.search(r\"(\\d{4}-\\d{2})\", name)\n",
|
| 380 |
+
" if match:\n",
|
| 381 |
+
" return match.group(1)\n",
|
| 382 |
+
" return name\n",
|
| 383 |
+
"\n",
|
| 384 |
+
"def prepare_summary_bars(data):\n",
|
| 385 |
+
" return {\n",
|
| 386 |
+
" key: {\"x\": [rename_ind_dedups(key)], \"y\": [value[\"summary\"][\"mean\"]], \"label\": rename_ind_dedups(key), \"color\": \"blue 1.0\"} for key, value in data.items()\n",
|
| 387 |
+
" }\n",
|
| 388 |
+
"\n",
|
| 389 |
+
"\n",
|
| 390 |
+
"OUTPUT_DIR = \"../assets/data/plots/synth_data_contamination\"\n",
|
| 391 |
+
"TABBED_PLOT = {}\n",
|
| 392 |
+
"\n",
|
| 393 |
+
"\n",
|
| 394 |
+
"def filter_old_2(x):\n",
|
| 395 |
+
" year = int(re.search(r\"(\\d{4})-(\\d{2})\", x).group(1))\n",
|
| 396 |
+
" week = int(re.search(r\"(\\d{4})-(\\d{2})\", x).group(2))\n",
|
| 397 |
+
" return year >= 2021 \n",
|
| 398 |
+
"\n",
|
| 399 |
+
"with open(\"../src_data/words_contamination_delve.json\", \"r\") as f:\n",
|
| 400 |
+
" data = json.load(f)\n",
|
| 401 |
+
" bars = prepare_summary_bars(data)\n",
|
| 402 |
+
" filtered_bars = {k: v for k, v in bars.items() if filter_old_2(k)}\n",
|
| 403 |
+
" print(len(filtered_bars))\n",
|
| 404 |
+
" print(len(result_2021_plus))\n",
|
| 405 |
+
" assert len(filtered_bars) == len(result_2021_plus)\n",
|
| 406 |
+
" TABBED_PLOT[\"words_contamination\"] = {\n",
|
| 407 |
+
" \"data\": filtered_bars,\n",
|
| 408 |
+
" \"traces\": [cc_traces],\n",
|
| 409 |
+
" \"layout\": {\n",
|
| 410 |
+
" \"showlegend\": False,\n",
|
| 411 |
+
" \"title\": {\n",
|
| 412 |
+
" \"text\": \"Synthetic Data Contamination\"\n",
|
| 413 |
+
" },\n",
|
| 414 |
+
" \"margin\": {\n",
|
| 415 |
+
" \"b\": 80,\n",
|
| 416 |
+
" },\n",
|
| 417 |
+
" \"xaxis\": {\n",
|
| 418 |
+
" \"title\": {\n",
|
| 419 |
+
" \"text\": \"Year\",\n",
|
| 420 |
+
" \"standoff\": 20\n",
|
| 421 |
+
" },\n",
|
| 422 |
+
" \"type\": \"category\"\n",
|
| 423 |
+
" },\n",
|
| 424 |
+
" \"yaxis\": {\n",
|
| 425 |
+
" \"title\": {\n",
|
| 426 |
+
" \"text\": \"Synthetic proxy Words Ratio\",\n",
|
| 427 |
+
" },\n",
|
| 428 |
+
" \"side\": \"right\"\n",
|
| 429 |
+
" },\n",
|
| 430 |
+
" \"shapes\": [\n",
|
| 431 |
+
" {\n",
|
| 432 |
+
" \"type\": \"line\",\n",
|
| 433 |
+
" \"x0\": 13.92,\n",
|
| 434 |
+
" \"y0\": 0.424,\n",
|
| 435 |
+
" \"x1\": 13.92,\n",
|
| 436 |
+
" \"y1\": 0.439,\n",
|
| 437 |
+
" \"xref\": \"x\",\n",
|
| 438 |
+
" \"yref\": \"y2\",\n",
|
| 439 |
+
" \"line\": {\n",
|
| 440 |
+
" \"color\": \"red 1.0\",\n",
|
| 441 |
+
" \"width\": 2,\n",
|
| 442 |
+
" \"dash\": \"dashdot\",\n",
|
| 443 |
+
" }\n",
|
| 444 |
+
" }\n",
|
| 445 |
+
" ],\n",
|
| 446 |
+
" \"annotations\": [\n",
|
| 447 |
+
" {\n",
|
| 448 |
+
" \"text\": \"Chat-GPT Release\",\n",
|
| 449 |
+
" \"x\": 12.3,\n",
|
| 450 |
+
" \"y\": 0.4357,\n",
|
| 451 |
+
" \"xref\": \"x\",\n",
|
| 452 |
+
" \"yref\": \"y2\",\n",
|
| 453 |
+
" \"showarrow\": False\n",
|
| 454 |
+
"\n",
|
| 455 |
+
" }\n",
|
| 456 |
+
" ],\n",
|
| 457 |
+
" \"yaxis2\": {\n",
|
| 458 |
+
" \"title\": {\n",
|
| 459 |
+
" \"text\": \"Aggregate Score\"\n",
|
| 460 |
+
" },\n",
|
| 461 |
+
" \"overlaying\": 'y',\n",
|
| 462 |
+
