diff --git a/best_hn/scraped_stories/__init__.py b/best_hn/scraped_stories/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/best_hn/scraped_stories/prepare_data.ipynb b/best_hn/scraped_stories/prepare_data.ipynb
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@@ -0,0 +1,778 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "75c7e7d9bc12404e8380e1b1cb1c82ef",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "train-00006-of-00012.parquet:   0%|          | 0.00/287M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "a8ebc5f2d9144fc5ad740ae276c56e6f",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "train-00007-of-00012.parquet:   0%|          | 0.00/318M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "train-00008-of-00012.parquet:   0%|          | 0.00/323M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "e6f71b3519754739bd552b8b3fd0800a",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "train-00009-of-00012.parquet:   0%|          | 0.00/318M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "434f2d8e9411479db7d595224632e359",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "train-00010-of-00012.parquet:   0%|          | 0.00/290M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "88a7812451c346589599dc9a46393ddb",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "train-00011-of-00012.parquet:   0%|          | 0.00/287M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "fd436bd1482c4de3b462f062ae125675",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "val-00000-of-00001.parquet:   0%|          | 0.00/69.0M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "8904c0992f194b8c88ea55e238414716",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "test-00000-of-00002.parquet:   0%|          | 0.00/132M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "a84ecddb4e22491da585e53cf82349af",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "test-00001-of-00002.parquet:   0%|          | 0.00/211M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b91ce151c51042f5bf92beb280d6d4dc",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating train split:   0%|          | 0/4825995 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "78cb89dffcbe4a3488c134fc58e8e161",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating val split:   0%|          | 0/109316 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "4eff1d8da02c496584e500f0176449db",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating test split:   0%|          | 0/548147 [00:00<?, ? examples/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "DatasetDict({\n",
+              "    train: Dataset({\n",
+              "        features: ['id', 'by', 'time', 'title', 'text', 'url', 'score', 'descendants', 'kids', 'deleted', 'dead', 'scraping_error', 'scraped_title', 'scraped_published_at', 'scraped_byline', 'scraped_body', 'scraped_at', 'scraped_language', 'split'],\n",
+              "        num_rows: 4825995\n",
+              "    })\n",
+              "    val: Dataset({\n",
+              "        features: ['id', 'by', 'time', 'title', 'text', 'url', 'score', 'descendants', 'kids', 'deleted', 'dead', 'scraping_error', 'scraped_title', 'scraped_published_at', 'scraped_byline', 'scraped_body', 'scraped_at', 'scraped_language', 'split'],\n",
+              "        num_rows: 109316\n",
+              "    })\n",
+              "    test: Dataset({\n",
+              "        features: ['id', 'by', 'time', 'title', 'text', 'url', 'score', 'descendants', 'kids', 'deleted', 'dead', 'scraping_error', 'scraped_title', 'scraped_published_at', 'scraped_byline', 'scraped_body', 'scraped_at', 'scraped_language', 'split'],\n",
+              "        num_rows: 548147\n",
+              "    })\n",
+              "})"
+            ]
+          },
+          "execution_count": 1,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from datasets import load_dataset\n",
+        "\n",
+        "dataset = load_dataset(\"OpenPipe/hacker-news-scraped-stories\")\n",
+        "\n",
+        "dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div><style>\n",
+              ".dataframe > thead > tr,\n",
+              ".dataframe > tbody > tr {\n",
+              "  text-align: right;\n",
+              "  white-space: pre-wrap;\n",
+              "}\n",
+              "</style>\n",
+              "<small>shape: (5_483_458, 19)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>id</th><th>by</th><th>time</th><th>title</th><th>text</th><th>url</th><th>score</th><th>descendants</th><th>kids</th><th>deleted</th><th>dead</th><th>scraping_error</th><th>scraped_title</th><th>scraped_published_at</th><th>scraped_byline</th><th>scraped_body</th><th>scraped_at</th><th>scraped_language</th><th>split</th></tr><tr><td>i64</td><td>str</td><td>datetime[μs]</td><td>str</td><td>str</td><td>str</td><td>i64</td><td>i64</td><td>list[i64]</td><td>list[str]</td><td>bool</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>datetime[μs]</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>2</td><td>&quot;phyllis&quot;</td><td>2006-10-09 18:30:28</td><td>&quot;A Student&#x27;s Guide to Startups&quot;</td><td>null</td><td>&quot;http://www.paulgraham.com/mit.…</td><td>16</td><td>0</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>&quot;train&quot;</td></tr><tr><td>3</td><td>&quot;phyllis&quot;</td><td>2006-10-09 18:40:33</td><td>&quot;Woz Interview: the early days …</td><td>null</td><td>&quot;http://www.foundersatwork.com/…</td><td>7</td><td>0</td><td>[531602]</td><td>null</td><td>null</td><td>&quot;http_404&quot;</td><td>&quot;404 - Page Not Found&quot;</td><td>null</td><td>null</td><td>&quot;\n",
+              "\t\n",
+              "\tError - Page Not Found\n",
+              "\t\n",
+              "\t…</td><td>2024-11-08 00:09:58</td><td>null</td><td>&quot;train&quot;</td></tr><tr><td>4</td><td>&quot;onebeerdave&quot;</td><td>2006-10-09 18:47:42</td><td>&quot;NYC Developer Dilemma&quot;</td><td>null</td><td>&quot;http://avc.blogs.com/a_vc/2006…</td><td>5</td><td>0</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>&quot;train&quot;</td></tr><tr><td>5</td><td>&quot;perler&quot;</td><td>2006-10-09 18:51:04</td><td>&quot;Google, YouTube acquisition an…</td><td>null</td><td>&quot;http://www.techcrunch.com/2006…</td><td>7</td><td>0</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>&quot;train&quot;</td></tr><tr><td>6</td><td>&quot;perler&quot;</td><td>2006-10-09 18:56:40</td><td>&quot;Business Intelligence the Inkl…</td><td>null</td><td>&quot;http://360techblog.com/2006/10…</td><td>4</td><td>0</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>&quot;train&quot;</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>42070630</td><td>&quot;Hooke&quot;</td><td>2024-11-06 22:37:34</td><td>&quot;Hard Cases for a Handle Theory&quot;</td><td>null</td><td>&quot;https://www.cabinetmagazine.or…</td><td>1</td><td>0</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>&quot;test&quot;</td></tr><tr><td>42070962</td><td>&quot;june07&quot;</td><td>2024-11-06 23:05:35</td><td>null</td><td>null</td><td>null</td><td>1</td><td>null</td><td>null</td><td>null</td><td>true</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>&quot;test&quot;</td></tr><tr><td>42071238</td><td>&quot;bentocorp&quot;</td><td>2024-11-06 23:32:57</td><td>&quot;New PFAS testing method makes …</td><td>null</td><td>&quot;https://phys.org/news/2024-11-…</td><td>1</td><td>0</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>&quot;test&quot;</td></tr><tr><td>42071435</td><td>&quot;pentagrama&quot;</td><td>2024-11-06 23:50:11</td><td>&quot;Gimp 3.0 RC1 Released&quot;</td><td>null</td><td>&quot;https://www.gimp.org/news/2024…</td><td>1</td><td>0</td><td>null</td><td>null</td><td>null</td><td>&quot;no_error&quot;</td><td>&quot;GIMP - GIMP 3.0 RC1 Released&quot;</td><td>&quot;2024-11-06T00:00:00+01:00&quot;</td><td>&quot;by                           G…</td><td>&quot;\n",
+              "                    We are ve…</td><td>2024-11-08 06:29:21</td><td>&quot;en&quot;</td><td>&quot;test&quot;</td></tr><tr><td>42071457</td><td>&quot;mixeden&quot;</td><td>2024-11-06 23:53:08</td><td>&quot;Node Identifiers in Graph Neur…</td><td>null</td><td>&quot;https://synthical.com/article/…</td><td>1</td><td>0</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>&quot;test&quot;</td></tr></tbody></table></div>"
+            ],
+            "text/plain": [
+              "shape: (5_483_458, 19)\n",
+              "┌──────────┬────────────┬────────────┬────────────┬───┬────────────┬───────────┬───────────┬───────┐\n",
+              "│ id       ┆ by         ┆ time       ┆ title      ┆ … ┆ scraped_bo ┆ scraped_a ┆ scraped_l ┆ split │\n",
+              "│ ---      ┆ ---        ┆ ---        ┆ ---        ┆   ┆ dy         ┆ t         ┆ anguage   ┆ ---   │\n",
+              "│ i64      ┆ str        ┆ datetime[μ ┆ str        ┆   ┆ ---        ┆ ---       ┆ ---       ┆ str   │\n",
+              "│          ┆            ┆ s]         ┆            ┆   ┆ str        ┆ datetime[ ┆ str       ┆       │\n",
+              "│          ┆            ┆            ┆            ┆   ┆            ┆ μs]       ┆           ┆       │\n",
+              "╞══════════╪════════════╪════════════╪════════════╪═══╪════════════╪═══════════╪═══════════╪═══════╡\n",
+              "│ 2        ┆ phyllis    ┆ 2006-10-09 ┆ A          ┆ … ┆ null       ┆ null      ┆ null      ┆ train │\n",
+              "│          ┆            ┆ 18:30:28   ┆ Student's  ┆   ┆            ┆           ┆           ┆       │\n",
+              "│          ┆            ┆            ┆ Guide to   ┆   ┆            ┆           ┆           ┆       │\n",
+              "│          ┆            ┆            ┆ Startups   ┆   ┆            ┆           ┆           ┆       │\n",
+              "│ 3        ┆ phyllis    ┆ 2006-10-09 ┆ Woz        ┆ … ┆            ┆ 2024-11-0 ┆ null      ┆ train │\n",