" \"side\": 'left'\n",
|
| 463 |
+
" }\n",
|
| 464 |
+
" }\n",
|
| 465 |
+
" }\n",
|
| 466 |
+
"\n",
|
| 467 |
+
" # Create the folders\n",
|
| 468 |
+
"os.makedirs(OUTPUT_DIR, exist_ok=True)\n",
|
| 469 |
+
"\n",
|
| 470 |
+
"\n",
|
| 471 |
+
"for name, plot in TABBED_PLOT.items():\n",
|
| 472 |
+
" with open(f\"{OUTPUT_DIR}/{name}.json\", \"w\") as f:\n",
|
| 473 |
+
" print(plot)\n",
|
| 474 |
+
" json.dump(plot, f)\n",
|
| 475 |
+
"\n",
|
| 476 |
+
"with open(f\"{OUTPUT_DIR}/index.json\", \"w\") as f:\n",
|
| 477 |
+
" mapping = {k: {\n",
|
| 478 |
+
" \"file\": f\"{k}.json\"\n",
|
| 479 |
+
" } for k in TABBED_PLOT.keys()}\n",
|
| 480 |
+
" json.dump({\n",
|
| 481 |
+
" \"files\": mapping,\n",
|
| 482 |
+
" \"settings\": {\n",
|
| 483 |
+
" \"defaultMetric\": \"words_contamination\",\n",
|
| 484 |
+
" \"slider\": None,\n",
|
| 485 |
+
" \"type\": \"bar\"\n",
|
| 486 |
+
" }\n",
|
| 487 |
+
" }, f)\n",
|
| 488 |
+
"\n",
|
| 489 |
+
"\n"
|
| 490 |
]
|
| 491 |
},
|
| 492 |
{
|
| 493 |
"cell_type": "code",
|
| 494 |
+
"execution_count": 183,
|
| 495 |
"metadata": {},
|
| 496 |
"outputs": [
|
| 497 |
{
|
| 498 |
"data": {
|
| 499 |
"text/plain": [
|
| 500 |
+
"21"
|
|
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|
| 501 |
]
|
| 502 |
},
|
| 503 |
+
"execution_count": 183,
|
| 504 |
"metadata": {},
|
| 505 |
"output_type": "execute_result"
|
| 506 |
}
|
| 507 |
],
|
| 508 |
"source": [
|
| 509 |
+
"len(result_2021_plus)"
|
| 510 |
]
|
| 511 |
},
|
| 512 |
{
|
src/colors.mjs
CHANGED
|
@@ -56,6 +56,8 @@ export const COLORS = [
|
|
| 56 |
|
| 57 |
export const NAMED_COLORS = {
|
| 58 |
"red": ["251", "13", "13"], // Vivid Red
|
|
|
|
|
|
|
| 59 |
};
|
| 60 |
|
| 61 |
export const getColor = (i, opacity=1) => {
|
|
@@ -66,6 +68,10 @@ export const getColor = (i, opacity=1) => {
|
|
| 66 |
};
|
| 67 |
|
| 68 |
export const getNamedColor = (name, opacity=1) => {
|
| 69 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
| 70 |
};
|
| 71 |
|
|
|
|
| 56 |
|
| 57 |
export const NAMED_COLORS = {
|
| 58 |
"red": ["251", "13", "13"], // Vivid Red
|
| 59 |
+
"black": ['13', '42', '99'], // Black
|
| 60 |
+
"blue": ["46", "145", "229"], // Sky Blue
|
| 61 |
};
|
| 62 |
|
| 63 |
export const getColor = (i, opacity=1) => {
|
|
|
|
| 68 |
};
|
| 69 |
|
| 70 |
export const getNamedColor = (name, opacity=1) => {
|
| 71 |
+
if (NAMED_COLORS.hasOwnProperty(name)) {
|
| 72 |
+
return `rgba(${NAMED_COLORS[name].join(",")}, ${opacity})`;
|
| 73 |
+
} else {
|
| 74 |
+
return undefined; // Return undefined if name doesn't exist
|
| 75 |
+
}
|
| 76 |
};
|
| 77 |
|
src/index.html
CHANGED
|
@@ -689,7 +689,12 @@
|
|
| 689 |
not contain any of these phrases), but assuming that the amount of synthetic data were to not change across
|
| 690 |
crawls, one would expect these frequencies to remain approximately constant over time.</p>
|
| 691 |
<p>The results are shown in the following plot:</p>
|
| 692 |
-
<
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 693 |
<p>While the frequency remained approximately constant until
|
| 694 |
2023-14 (ChatGPT was released at the end of 2022), we find a steep increase of our proxy metric
|
| 695 |
in recent crawls. While this simple test is not enough to conclude that ChatGPT completions and other synthetic data is improving the quality of the most recent crawl, it at the very least does not seem to drastically harm it.</p>
|
|
|
|
| 689 |
not contain any of these phrases), but assuming that the amount of synthetic data were to not change across
|
| 690 |
crawls, one would expect these frequencies to remain approximately constant over time.</p>
|
| 691 |
<p>The results are shown in the following plot:</p>
|
| 692 |
+
<div class="main-plot-container l-page-outset">
|
| 693 |
+
<figure>
|
| 694 |
+
<img src="assets/images/synth_data_contamination.png">
|
| 695 |
+
</figure>
|
| 696 |
+
<div id="plot-synth_data_contamination"></div>
|
| 697 |
+
</div>
|
| 698 |
<p>While the frequency remained approximately constant until
|
| 699 |
2023-14 (ChatGPT was released at the end of 2022), we find a steep increase of our proxy metric
|
| 700 |
in recent crawls. While this simple test is not enough to conclude that ChatGPT completions and other synthetic data is improving the quality of the most recent crawl, it at the very least does not seem to drastically harm it.</p>
|
src/plotting.js
CHANGED
|
@@ -21,6 +21,24 @@ const BAR_SETTINGS = {
|
|
| 21 |
}
|
| 22 |
}
|
| 23 |
|
|
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|
|
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|
| 24 |
const TASK_ID_TO_NAME = {
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| 25 |
// Ablations
|
| 26 |
agg_score: "Aggregate Score",
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@@ -111,6 +129,20 @@ const DEFAULT_LAYOUT = {
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| 111 |
family: "apple-system, Arial, sans-serif",
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| 112 |
},
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| 113 |
},
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| 114 |
legend: {
|
| 115 |
orientation: "v",
|
| 116 |
xanchor: "right",
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@@ -170,12 +202,12 @@ const createAblationPlottingElements = (
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| 170 |
);
|
| 171 |
// Dropdown
|
| 172 |
let dropdown = undefined
|
| 173 |
-
console.log(metricOptions)
|
| 174 |
if (metricOptions.length > 1) {
|
| 175 |
const dropdownLabel = document.createElement("label");
|
| 176 |
dropdownLabel.textContent = "Metric:";
|
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dropdown = document.createElement("select");
|
| 178 |
dropdown.innerHTML = metricOptions
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| 179 |
.map(
|
| 180 |
(option) =>
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| 181 |
`<option value="${option}">${TASK_ID_TO_NAME[option]}</option>`
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@@ -250,6 +282,34 @@ const rollingWindow = function (data, windowSize) {
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|
| 250 |
return rollingData;
|
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};
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| 252 |