+              "│          ┆            ┆ 18:40:33   ┆ Interview: ┆   ┆ \t          ┆ 8         ┆           ┆       │\n",
+              "│          ┆            ┆            ┆ the early  ┆   ┆ \tError -   ┆ 00:09:58  ┆           ┆       │\n",
+              "│          ┆            ┆            ┆ days …     ┆   ┆ Page Not   ┆           ┆           ┆       │\n",
+              "│          ┆            ┆            ┆            ┆   ┆ Found      ┆           ┆           ┆       │\n",
+              "│          ┆            ┆            ┆            ┆   ┆ \t          ┆           ┆           ┆       │\n",
+              "│          ┆            ┆            ┆            ┆   ┆ \t…         ┆           ┆           ┆       │\n",
+              "│ 4        ┆ onebeerdav ┆ 2006-10-09 ┆ NYC        ┆ … ┆ null       ┆ null      ┆ null      ┆ train │\n",
+              "│          ┆ e          ┆ 18:47:42   ┆ Developer  ┆   ┆            ┆           ┆           ┆       │\n",
+              "│          ┆            ┆            ┆ Dilemma    ┆   ┆            ┆           ┆           ┆       │\n",
+              "│ 5        ┆ perler     ┆ 2006-10-09 ┆ Google,    ┆ … ┆ null       ┆ null      ┆ null      ┆ train │\n",
+              "│          ┆            ┆ 18:51:04   ┆ YouTube    ┆   ┆            ┆           ┆           ┆       │\n",
+              "│          ┆            ┆            ┆ acquisitio ┆   ┆            ┆           ┆           ┆       │\n",
+              "│          ┆            ┆            ┆ n an…      ┆   ┆            ┆           ┆           ┆       │\n",
+              "│ 6        ┆ perler     ┆ 2006-10-09 ┆ Business   ┆ … ┆ null       ┆ null      ┆ null      ┆ train │\n",
+              "│          ┆            ┆ 18:56:40   ┆ Intelligen ┆   ┆            ┆           ┆           ┆       │\n",
+              "│          ┆            ┆            ┆ ce the     ┆   ┆            ┆           ┆           ┆       │\n",
+              "│          ┆            ┆            ┆ Inkl…      ┆   ┆            ┆           ┆           ┆       │\n",
+              "│ …        ┆ …          ┆ …          ┆ …          ┆ … ┆ …          ┆ …         ┆ …         ┆ …     │\n",
+              "│ 42070630 ┆ Hooke      ┆ 2024-11-06 ┆ Hard Cases ┆ … ┆ null       ┆ null      ┆ null      ┆ test  │\n",
+              "│          ┆            ┆ 22:37:34   ┆ for a      ┆   ┆            ┆           ┆           ┆       │\n",
+              "│          ┆            ┆            ┆ Handle     ┆   ┆            ┆           ┆           ┆       │\n",
+              "│          ┆            ┆            ┆ Theory     ┆   ┆            ┆           ┆           ┆       │\n",
+              "│ 42070962 ┆ june07     ┆ 2024-11-06 ┆ null       ┆ … ┆ null       ┆ null      ┆ null      ┆ test  │\n",
+              "│          ┆            ┆ 23:05:35   ┆            ┆   ┆            ┆           ┆           ┆       │\n",
+              "│ 42071238 ┆ bentocorp  ┆ 2024-11-06 ┆ New PFAS   ┆ … ┆ null       ┆ null      ┆ null      ┆ test  │\n",
+              "│          ┆            ┆ 23:32:57   ┆ testing    ┆   ┆            ┆           ┆           ┆       │\n",
+              "│          ┆            ┆            ┆ method     ┆   ┆            ┆           ┆           ┆       │\n",
+              "│          ┆            ┆            ┆ makes …    ┆   ┆            ┆           ┆           ┆       │\n",
+              "│ 42071435 ┆ pentagrama ┆ 2024-11-06 ┆ Gimp 3.0   ┆ … ┆            ┆ 2024-11-0 ┆ en        ┆ test  │\n",
+              "│          ┆            ┆ 23:50:11   ┆ RC1        ┆   ┆ We are ve… ┆ 8         ┆           ┆       │\n",
+              "│          ┆            ┆            ┆ Released   ┆   ┆            ┆ 06:29:21  ┆           ┆       │\n",
+              "│ 42071457 ┆ mixeden    ┆ 2024-11-06 ┆ Node Ident ┆ … ┆ null       ┆ null      ┆ null      ┆ test  │\n",
+              "│          ┆            ┆ 23:53:08   ┆ ifiers in  ┆   ┆            ┆           ┆           ┆       │\n",
+              "│          ┆            ┆            ┆ Graph      ┆   ┆            ┆           ┆           ┆       │\n",
+              "│          ┆            ┆            ┆ Neur…      ┆   ┆            ┆           ┆           ┆       │\n",
+              "└──────────┴────────────┴────────────┴────────────┴───┴────────────┴───────────┴───────────┴───────┘"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import polars as pl\n",
+        "\n",
+        "# Convert each split to polars and combine\n",
+        "train_df = (\n",
+        "    dataset[\"train\"]\n",
+        "    .to_polars()\n",
+        "    .with_columns(pl.Series(name=\"split\", values=[\"train\"] * len(dataset[\"train\"])))\n",
+        ")\n",
+        "val_df = (\n",
+        "    dataset[\"val\"]\n",
+        "    .to_polars()\n",
+        "    .with_columns(pl.Series(name=\"split\", values=[\"val\"] * len(dataset[\"val\"])))\n",
+        ")\n",
+        "test_df = (\n",
+        "    dataset[\"test\"]\n",
+        "    .to_polars()\n",
+        "    .with_columns(pl.Series(name=\"split\", values=[\"test\"] * len(dataset[\"test\"])))\n",
+        ")\n",
+        "\n",
+        "# Combine into single dataframe\n",
+        "df = pl.concat([train_df, val_df, test_df])\n",
+        "\n",
+        "df"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div><style>\n",
+              ".dataframe > thead > tr,\n",
+              ".dataframe > tbody > tr {\n",
+              "  text-align: right;\n",
+              "  white-space: pre-wrap;\n",
+              "}\n",
+              "</style>\n",
+              "<small>shape: (368_679, 19)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>id</th><th>by</th><th>time</th><th>title</th><th>text</th><th>url</th><th>score</th><th>descendants</th><th>kids</th><th>deleted</th><th>dead</th><th>scraping_error</th><th>scraped_title</th><th>scraped_published_at</th><th>scraped_byline</th><th>scraped_body</th><th>scraped_at</th><th>scraped_language</th><th>split</th></tr><tr><td>i64</td><td>str</td><td>datetime[μs]</td><td>str</td><td>str</td><td>str</td><td>i64</td><td>i64</td><td>list[i64]</td><td>list[str]</td><td>bool</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>datetime[μs]</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>92</td><td>&quot;greg&quot;</td><td>2007-02-19 18:09:20</td><td>&quot;Track users&#x27; mouse movements o…</td><td>null</td><td>&quot;http://www.clicktale.com/&quot;</td><td>10</td><td>0</td><td>null</td><td>null</td><td>null</td><td>&quot;no_error&quot;</td><td>&quot;Clicktale Is Now Contentsquare…</td><td>null</td><td>null</td><td>&quot;We&#x27;ve joined forces to create …</td><td>2024-11-07 18:14:40</td><td>&quot;en&quot;</td><td>&quot;train&quot;</td></tr><tr><td>99</td><td>&quot;pg&quot;</td><td>2007-02-19 18:37:29</td><td>&quot;The Google-Powered Business&quot;</td><td>null</td><td>&quot;http://blog.radioactiveyak.com…</td><td>5</td><td>0</td><td>null</td><td>null</td><td>null</td><td>&quot;no_error&quot;</td><td>&quot;GoogleOffice (Beta): The Googl…</td><td>null</td><td>null</td><td>&quot;\n",
+              "[ Google Office Tools Homepag…</td><td>2024-11-08 05:11:41</td><td>&quot;en&quot;</td><td>&quot;train&quot;</td></tr><tr><td>124</td><td>&quot;herdrick&quot;</td><td>2007-02-19 22:44:06</td><td>&quot;Startup news link submission s…</td><td>null</td><td>&quot;http://news.ycombinator.com/&quot;</td><td>58</td><td>19</td><td>[133, 139, … 218215]</td><td>null</td><td>null</td><td>&quot;no_error&quot;</td><td>&quot;Hacker News&quot;</td><td>null</td><td>null</td><td>&quot;\n",
+              "            \n",
+              "      1.      Ha…</td><td>2024-11-08 03:12:42</td><td>&quot;en&quot;</td><td>&quot;train&quot;</td></tr><tr><td>163</td><td>&quot;boris&quot;</td><td>2007-02-20 09:10:33</td><td>&quot;How to market to smart people …</td><td>null</td><td>&quot;http://codesynthesis.com/~bori…</td><td>12</td><td>0</td><td>null</td><td>null</td><td>null</td><td>&quot;no_error&quot;</td><td>&quot;How to market to smart people&quot;</td><td>null</td><td>null</td><td>&quot;\n",
+              "\t\t\t\tIt should be pretty obvio…</td><td>2024-11-08 04:30:27</td><td>&quot;en&quot;</td><td>&quot;train&quot;</td></tr><tr><td>190</td><td>&quot;farmer&quot;</td><td>2007-02-20 21:36:43</td><td>&quot;Interview with Michael Wesch (…</td><td>null</td><td>&quot;http://battellemedia.com/archi…</td><td>2</td><td>0</td><td>null</td><td>null</td><td>null</td><td>&quot;no_error&quot;</td><td>&quot;John Battelle&#x27;s Search Blog A …</td><td>&quot;2007-02-18T21:48:04+00:00&quot;</td><td>null</td><td>&quot;Michael Wesch, PhD, is Assista…</td><td>2024-11-08 06:28:03</td><td>&quot;en&quot;</td><td>&quot;train&quot;</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>42057787</td><td>&quot;robaato&quot;</td><td>2024-11-06 07:18:46</td><td>&quot;Early morning and evening acti…</td><td>null</td><td>&quot;https://www.theguardian.com/so…</td><td>2</td><td>0</td><td>null</td><td>null</td><td>null</td><td>&quot;no_error&quot;</td><td>&quot;Early morning and evening acti…</td><td>&quot;2024-11-06T05:00:04.000Z&quot;</td><td>&quot;Andrew Gregory&quot;</td><td>&quot;Being more active in the morni…</td><td>2024-11-08 06:04:44</td><td>&quot;en&quot;</td><td>&quot;test&quot;</td></tr><tr><td>42061756</td><td>&quot;DocFeind&quot;</td><td>2024-11-06 13:12:33</td><td>&quot;X has always had a Nazi proble…</td><td>null</td><td>&quot;https://www.theatlantic.com/te…</td><td>3</td><td>0</td><td>null</td><td>null</td><td>null</td><td>&quot;no_error&quot;</td><td>&quot;X Is a White-Supremacist Site&quot;</td><td>&quot;2024-11-05T19:41:17Z&quot;</td><td>&quot;Charlie Warzel&quot;</td><td>&quot;Elon Musk has made one of Twit…</td><td>2024-11-07 22:12:22</td><td>&quot;en&quot;</td><td>&quot;test&quot;</td></tr><tr><td>42063552</td><td>&quot;Olshansky&quot;</td><td>2024-11-06 15:13:49</td><td>&quot;Vibe Checks Are All You Need&quot;</td><td>null</td><td>&quot;https://olshansky.substack.com…</td><td>3</td><td>0</td><td>null</td><td>null</td><td>null</td><td>&quot;no_error&quot;</td><td>&quot;Vibe Checks Are All You Need&quot;</td><td>&quot;2024-05-29T20:49:40+00:00&quot;</td><td>&quot;Daniel Olshansky&quot;</td><td>&quot;I’ve been using ChatGPT since …</td><td>2024-11-08 14:57:45</td><td>&quot;en&quot;</td><td>&quot;test&quot;</td></tr><tr><td>42068691</td><td>&quot;0xlogk&quot;</td><td>2024-11-06 20:18:46</td><td>&quot;Are You Playing to Play, or Pl…</td><td>null</td><td>&quot;https://commoncog.com/playing-…</td><td>3</td><td>0</td><td>null</td><td>null</td><td>null</td><td>&quot;no_error&quot;</td><td>&quot;Are You Playing to Play, or Pl…</td><td>&quot;2021-09-21T14:39:12.000Z&quot;</td><td>&quot;Cedric Chin&quot;</td><td>&quot;\n",