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| 253 |
export const init_ablation_plot = function () {
|
| 254 |
const plotElements = document.querySelectorAll('[id^="plot-"]');
|
| 255 |
plotElements.forEach(async (plotElement) => {
|
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@@ -290,27 +350,11 @@ export const init_ablation_plot = function () {
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|
| 290 |
const metricData = await fetch(
|
| 291 |
`${DATA_FOLDER}/${plotName}/${indexMapping[metricName]["file"]}`
|
| 292 |
).then((response) => response.json());
|
| 293 |
-
const traces = metricData?.traces ?? []
|
| 294 |
-
for (const [index, [key, traceData]] of Object.entries(metricData?.data ?? []).entries()) {
|
| 295 |
-
const y = rollingWindow(traceData.y, sliderValue);
|
| 296 |
-
const x = traceData.x.slice(0, y.length);
|
| 297 |
-
const plotSettings = settings.type === "bar" ? BAR_SETTINGS : LINE_SETTINGS;
|
| 298 |
-
const traceColor = getColorForTrace(key, colorsMapping, index)
|
| 299 |
-
const trace = _.merge({}, {
|
| 300 |
-
x: x,
|
| 301 |
-
y: y,
|
| 302 |
-
name: traceData.label ?? DATASET_ID_TO_NAME[key] ?? key,
|
| 303 |
-
marker: {
|
| 304 |
-
color: traceColor,
|
| 305 |
-
},
|
| 306 |
-
line: {
|
| 307 |
-
color: traceColor,
|
| 308 |
-
},
|
| 309 |
-
}, plotSettings);
|
| 310 |
-
traces.push(trace);
|
| 311 |
-
}
|
| 312 |
-
console.log(traces)
|
| 313 |
const width = plot.parentElement.offsetWidth;
|
|
|
|
|
|
|
|
|
|
| 314 |
const layout = _.merge(
|
| 315 |
{},
|
| 316 |
DEFAULT_LAYOUT,
|
|
@@ -318,13 +362,12 @@ export const init_ablation_plot = function () {
|
|
| 318 |
width: width,
|
| 319 |
yaxis: { title: { text: TASK_ID_TO_NAME[metricName] } },
|
| 320 |
xaxis: {
|
| 321 |
-
range:
|
| 322 |
-
? getAutoRange(traces)
|
| 323 |
-
: undefined,
|
| 324 |
},
|
| 325 |
},
|
| 326 |
metricData.layout
|
| 327 |
);
|
|
|
|
| 328 |
Plotly.react(plot, traces, layout);
|
| 329 |
|
| 330 |
window.addEventListener("resize", () => {
|
|
@@ -337,6 +380,7 @@ export const init_ablation_plot = function () {
|
|
| 337 |
});
|
| 338 |
});
|
| 339 |
}
|
|
|
|
| 340 |
// Initial plot
|
| 341 |
updatePlot(dropdown, slider);
|
| 342 |
});
|
|
|
|
| 21 |
}
|
| 22 |
}
|
| 23 |
|
| 24 |
+
const METRIC_ID_TO_PRIORITY = {
|
| 25 |
+
"agg_score": 0,
|
| 26 |
+
"hellaswag/acc_norm": 1,
|
| 27 |
+
"arc/acc_norm": 2,
|
| 28 |
+
"mmlu/acc_norm": 3,
|
| 29 |
+
"openbookqa/acc_norm": 4,
|
| 30 |
+
"commonsense_qa/acc_norm": 5,
|
| 31 |
+
"piqa/acc_norm": 6,
|
| 32 |
+
"siqa/acc_norm": 7,
|
| 33 |
+
"winogrande/acc_norm": 8,
|
| 34 |
+
|
| 35 |
+
|
| 36 |
+
// Stats
|
| 37 |
+
"lines_ended_with_punct": 0,
|
| 38 |
+
"lines_chars": 1,
|
| 39 |
+
"short_lines": 2,
|
| 40 |
+
}
|
| 41 |
+
|
| 42 |
const TASK_ID_TO_NAME = {
|
| 43 |
// Ablations
|
| 44 |
agg_score: "Aggregate Score",
|
|
|
|
| 129 |
family: "apple-system, Arial, sans-serif",
|
| 130 |
},
|
| 131 |
},
|
| 132 |
+
yaxis2: {
|
| 133 |
+
title: {
|
| 134 |
+
text: "Words Contamination",
|
| 135 |
+
font: {
|
| 136 |
+
size: 15,
|
| 137 |
+
family: "apple-system, Arial, sans-serif",
|
| 138 |
+
},
|
| 139 |
+
standoff: 10,
|
| 140 |
+
},
|
| 141 |
+
tickfont: {
|
| 142 |
+
size: 14,
|
| 143 |
+
family: "apple-system, Arial, sans-serif",
|
| 144 |
+
},
|
| 145 |
+
},
|
| 146 |
legend: {
|
| 147 |
orientation: "v",
|
| 148 |
xanchor: "right",
|
|
|
|
| 202 |
);
|
| 203 |
// Dropdown
|
| 204 |
let dropdown = undefined
|
|
|
|
| 205 |
if (metricOptions.length > 1) {
|
| 206 |
const dropdownLabel = document.createElement("label");
|
| 207 |
dropdownLabel.textContent = "Metric:";
|
| 208 |
dropdown = document.createElement("select");
|
| 209 |
dropdown.innerHTML = metricOptions
|
| 210 |
+
.sort((a, b) => (METRIC_ID_TO_PRIORITY[a] ?? 0) - (METRIC_ID_TO_PRIORITY[b] ?? 0))
|
| 211 |
.map(
|
| 212 |
(option) =>
|
| 213 |
`<option value="${option}">${TASK_ID_TO_NAME[option]}</option>`
|
|
|
|
| 282 |
return rollingData;
|
| 283 |
};
|
| 284 |
|
| 285 |
+
const createTraces = (data, settings, colorsMapping, sliderValue) => {
|
| 286 |
+
if (!data) {
|
| 287 |
+
return []
|
| 288 |
+
}
|
| 289 |
+
const res = Array.from(Object.entries(data)?.entries().map(([index, [key, traceData]]) => {
|
| 290 |
+
const y = rollingWindow(traceData.y, sliderValue);
|
| 291 |
+
console.log(y)
|
| 292 |
+
const x = traceData.x.slice(0, y.length);
|
| 293 |
+
const plotSettings = settings?.type === "bar" ? BAR_SETTINGS : LINE_SETTINGS;
|
| 294 |
+
const traceColor = traceData.color ?? getColorForTrace(key, colorsMapping, index)
|
| 295 |
+
console.log(traceColor)
|
| 296 |
+
const trace = _.merge({}, {
|
| 297 |
+
x: x,
|
| 298 |
+
y: y,
|
| 299 |
+
name: traceData.label ?? DATASET_ID_TO_NAME[key] ?? key,
|
| 300 |
+
marker: {
|
| 301 |
+
color: traceColor,
|
| 302 |
+
},
|
| 303 |
+
line: {
|
| 304 |
+
color: traceColor,
|
| 305 |
+
},
|
| 306 |
+
yaxis: traceData.yaxis ?? "y1"
|
| 307 |
+
}, plotSettings);
|
| 308 |
+
return trace
|
| 309 |
+
}));
|
| 310 |
+
return res
|
| 311 |
+
}
|
| 312 |
+
|
| 313 |
export const init_ablation_plot = function () {
|
| 314 |
const plotElements = document.querySelectorAll('[id^="plot-"]');
|
| 315 |
plotElements.forEach(async (plotElement) => {
|
|
|
|
| 350 |
const metricData = await fetch(
|
| 351 |
`${DATA_FOLDER}/${plotName}/${indexMapping[metricName]["file"]}`
|
| 352 |
).then((response) => response.json());
|
| 353 |
+
const traces = (metricData?.traces ?? []).concat(createTraces(metricData.data, settings, colorsMapping, sliderValue))
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 354 |
const width = plot.parentElement.offsetWidth;
|
| 355 |
+
if (plotName == "plot-synth_data_contamination") {
|
| 356 |
+
console.log(metricData.layout, DEFAULT_LAYOUT, emptyData)
|
| 357 |
+
}
|
| 358 |
const layout = _.merge(
|
| 359 |
{},
|
| 360 |
DEFAULT_LAYOUT,
|
|
|
|
| 362 |
width: width,
|
| 363 |
yaxis: { title: { text: TASK_ID_TO_NAME[metricName] } },
|
| 364 |
xaxis: {
|
| 365 |
+
range: null
|
|
|
|
|
|
|
| 366 |
},
|
| 367 |
},
|
| 368 |
metricData.layout
|
| 369 |
);
|
| 370 |
+
console.log(traces)
|
| 371 |
Plotly.react(plot, traces, layout);
|
| 372 |
|
| 373 |
window.addEventListener("resize", () => {
|
|
|
|
| 380 |
});
|
| 381 |
});
|
| 382 |
}
|
| 383 |
+
|
| 384 |
// Initial plot
|
| 385 |
updatePlot(dropdown, slider);
|
| 386 |
});
|
src/style.css
CHANGED
|
@@ -242,6 +242,16 @@ d-contents nav > div > a {
|
|
| 242 |
font-weight: 600;
|
| 243 |
}
|
| 244 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 245 |
d-contents nav > div > a:hover,
|
| 246 |
d-contents nav > ul > li > a:hover {
|
| 247 |
text-decoration: none;
|
|
|
|
| 242 |
font-weight: 600;
|
| 243 |
}
|
| 244 |
|
| 245 |
+
d-article aside {
|
| 246 |
+
margin-bottom: 1em;
|
| 247 |
+
}
|
| 248 |
+
|
| 249 |
+
@media (min-width: 768px) {
|
| 250 |
+
d-article aside {
|
| 251 |
+
margin-bottom: 0;
|
| 252 |
+
}
|
| 253 |
+
}
|
| 254 |
+
|
| 255 |
d-contents nav > div > a:hover,
|
| 256 |
d-contents nav > ul > li > a:hover {
|
| 257 |
text-decoration: none;
|
webpack.config.js
CHANGED
|
@@ -10,27 +10,41 @@ const COLOR_KEYS = ["color", "bgColor", "fillcolor"];
|
|
| 10 |
const transformDataColors = async (data, path) => {
|
| 11 |
const {getNamedColor} = await import('./src/colors.mjs');
|
| 12 |
// if not json file, return
|
| 13 |
-
if (!path.endsWith(".json")
|
| 14 |
return data;
|
| 15 |
}
|
| 16 |
-
console.log(path)
|
| 17 |
const parsedData = JSON.parse(data);
|
| 18 |
// Change the color of the data
|
| 19 |
-
const deepIterateAndSetColor = (
|
| 20 |
-
|
| 21 |
-
|
| 22 |
-
|
| 23 |
-
|
| 24 |
-
|
| 25 |
-
|
| 26 |
-
|
| 27 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 28 |
}
|
| 29 |
-
}
|
|
|
|
| 30 |
};
|
| 31 |
-
|
| 32 |
-
deepIterateAndSetColor(parsedData);
|
| 33 |
-
return JSON.stringify(parsedData);
|
| 34 |
};
|
| 35 |
|
| 36 |
module.exports = {
|
|
|
|
| 10 |
const transformDataColors = async (data, path) => {
|
| 11 |
const {getNamedColor} = await import('./src/colors.mjs');
|
| 12 |
// if not json file, return
|
| 13 |
+
if (!path.endsWith(".json")) {
|
| 14 |
return data;
|
| 15 |
}
|
|
|
|
| 16 |
const parsedData = JSON.parse(data);
|
| 17 |
// Change the color of the data
|
| 18 |
+
const deepIterateAndSetColor = (key, val) => {
|
| 19 |
+
if (val === null) {
|
| 20 |
+
return null;
|
| 21 |
+
}
|
| 22 |
+
if (val == undefined) {
|
| 23 |
+
return undefined;
|
| 24 |
+
}
|
| 25 |
+
if (Array.isArray(val)) {
|
| 26 |
+
return val.map(item => deepIterateAndSetColor(key, item));
|
| 27 |
+
}
|
| 28 |
+
if (typeof val === "object") {
|
| 29 |
+
return Object.entries(val).reduce((newObj, [key, value]) => {
|
| 30 |
+
newObj[key] = deepIterateAndSetColor(key, value);
|
| 31 |
+
return newObj;
|
| 32 |
+
}, {});
|
| 33 |
+
}
|
| 34 |
+
if (COLOR_KEYS.includes(key)) {
|
| 35 |
+
const [colorName, opacity, ...rest] = val.trim().split(/\s+/);
|
| 36 |
+
const floatOpacity = parseFloat(opacity);
|
| 37 |
+
const newColor = getNamedColor(colorName, floatOpacity);
|
| 38 |
+
if (newColor !== undefined && rest.length === 0 && !isNaN(floatOpacity)) {
|
| 39 |
+
console.log(`key: ${key} in file ${path} changed from ${val} to ${newColor}`);
|
| 40 |
+
return newColor;
|
| 41 |
+
} else {
|
| 42 |
+
return val;
|
| 43 |
}
|
| 44 |
+
}
|
| 45 |
+
return val;
|
| 46 |
};
|
| 47 |
+
return JSON.stringify(deepIterateAndSetColor(undefined, parsedData))
|
|
|
|
|
|
|
| 48 |
};
|
| 49 |
|
| 50 |
module.exports = {
|