+              "      \n",
+              "      My friend Lesley…</td><td>2024-11-08 10:31:35</td><td>&quot;en&quot;</td><td>&quot;test&quot;</td></tr><tr><td>42071435</td><td>&quot;pentagrama&quot;</td><td>2024-11-06 23:50:11</td><td>&quot;Gimp 3.0 RC1 Released&quot;</td><td>null</td><td>&quot;https://www.gimp.org/news/2024…</td><td>1</td><td>0</td><td>null</td><td>null</td><td>null</td><td>&quot;no_error&quot;</td><td>&quot;GIMP - GIMP 3.0 RC1 Released&quot;</td><td>&quot;2024-11-06T00:00:00+01:00&quot;</td><td>&quot;by                           G…</td><td>&quot;\n",
+              "                    We are ve…</td><td>2024-11-08 06:29:21</td><td>&quot;en&quot;</td><td>&quot;test&quot;</td></tr></tbody></table></div>"
+            ],
+            "text/plain": [
+              "shape: (368_679, 19)\n",
+              "┌──────────┬────────────┬────────────┬────────────┬───┬────────────┬───────────┬───────────┬───────┐\n",
+              "│ id       ┆ by         ┆ time       ┆ title      ┆ … ┆ scraped_bo ┆ scraped_a ┆ scraped_l ┆ split │\n",
+              "│ ---      ┆ ---        ┆ ---        ┆ ---        ┆   ┆ dy         ┆ t         ┆ anguage   ┆ ---   │\n",
+              "│ i64      ┆ str        ┆ datetime[μ ┆ str        ┆   ┆ ---        ┆ ---       ┆ ---       ┆ str   │\n",
+              "│          ┆            ┆ s]         ┆            ┆   ┆ str        ┆ datetime[ ┆ str       ┆       │\n",
+              "│          ┆            ┆            ┆            ┆   ┆            ┆ μs]       ┆           ┆       │\n",
+              "╞══════════╪════════════╪════════════╪════════════╪═══╪════════════╪═══════════╪═══════════╪═══════╡\n",
+              "│ 92       ┆ greg       ┆ 2007-02-19 ┆ Track      ┆ … ┆ We've      ┆ 2024-11-0 ┆ en        ┆ train │\n",
+              "│          ┆            ┆ 18:09:20   ┆ users'     ┆   ┆ joined     ┆ 7         ┆           ┆       │\n",
+              "│          ┆            ┆            ┆ mouse      ┆   ┆ forces to  ┆ 18:14:40  ┆           ┆       │\n",
+              "│          ┆            ┆            ┆ movements  ┆   ┆ create …   ┆           ┆           ┆       │\n",
+              "│          ┆            ┆            ┆ o…         ┆   ┆            ┆           ┆           ┆       │\n",
+              "│ 99       ┆ pg         ┆ 2007-02-19 ┆ The Google ┆ … ┆            ┆ 2024-11-0 ┆ en        ┆ train │\n",
+              "│          ┆            ┆ 18:37:29   ┆ -Powered   ┆   ┆ [ Google   ┆ 8         ┆           ┆       │\n",
+              "│          ┆            ┆            ┆ Business   ┆   ┆ Office     ┆ 05:11:41  ┆           ┆       │\n",
+              "│          ┆            ┆            ┆            ┆   ┆ Tools      ┆           ┆           ┆       │\n",
+              "│          ┆            ┆            ┆            ┆   ┆ Homepag…   ┆           ┆           ┆       │\n",
+              "│ 124      ┆ herdrick   ┆ 2007-02-19 ┆ Startup    ┆ … ┆            ┆ 2024-11-0 ┆ en        ┆ train │\n",
+              "│          ┆            ┆ 22:44:06   ┆ news link  ┆   ┆ 1.         ┆ 8         ┆           ┆       │\n",
+              "│          ┆            ┆            ┆ submission ┆   ┆ Ha…        ┆ 03:12:42  ┆           ┆       │\n",
+              "│          ┆            ┆            ┆ s…         ┆   ┆            ┆           ┆           ┆       │\n",
+              "│ 163      ┆ boris      ┆ 2007-02-20 ┆ How to     ┆ … ┆            ┆ 2024-11-0 ┆ en        ┆ train │\n",
+              "│          ┆            ┆ 09:10:33   ┆ market to  ┆   ┆ \t\t\t\tIt     ┆ 8         ┆           ┆       │\n",
+              "│          ┆            ┆            ┆ smart      ┆   ┆ should be  ┆ 04:30:27  ┆           ┆       │\n",
+              "│          ┆            ┆            ┆ people …   ┆   ┆ pretty     ┆           ┆           ┆       │\n",
+              "│          ┆            ┆            ┆            ┆   ┆ obvio…     ┆           ┆           ┆       │\n",
+              "│ 190      ┆ farmer     ┆ 2007-02-20 ┆ Interview  ┆ … ┆ Michael    ┆ 2024-11-0 ┆ en        ┆ train │\n",
+              "│          ┆            ┆ 21:36:43   ┆ with       ┆   ┆ Wesch,     ┆ 8         ┆           ┆       │\n",
+              "│          ┆            ┆            ┆ Michael    ┆   ┆ PhD, is    ┆ 06:28:03  ┆           ┆       │\n",
+              "│          ┆            ┆            ┆ Wesch (…   ┆   ┆ Assista…   ┆           ┆           ┆       │\n",
+              "│ …        ┆ …          ┆ …          ┆ …          ┆ … ┆ …          ┆ …         ┆ …         ┆ …     │\n",
+              "│ 42057787 ┆ robaato    ┆ 2024-11-06 ┆ Early      ┆ … ┆ Being more ┆ 2024-11-0 ┆ en        ┆ test  │\n",
+              "│          ┆            ┆ 07:18:46   ┆ morning    ┆   ┆ active in  ┆ 8         ┆           ┆       │\n",
+              "│          ┆            ┆            ┆ and        ┆   ┆ the morni… ┆ 06:04:44  ┆           ┆       │\n",
+              "│          ┆            ┆            ┆ evening    ┆   ┆            ┆           ┆           ┆       │\n",
+              "│          ┆            ┆            ┆ acti…      ┆   ┆            ┆           ┆           ┆       │\n",
+              "│ 42061756 ┆ DocFeind   ┆ 2024-11-06 ┆ X has      ┆ … ┆ Elon Musk  ┆ 2024-11-0 ┆ en        ┆ test  │\n",
+              "│          ┆            ┆ 13:12:33   ┆ always had ┆   ┆ has made   ┆ 7         ┆           ┆       │\n",
+              "│          ┆            ┆            ┆ a Nazi     ┆   ┆ one of     ┆ 22:12:22  ┆           ┆       │\n",
+              "│          ┆            ┆            ┆ proble…    ┆   ┆ Twit…      ┆           ┆           ┆       │\n",
+              "│ 42063552 ┆ Olshansky  ┆ 2024-11-06 ┆ Vibe       ┆ … ┆ I’ve been  ┆ 2024-11-0 ┆ en        ┆ test  │\n",
+              "│          ┆            ┆ 15:13:49   ┆ Checks Are ┆   ┆ using      ┆ 8         ┆           ┆       │\n",
+              "│          ┆            ┆            ┆ All You    ┆   ┆ ChatGPT    ┆ 14:57:45  ┆           ┆       │\n",
+              "│          ┆            ┆            ┆ Need       ┆   ┆ since …    ┆           ┆           ┆       │\n",
+              "│ 42068691 ┆ 0xlogk     ┆ 2024-11-06 ┆ Are You    ┆ … ┆            ┆ 2024-11-0 ┆ en        ┆ test  │\n",
+              "│          ┆            ┆ 20:18:46   ┆ Playing to ┆   ┆            ┆ 8         ┆           ┆       │\n",
+              "│          ┆            ┆            ┆ Play, or   ┆   ┆ My friend  ┆ 10:31:35  ┆           ┆       │\n",
+              "│          ┆            ┆            ┆ Pl…        ┆   ┆ Lesley…    ┆           ┆           ┆       │\n",
+              "│ 42071435 ┆ pentagrama ┆ 2024-11-06 ┆ Gimp 3.0   ┆ … ┆            ┆ 2024-11-0 ┆ en        ┆ test  │\n",
+              "│          ┆            ┆ 23:50:11   ┆ RC1        ┆   ┆ We are ve… ┆ 8         ┆           ┆       │\n",
+              "│          ┆            ┆            ┆ Released   ┆   ┆            ┆ 06:29:21  ┆           ┆       │\n",
+              "└──────────┴────────────┴────────────┴────────────┴───┴────────────┴───────────┴───────────┴───────┘"
+            ]
+          },
+          "execution_count": 11,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df = df.filter((pl.col(\"scraping_error\") == \"no_error\") & pl.col(\"text\").is_null())\n",
+        "\n",
+        "df"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 32,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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q5OvuF1sAQCc0mgMAAAAAACD47HtLqiow16ZfL0XH283jL0u+J8nhC6zXviN528y1rHkBiwQElfRJ5nUmmgNBwev16emNB0+679DxJn31yc00GQ8QK/dV6d3d5V3WX9x6WN9/bpu8/HMQ1O5dtl91ze3G2o0LRio9PtpyIgBAfzQyJU6zRg4x1p7fdIgnncCKp9Yf1A0PrXP82UaS4iLD9OCNs3Td3GyLyQLjunnZGpc+yFjbfbRO/1pXYi2Lx+vTq9vNjeaLx6VqcHSEtSwft3BsinG9sr5Fu4/ytAUA6CkazQEAAAAAABB8Vv/FvO4Kk+Z8yW4Wf0qbKE260lxrrTevRydIabmBywQEk3SHf9bLaDQHgsHaomodrHaeDPhxywsq9Ye39gQ4EYLBQyuLHGtPbSjVT17KZ1pgkDpa06xHVh4w1gZHh+v2xWPsBgIA9GtXOUw1L69r0Yp9lZbTYCDxen36zeu79Z1nt6m9m5schyVE6+nb5uus8WkW0wVORJhbP74sz7H++zf3WnvS1IYD1SqvazHWLpkyzEoGk7ljkhXuMLV+RQF/LgFAT9FoDgAAAAAAgOByZJtU9L65lnu5lJhlN4+/Lf6e5OrFx3KZcyU3H+NhgHC6qaL+qNRQZTcLgC56Ms384/763n69mX80QGkQDAor6o3TzD/u0dXF+tXru2k2D0J/erdALe3mCbO3LR6jxNhIy4kAAP3ZRVOGKTrC/PnFMxtLLafBQNHc5tFdT27WPUv3d7svb3i8/nPnAuUO76dPSXQwf0yKLp5sbuSuaWrTb9+wc/PvKw7TzCPD3TpnYrqVDCaDosI1Pcv8tIXl3AADAD3GN1QAAAAAAAAILmvuca7Nu9NejkBJHSdNurrn+7PmBi4LEGzSnSdxqTzfXg4AXdQ1t+m17b1vGv/mU1tVWOHw1A70e4+sOtCjfX9bVqg/vbMvsGHQK0WVDfr3evPNIymDonTTgpF2AwEA+r346AhdkDfUWHtzZ5lqmtosJ0Koq6pv0eceWKuXt5mbnD9w7sQ0PfWleUqPj7aUzK4fXDxRMRFhxtqT60u0rfR4QK/v8fr0qsPvimeNT9WgqPCAXv9kFuakGNfXFVWpuc1jOQ0A9E80mgMAAAAAACB41B2Vtj9jrmXOkTJm2s0TKIu/2/Op5tnzA5sFCCaD0qWYJHOtbKfdLAA6eWXbETU5fAn//y7J1eBoc/NAXUu7bvvHRjW0tAcyHvpATWObnt7Q8+mkf3x7r/7+fveTJmHPH97aK4/XPGX+rnPGKjaybxuCAAD909UzMozrre1evXKSZmCgN/ZX1OuKe1ZpY/GxbvfdOH+k/nbdTMX1cbNzII1IjNGdZ40x1nw+6Ucv5svr8HOfP6wrqlZlfYuxdvGU4QG7bk85NZo3t3m16ST//AAAOtBoDgAAAAAAgOCx7n7J6zDhKhSmmX8gZaw05dMn3xcWJQ2fFvg8QLBwuZynmjPRHOhTT280NxSnDo7SDfOy9b+fnup47N6yen332W3y+QLX3AD7/r2hxPHmAye/eHW3Hl99IDCB0GP5h2v00tbDxlpmUow+MyvLciIAQKiYPyZFQx2mRj+z0fwkDaC3Vu+v0pX3rFJJdaPjHrdL+tGlufrxZXkKc7sspusbXzhztLKSYo21zSXH9dzmQwG79ivbzT9XRoW7dc6EtIBdt6emjEhwvDF6+b5Ky2kAoH+i0RwAAAAAAADBobVR2vCguZaYLU24xG6eQFv0bcllfqzthzJmSuFRdvIAwSIt17zORHOgz+yvqHecFHjltBEKD3PrnInpuuvssY7neHnbET24oihQEWFZu8erR1cVG2uDo8O7beb5fy/k66kNNJr1pd++scex9o3zxikynK+QAQCnJszt0pXTRxhrm0qOq7Ci3nIihJpnN5bq+ofWqqbJYVCFpNjIMN1//UzdtGCUxWR9KzoiTD+8xOHzFEm/em23apud/56dKo/Xp9d3HDXWzp6QFhST5MPD3Jo/JtlYW1FAozkA9ASfEgAAAAAAACA4bH1CanJ4XOnc2yX3SZqy+5vkMdIZ13a/J2uenSxAMHGcaL5L8nrtZgEgSXp6g3mauSR9ambGh//9q+eO0+JxqY57f/nabq0trPJrNvSNN3eW6dDxJmPtCwtH638/PVXdDY787rPb9KLDRG0E1trCKi3dU2GsjU8frMvOMDcHAgDQU1fNyHCsPbvJ+edKoDs+n09/eGuvvvn0/2fvruMjq6//j79nJhN393XPuhsui0Nht2iRtliBFur9fdtS95bS4u6FRUopLE5h3d09snH3SWbm90dYyebebGxuJsnr+XjsY3M/5869Z1hhc+d9z92sJrf5k5KSIoP02m2zdfaYJAu78w9nj0nUmaOMvx8rqWnU3z/e2+PnXH2wVCU1LsPaRRNSevx8XTVvhPF/l21HKlVWa9w/AOA4guYAAAAAAADofR6PtOph41pQpDT5emv7scpp35Ps7Uz2GUTQHAOQWdC8qVaqMJ6eC8B3mt0evWkSCJqcGa3hiRHHth12m/5+9SSlx4QYw+sx+wABAABJREFU7u/2ePWtlzeqsKrBJ73COs8sN55OH+iw67pZmbpkYqr+cOUE09d7vdK9r27SB9uNpx/CN7xer/7YzjTz750/qt1p9AAAdMSwhHBNyog2rL25IU9uj3lIGDDS2OzWd17dpAc/aT8oPSYlUv/+1lxlpUVZ1Jl/sdls+tkl4xToMI4DPrfikPYWVvfoOd/dkm+4Huy066zRiT16ru6YPzzecN3rlZbvY6o5AJwKQXMAAAAAAAD0vr0fSqX7jGtTviYFRRjX+rrYIdKka41rNruUPsPafgB/kDDavFa43bo+AEiSlu4tUVF1o2Ft4dSMNmvRoYF69PqpCgow/giqpKZRd7y4Xq5mnlDQV23JrdDaQ8ZPoblsUqriw4MkSQunZehXl5ncPKSWGw/ufnmjPt9jPF0bPe/TXUVaf9j4125KZrTOGeM/YSAAQN92lclU8/zKBq3czxNu0HHltS7d8OQavb2p/afhnDkqQYtvn62UKOObXgeKIfFh+vr8IYa1Zo9X97+zXV5vz9zs0ez26P1txjeOnjU6UaGB7QzXsNiguFDTG6KX7SVoDgCnQtAcAAAAAAAAvW/VQ8brNoc08zZre7Ha/O9JjsC26yPOl4Ijre8H6G1B4VLMYONa0Q5LWwEgvbYux3A92GnXxRONH4WelRal31wx3vSYG7Ir9Jt3+fPcVz2z/JBp7ea5rUMtN8werJ9caH4Dkcvt0a3PryNwZgGPx6s/tTPN/AcLRstmY5o5AKBnXDIhVYEmNx6+YfK0HOBkB0tq9ZVHVmjNobJ297th1iA98bVpCg/yn2Bzb7rrzOFKjgw2rC3fV2oaDu+s1QfLVFrrMqxdND61R87RU2w2m+aPMJ5qvmxfSY+F7wGgvyJoDgAAAAAAgN6Vv0U6+IVxbexlUnSmtf1YLWaQdNlJQfvINOmc+3ulHcAvJJpMwGWiOWCpslqXPt5ZaFi7ICtFkcFO09deNTVd1800/3/4cysP662NhIz6msKqBv13i/E0ydlD4zQ2te1NcreeNkz3njPS9JiNzR59/bm12pBtPGkbPeM/m49oV0G1Ye30kQmaNTTO4o4AAP1ZVKhT545NMqwt2Zav6oYmiztCX7PmYJmueHi5DpbUmu5js0n/d9EY/fKycQpwEIE7KiwoQD+5aIxp/dfv7lS9y93t8/x3S77heojToTNHJ3T7+D1t/gjjnvIq6tv9fQYAIGgOAAAAAACA3rbqYfPa7Lus66M3TVgk3b1BOu830qX/lO5YLiWaT/8E+r2kscbrTDQHLPX2pjw1uY0nuy2cmn7K1//skrGalBFtWv/xm1u140hVV9tDL3hx1WHT3xO3zBtiuC5J95w9XLedPtS0Xudy68an12hbXmW3e0RbrmaP/vrRHtP6988fZWE3AICB4qopxv9ebGjyaMnWnpmojP7H6/XqrY25uv7J1aqoM78hIdhp16PXT9U35g/lqSwGLpmQoplDYg1reRX1euR/+7p1/Ga3R+9vMw6anzUmUaGB/jddfs6wOJn9Vlm2r8TaZgCgjyFoDgAAAAAAgN5TlS9tfd24ljFLSp9qbT+9KW6YNOcuacoNUkhMb3cD9K5Ek6B56X6pqcHaXoABbPE644nj6TEhHZp+HBTg0CPXT1F8eKBhvaHJo9tfXK/KdgIk8B8NTW69tDrbsDYoLlRnjU40fa3NZtOPFozWjbMHme5T3dCsG55arT2FxlO30XWvrs1WdlmdYe2iCSnKSouyuCMAwEAwf0S8EiKCDGuvb+DJNmitrNalJ5ce0Hl/+0L3vrpZLrfHdN+EiCC9dttsnT8u2cIO+xabzab7Lx0nu0mw+tEvDii71Pjfhx2x8kCpyk2+j7t4fEqXj+tL0aGBmmDy796lewmaA0B7CJoDAAAAAACg96x9QvKYhMtmf8vaXgD4j6Qs43WvWyrZbW0vwAC1La9SO/KNp41fNTVddrPEwklSokL0j2umyGGyf3ZZnb7z6kZ5PMZTsuE/3t6Up7Jal2HtpjmDTX+Nj7LZbPr5JeP01WkZpvuU1zXp2idW8+j6HlTnataDnxpPrHTYbfruuSMt7ggAMFAEOOy6YnKaYW3NwbJuhVzRP3g8Xi3fV6K7Xt6gWb/9RL9+d6f2FtW0+5pRSRF66845mpAebU2TfdiYlEh9bfZgw5qr2aNfvdv1p8a9u8V4mnlooENnjDK/AbW3zRsRb7i+an+pmtu5uQEABjqC5gAAAAAAAOgdrjpp3dPGtehB0uiLrO0HgP+IHSo5jCffqfyQpa0AA9Xr682nTF45Jb1Tx5o9LE4/XDDKtP7Z7mI9+OneTh0T1vJ6vXp62SHDWkRQgBa2Ex4/kd1u02+/Ml6XTUo13aekplHXPbFKOSYTuNE5zyw/pOLqRsPaomnpGpoQbnFHAICBpL1/N77BVPMBq6iqQQ99tk9n/Pl/uu7J1frvlvx2J5gfNX9EvBbfMVvpMaEWdNk/3HvOSMWGGT9h6qMdhfrf7qJOH7PJ7dH72wsMa2ePSVJIoKPTx7TKvOEJhuvVjc3anFthbTMA0IcQNAcAAAAAAEDv2Pa6VF9uXJt1h2T33w8lAPiYI0CKMp58p6oj1vYCDECNzW79e1OeYW3OsDhlxHY+2PHN+UN1UTuPUP/7J3v12a7OhxxgjRX7S7W7sNqwtmh6hsKDAjp8LIfdpj8vnKjzxyWZ7nOkskHXPblaBZUNne4Vx1XWNemxz/cb1oIC7Lrn7BEWdwQAGGhGJUdofFqUYe3Njbk81WYAaXZ79MnOQn3z+XWa/ftP9acPdiu7EzcWXjMjQ0/fNF2RwU4fdtn/RIU69YPzzW/6/eU7O+Rq7twk7xX7S1VRZ/yEyva+5/MHUwZFK8RpfM156d4Si7sBgL6DoDkAAAAAAAB6x76PjdeDIqXJ11vbCwD/E2kWNDcOvwLoOR/vKDINDizq4OTqk9lsNv3hqgkanmg8Pdnrlb79r43KLmWKtT96etlBw3WbTbpx9uBOH8/psOvBaybr9JHGEwUlKbusTtc9uUolNcbTuHFqj3y+X1UNzYa1G+cMVkpUiMUdAQAGoiunGH9vl1NWrzWHyizuBlbLKavTXz/crXl/+Exff26dPtpRKHcnbzD48QWj9dsrxsvpIObWFYumZWhCuvENHwdKavX0cuN/65t5d4vxAICwQIfOGGX+73t/EBTg0MyhsYa1ZQTNAcAU/wcGAAAAAACA9bxeKWeNcW3SdVJQhLX9APA/kanG60w0B3xu8focw/WIoACdPy65y8cNDwrQo9dPNZ1+XdXQrNteXK96l7vL50DPO1hSq09Mps2fOyZJmXGdn3AvtYQ8HrthqmYPjTPdZ39xra5/crUq6lxdOsdAVlTVoGdXGIeGIoICdMfpwyzuCAAwUF06KU1Oh82w9sb6XIu7gRVczR69tzVfNzy1Wqf96TM9+Ok+FVR17kk1dpt01uhEvXnnHN12+jDZbMa/h3BqdrtNv7h0nGn9H5/s7fCThFzNHn2wvdCwds7YJAWbTAv3J/OGxxuub8ypUFWD8Q3XADDQETQHAAAAAACA9Spzpep849qws6ztBYB/ImgO9IqCygZ9safYsHbxxFSFBHYvODA8MVx/XjjBtL4zv0r/762t8no7N+UQvvNsOxMOb5k3pFvHDnY69OSN0zQlM9p0n10F1brx6TWqJvTRKQ9+ulcNTR7D2q2nDVVMWKDFHQEABqrYsECdNTrRsPbe1nzVuYyfvoG+Z39xjX773k7N/t0nuvOlDVq6t0Sd/Wd9WnSI7jt3pJb/6Cw9fdN0TcmM8U2zA8zkzBgtnJpuWKt1ufW7JTs7dJzl+0tUWW/87/KLxqd0uT8rzR9hPHXd7fFq1f5Si7sBgL6BoDkAAAAAAACsl7PavJY+zbo+APivSOPHq6sqz9o+gAHmjQ25MnuS/cJpxsGEzlqQlaLb25mm/ObGPL246nCPnAvdU1nfpMUmk0bHpkRq5hDjx853RlhQgJ69ZYay0iJN99mcW6lbnl1LEK2DDpfW6l9rjJ9MEB8e2O0bBAAA6KyrpmYYrte63Hp/W4HF3aAnNTS59eaGXC16dKXO/svnevyLAyqt7dzTaJwOmy4cn6znb5mhpT84U/ecPUIpUSE+6njg+sGC0YowebrU25uOaM3BslMe490txoNDwoMCdNpI4wC3vxmZFK7EiCDD2rJ9JRZ3AwB9A0FzAAAAAAAAWC9njfF6/CgptPuBJQD9gOlE83zJYzyhFUD3eL1evW4SKh6eGK7JGdE9dq7vnTdSc4fHmdZ/+d8dWn+4vMfOh655bW2O6lxuw9ot84bIZrP1yHkig5164ZaZGpUUYbrP2kPluvX59WpoMu4Hx/31oz1qNrlj5K4zhyvMJGAEAICvnDEqQXEmT9Mw+/cn/NuOI1X62dvbNP03H+u+1zZrzaFTh5RPNjQ+TD+5cLRW/vhsPXzdVJ02MkF2e8/8+xJtJUQE6TvnjjSt//w/2+U2u+tYkqvZow+2G98Ycu7YJAU7u/f0K6vYbDbNGxFvWFu2l6A5ABghaA4AAAAAAADrmU00z5hhbR8A/JdZ0NzTJNXxwR/gC+sPl+tgSa1hbeHU9B4LFUtSgMOuB6+erNSoYMN6k9urO19ar6Lqhh47Jzqn2e3RsysOGdbiwwN1ycSUHj1fTFigXvjGDA2NDzPdZ9m+Et350ga5mrnhSGq5OaSyrkk786v06a5CvbjqsH733k69vemI4f5p0SG6ZmamxV0CACA5HXZdOsn4e7yVB0qVV1FvcUfoiprGZr28OluX/XOZLnxwqZ5feVjVDZ174kxQgF1fmZymV2+dpU++e7puPW2Y4sONp0uj531t9iCNTAo3rO3Mr9LLq82fLLVsX7Hpr/dF43v2ewNfm28SND9QUsvfRwBggNvVAQAAAAAAYC1XrVSw1bhG0BzAUZFp5rWqPCk80bpegAHitXU5husOu01XTGnnz2QXxYUH6ZHrp2rhoyvlcrcNDhdWNequlzfqpW/MlNPB7CSrfbSj0DRkcf2sQQoK6PmJhYkRwXrxGzO16LGVyi03Pvenu4r0nVc36sGrJyugn/++qHe5daSyXkcq6pVf0aAjlcd/PlJRr/zKBtOJ80buO3ekT37dAADoiKumpuuZ5YfarHu90lsbcnXXWSOsbwqn5PV6tSmnQv9ak6N3thzp1L89TjQ6OULXzMjU5ZPSFBXq7OEu0VFOh133XzpO1z5hPATkzx/u0UUTUhVr8ASC/27JN3xNRFCA5o80Dm77q7nDzftdtrdYX53OzZkAcCKC5gAAAAAAALDWkY2S1+RDqYyZ1vYCwH+Fxkt2Z8sE85NVHZFSJ1vfE9CP1bma9a5JcODMUQlKjDCePN5dEzOi9YvLxunHbxrfhLbmYJn+sGSX/u/isT45P8w9vfyg4Xqgw67rZg7y2XlTo0P08jdmadFjK1VQZTzR/r2tBQoO2KI/L5wou73nJu1bydXsUWFVw7HAeOtAeYPyK+tVUWfw/8AuGpkUrssn9/wNIwAAdNS41CiNTo7QroLqNrU3NuTpW2cO79En6KB7Kupcemtjnl5dm2P4a9YRYYEOXTopVVdPz9SE9Ch+ff3EnGHxumh8it7d2vb7v8r6Jv3pg9363VfGt1pvbHbro+2Fhsc7d1xSn7uZMTEi2PTvo6V7SwiaA8BJCJoDAAAAAADAWjnGE3MUHC3FMb0KwJfsdikyRarIblurOmJ9P0A/997WAtWaTCe8amqGT899zYxMbcqu0KsmE9WfXHZQEzOidcnEVJ/2geO25FZo7aFyw9qlk1KVEBHk0/NnxoXqxW/M1NWPr1RJjctwnzc35inI6dBvr8jyu9CS2+NVSU2j8r4MjudX1uvI0Z8rW8LlJTWN8nqt6+m7542So4+G8gEA/cdVU9P163d3tlk/WFKrDdnlmjoothe6wonWHCzTy6sP671tBXI1t33qUEdMyojWNTMydPGEVIUFEU3zRz+5aIw+2VWohqa2v8b/Wputa2dkanx61LG1pXtKVN3YbHisiyek+KxPX5o3PN4waL5if6k8Hm+fvaEVAHyB/5sDAAAAAADAWjlrjNczZrQESwHgqMg0k6B5nvW9AP3cYpOQd2xYoM4anejz8//isnHakV+lrXmVhvUfvrFFo5IjNDIpwue9QHpm+SHT2s1zB1vSw/DEcL3w9Zm65olVppO9X1mTrRCnQz+9eEyvhM29Xq/yKxu0M79KuwqqtTO/Sjvzq3S4tE7NHgtT5KcwOTNa541N6u02AADQZZPS9Lslu+Q2+P/k6+tzCZr3oqLqBv3kzW36eKfx1OpTiQpx6orJabp6RoZGJ0f2cHfoaWnRIbrrzOH684d72tS8Xunn/9mm12+fcyxsbTT9XJIiggM0b3iCT3v1lXkj4vXksrZPcSqrdWlHfpWy0qIMXgUAAxNBcwAAAAAAAFjH6zUPmqfPsLYXAP4v0mR6MRPNgR51uLRWqw+WGdaumJymwADf3wgW7HTokeun6JJ/LFO5Qai4zuXW7S+s17/vmqvIYKfP+xnICqsa9N8txn/Pzhoaq3Gp1gUuxqRE6vlbZui6J1abTlB8evlBhQY69L3zR/m0l3qXW7sLq7Xry1D5jvwq7cqvUlWDcV/+IjkyWA9dO8Xvpr4DAAamhIggnTkqQR/vLGpT++/mfP38knEKdjp6obOB7Z3NR/TTt7eZ3tzXnllDY3X19EwtyErm166P+cb8oXptXa6yy+ra1DZkV+jNjXm6amq6Gprc+miH8Q0I549LtuT7RV+YOSROgQ67XO62U92X7i0haA4AJyBoDgAAAAAAAOuU7pfqjYNsyiBoDuAkBM0BS7y+Pte0tnBaumV9pMeE6sFrJuvGp9fIaBj0gZJafe+1zXr0+qk8xtyHXlx1WE1u42nct8wdYnE30oT0aD1z83Td8NQa1Te5Dff552f7FBLo0LfOHN7t83m9XuVV1Gtn/vFQ+c78Kh0srZXXf4aUt2G3SUmRwUqJClZKdIhSo4KVlRal88YmKySQ0BcAwH9cOSXdMGhe3disD7YX6LJJab3Q1cBUVuvST/+9zXRatZn48EBdOTVdV0/P1JD4MB91B18Ldjr0s4vH6hvPrzOs/37JLp03Lkmr9peqxuSmz4smpPiyRZ8KCXRo6qAYrTxQ2qa2dG+x7jhjWC90BQD+iaA5AAAAAAAArJOz2njdZpfSplrbCwD/F2kSMKjKs7YPoB9ze7x6wyRoPj4tyvLH3s8fkaDvnjdKf/pgt2H9wx2FeuTz/T0SKEZbDU1uvbQ627CWGRuqs8ckWdxRi2mDY/XkjdN087Nr5WpuO3FQkv70wW4FOx36+ryOh+FrG5u/nFJerV0FVdqZX6Vd+dWm09N7U3x4oFKiQpQSFazU6JBjgfK06GClRIUoMSJIAY6+OU0SADCwnDUmUdGhTsPp2W9syCNobpEPtxfoJ29tVUmNq0P722zSaSMSdM2MDJ09JklO/t3RL5w9JlFnjkrQZ7uL29RKahr14Md7VVzTaPjaqBCn5g6L93WLPjVvRLxh0HzdoXLVu9zcsAkAXyJoDgAAAAAAAOuYBc2TsqSgcGt7AeD/2pto7vW2fNINoFuW7yvRkcoGw5qV08xPdMfpw7Qpp8L08ex/+XC3JqRHaf6IBIs76//e3pSnslrjsNFNcwbL0YuT5OcOj9dj10/VrS+sM524/qv/7lCI06FrZ2a2Wvd4vMotr9fOE8LkuwqqdLiszi+mlEcEByg1KkQpX4bGj4bHU6KDlRoVouSoYAU7CbkAAPqHoACHLp2YqudXHm5TW7a3WAWVDUqOCu6FzgaGyvom/eKd7XpzQ8du4E6NCtbCaRlaND1DadEhPu4OVrPZbPrZJeO0fN8Xcrnb3tD57IpDpjcVnD8uSYEBffuGg9NGJBje5Oxye7TmUJlOH8n3nAAgETQHAAAAAACAlXLWGK9nzLS2DwB9g9lE8+YGqb5cCo21th+gH1psMs08MMCuSyea3OzhY3a7TX9ZNFGX/3O5DpTUtql7vNI9r2zUO3fPU3pMaC902MLr9aqirkm55fUKDw5QWnRInw5aeL1ePb3skGEtPCig1248ONGZoxP14NWTddcrG+X2GCfE/9+/t6rO1axgp6MlVF5Qrd0F1arppSnlQQF2pUUfD5GnfjmJPCUq+Mv1EIUH8ZEtAGBguXJKumHQ3OOV3tqYpzvOGNYLXfV/n+8p1g9f36KCKuMbTU80LjVS3ztvlE4bmdCrNxvC94bEh+nr84fokf/tb1Nr9njV7HEbvu6iCb3z/WJPGpcaqZhQp8oNnrCwbG8xQXMA+BJXLQAAAAAAAGCN+gqpeJdxLWOGpa0A6CPMJppLLVPNCZoD3VJZ16QPthcY1s4bm6To0ECLOzouMtipR2+YqssfWq46V9tgQ3ldk+54cYMW3z7bp5Oem90eHaloUHZZnQ6X1Sq7rE45ZXU6XFqn7LI6VTccDy9nxobq4eumKCstymf9+NLK/aXaXVhtWFs0LUMRwU6LOzJ2wfgU/aXZo3tf22Q4jdzrlX797k7L+0qLDtGYlAiNTo7U6JQIDY4LU2p0iGJCnbLxBA4AAFqZkB6lEYnh2ltU06b2xoZc3X76UP7/2YNqGpv1m3d36pU12afcN8Bu011nDde3zhxuOska/c9dZw7XWxvyOnQTgiRFhzo1Z1icj7vyPbvdpjnD4/Xulvw2taV7S3qhIwDwTwTNAQAAAAAAYI28dZKMJ08SNAdgKDxJsjkkr8H0rKojUnKW9T0B/ch/NufJ1dz28eiStHBahsXdtDUyKUJ/uHKC7n5lo2F9a16lfv72dv3hqgndOk9VQ5OyS78MkJe1BMizvwyS51XUm07OPll2WZ2ueWKV3rlrngbHh3Wrp97w9PKDhus2m3TTnMHWNnMKl09OU32TWz9+c6vl5w5xOjQqOeJYqHxMSqRGJUcoKsQ/gvgAAPQFNptNV05N1++XtB1IsK+oRhtzKjQlM6YXOut/Vh0o1fcWb1Zuef0p9x2ZFK6/LJyk8el988ZJdF1YUIB+ctEY3WPyvdfJzh+b3G9uRJhvEjTfVVCt4upGJUQE9UJXAOBfCJoDAAAAAADAGjlrjNfDk6ToQdb2AqBvsDukiGSpKq9tzWgNQKcsXp9ruJ4SFax5w+Mt7sbYJRNTtSmnQk8tMw5Bv7ouR5Mzo3X1jEzTY7g9XhVUNXwZHm+ZSp5dVq/s0pavjR6T3lXVDc26/cX1evPOOQoN7Dsfwx0sqdUnu4oMa+eOSVJmXKjFHZ3aNTMy1dDk1i/e2eGzc2TEhrSEyZMjNCYlUqNTIpUZGyqHnQmrAAB01xWT0/TH93fJ6J6+n/57m968c46CAnz35Jr+rt7l1h8/2KVnlh865b52m3TracN077kj+G8+gF0yIUUvrTqs1QfLTrnvRRNSLOjIGvNGmH/vu3xfiS6fnGZhNwDgn/rOFS4AAAAAAAD0bTmrjdczZrSMygQAI5GpJkHzI9b3AvQjuwuqtSW30rB25ZR0vwrS/uiC0dqaV6k1JoGHn729XUPiwxQV6jw2iTy7rE6Hv5xSnlteL5fbeHK7L+wqqNaP39yqB746SbY+8m+c51YcktdkcPst84ZY20wn3Dx3iOqb3Prj+7u7dZywwJYp5aNTWiaUj0mO0KjkCEUEM6UcAABfSYoM1vwRCfp8T3Gb2vYjVfrDkt362SVje6Gzvm9Ddrm+99pmHSipPeW+Q+LD9OeFEzV1EBPkBzqbzab7Lx2nix5cangDyFExoU7NHhZnXWM+lh4TqiHxYTpo8Odl6V6C5gAgETQHAAAAAACAFTxuKXedcS19hrW9AOhbIlON1wmaA92yeF2Oae2qqekWdnJqToddD107RRf/Y6kKqxrb1F1uj776+Kpe6Mzc25uOaFJGtG6e678h7aMq65v0msnvh7EpkZo5JNbijjrnzjOGq8Hl1oOf7uvQ/oPiQjX66ITy5EiNSYlQRkyo7H50cwUAAAPFtTMzDYPmkvT08oOaOzxOZ49Jsrirvqux2a0HPt6rxz7f325Q+Kib5gzWDxeMVkggU8zRYkxKpG6YNUjPrTxsus+CrGQ5HXYLu/K9ecPjDYPmy/YVy+v19pkbiAHAVwiaAwAAAAAAwPeKdkquGuNaxkxrewHQt0SYBc0NppwD6JAmt0dvbTT+MzRjSKwGx4dZ3NGpJUQE6eHrpurqx1eqyd2B1Iwf+M27O5WVFqXpg/07qL14XY7qXG7D2i3zhvSJUMW9545UVGig/vzBbtU3tbyX8KCA44HylAiNTo7UqOQIhQfx8SgAAP7ivLFJOn2k8VRzSfre4s1a8u3TlBwVbHFnfc+2vEp997XN2l1Yfcp906JD9KeFEzRnWLwFnaGvue/cUXpnS77Kal2G9YvGm1yn6cPmjYjXC6vahusLqxq1r6hGI5IieqErAPAfXEkBAAAAAACA7+WsNl53BEopE63tBUDfwkRzoMd9uqtIpSahgYV+Ns38RFMHxeinF4/Vz97e3ivnjw8PUmZsiDJjQ5UZF6bM2FClRAXrB69vUV5FfZv9mz1e3fnSBr179zwlRvpnOKrZ7dEzyw8Z1uLDA3XJxBRrG+oim82mr88boqumpquwqkEhTofSY0L6REgeAICBzGaz6S+LJuqCvy9VcXXbJ9eU1zXpO69u1EvfmCUHTx8x1OT26OHP9usfn+5VcwfGmF8zI0P/76Kx3HwHU1GhTv3g/FH60Ztb29TiwgI1a6h/30jbFbOHxclht8lt8Gfoi70lBM0BDHj8qwEAAAAAAAC+l7PGeD1lkuT0z+AVAD9B0BzocYvX5RquhwY6dOF4/w4W3zBrkDZlV+hNk4ns3eF02JQRE6qM2FANigtVZuzxrzNiQhVmEsZ57Iap+sojK+Rq9rSpFVc36s6XNuiVW2f55ePlP95ZaBiSl6TrZg5SUIDD4o66JyrEqagQZ2+3AQAAOiE+PEh/WzRJNzy9Wl6DnPSqA2V6+LN9uvvsEdY35+f2FFbru69t1ta8ylPumxQZpN9fOUFnjkq0oDP0dYumZeidLUe0fF9pq/V7zx2pAD/8vqa7IoOdmpQRrfWHy9vUlu0t1tfnDemFrgDAfxA0BwAAAAAAgO+ZTTTPmGFtHwD6nsg043VXtdRQJQVHWtsP0McVVTfos91FhrWLxqeYhqn9hc1m02+uGK+dBdXamV/V6dfHhDpbBcgzY0OVGRumzLhQJUcGd2lSZlZalH59eZZ+8PoWw/q6w+X6zbs7df+l4zp9bF97etkhw/VAh13Xzcq0thkAADBgzRsRr9tPH6ZH/rffsP63j/do1rA4TR/c/yYpd4Xb49WTSw/oLx/ukcvd9mbHk10xOU33XzJOUaHckIeOsdttevyGafr9kl36aEehIoIDdNPcwbpmev/9HmHe8HjDoPnqg2VyNXsUGND/AvYA0FH+fbUQAAAAAAAAfV9NsVR+0LhG0BzAqZhNNJdappoTNAc65d8b8wwfBy5Ji6ZnWNxN14QEOvTUjdN0x0sbtDmnolXNYbcpLTqkJUB+LEh+fDq5r6ZdL5qWoU05FXp5dbZh/dkVhzQ5M1qXTTK5eaYXbM2t1JpDZYa1SyamKjGCp84AAADr3HfuSK06UKqN2RVtah6v9O1XNuq9b89XdGig9c35kYMltfre4s2GgdiTxYUF6jdXjNeCrGQLOkN/ExYUoF9dnqVfXZ7V261YYv6IeP39k71t1utcbm3ILtesoXG90BUA+AeC5gAAAAAAAPCt3DXmtXSC5gBOISLFvFaVJyWOtq4XoI/zer1avC7XsDYkPkzTBsVY3FHXpUaHaPFts7X+cLkKquoVHx6kQbFhSokOlrOXHuX+80vGavuRqjbh96N++MYWjUyK0JgU/7hB5pnlJjcCSrp57mDrGgEAAJDkdNj14NWTdeGDS1Xd0NymfqSyQT98Y4sevX6qbLbOP4Wmr/N4vHph1WH9bslONTSdeor5BVnJ+vXlWYoLD7KgO6Dvm5gRrfCgANU0tv37Z9neEoLmAAY0nukAAAAAAAAA38pZbbwenSlFthMgBQBJCgiUwhKNa1VHrO0F6OM25VRob1GNYe2qqel9LrATGGDX7GFxumJyuuaPSFBmXGivhcwlKSjAoUeum6K4MOMpmw1NHt3+4npV1jdZ3FlbRVUNemeL8d+hM4fEKistyuKOAAAApIzYUP3+KxNM6x9sL9SLqw5b2JF/yCmr03VPrtbP/7P9lCHzqBCn/n71JD183RRC5kAnOB120zD50n0lFncDDBBNDdKBz6U1T0hf/Ela/ndp6+tSmfmN8egdTDQHAAAAAACAb+WYTDTPmGltHwD6rshUqbao7TpBc6BTFq83nmZut0lfmZJmcTf9U2p0iP5xzWRd/9Rqebxt64dL63Tfq5v0xNemyW7vvWD/i6sOq8lt0KCkW+YNsbgbAACA4y6akKJl+zL0ypocw/qv3t2paYNj/eYpMb7k9Xr16toc/eq/O1Trcp9y/7NGJ+p3XxmvpMhgC7oD+p/5I+L18c7CNutbcytUWdekqFBnL3QF+InXb5G2vdF6LSpTundr549VWyL97/fSppelplrjfVImSaf/QBp9UeePjx7HRHMAAAAAAAD4TrNLyttgXEufYW0vAPquSJMAbFWetX0AfVhDk1vvbDa+OWP+iASlRIVY3FH/NWd4vH64YLRp/ZNdRfrnZ/ss7Ki1hia3XlydbVjLiA3ROWOSLO4IAACgtZ9dPE4jEsMNa65mj+5+ZaPqXM0Wd2WtwqoG3fzsWv3oza2nDJmHBwXoj1dO0FM3TiNkDnTDvBHxhuser7RiP1PNMYDtXtI2ZN5VB5dKD82Q1j5hHjKXpPxN0r+uld66veVzJvQqguYAAAAAAADwnYKtkrvRuJZB0BxAB0WmGq8z0RzosA+2F6i6wTiMs3BausXd9H+3njZUF45PNq3/7eM9+my3wZMaLPCfTUdUVmv8Ie1Nc4bI0YuT1gEAACQpJNChf147RUEBxrGmfUU1+uU7Oyzuyhper1f/3pinc//6uf63u/iU+88ZFqf3vzNfi6ZnyGbj33FAdwyND1NqlPHNGkv3ETTHANVQKf33vp451uGV0ksLpbrS1uvBUVLyBCk6U7I5Wtc2vyK9cYvkNX4qG6xB0BwAAAAAAAC+k7PaeN0ZKiVlWdsLgL6LoDnQba+tyzFcjw516tyxTLDuaTabTX+8aqKGJYQZ1r1e6Tv/2qTs0jpL+/J6vXp6+UHDWnhQgBZx0wEAAPATo5Ij9NOLx5rW/7U2x/SJPX1VSU2jbn9xvb7z6iZVmdwkelSI06FfXjZOL359ptJjQi3qEOjfbDab6VTzZXsJmmOA+vCnUvWX/791Gl/j6JD6cun1m6Xm+uNrUZnS1S9LPzws3b5U+s7Wlh9Tb2792p3vSCsf6vq50W0EzQEAAAAAAOA7ZkHztKmSI8DaXgD0XZFpxutVedb2AfRRueV1WrG/1LB22cRUBQU4DGvonvCgAD12wzSFBRr/962sb9LtL65XvcttWU8r95dqV0G1YW3htHRFBDst6wUAAOBUrpuZqQuyzJ8S85M3tyqnzNob93zl/W0FOu9vX+iD7YWn3HfaoBgt+fZ8fW32YNl5Gg3Qo+aNSDBczy6r0+HSWou7AXrZwaXShudbvrbZpTN+2PVjLX9Qqs4/vh09SPr6h9Loi6QTn8gRlSZd8oB01k9bv/7zP7aE1dErCJoDAAAAAADAd3LWGK9nzLC2DwB9m9lE84YKycWHfMCpvLE+z/QJwwunZVjbzAAzPDFcf1440bS+I79K/+/fW+W16BHQZtPMbTbppjmDLekBAACgo2w2m37/lQlKiw4xrFc3NuuuVzaqye2xuLOe4/V69beP9uj2F9errNbV7r6BAXb95MLRevW22Roc342psgBMzRse3yrzeqKlTDXHQNJUL/3nbklfXq+YcZuUOqVrx6otkdY83nrt0gelyBTz18z/rjRo7vHtxkppxT+6dn50G0FzAAAAAAAA+EZl7vFHKp4sY6a1vQDo28yC5pJUlW9eQ4/xer36dFeh7nttk3729jbtKzKeiAz/4/F4tXh9jmFtdHKExqVGWtzRwHPB+BTddvpQ0/qbG/L04qrDPu/jYEmtPtlVZFg7Z0ySBsURVgIAAP4nKtSpB6+ZJIfJ5O7NORX684e7Le6qZ7iaPfrua5v190/2nnLfCelRevfuebr1tGGm/y0AdF9sWKDp98nLCJpjIPn011L5lzerR2VIZ/1f14+17Q3JVXN8e9BcaegZ7b/GZpNOP2mC+sYXZTpJAT5F0BwAAAAAAAC+kbPavJY+3bo+APR97QbN86zrYwD7+yd7dcuz6/Tmhjw9v/KwLnxwmd7ZbHIzEfzKqoOlyi2vN6wtmpYhm9moNvSo7583SnOGxZnWf/nfHVp/2LePgH5uxSHTz2NvmTvEp+cGAADojqmDYnXvOSNM6499fkBf7Cm2sKPuq6xr0teeXq03N7b/PW2A3abvnjtSb9wxRyOSIizqDhjY5g1PMFxfsb9Ebg8hVwwAeeulVY8c377wz1JQeNePt+vd1tuTb+jY64acJkUPOr5dUyjlru16H+gyguYAAAAAAADwjZw1xuvxI6XQWGt7AdC3OUOkEJO/N6qZaO5rOWV1+uen+1qtHZ28t+pAaS91hY56fV2u4brTYdPlk9Ms7mbgCnDY9Y9rJislKtiw3uT26s6X1qu4utEn56+sb9Jr64wn249JidSsofzbDAAA+Lc7zhje7o179722SUXVDRZ21HU5ZXX6yiPLtepAWbv7jU6O0Nt3zdXdZ4+Q00HEC7DK/BHxhutVDc3aklthbTOA1dxN0tt3S153y/bYy6VRC7p+vMYa6fCK1mvDzurYa222tpPP97zf9V7QZfwrBAAAAAAAAL5hNtE8fYa1fQDoHyJNArFMNPe5d7fmq9lgYpfL7dGtz6/TvqLqXugKHVHV0KT3thnfjHH26CTFhgVa3NHAFhcepEeun6pAk5BQYVWj7np5g5rdnh4/9+J1OapzuQ1rt8wdzGR7AADg9xx2m/721Umm/4YtqXHpu69tlsfPpw1vyqnQFQ8v1/7iWtN9bDbpzjOG6e275mpcapSF3QGQpKmDYhQUYPx927K9JRZ3A1hs6V+lou0tXwdHSRf8sXvHK94leZqOb0cPkiKSOv76zFmttwu2dq8fdAlBcwAAAAAAAPQ8V535Bb8MguYAuiAy1Xi96oi1fQxAS7YVmNaqGpp149Nr+8zkwIHm3S35amgyDi0vmp5ucTeQpEkZ0br/0nGm9dUHy/T7Jbt69JzNbo+eWX7IsBYfHqhLJpr8/QoAAOBnkiKD9ZeFE03rS/eW6PGlByzsqHM+2F6gqx9fqZIal+k+wU67Hrluqn6wYLSCAhwWdgfgqGCnQzOGGD/1aek+gubox4p2SUv/fHz7nF90LhRupHh36+2E0Z17fcKo9o8HSxA0BwAAAAAAQM87slHyNBvXMmZa2wuA/oGgea84UlGvzTkV7e6TV1GvW55dq9pGk7/30WsWr8sxXE+MCNJpIxIs7gZHXTMjQ4ummQf9n1x2UO9s7rm/2z7eWai8inrD2nUzBynYSYAJAAD0HWeOTtQ35g0xrf/5g93amF1uYUcd8/Syg7r9xfWmN4JKUlxYoF755iwtyEq2sDMARuaPiDdc35hdzvUP9E8ej/SfuyT3lzdDZc6Rpt7U/eOW7m29HWXy1EozkSddP6nMkZoYeGE1guYAAAAAAADoeTmrjdeDo6T4kdb2AqB/iDT5EKIqz9o+BpgPtptPMz/Rtrwq3f3KRjW7zUMTsNa+ohptyK4wrF0xJU0BDj4i6i02m02/vCxL49OiTPf54RtbtKewukfO9/SyQ4brgQ67rpuV2SPnAAAAsNIPFow2/bdUs8ere/61UVUNTRZ3Zczt8er+/2zXL/+7Q16v+X5DE8L01p1zNTkzxrrmAJiaN9z45uwmt1erD5Za3A1ggdWPSrlrW752BEqX/F2y2bp/3Nri1ttmw0TMhCdK9oDj216PVF/W/b7QKQGn3gUAAAAAAADopJw1xuvpMyQ7wTYAXcBE816xZFvHguaS9OmuIv38P9v168uzZOuJD6LQLYvXG08zl6SFUzMs7ARGgp0OPXL9FF38j2WqqGsbgqpzuXX7C+v177vmKjLY2eXzbMur1JpDxh/AXjIxVYkRwV0+NgAAQG8JDLDrH9dM1kUPLlWty92mnlNWr5+8uVX/uGZyr35vUudq1j2vbNLHOwvb3W/mkFg9dsNURYcGWtQZgFMZnRyh+PBAldS42tSW7i3RWaOTeqEr9FfZ2dnavn17p16TkJCgxMTEnmmg/JD06a+Pb8+7T0rooYFBrtrW286wzr3eZpMCQiTXCTfjn3xM+BxBcwAAAAAAAPQsr1fKNQmaZ8ywthcA/YdZ0Ly2WGpulAKCrO1nACiubtRak4CqmZdWZys9JlR3nDHMR12hI5rdHr25wXja/5TMaA1PDLe4IxhJjwnVg1dP1o3PrDGcbnmgpFbfe22zHr1+quz2rgWknl5+0LR289zBXTomAACAPxgcH6ZfX5Gle1/dbFj/75Z8zR8Rr69O750nuBRVN+jrz67T1rzKdve7YnKafn/leAUFOCzqDEBH2O02zR0er7c3tR1w8MzyQ/pkZ5FGJUdodHLEsZ8Hx4Xx9DB0yT333NPp1/z85z/X/fff3zMNvPNtqenL8Hb8SGn+d3vmuFLbUHhXruE6g08Kmtd0ryd0GkFzAAAAAAAA9KyyA1KdyeNDCZoD6Kr2HqtanS/FDLaslYHiwx0Fpo92N5vqJUl/eH+XUqODddmkNB92h/Z8sbdYxdWNhrWF05hm7k9OG5mg7503Sn/6YLdh/cMdhXrk8/361pnDO33souoGvbPZ+KkPM4bEKistqtPHBAAA8CdXTE7Xsr2lemNDrmH95//ZrqmDYjQ8McLSvvYUVuvmZ9Yqr6K+3f3uOWu47j13JE+EAvzU/BEJhkFzScouq1N2WZ0+2nH8iQWBAXYNTwjXqC/D50cD6MmRwfw5h//a8Lx04H9fbtikS/4uBfTgEzaaG1pvO7pwbMdJ4fSmBuP94DMEzQEAAAAAANCzclYbr9vsUtpUa3sB0H+0FzSvOkLQ3Afe31ZguD40PkyPf22qrnxkpSrrmwz3+f7iLUqODNbMoXG+bBEmXltrHLQJdtp18YQUi7vBqdxx+jBtyqloFVA40V8+3K0J6VGaPyKhU8d9cVW2mtzGd4vcMndIp/sEAADwR7+8bJw2ZpfrQEltm1pDk0d3vbxR//7WXAU7rZkYvmJfiW57cb2qG5pN9wmw2/S7r4znJlDAz80bHt+p/V3NHu3Ir9KO/KpW61EhTo1Kah0+H5kcochgZ0+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44IrgBzpJkiSpM4rFgqnmy2c0KQ2NrWiyzYnmkiRJkiRJktRJrQ2bz3sbXv8brFoEE48OBo70UD1iJGB8gw9JkiRJaRaPw/O/aTlkvs9PDZlLkiSpaygcHrp5SEjQvDDPoLkkSZIkSZIkdTprp5nHYhBvgLlvwmPfgT+Ph7tPh88ehfratLaYDp1+ojl0PCC+NpZi2FySJElKs3gcnvk5vHlV8+sO+HVwGypJkiSpKygcFrp544nmBblZZGX2iPkvkiRJkiRJktR1nH4ffHA7zHgS6qrXb4/HoW4NTH88+OhVEEw43/pEGLV72tpNpU4fNP/yf7/R5n2qa+tZUVHDtHmlPDZtEU9/spiGeJxR/Xtz1anbMXFozx1hL0mSJKVNQwM8+UN49/rm1x3yB9jlotT0JEmSJCVCQesmmhf3zklFN5IkSZIkSZKkttjsgOCjuhw+vg8+vBPmvxvU1t6FPR6H6jJ4/5bgo2A4TDohCJ0P2jx9vSdZtxydkpudybCiPA7deghXn7Y991+8G8OK85izooKT//0W7321Mt0tSpIkST1LQz08clkLIfMYHP43Q+aSJEnqeiImmg+JNX4tuijfoLkkSZIkSZIkdVq5BbDjuXD+s3DZlOBO7H2HBiFzaBw6L5sHr/0f/GtXuHYPeOMqWLU4fb0nSbcMmm9smxFF3HbezhTmZbO6po5v3jqFpauqW95RkiRJUsfV18ID34QPb4teE8uAY66FHc9JXV+SJElSohSEB80HxMrpRc26x8X52anqSJIkSZIkSZLUEf3HwgG/gu9+DGc8CFsfD1m5jUPn8XjwsfhjePYX8H8T4ckfp7fvBOsRQXOATfv35tL9xgFQWlnDH56ckeaOJEmSpB6grgbuPTu4tVSUjCw4/gbY5uSUtSVJkiQlVOGIyNKQ2Ip1Xxc70VySJEmSJEmSupZYDMbuC8ddDz/4HI74O4zctXHgfG3ovKEeln++fvI5wJw309N3gvSYoDnA8dsPJyMWIw48OnUh5dW16W5JkiRJ6r5qq+Hu02D6Y9FrMnPgxFth4jGp60uSJElKtIKhkaUhsZXrvi5yorkkSZIkSZIkdV29+sIOZ8G5T8LlH8BeP4DC4Y1D52utDZ/fdBj8dUt45uew4P309N0BPSpoXpifzch++QDUNTTw9uyVLewhSZIkqV1qKuCOE+GLZ6LXZOXCKXfC5oelri9JkiQpGXLyIa9faGkoTjSXJEmSJEmSpG6n32jY7+fwnY/grEdg0kmQnR+EyzcMnsfjUL4Q3rwart8f/rE9vPA7WDo9vf23Ula6G0i1wrz1E2PmrKhIYyeSJElSN1VTCbcdB3Obuf1Tdm849W4YvWfq+pIkSZKSqXAYVDUdbjIktmHQ3InmkiRJkiRJktTtjN4r+KipgE8ehKl3wZzXg5D52inna8PnK2fDq38OPgZtCRe/nr6+W6FHTTQHKK+uXff1mrqGNHYiSZIkdVMv/Lb5kHmvAjjjQUPmkiRJ6l4KhoduHhpbvu7rIieaS5IkSZIkSVL3ldMbtjsdzn4MLv8Q9v4xFI1sPOF87ZTzeByWfprWdlujRwXNl5ZX89XyCr6+NsDblEqSJEmJVrEC3r0+up5XHNwyauTOqetJkiRJSoXCqKD5+innviYtSZIkSZIkST1E8aaw70/h21Ph7Mdh29Mgp8/60HkX0aOC5n9+ZgZxYO1v0agB+elsR5IkSep+PrgF6teE1/IHwFmPwdDtUtuTJEmSlAqFw0I3D4mtWPd1UX52qrqRJEmSJEmSJHUWo3aHo6+GH3wOR/8LRu8VTDbvArLS3UAqrKqu5X+fnM69U+YTIwia987JYvKofuluTZIkSeo+Gurh3RvCa/kD4JwnYeD41PYkSZIkpUpB+ETzDYPmxb2daC5JkiRJkiRJPVZ2Hmx7SvBROg+m3ZXujlrU6YPm90+Z3679quvqWb6qhk8WlvHazOVU19YDQcg8Bpyzx2iyMnvUQHdJkiQpub54Bsrmhtd2udiQuSRJkrq3iInmBbEq+lLJKvIpdqK5JEmSJEmSJAmgaATs9cN0d9GiTh80/8F9U+nocPj415/XTjOfNLyIS/YZ28GjSpIkSWrknevCt2fmwPZnpbYXSZIkKdUKwoPmEEw1X5PZh7zszBQ2JEmSJElSF9NQD+/8B6Y/Bjm9YacLYNwB6e5KkqQerdMHzdeKt7ykidgGn+Nff+y/+SD+csK25PqCviRJkpQ4y2fCrBfCa1seDX0GprQdSZIkKeUKhrL+1ejGhsZWUJa/GbFYR8eqSJIkSZLUjT3+PZhy0/rHnz8F+/4/2PtHaWtJkqSerksEzdsTMt9wv7zsTPYaN5CTJ49gnwmDEtWWJEmSpLXe+290bfIFqetDkiRJSpfMbOg7GFYtalIaElvB4vycNDQlSZIkSVIX8cVzjUPma734u+DuuXt8J9UdSZIkukDQ/E/Hb9PmfWJAr+wM+vTKYnhxHmMG9CEjw0kxkiRJUlLUVMAHt4fXhmwDw3dKbT+SJElSuhQMiwyaf5mfnYaGJEmSJEnqAupq4KmfRNef+xVk5cIuF6WuJ0mSBHSBoPnxOwxPdwuSJEmSmjPtHlhTFl7b6QKIedGnJEmSeojCYbDgvSabh8ZWUuxEc0mSJEmSwr3zb1jxRfNrnvoxZPWCHc9JTU+SJAmAjHQ3IEmSJKkLi8fh3evDa7lFsNVxKW1HkiRJSquC8MEpQ1lOkUFzSZIkSZKaWrUEXvpD69Y+9l348M7k9iNJkhoxaC5JkiSp/ea+BUs+Dq9tdzrk5Ke2H0mSJCmdCsOD5kNiKyjOz05xM5IkSZIkdQHPXwE1q1q5OA4PXwIf35/UliRJ0noGzSVJkiS13zvXRRRisNN5KW1FkiRJSrvCYaGbh8RWUpxn0FySJEmSpEbmvwcf3t62feINcP8F8NljyelJkiQ1YtBckiRJUvusWgyfPRJeG3cg9BuT2n4kSZKkdCsIn2ieG6tlUNbqFDcjSZIkSVIn1tAAT/wwur7pHtG1eD3cezZ8/kzC25IkSY0ZNJckSZLUPlNuhoa68NpOF6S2F0mSJKkTiBcMjawNji9PYSeSJEmSJHVyU++Ahe+H1/pvBmc8CPv/Mnr/hlq4+3SY/VJS2pMkSYGsdDewqro2befum+utSiVJkqR2qa+FKTeG14pHwWYHpLQdSZIkqTOo6tWfrHgmObH6JrX+DcvS0JEkSZIkSZ1QdRk89+vo+iF/gKwc2PP7ULcGXv5D+Lr6NXDnKXD6/bDpbklpVZKkni7tQfNJVzxDLA3njcVizPr9YWk4syRJktQNTH8MVi0Kr+10PmR48yRJkiT1PCVV9dTH+zEy1jRUXli7NA0dSZIkSZLUCb38R6iIuCB7/KEwboOBRvv8FGqr4I1/hK+vrYTbT4AzH4bhOya+V0mSerhOkf6Ip+MjHk/Jc5MkSZK6pXeuD9+elQvbnpbaXiRJkqROoqSihkX0D631qV6S4m4kSZIkSeqEls2At68Nr2XmwMG/a7wtFoMD/wcmXxh9zJrVcOuxsPDDhLUpSZICnSJoHkvyx8bnkCRJktQBSz6FOa+F17Y+HvL7pbYfSZIkqZMoraxlYTw8aN6rMuKOQJIkSZIk9RTxODz5I2ioC6/vein0H9t0eywGh/4Btj8r+thryuDWY2DJJ4npVZIkAZCV7gaGFuYRS3L6u64+zpJV1YbMJUmSpER4N2KaOcBOF6SuD0mSJKmTKamsYVFE0DyjfH6Ku5EkSZIkqZOZ/jjMfim81nco7Pn96H1jMTj8b1C3BqbdFb6maiXcchSc/QQMHN/RbiVJEp0gaP76T/ZL2rEbGuI88MEC/v7850k7hyRJktSjVJfB1IgX74ZPhqHbprQdSZIkqTMprayJnGhO2YLUNiNJkiRJUmdSWwVP/zS6ftBvoFef5o+RkQFHXQ31NfDJA+FrKpbBLUfCOU9AvzHt71eSJAGdIGieLE9+tIi/Pvs5s5atBlg3zTz+9eeDthyclr4kSZKkLm3qXVBbEV6b7DRzSZIk9WwllbXRQfNVi6ChHjIyU9uUJEmSJClcQz2UfAXLpsPyz6G+FsbuB8N3THdn3dMbV0Hp3PDayF1hq+Nad5zMLDj2uiBsPv2x8DWrFsHNX4fNi0a2r19JkgR0w6D5izOW8pdnZvDpwvJ1ofIY6wPme2w2gB8ePIFJw4vS06AkSZLUVcXj8O714bX8AbDlUantR5IkSepkSiprWBQVNI/Xw6rFUDgstU1JkiRJUk9XXwcrZweB8mUz1n9e/jnUr2m89sXfwWYHwoFXwCYT09Nvd1Q2H179S3gtlgGH/hFisfB6mMxsOP4GuOs0mPlsxDnnrQ+bFwxte8+SJAnoRkHzt2ev4M/PzGDKnBIgCJavDZjHgR03LeYHB01g5zERL/JLkiRJat6XLwcvuobZ4WzI6pXSdiRJkqTOprS5ieYA5QsMmkuSJElSstTVbBAon75BoPwLaKht/XFmPgszn4NtT4N9f+bPcYnwzC+griq8tsPZMGRS24+Z1QtOuhXuOBG+fCV8TcmX68PmfQa1/RySJKnrB82nzS/lT0/P4PWZy4H1AfO1IfOJQwv4/kET2HeC3yxIkiRJHfLOf8K3xzJgx3NS24skSZLUCZVU1lBGbyrjvciPrWm6oGwejJic+sYkSZIkqTupWwMrZjaeUL50OqycBQ11CTpJHD68DT6+D3a5BPb4DuQWJujYPcxXr8EnD4TXcotg35+3/9jZeXDKXXDbcTD3zfA1K76AW46Csx6D3g4olSSprbps0PzzJav4yzMzePbTJUDTgPnYgX343oHjOWzrIWnsUpIkSeomSufBjCfCaxMOg8Lhqe1HkiRJ6oRKKmuBGAvj/dkstrDpgrIFKe9JkiRJkrqs2uogJLx0ownlK2dDvD41PdRVw2t/hSk3wd4/hh3Phayc1Jy7O6ivgyd/HF3f7+cdD3/n9IZT74Fbj4EF74WvWfop3Ho0nPUo5BV17HySJPUwXS5oPmdFBf/37Oc8Nm0RDfF4k4D58OI8vrP/eI7ZbhgZGbH0NitJkiR1F1NuhHhDeG3yBantRZIkSeqkSitrAIKgOSFB83KD5pIkSZLURH0dLPl4/XTytR8lX0W/N5FqVSvhqR/D2/+C/X8FE4+BmLmkFk25Mfi9DTNoIuyQoDvm5hbA6ffBzUfC4mnhaxZPCyafn/kQ9OqbmPNKktQDdJmg+eKyav7+/BfcP2U+dQ0NTQLmg/r24tL9xnHyTiPIzsxIb7OSJElSd1K3BqbcHF4bMB5G753afiRJkqROqqQiCJovikdMYyubn8JuJEmSJKkL+PgBeOKHULk8jU3EoPcAqFjW8tKSr+C+c+DNq+DA38Co3ZPeXZdVuRJe+G10/dA/QGYCo2t5xXDGQ3Dz4cEE8zAL3oPbTwxC6Tm9E3duSZK6sU4fNF+xeg1XvziLO96ZQ01d04B5cX4OF+09hjN3HUVudmZ6m5UkSZK6o08ein6Bd6cLnNghSZIkAXX1DZRX1wGwiH7hi5xoLkmSJEnrffwA3HcuQQIoBWIZ0G8MDNwcBk74+vPmMGAcZOXC9MfguV/DipktH2vBFLjpMBh/KBzwaxi0ebK773pe+C1Ul4bXJh4Do/dM/Dl794czH4YbD4MVX4SvmfsG3HkKnHo3ZOclvodEaaiH2irI6gWZ2enuRpLUg3XaoHl5dS3XvTybm974isqauiYB8z45WZy352jO33MMfXp12qchSZIkdX3v/id8e04f2Obk1PYiSZIkdVJlVbXrvl7oRHNJkiRJat5Xr8GDF5KUkHksE/qPbRwmH7g59N8MsnOj99viCBh/CLx/M7x0ZesmnH/+JHzxNGx3Buz7M+g7OHHPoytbNA2m3Bhey8oLpsEnS59BcNYjcOOhwQT6MF++DPecCSfdDlk5yeulrSqWw+dPwYwnYdYLUFsJBcODixkmnZDu7iRJPVSnS2hX1dRzw+tfct0rs1lVXbvu28m1AfPcrEzO3HVTLt5nLEX5neg/ekmSJKk7WvgBzH83vDbpJMgtSG0/kiRJUidVUrlh0HxA+KKKZVC3JphGJkmSJEk91ZJP4M5Tob6mY8fJyArC4+vC5BPWB8rbGx7OzIadzg/eA3njn8FHbWXz+8QbgnD6R/fCrpfC7pdDr77tO393EI/Dkz8Ofl3C7Pk9KBqR3B4KhsJZjwaTzcvmha/54hm47xw44ab0TQyPx2H5FzDjiSBcPu9tmlx8UT4fHjgfSr6EvX7onYYlSSnXaYLmNXUN3PbWHK55aRYrK9Y0CZhnZWRw8uQRXLrfZgzq28zVhZIkSZIS553ro2uTL0hdH5IkSVInV1q5PiCxKN4vemH5guBW7ZIkSZLUE5XNh9uOhzVlrd8nMwf6j1sfJB/0dbC835jkBYR79Q0mlO94Lrz0v/D+LdHB6bVqK+GVPwaTvPf5CWx/VvoCzOn08f0w943wWtGmsNvlqemjaCSc+TDc9A1YtSh8zfTHgsn6x/4HMjJT01d9XRAoXxsuXzmrdfu9+DuoLoODfmvYXJKUUmkPmtc3xLnnvXn88/kvWFxe3SRgnhGLcfR2w/j2/uMY0S8/jZ1KkiRJPUzlSvj4vvDaqD1h0Bap7UeSJEnqxBpPNO8fvbDMoLkkSZKkHqqqJAiZr1oYvWbQll9/bL5+UnnxaMhMU8Sp72A44u+w88Xw/BVBOLglFcvg8e/DW/+CA34Nmx/ec4LBa1bDM7+Irh/8e8hO4YDR/mPhzEfgpsOC35cwH98Pmb3gqKshIyM5faxZBTOfD4LlXzwd/F1ojzevCo51+P+lLhgvSerx0h403/8vLzF3ZWWTgDnAYVsN4bsHjmezQX3S1J0kSZLUg31wG9RVh9d2Oj+1vUiSJEmdXMkGE82ryKU03puiWEXTheULUtiVJEmSJHUStdVw12mw7LPoNTueC9/4a+cMZQ/aHE65E756HZ79BSyY0vI+K2bC3afDiJ3hwN/AyJ2T32e6vfbX6AsJxuwLm38jtf0ADBy/frJ5VMB76h2QlQOH/y1xf/7K5gfB8hlPwlevQn1Ny/u0xvs3Q81qOObfPXNiviQp5dIeNJ+zspIYjQPmO25azPcPmsDEoQUArKqujdq9Q/rm+p+tJEmSFKqhHt77b3it79D0vBAoSZIkdWKllY3fMF4U7x8eNC+bl6KOJEmSJKmTaGiAB78Jc16PXjPhG3DYnztnyHxDo3aH85+HTx+C566Aki9b3mfe23DDQbDFEbD/r2HAZsnuMj1WzoY3/hley8iCQ/+Qvt/fTSbCGQ/BzUfCmrLwNVNugqxcOOTK9vUZj8PiaUGwfPrjwdfJ8vH9UFMBJ9wE2XnJO48kSXSCoPnG4sCUOSWc+p+3knqeWCzGrN8fltRzSJIkSV3WzOeg5Kvw2o7nOCFBkiRJ2khJZeOBKQvi/dmCuU0XljnRXJIkSVIPEo/D0z+FTx+OXjN8Mhx3PWRkpq6vjojFYOIxQTh+yo3w0pVQtbLl/T57FKY/EbzPsvePoc+g5PeaSk//v+ip3ZMvhIETUtvPxoZuC6ffD7ceHUwED/P2tZDVCw64onVh87o1wbTytZPLE3UXs35jYORuMO0uaKgLX/P5U3D7CcGk/V59E3NeSZJCdKqg+dr/nuPNrkqQeErOIkmSJHVN7/wnfHtGNmx/Vmp7kSRJkrqAsInmoRL1prMkSZIkdQVv/DMI70bpPw5OvRty8lPXU6Jk5cDOF8I2J8Nrf4O3roG66ub3idfDu9fD1Ltg92/Drt+CnN4paTepvngOZjwRXus9EPb5cWr7iTJiJzj1HrjtOKirCl/z+t8hKw/2/Wl4vXIlfPFM8HxnPh8dWm+TGIzYGSYcChMOgwHjgqD7FkfAPWdC/Zrw3b56FW45Gk67F/L7JaAPSZKa6lRB87WSfZMUI+bJV11bz/tzSpi1bDVlVbVkZ2YwuDCX7UYUM7J/Yn84mLOigg/nlbK4rJra+gYK87IZO7AP229aTG524q52TeVzkiRJSquVs4OJ5mG2PBL6bpLafiRJkqQuoKSi8UTzyKC5E80lSZIk9RTT7oVnfxFd77NJMGG6qwdkcwvhgF/BTufDi7+HD2+nxXRSzWp48Xfw7n+DQPO2p0Nmp4xxtayuBp76SXR9/18Fv0adxajdgyngd5wUHeB++cpgsvme3wser5gVBMtnPAlz34R4Q8f7yM6HsfsF4fJxB0OfgU3XTDgETr8P7jwlOtC+4D246XA440Hfw5MkJUXav0MZWpjXqjuNKHkuu/MDHp26sNG2YUV5vP6T/dp8rBWr1/D357/gvinzqaypD12z9bBCLttvMw6aOLhd/a719CeL+ecLX/DxgvLQeu+cTI7fYTjfPmA8/XrntPs8qXxOkiRJncK7/yXyBdDJ30xpK5IkSVJXUbLRRPOFkRPN56egG0mSJElKs9kvwUMXR9dz+gRTmIs3TVlLSVc4DI6+Gna5GJ77Ncx8tuV9Vi+GR78Nb14DB14B4w+hywWp3vk3rPgivDZ0O9j2tNT20xpj94WTboO7ToWG2vA1z18BSz6GxR/D8hmJOW+fwUF4fMJhMHovyM5reZ/Re8GZDwdT2KtLw9cs/QRuPCRYVzQyMb1KkvS1tAfN2xNmVuI89+mSJiHz9npz1gq+dcf7rKyoaXbdRwvK+OatUzh2+2FceewkcrIy2nSeNXX1/Pi+aTz0YfN9V9TUc/Obc3hs2iKuOW17dh4T8cZOM1L1nCRJkjqNmkr44Nbw2iZbB7ftkyRJktREaWUrJ5pXl8GaVdCrbwq6kiRJkqQ0WPwR3HV6dIA3IwtOuhWGbJPavlJl8FbBFOrZL8Gzv4RFU1veZ/kMuPNk2HR3OPA3MHyHpLeZEKuWwEt/iK4f+ifI6KQZmvEHwQk3wj1nQTx88CQf39/x82yyVTC1fMKhMGS79v16DN8RznkCbjkaKpaGr1k5G244NAibD9isQy1LkrShTvo/uVKhvLqWnz/0cUKO9e5XKznnpneaBLILcrOYOLSA4cV5ZGY0vuLygfcXcPmdHxCPt3C7oA00NMS59I4PmoTMMzNijOiXx5ZDCuib2/j6iRUVNZx947tMmVPSKZ+TJElSp/LxfUHwJczk87veFA1JkiQpRTaeaL6AZgZflC1IcjeSJEmSlCalc+G246FmVfSao66GsT1gMOWYfeCCl+DY66GwlVOm57wO1+8HT/wIaiqS2V1iPH9F9O/1NqfCiJ1S209bbXEEHPcfiCUwQpeRFfzeH/pH+PY0uPh12O/nMGyHjoXuN5kI5z4FhSOi15TPDyabL/6o/eeRJGkjaZ9orvT53yc+Y3F5NQD5OZlU1kRcndeCsspaLr3jfaprG9ZtG1aUx6+O2JIDt9yE2NdhpEVlVfzzhZnc8fbcdeue+mQx/33tS87fc0yrzvXvV2bz7KdLGm07beeRXL7/ODYpyAWCMPozny7hN499yoLSKgCqauu59I73efq7e1GQm92pnpMkSVKnEY/DO9eF13oVwtYnpLYfSZIkqYuIx+NNJpovifejIR4jIxYylKJ8PgzaPEXdSZIkSVKKVK6E246D1Yuj1xzwa9jm5JS1lHYZGTDphCDQ/O718MqfoLq05f3e+TfMfBaOvhZGdtK7zc5/Dz68PbyW0xcO+FVq+2mvrY6DujXw0MXtP0avQhh3IGx+GGx2AOQWJq6/DfUfC+c8CbceDStmhq+pWAY3fQNOu7/zB/0lSV2CE817qDdnreCud+cBkBGDb+8/rt3H+vcrs1hSvmbd4xH98njgkt04aOLgdYFsgCGFefz+mK354cETGu3/9+e/oKwy4nZJGyipqOHqFxt/k/SjQybwu2O2XhcyB8jIiHHIVoO5/+LdGF6ct277orJqrn/1y071nCRJkjqVee9ETzjY7nTI6Z3afiRJkqQuorKmnpr6hkbbasliORFvLDvRXJIkSVJ3U1sFd54Cyz+PXjP5m7D7d1LWUqeSnQu7XQrf/hB2uxwye7W8z8rZcMPB8MwvoLY66S22SUMDPPHD6PreP4K+g1PXT0dteyoc/re27VO0Kex8MZz5CPxoFhz/3yC0nqyQ+brzjgjC5ptsHb2mugxuOQpmv5TcXiRJPYJB8x6ouraenzwwjfjXg3TO2m0Uk4YXtetYK1av4eY3vmq07cpjJzUKfm/skn3GMnl0v3WPV1XXcd2rs1o817WvzGL1mrp1jyeP7sfFe4+NXD+4MJc/HDep0bYbXvuSkoqaiD0CqXxOkiRJncq7/4mu7XRe6vqQJEmSupiSyvDXHBfG+4Vup2x+EruRJEmSpBRrqIf7z4d5b0Wv2eIIOORK2GC4X4+UVwwH/QYuew8mndSKHeLwxj/gur1hwftJb6/Vpt4BCyP66T8Odr4otf0kwo7nwCF/aH7NsB1hv1/AxW/Ct6fCoVfCmL0hMzs1Pa7VZxCc/SgMb2ZieW0F3H4iTH8idX1Jkrolg+Y90F+emcGcFZUADCvK4wcHTWhhj2iPTl1IRU39useTR/dj980GNLtPLBbjOxtNUL/nvfnE4yG3kP1aQ0Oc+95r/ObLdw4Y12i6eJjdNxvA5FHr38xZvaaOxz5a1Ow+qXpOkiRJncrqpfDJQ+G1sfsHt+KTJEmSFKo04u6Gi+L9w3cod6K5JEmSpG4iHocnfwTTH4teM3JXOPY/kJGZur46u6KRcOx1cOErMHrvltcvmw7XHwAv/Bbqmh+wmHTVZfDcr6Prh1wJWTkpayehdrkIjr8B+g4NHmflwvhD4Yh/wPdnwAXPw14/gE22TP9FE3nFcMZDzf/5qV8Dd58O0+5NWVuSpO7HoHkPM3VeKTe8/tW6x/9z1ER698pq9/Ge/WxJo8cn7TiiVfvtOrY/I/rlrXu8bNUa3p9bGrl+ytwSVmwwiXxkv3x2HRPxJs1GTtypcU/PfLK42fWpek6SJEmdyvs3Q0N4OIbJF6S2F0mSJKmLiZ5oHjHAwonmkiRJkrqL1/4P3r0+uj5gApx8B2TnRa/pyYZsA2c+DKfesz7cHCVeD6/8Cf6zHyz+KDX9hXnpD1CxLLw24TAYd0Bq+0m0rY6D70yDny4IPk69C3Y4C/oOTndnTfXqE/zZmXBY9Jp4PTxwAbx3Y+r6kiR1KwbNe5Da+gZ+fP806huCKdvf2HoI+2+xSbuPV7Gmjne+XNlo257jm5/8vVYsFmOPjaaEvzB9ScRqeGH60kaP9xg3oMVp5ut6Gtf4PG/PXkllTV3o2lQ+J0mSpE6jvi76xaWikTDuoNT2I0mSJHUxJRETzUuyB4bv4ERzSZIkSd3Bh3fC81dE1/sOgdPvh/x+0WsUTMYefzBc8iZsc2rL65d8BNftG4TO68PzL0mzdDq88+/wWmYOHPy71PaTLJnZQYg7s/3DO1MmOxdOvAW2PqGZRXF47Dvw+t9T1ZUkqRsxaN6DXPPiLKYvXgVAQW4Wvzpyyw4d7/Mlq6itj697PKJfHoP65rZ6/x02bfyDxKcLyyPXblzbYWRxq8+zSUEuw4vXXxlbU9/AF0tWh65N5XOSJEnqNGY8ER102fE8b2UpSZIktaA0YqL56l4R087KFgS3l5ckSZKkrmrm8/DIpdH1XgVw2n1Q1Lq7yAvIK4Jj/gUn3wm9BzW/tqEWXvgt/PdAWDYjJe0Rj8NTP4aGiHD7bpdBvzGp6UWNZWbDMdfBjuc2v+7ZXwZ/bnxNou2qy2HlbH/tJPVIBs17iC+WrOLqF2eue/yTQ7doU4A6zMyljcPa4wb1bdP+4wb1aXy8ZeHh79BzbdInYmUrz7U0/FypfE6SJEmdxrv/Cd+e2Qu2OyO1vUiSJEldUElF+ETzqryIoHldFVSuDK9JkiRJUme38EO458zowHFGNpx0GwzeKqVtdRubHwbfehu2Oq7ltQvfh2v3hNf/AQ31ye1r+uMw+6XwWt+hsMf3knt+NS8jA77xV9j9282ve+VP8OSPoaEhNX11dTWV8PC34MoR8I/tgo+pd/nrJ6lHMWjeAzQ0xPnR/dOoqQ/+g5s8qh+nTO74FaOzl1c0ejyksG3B9SFFjdcvKKmiurbpN73VtfUsLKva6Fx5TdY1f67G62cvDw+Ap+o5SZIkdRrLZsCXr4TXtjoOevdPbT+SJElSF1QSMdG8tvfQ6J3K5yepG0mSJElKopKv4PYToKaZwXvHXAtj9k5ZS91Sfj84/gY44SbI69f82vo18Owv4MbDYMWs5PRTWwVP/zS6ftBvoFfbhkYqCWIxOOAK2O8Xza9759/BHQnqIy4WUaChHu4/Dz64bf22ki/hwQuDuwnMfy99vUlSChk07wFufOMrPphbCkBOZga/P3ZrYrFYh4+7YvWaRo/bGsoe2KcXWRnr+2iIQ2ll08k/KytqGt11JDszxoA+OW061+CCxr2tWB3+xk+qnpMkSVKn8U7ENHOAyRekrg9JkiSpCyuNCJrH+g6CjKzwncoWJLEjSZIkSUqCihVw23FQsTR6zUG/g62PT11P3d3EY4Lp5psf3vLaeW/BtXvA29clftryG1dB6dzw2shdWzd9XakRi8FeP4BD/9j8ug9vh/vPhbrw1zQEPPcrmPFEeG3Be3D9/vDAhVC+KLV9SVKKRbzCre5i3spK/vLMjHWPL95nLJsNSswVhBU1jSd15+W07Y9TLBYjNzuT1WvWXx1XUdP0SrnKjbblZme2OSifn5PZ6PHGvUdtT9Zzao+lS5eybNmyNu0zd27EN/mSJEkAa1YFt3YLM2wHGLZ9avuRJEmSuqiSiGETRb3zgtuHl4W8Tldu0FySJElSF1JTCXeeBCtmRq/Z5Vuw26Wp66mn6DMITroNpt0DT/4Qqsui19ZWBms+ewSOuhqKN+34+cvmw6t/Ca/FMoJAcwIGXirBdr4QevWFh78F8YgLDz59GGoq4MRbISc/tf11dlNuhjf+2fK6aXfBZ4/Cnt+DXS+F7LYNNZWkrsCgeTf30wc+ovLr8PTYgb351r6bJezYlWsaB6h7ZbV9QH5udgYbDhGvXNM0AF6x0bZeWZlN1rSkV3bjfTbuPWp7sp5Te1xzzTVcccUVCTmWJEkSEITMa1aF13ZymrkkSZLUWlETzYvzs6FwWHjQvGx+kruSJEmSpASpr4P7z4P570avmXgMHPTb1PXU08RisM1JMHpPeOQymPlc8+u/ehX+tRsc/HvY/syOBcGf+QXUVYXXdjgbhkxq/7GVXNueCjm94b7zoCH8InlmPhfcqeDUuyC3MLX9dVazX4bHv9f69bUV8MJv4P2bg7s6bHGEF19I6lbanqJVl3H3u3N5beZyIPi/63+PnUROO4LTUdbUNb7aLSez7cfeeJ/quqah7Kbnaft/xL1acZ7wcyXnOUmSJKVdPA7vXh9ey+8fvCAsSZIkqVUiJ5rn50DBsPCdDJpLkiRJ6gricXji+zDjieg1o/aEY/4NGcaQkq5gKJx2HxzxD8jp2/zamtXw6OVw+wlQvrB95/vqNfjkgfBabhHs+/P2HVeps+VRQYg8Ky96zdw34OYjoWJF6vrqrJZ/AfecAQ3hQ0ybVTo32PfmI2Dxx4nvTZLSxO/wuqml5dX87vHP1j0+eacRTB7dL6Hn2Hjad019xG1WmrHxPmETxJueJ97m86xpcp7wqeipek6SJElp99WrsGx6eG37M72tmyRJktQGJZETzXOgcHj4TuULktiRJEmSJCXIK3+GKTdF1wdtCSfdBlm9UtZSjxeLwQ5nwSVvwOi9Wl4/81m4ZpfgTrfxNmRu6uvgyR9H1/f7OfTu3/rjKX02OwDOeAB6FUSvWfQh3HQYlC9KWVudTuVKuONEqC7r2HG+ehX+vSc89j3D+5K6hax0N6Dk+MXDH1NeHVxZNbBvL35y6BYJP0d+r8Z/fDaeBt4a1bWN98nPafpHsnevxqHwNe2YEL6mtvE++TnhQfNUPaf2uOSSSzjhhBPatM8LL7zA5ZdfnpDzS5Kkbuad/4Rvj2XAjuemthdJkiSpC6urb2BVdfiUq+L87OigeZlBc0mSJEmd3Ae3wYu/ja4XDAuma+cVpawlbaBoJJzxMLz3X3j2l1BbGb22ugwevBA+fQSO+Bv0GdTy8afcCEsipjJvshXscE672laabLobnPUo3HoMVK0MX7NsOtx4CJz5MBSPSml7aVdXA3efAStnR6857M9QPBqe/iks/7z548Ubgr+bH98H+/wUdjofMrMT27MkpYhB827o8WmLePqTJese/+qILSnMS/x/VL03CmtX1bTtliHxeJzqjQLgG4fKoWlQu7q2nng8TiwWa/W5Kms2Ok9E+DtVz6k9Bg0axKBBrfhGfwMzZ85MyLklSVI3U7YApj8eXht/SPDCpCRJkqRWKa2qjawV5edA3bDw4qqF0FAPGYl5/VCSJEmSEuqLZ+GRZgbb5RbC6fdDYcTPPEqNjAyYfAGM3Q8eugTmvdX8+hmPw9w34fC/wsRjotdVroQXmrnI4NA/QKaxsy5n6LZwzpNw69GwKmJyeclXcMMhcMZDMGjz1PWWTvE4PPZdmPNa9JrJFwZ/1wDG7A3v/hde+n3L08+ry+Cpn8B7N8Ah/xtMl5ekLiYj3Q0o8X7/xGfrvt5v80EcPmloUs7Tv0/j2x4tKqtu0/7LVq+hrmH9LXkyYl/fSnYj/XrnsGGmvLY+zvLV4beijbK4vHFv/fs0PU+wPTXPSZIkKa2m3AjxiLvErH2BRJIkSVKrlFZGv1ZZ3Ds7OnTRUAerlyapK0mSJEnqgAVT4J4zo99LyMyBk++EQVukti9F6z8WznkCDvotZPZqfm3VSrj3bLj3nCBQHuaF30B1aXht4rEwao+OdKt0GrQ5nPsUFG0avWbVIrjpMFj4YcraSqvX/w4f3hZd3+xAOPj36x9nZsMuF8FlH8CO5wV3jG7J8s/htuPgjpNguYNDJXUtXlrWDZVXr5+g88L0pYz6ScS0ymYsKK1qst/jl+/BxKGF6x6PGdC7UX1haVWbzrGwtHGIe1hxHrnZTaf35GZnMrQwjwUbHH9haRUD+7bwjXEzvY0d2Cd0XaqekyRJUtrU1cCUm8Nr/TeD0fukshtJkiSpyyupjJ5oXpyfA1nDo3cumw8FQ5LQlSRJkiS108rZcPuJUFsZsSAGx14Ho3ZPaVtqhYxM2O0yGHcQPHgRLHy/+fWfPABfvQZH/B02P2z99kVT4b0bw/fJyoODfpO4npUexaOCsPktR8PyGeFrKlfAzUfAqffAprumsrvU+uxReO7X0fWBW8DxN4RP8O/dP7g7wI7nBlPLv3q15fN9/hTMfD4Iqu/1w+DuEJLUyTnRXO02dlDjsPYXS1e3af+ZG62PCn8n5VyDws+VyuckSZKUFp89AhURUxN3Oj+4xaIkSZKkViupCJ9onpudEQyhyO8XvBEfpnx+EjuTJEmSpDZavQxuPRYql0evOeRKmHhM6npS2w2cAOc9C/v9AjKym19bsRTuOiUIpleVQjwOT/4YiIev3/P7UNjMBdXqOgqGwjlPwpBto9esKYdbj4GZz6WsrZRa+AHcfwGRf957D4RT74bcguaPM3grOOtROPFWKBrZ8nkbauGNf8I/d4D3b4GGiLtHSFInYYpE7TZ+k75kZ8bWPZ5fUsXS8upm9mhsypzGt9/Zckj0f8ob16bMKWn1eZaWVzO/ZP1k8uzMGOMiguapfE6SJElp8c5/wrdn58M2p6S2F0mSJKkbKI2YaF6cnxN8EYtB4bDwncsWJKkrSZIkSWqjmgq440Qo+TJ6zW6XB1N41fllZsFeP4BvvgSDt255/dQ74Zpd4en/B3PfDF9TtGkwMV3dR+/+cNYjMLKZieV1VXDHyfDpI6nrKxXKF8KdpwTPL0xmLzj5DijetHXHi8VgyyPhW+/C/r+E7N4t71OxDB65DP6zL8yJ+HsnSZ1AyD0d1NX958wdqauPuNIqwmeLyvndE5+tezygTy/+dtK2jdaM6t/4P8A+vbKYPLofr89csW7bq18s57gdWr5yMR6P89rMxlfA7r/FJpHr999iENe+PGvd49dnLicejxOLxSL3WeuVLxqfZ5cx/endK/yPfiqfkyRJUsotmgbz3gqvTToJ8opS2o4kSZLUHZRUhk80L1obNAcoGAYrZjZdVG7QXJIkSVInUF8H954NC9+PXrP1CXDAFSlrSQkyeCs4/wV45U/w6l8g3szk5FUL4a2ro+sH/x6ycxPfo9IrtxBOfwDuOSN6cnlDLdx7VvBvwG6XBaHqrqymAu44CVYtil5z9DUwYnLbj52dG0z+3+ZUeP6K4CKOliyaCjceAhOPhQP/B4pGtP28kpREBs27oV3G9G/zPpkZjb8B6JWVwR7jBrS43wFbbNIolH33e/NaFcp+c9YK5q1cf0XYgD692G5EUeT6HUYW0693Diu/vg3t3JWVvDl7BbuNbbnHe96d1+jxQVs2H/5O1XOSJElKuXcjppkDTL4gdX1IkiRJ3UhJ5ETzDW5PHnVb8bL5SehIkiRJktogHofHvgNfPBO9ZvTecNQ1kJGRsraUQFk5sN//gwmHwkMXw7LpbT/GmH1h828kvjd1Djn5cPKdcP958FnE5PJ4Azz7C1j4ARx1FeS0YmJ3Z9TQAA98ExZPi16zz09h6+M7dp6CIXDMtbDT+fDkj2HBey3v88kDMOMJ2P07sPu3g98XSeoE/A5QHXLENkPJz8lc9/idL1fyxkZTvTcWj8f52/NfNNp2wo7DyciIvtotIyPG8RuFvf/+3BfE481Pbn995nLe+Wrlusd9emXxjUlDm90nVc9JkiQppapKYNq94bWRu8EmE1PbjyRJktRNlEZMNC/eeKJ5GIPmkiRJktLtpSvhg1uj65tsDSfdFoSV1bUN2x6++XIQYKUNeZaMLDj0D11/irWal5UDx98I257W/LpPHoDrD4SVs1PTV6I9/2uY/lh0favjYe8fJ+58w3eE856FY/4NfQa3vL6uGl6+Eq7aET66L7gYSJLSzKC5OmRAn16cueuoRtt+/MA0lpRXR+5zzUuzeOfL9eHvvrlZXLjXmBbPddHeY+m9QQD87S9X8q+XZ0WuX1xWzY/vb3z12Tm7j6Jf7+Z/+Enlc5IkSUqZD++Auqrw2uTzU9uLJEmS1I2URATNi1oz0bx8QRI6kiRJkqRWmnJTEGiMUjgCTrsXcgtS1pKSLDsXDvwfOPdp6De2dftMvhAGTkhuX+ocMrPgyKtg54uaX7f0E7huH/jiuZS0lTDv3wqv/z26PnwnOOrqxF9UkZEB25wMl02BPX8Amb1a3qd8QTBh/oZDginyUirE41DyFUx/At75D7zyZ3jjn8HfnTlvQG10frLd1qwK/i1561p4+U/B+T55CMoXJf5caresdDegru+ivcdw//vzWbZqDQDzVlZx7DVv8OsjJ3LAFoOIff2f76KyKv75wkzueHtuo/0v3XczivJbvvK1X+8cLtl3M/709Ix12/741AwWllZx2X7j2KQgF4CGhjjPfraE/3n0UxaUrg9TbVLQi/P3bF34O1XPSZIkKSUaGoIfBMP0GQybH5HafiRJkqRupKSyNnR746B5xETz1UuhrsbJgJIkSZJSq6YSXvgtvHVN9JrcIjj9figYkrK2lEIjd4aLXoPnr4C3r41e13sg7JPA6c7q/DIy4JAroVcBvPLH6HXVZXD78bDfz2HP73f+ifdfvgqPfSe6XjgSTr4juBgjWXr1gf1/AdufAc/8Aj57pOV95r0F1+0L250G+/0S+m6SvP7UM1WVwPTHYeZz8OUrULkiem1GNow/GHa5GEbt0bHzlnwFL/4ePnkQ6sMGecSCc+zzUxi1e8fOpQ4zaK4OK8rP4apTtuPMG95hTV0DAAtKq7jglvcoyM1iRL98yqtrWVhaTX1D49t5HLjlJnyzDZO/L957LO/PKeH56UvXbbvtrbnc+c48hhXl0Tc3i3krKymvrmu0X252Blefuj2FedkbHzLtz0mSJCnpZr0AJV+G13Y421CLJEmS1AGlERPNizccRFEQMdGcOKxaCMWjEt6XJEmSJIX68lV45LLo9w0AsnLh1LudYt3d5eTDoX+AzQ+Hhy+B0rlN1xxwBeQWpr43pVcsBvv9PxgwPvj3IuquycThhd8EE7ePuRZ69U1pm622YhbcfTo01IXXc/oG/+b1GZSafopHwUm3BqHep34KSz5uYYc4fHAbfPIw7P3DYOJ8Viumokstefz78P4tEUHvEA21MP2x4GObU+DQP7bvricfPwAPfwtqK5tZFIevXoWbXoPdvw0H/LrzX9DSjWWkuwF1DzuP6c+NZ+/UeEoPUF5dxycLy5m3sqpJIPuobYdy1anbrZsO3hoZGTGuPm17jthmaKPt9Q1x5q6s5JOF5U1C5sX52dx49mR2HNWvUz4nSZKkpHs3Ypp5RlYQNJckSZLUbtETzTcImkdNNAcoW5DgjiRJkiQpxJpV8Nj34ObDmw+ZE4PjroeRu6SsNaXZ6D3h4jdgx/Mgc4OfZff/ZTBFWT3XpBPgvGegaGTz66Y/Bv/ZD5Z/kZq+2qJyJdxxIlSXhtdjGXDCjbDJliltC4DRe8E3X4Zv/BXyWpFrq1kFz/4SrtkFZjwJ8XjL+0jNmf9eeMg8lgkFw2DItrDJVtAr5IKjqXfCrUfDmtVtO+cnD8L95zUNmecPgCHbBOdlw+xlHF7/Gzz9s7adRwnlRHMlzG6bDeDZ7+7N35//nPunLKCqtj503cShBVy232YcslX7bq+Um53JP0/ZjsO2Gsw/X5jJp4vKQ9fl52Ry3PbD+fYB4xjQp31XcaXqOUmSJCVNyVfw+dPhtS2O8JaXkiRJUgfE4/FmJppvMMCiV9/gDZk1ZU0Xls1PUneSJEmS9LWZz8Oj34ayeS2vPexPwfsH6ll69YXD/woH/AoWTYPBW0Fecbq7UmcwZFIQhr7vXJj9YvS65Z/DdfvCsf+Gzb+Ruv6aU1cD95wJK2ZGrznkShh3YOp62lhmFux0Hmx1LLz8R3jnuujJ62utnA13ngxj9oXD/w/6jU5Nr+recgth6xNg3MGw6a6N71DQUA9z3oAXfw9z31i/fcEUeOjiYEJ/a6ycDQ99C+IN67dtsjUc8vvgwou1ln8Bz18Bnz26fttb18DIXWHLI9v3/NQhBs0FwK5j+/PVlR3/T35g31789uit+fk3tmTKnBJmLl1NeVUt2VkZDC7IZdsRRYwa0DsBHcOhWw/h0K2H8NXyCj6cV8ri8mpq6xooyMtms0F92GHTYnKzMzt8nlQ+J0mSpIR7979AxNXsO12Q0lYkSZKk7qaipp7a+vDvtxtNNAcoHA5LQ4Lm5QbNJUmSJCVJVSk88//gg9tat36fn8Fk3zvo0XILgwnn0oby+8Hp98Pz/xNMFo5SswruOhX2+hHs81PIyEhZi03E4/D49+CrV6PX7HQ+TP5m6npqTl4xHPK/wd2on/opzHq+5X3mvhXcwVrqiKKRsNcPg5B5dl74mozM4P+GTR8L/l5NuWl97bNH4MtXGgfFo7zwO6itWP946PZw5sOQW9B43YBxcOKt8Nh3Gp/r2V/ChMOCCzSUUv6KKylyszPZfbMB7L7ZgKSfa9SA3ikJeqfyOUmSJCVEbRV8EHH18KAtYdPdUtuPJEmS1M2UVIRPM4eNJpoDFA6DpZ80XVi2IMFdSZIkSRIw46kgoLVqUctr+w6Bw/8GEw5JdleSuqqMTDjwChi6bTCReMOw6MZe+SMsmgrHXgd5RanqsLE3/hn9PinA2P3gkD9ALJa6nlpj4IQg1P/FM0HgfOWs6LV7fAeKRqSsNXVD+/4smIyfldPyWgj+HfjGX4O/3ws/WL/9/VtaDpov/Qw+eWD948wcOObapiHztWKx4O/ol6+u/3tQ8iV8eFtwQYZSKo2XDUmSJElKqo8fgKqS8NpO53e+F04kSZKkLqa0sjayVrzxRPOCYeELyw2aS5IkSUqgypVw/wVw50mtC5lvdwZc8pYhc0mtM/EYOP856Dem+XVfPA3/2TcIl6ba9MeDycdRBm4OJ9zUeacix2Iw/uDg3+aDfgu9QoK4BcNht8tT35u6l/EHtz5kvlZGJuz+7cbbZrZiAv8Ht0G8Yf3jrY4LLqxoTnYu7PHdxtvev6V1fSqhDJpLkiRJ3VE8Du9cF17rVQCTTkptP5IkSVI3VFIZPtE8FoOCvJCJ5mHK5ie4K0mSJEk91icPwdWT4aN7Wl5bOBLOeBCOuip9E4cldU2bbAkXvAjjDmp+3crZ8J/9g3+bUmXRVLj/fCAeXs/vD6feDbmFqeupvbJyYLfL4LL3YfuzgA2GiB30P5CTn7bW1MON3OjO6VUroaay+X1mPNH48XZntO5cWx0L2b3XP17wPpS34kI6JZRBc0mSJKk7WjAFFn0YXtv2VOjVJ6XtSJIkSd1RVNC8MC+bzIyN7iBUMDz8IAbNJUmSJHXU6qVw9xlw71lQsazl9TtdAJe8AWP3S35vkrqnvCI45W7Y+8fNr6utCP5tevZX0FCf3J7KF8EdJ0NtROA1MwdOvgOKRyW3j0TrMxCO/Adc+HIQ8B25K0w8Nt1dqScLu0BtTXn0+uVfBBeerJXdG0bs3Lpz5fSGEZM32BAP7piglOqk93+QJEmS1CHv/Ce6ttP5qetDkiRJ6sZKK2tDtxfnh9xytjAiaF5dCjUVwZsmkiRJktQW8Th8dC88+SOoKml5ffFoOOpqGLV78nuT1P1lZMC+P4Mh28ID34SaVdFrX/9bMG38+Bsgv1/ie6mphDtPhlULo9ccdTWM3CXx506VIdvAOU8EryXFYi0ul5KmPOTvWV4zf68XT2v8eNj2kNmG6PLIXWD2ixsc76PW76uEcKK5JEmS1N1ULIdPHgivjdkHBoxLaTuSJElSdxU10bwoP7vpxsJh0QcqW5CgjiRJkiT1GOULg1DlAxe0ImQeg10vhYvfMGQuKfE2Pwy++SIMGN/8utkvwnV7w6Jpza9rq4YGePCb0Xd7BtjrRzDpxMSeNx1iMcgrTncX6unmvtn4ceFIyAoZvLHWss8bPx64edvON3DCRseb0bb91WEGzSVJkqTu5v2boT488MLkb6a2F0mSJKkba9NE84Jmgubl8xPUkSRJkqRuLx6H92+Fq3eBz59qef2A8XDes3Dw7yAnP/n9SeqZBoyD85+HzQ9vfl3pXPjvQTDtnsSd+4XfwGePRtcnHgP7/DRx55N6ug9ua/x43IHNr1/xRePHzQ3kCFOw0Z0iV8xs2/7qMIPmkiRJUnfSUA/v3RheKxwB4w9JbT+SJElSN9amieZZvaD3wPADlRk0lyRJktQKpXPh1mPgkUthTVnza2OZsOf34cJXYcROqelPUs+WWwAn3gr7/QKIRa+rqwruxvDUT6E+/CL+Vvvgdnjtr9H1YTvA0f+CDGOSUkJ8/gzMeb3xtm1Pa36fimWNHzc3kCNMwZCNjre8bfurw7LS3YAkSZKkBPr8aSibF17b8RzIyExtP5IkSVI3VtKWieYQvImy8RsrAGULEtiVJEmSpG6noQHe+y8892uoWd3y+k22gqOugqHbJb01SWokIwP2+gEM2QbuPw+qm7ko5q1rYNE0OOEm6BNxcX5zvnodHv12dL1wBJx8J2Tntf3YUgrNnTuXTz75pE37DBw4kEGDBiWpowiVK+Gx7zTetvnhMHyH5verqWj8OLuNd1jZeH1DLdStCQZ7KCUMmkuSJEndyYe3h2/PzIHtz0ptL5IkSVI3Vxox0bw4bKI5QOFwWPRh0+3lTjSXJEmSFGHFLHjkcpjzWstrM7Jhrx/CHt+FrIgLYCUpFcYdCN98Ce46HZY2E6Cd8xpctzecdGswfby1VsyCu08LAqdhcvrAKXdB303a1LaUDpdffnmb9/nVr37Fr3/968Q3E6WhAR74JpRvMDCjVyEc+oeW9904aJ6V27Zzh10sUlNh0DyFvCeEJEmS1F1UrgwmmoeZeAz0HpDafiRJkqRuriQiaF4UNdG8cHj4dieaS5IkSdpYQz28eTX8a/fWhcyHbAsXvgz7/NiQuaTOod8YOP9ZmHhs8+vKF8ANh8L7t7buuFUlcMdJwecwsQw4/gYYvFXb+pUU7dlfwMxnG2874v+iX+/cUF1148eZEUM6omSGBMprq9p2DHWIE80lSZKk7uKTB6Ov2t/+zNT2IkmSJPUApRXh338XRwXNC4aFby83aC5JkiRpA8tmwMPfgvnvtrw2sxfs+1PY9TLINAYkqZPJ6R2EvoduB8/9CuIN4evq18Ajl8LCD+CQK6MvmKmvhXvOghVfRJ/zoN/B+IM73rukwFvXwptXNd62+7dhq+Nat//GE8zrIzINUerXtHxMJZXfYUqSJEndxbS7w7cXjoCRu6W2F0mSJKmbq61vYNWautBacX7EVJ7CiKB52XyIxyEWS1B3kiRJkrqk+jp44+/w0pVQH34HpUaGT4ajroaB45PfmyS1VywGu18OQybBvedA1crote/9F5Z8DCfeAn0HN67F4/DED+DLl6P33/Fc2OXixPQtpcg//vEP9ttvvzbtM3DgwCR1s5Fp98JTP2m8bdvT4IArWn+MnN6NH2884bwlYdPLNz6mksqguSRJktQdrJwN894Or006ETIyUtuPJEmS1M2VVkZP3imKmmheOCJ8e21lcLvn/H4J6EySJElSl7T4Y3j4Elg0teW1WXmw/y9h5wshIzP5vUlSIozZBy58Ge46DRZPi1437234995B2Hzkzuu3v3UNTLmpmePvC4f+0Qv51eWMHDmSiRMnpruNpmY8BQ9dBMTXb9viCDjyn237e7ZxKLy2sm19bLw+IwuynWieSqZNJEmSpO5g2j3RtUknpa4PSZIkqYcorYyeLljcO2KieUHERHOA8gUd7EiSJElSl1RXAy/+L1y3d+tC5pvuAZe8AbteYshcUtdTNBLOewa2OaX5dasXw03fgHf/G0wyn/EkPP3/otcPGA8n3ASZEa/JSGqbL1+Be8+Chg3u6DhmXzjuhrZ//9F7o+nrbX0dtHxR48f5A9q2vzrMieaSJElSVxePw9S7wmtDtoWBE1LajiRJktQTlDQz0bw4aqJ538EQy4R4fdNa2QIYvHWCupMkSZLUJSz8AB76Fiz9pOW1OX3gwCtgh3O9i6mkri07D47+FwzdDp7+WeMg64YaauHx78FXr8Lnz9BoqvKG8vrBqXdDXlGyOpZ6lvnvwZ2nQF31+m0jdoaTb4esiNc9m9N/XOPHZfPbtn/ZvMaPB4wLX6ekMWguSZIkdXXz34OSL8Nr25yc2l4kSZKkHqIkYqJ5bnYGudkRU30yMqHvECgPeTMlbJskSZKk7qmuBl76Pbz+j/ALUTc2dj844u/BJGBJ6g5iMdj5Qthkq2BqcsWy6LWfPBhdy8gOwq/9xiS+R6knWvwx3HYc1Kxev23wJDjtXsjp3b5jbhwMXzajbfsv/3yj441vXx9qNy9xlCRJkrq6aRHTzGOZsNXxqe1FkiRJ6iFKI4LmkdPM1yocFr69rZN8JEmSJHVNNRVwxwnw2v+1HDLvVQhHXQ2nP2DIXFL3NGp3+ObLMGyH9u1/5D9h090S25PUUy3/Am49GqpL128bMAHOeBByC9t/3MGTGj9e8D7UR9zJIMzctzY6nneFTDWD5pIkSVJXVlcDH98fXttsf+gzMLX9SJIkST1ESWVt6PaiFoPmw8O3ly3oYEeSJEmSOr2qUrjlaJj9Ustrxx8K33obtjs9mPwrSd1V4TA450nY/sy27bfn92HbU5LTk9TTlM6FW45qfHeBok3hzIeg94COHXvgeCgevf5xbQXMe7t1+9ZUwPx3N9gQg/GHdKwftZlBc0mSJKkrm/ksVJWE1yadlNpeJEmSpB6kJHKieXbzOxZETDQvN2guSZIkdWurl8FNh8P8d5pfl1cMx14Pp9wJBUNS05skpVtWr2A6+eF/g4wWXlsB2PIo2PfnSW9L6hFWLYabj2z8+mTfoXDWI1AwNDHnmHBY48cf3Nq6/T5+AGpWr388dDu/P0oDg+aSJElSVzb1rvDtOX2b/rAmSZIkKWFKK8Inmhe3e6L5/A52JEmSJKnTKpsPNx4CSz5qft2WR8O33oFJJzjFXFLPtOM5cM4T0LeZIOnQ7eHoayHD6KPUYZUrg7utlHy5flv+gGCSefGoxJ1nu9OBDb63+fh+WDaj+X1qq+G1/2u8bfszEteTWs1/bSVJkqSuqqoUPn8qvLblkZCTn9J2JEmSpJ4kaqJ5Ubsnmi+EhoYOdiVJkiSp01kxC244BFbMjF6T1w9OvAVOvBn6DEpdb5LUGY2YDN98GUbu2rRWMCy444Pvg0odt2YV3HYcLPts/bbcQjjjQRg4IbHn2mRLmHjM+sf1NfDgRVBdHr4+HoenfgIrZ63fVjwKtjNong5Z6W5AkiRJUjt9+lDwA1iYSSeltBVJkiSppymtbO9E84igeUMtVCyFvoM72JkkSZKkTmPxx3DrMcH3+lEKhsGZD8OAcanrS5I6u76bwJmPwBt/h7evg9pK2HR3OOLvvnYiJcqdp8DC9xtv2/VSqFwBs15s27GGbgt5xc2v2e/nwSC92srg8cL34cbD4JD/hdF7rl+3fCY8/2v47NHG+x/wa8hsYciHksKguSRJktRVTb07fHvBMBi1Z3hNkiRJUkK0e6J54YjoWtkC3yyVJEmSuov578Ftx0J1WfSafmOCkHnRyNT1JUldRVYO7PVD2OP7kJGR7m6k7uerV5tue/F37TvWWY81DouH6T8Wjvwn3H8+EA+2LfkIbj4c8gdA4XCoWA7lC9bX15p8YeOJ6Eopg+aSJElSV1QyB+a+EV7b+gRfbJEkSZKSrKS9E83z+0NWLtRVN62Vzwd26HhzkiRJktLry1fgjpOhtiJ6zaAt4YyHgqm9kqRovu8pdR9bHx98fvhSqKtav71yefARZrfL4MDfJL83RfJfYUmSJKkrmnZPdG2bk1PXhyRJktQDxeNxSiMmmhf3bmGieSwGBUPDa2ULOtiZJEmSpLSb8STcdnzzIfNhO8DZjxsylyRJPc/Wx8Mlb349QK+Z11I33T34fumg3wavqSptnGguSZIkdTXxOEy7K7w2eGsYtEVq+5EkSZJ6mNVr6qhriIfWilqaaA5QMAxWzm66vdyguSRJktSlTbsXHrwQ4vXRa0btCafcCb36pq4vSZKkjf26LH3n7jcajrsevvFXmPsWrJwFa1ZDVi8oHA4jd4ke1qGUM2guSZIkdTUL34cVM8Nrk5xmLkmSJCVbaWVtZK24NUHzwuHh28vmtbMjSZIkSWn33g3w2PeA8ItSARh/KJxwE2TnpqorSZKkziu3AMYflO4u1IKMdDcgSZIkqY2m3h2+PZYR3GZKkiRJUlKVVNZE1orzm7nd61qRQXMnmkuSJEld0ut/h8e+S7Mh862Oh5NuNWQuSZKkLsWJ5pIkSVJXUl8LH98fXhuzL/QdnNp+JEmSpB6oJGKieUYMCnJbETQvGBa+vdyguSRJktSlxOPwwm/h1T83v26Hs+Ebf4WMzJS0JUmSJCWKQXNJkiSpK5n5PFQuD69tc3Jqe5EkSZJ6qNKIieaFedlkZMRaPkDURPNVi4OLSzNbEVaXJEmSlF4NDfDUT+Cdfze/brfL4cD/gVgrflaQJEmSOpmMdDcgSZIkqQ2m3RW+Pbs3bP6N1PYiSZIk9VAlFeFB8+L8nNYdIGqiOXEoX9i+piRJkiSlTn0dPPytlkPm+/3ckLkkSZK6NCeaS5IkSV1FdRnMeDK8tsURkNM7tf1IkiRJPVRJZW3o9qL8Vk4iL4wKmgPlC6B403Z0JUmStJGGBpj1Asx8FipXwriDYOvjDbxKHVW3Bu4/Dz57tPl1h/4Rdr4wNT1JkiRJSWLQXJIkSeoqPn0E6qrDa9uclNpeJEmSpB6stLKDE81zC6FXAawpb1orW9CBziRJkoBVS+CDW+H9m6F07vrtH90D89+Bw/6Uvt6krq6mAu4+PbiII0osA466GrY9NXV9SZIkSUli0FySJEnqKqbdHb697xAYvXdqe5EkSZJ6sOiJ5q0MmgMUDINlIUHz8vnt7EqSJPVoDQ3w5csw5UaY/jg01IWve+c6GLkLbHVcavuTuoPqMrj9RJj3VvSajGw4/gbY8sjU9SVJkiQlkUFzSZIkqSsonQdfvRpe2/p4yMhMbT+SJElSD1YSOdE8u/UHKRwGyz5rut2J5pIkqS0qlsOHt8OUm2Dl7Nbt8/j3YdM9oO8mSW1N6lYqlsOtx8DiadFrsvLg5NtgswNS15ckSZKUZAbNJUmSpK7go3uia5NOTl0fkiRJkiiNmGhe3LuNE83DlDnRXJIktSAehzmvw3s3wGePQn34RXCRqkrg0cvhlLsgFktOj1J3UrYAbj0aln8evaZXAZx6D2y6a8rakiRJklLBoLkkSZLU2cXjMPXu8NqgiTB4q9T2I0mSJPVwURPNi9o00Xx4+PZyg+aSJClC5UqYemcwvby5wGtrfP4UfHgHbHdaQlqTuq0Vs+CWo6FsbvSa/P5w+gMwdNtUdSVJkiSljEFzSZIkqbNbNBWWzwivbXNSanuRJEmSFD3RPL8NE82jguZlC9rRkSRJ6rbicZj3Nrx3I3zyINSvSdyxn/oJjN4LikYk7phSd7Lk02CS+eol0Wv6DoUzH4KBE1LVlSRJkpRSBs0lSZKkzm5axDRzYrD1CSltRZIkSerpauoaWL2mLrTWponmBcPCt1ethJpKyMlvR3eSJKnbqCqFaffAlBth6aftO0bxaNjhbPj4flg8rWl9TTk8cimc/iBkZHSkW6n7WTAFbjsOqkqi1xSPhjMfhuJNU9eXJEmSlGIGzSVJkqTOrL4OProvvDZmbygYmtp+JEmSpB6utKomspaQieYA5QthwGZt6EqSJHUL8XgQbn3vxiAcXlfV9mNkZMHm34AdzoHRewcB8nEHwnX7QH3I9zGzX4L3/guTL+ho91L38eWrcOfJULM6es3ALYJJ5n0Hp6wtSZIkKR0MmkuSJEmd2ewXoWJpeG3SyantRZIkSRKllbWRtTYFzZu7aLRsnkFzSZJ6kjWr1k8vX/xR+45ROBJ2OAu2OwP6btK4tslE2Pdn8Nyvw/d99pcwdj/oP7Z955a6k8+fhnvOhLrq6DVDt4fT74f8fqnrS5IkSUoTg+aSJElSZzb1rvDt2fmwxRGp7UWSJEkSJRXRE82L8rNbf6DsPMjvD5UrmtbKF7SjM0mS1OUs/DAIl390X/OTk6PEMmD8obDjOUFQPCMzeu1ul8P0x2H+u01rtZXw0CVwzhPNH0Pq7j6+Hx74JjTURa/ZdA845U7ILUhdX5IkSVIaGTSXJEmSOqs1q4I3f8Js/g3o1Se1/UiSJEmiJGKieV52JrnZbQxmFQ4PD5qXGTSXJKnbqqkIwqzv3QALP2jfMQqGwfZnBtPLC4e1bp+MTDj6Wrh2D6iralqf9xa8eTXsfnn7epK6uik3waPfAeLRa8YdDCfeHFw0KkmSJPUQBs0lSZKkzuqzR8Pf9AGYdHJqe5EkSZIEQGll+ETz4rZMM1+rYDgsmtp0e/n8th9LkiR1bos/DqaXT7sH1pS34wAxGHcg7HgubHYgZLbjrf4Bm8GBV8CTPwqvv/Cb4ByDtmhHf1IX9sZV8Mz/a37NxGPhmH9DVk5qepIkSZI6CYPmkiRJUmc19a7w7b0HwZh9UtqKJEmSpEDURPOi/HYETqImkDrRXJKk7qG2Cj55EN67Eea/075j9NkkmF6+/ZlQNLLjPe10QTDg4qtXm9bqa+DBi+D85yCzHRfRSV1NPA4v/S+8/Ifm121/Jhz+t+DOAJIkSVIPY9BckiRJ6ozKFsCXr4TXtj6hfROLJEmSJHVY5ETz3u2ZaB4VNHeiuSRJXVrlSnjnOnj731C1sn3HGLsf7HAOTDg0saHvjAw46mr41+5Qs6ppfdGH8OpfYZ8fJ+6cUmfU0ABP/wze/lfz63a9FA76LcRiqelLkiRJ6mRMp0iSJEmd0Uf3AvHw2jYnpbQVSZIkSeuVRATN2zfRfHj49vIFwXRFwyySJHUtpXPhzavh/VugtrLt++cPgO1Ohx3Ogn5jEt/fWsWbwiG/h0cuC6+/8kcYfzAM3TZ5PUjpUFUKyz+HZdPh86dh+mPNr9/3/8FeP/T7ckmSJPVoBs0lSZKkziYeh2l3h9cGbgGDJ6W2H0mSJEnrlFTWhm4vzm/HpNGooHnNaqgug7yith9TkiSl3uKP4fW/w8f3Q7y+7fuP2hN2PAc2PwKy2nHxWntsdwZ89ih88UzTWkMdPHgRXPgyZPVKTT9SosTjULEMls0IAuVrg+XLPofVi1t/nEOuhF0uTl6fkiRJUhdh0FySJEndR00lLP0UcgthwLh0d9N+Sz4OnkeYSSc6PUWSJElKo9KIiebF7ZloXjAsula+wKC5JEmdWTwOc96A1/4PZj7b9v3zimHb02CHs9PzWmYsBkf8A67ZBapLm9aXfQYv/h4OvCLlrSmJqsvglT/DjCehvgb6DoGCIcHnvkOgYOjXXw8Ovs7OS3fH0eJxKJsPy2d8HSr/+mP5DKgqaf9xYxnB343tz0hcr5IkSVIXZtBckiRJ3cNXr8N9566fSDJmXzjxFsgtSG9f7TH1rohCLAiaS5IkSUqbqInmRe0JmvcdEgRZ4g1Na2ULYJOJbT+mJElKroYGmPEEvP43mP9u2/cfuSvseC5scSRk5ya8vTYpGALf+Avcf154/Y1/wITDYOTOqe1LybHwQ7j3LCj5av220jnN75NbtEEYfejXnwdv8PUQ6D0QMjKT13dDfdDzxhPKl38R3AkokTKy4bj/wMRjEntcSZIkqQszaC5JkqSur7YK7j+/8W0vZ78ID1wAp9zVtSaAN9TDR/eF10btAYXDU9uPJEmSpEaiJ5pnt/1gmVnQZzCsWti0Vjav7ceTJEnJU7cGpt0Nr/8DVnzRtn1z+sK2p8KO58CgLZLTX3ttdRx89gh8+nDTWrwBHroILnoNcnqnvjclRjwO714PT/8smGLeFtWlwceyz6LXxDK/Dp9vMAl93YT0DQLqvfo2f666NbBiVtMJ5StmQv2atvXdHll5cNJtMO6A5J9LkiRJ6kIMmkuSJKnr+/yp8GDG50/BB7d1rVtczn6pcWB+Q9ucnNJWJEmSJDUWj8cpjZhoXtyeieYQXEwa9vNM+YL2HU+SJCVWdTlMuRHe+hesWtS2fXsPgl0uDiaY5xUlpb0Oi8XgG3+FOW9AxbKm9ZWz4bkr4LA/pr43dVx1OTz6bfjkgeSdI14ffO/a0vevOX02CJ8PWX93n7UTyld+GRwrHfKK4aTbYdTu6Tm/JEmS1IkZNJckSVLXN+uF6NpTP4Uxe0PRyNT10xHT7g7fnpUb3E5XkiRJUtqsWlNHXUM8tFbUnonmAIXDYH7I9jKD5pIkpdWqJfD2v+DdG2BNWdv27TcGdrsMtjkVsnOT018i9R4Ah/8N7j4tvP7Ov2HzbwSvs6rrWDQN7j0bVs5KdyeBmtXB3QDaekeAZMjpCwPHw8DNYfAk2OpY6DMo3V1JkiRJnZJBc0mSJHVt8TjMejG6XrMKHv4WnPEwZGSkrq/2WLMaPns0vDbhMMgtSG0/kiRJkhoprQifZg4dmGheMCx8uxPNJUlKjxWz4I1/wod3QP2atu07ZFvY4zvBwIiMzGR0lzxbHA7bnAJT7wyvP/wtuPgNX6PsCuJxmHITPPnjtv8Z7m7y+gVh8oET1n8MmAAFQ4Np/pIkSZJaZNBckiRJXduKmVA2r/k1X74C714PO38zNT211/THobYyvLbNyantRZIkSVITJZU1kbV2B80Lh4dvLwsbcy5JkpJmwfvw+t/g00eA8DuYRBq7H+z+HRi9V9cOrx5yJcx+GVYtbFormwfP/D848p+p70utt2Y1PPYd+OjeltducSQMGAfli4Lf81WLg6/bOsG/M+g7dP2E8gHj14fLew9Id2eSJElSl2fQXJIkSV3brBdat+7ZXwZv+AzYLLn9dMS0u8K35w8IepckSZKUVlFB84wY9M1t58vtzU00b2jo/HdmkiSpK4vHg9cXX/9bMKyiLWIZMPEY2P3bMGSbpLSXcnlFcNRVcNux4fX3b4HNj4DxB6W0LbXSkk/gnrNgxRfNr8vsBYdeCTucE35hRE3F1+Hzrz/Kvw6hr1q4wfbF0BB9t5/kiEHxpsFE8nUTyjcPwvK5hSnuRZIkSeo5DJpLkiSpa2tt0LyuCh66CM59unPetnbVYpj9Unht6+MhMzul7UiSJElqqrQyPExTlJ9DRkY7p5dGTTSvr4HK5dBnUPuOK0mSotXXwWcPw2t/g8XT2rZvVi5sdzrsein0G52U9tJqs/1hx3PhvRvC649cBpe8Cfn9UtuXosXj8MFt8MQPg9fBm9NvDJxwU/MXR+T0Dga2NDe0paEBKldsMAl94QbB9A0C6lUr2/58MrKg39gNJpR/HSofMA6y89p+PEmSJEkdYtBckiRJXVddDXz5auvXz38X3vgH7PHd5PXUXh/dC/GG8Nqkk1LbiyRJkqRQURPNi/I7cGFoVNAcoGy+QXNJkhKptioI5L55FZR81bZ9c4tg8gUw+ULoMzAZ3XUeB/4GZj4PpXOa1lYvhid/BMddn/q+1FRNBTz+fZh6Z8trtzwajvwn5BZ0/LwZGcHfgz4Dmw+t11YHf2ZCJ6QvCi6uLNo0CJSvDZb3G+PgFUmSJKkTMWguSZKkrmve21Bb0bZ9Xvw9jDsINpmYnJ7aa+rd4dsHjIeh26W2F0mSJEmhSiImmhfn57T/oPkDIDMnCNlsrHwBDNu+/ceWJEmBqhJ453p4+9rgjiFtUTAMdv0WbH8W9OqTnP46m1594Oh/wU3fAOJN6x/dC5sfDhOPTnVn2tDS6XDvWbBsevPrMnPg4N/DTudDrJ134Wmv7FwoHhV8SJIkSeqSDJpLkiSp65r1Qvj2XgUwdFv48pWmtfoaePBCOP8FyOpAGCSRlnwCSz4Kr006MfUv/kuSJEkKVRox0by4IxPNMzKgYGj4VNWy+e0/riRJCv4vffMamHJT2wdWDNwcdv82bHV853kdMZVG7R4E7N+8Krz++Pdg0928+0q6fHhn8HtQW9n8uqJN4cSbHWYiSZIkqd0y0t2AJEmS1G5RQfPRe8FR1wSB8zCLP4JX/pS8vtpqWsQ0c4CtT0xdH5IkSZKaFTXRvKgjE80BCoaHbzdoLklS28TjsGIWvPtfuOs0+Ps28NbVbQuZj9gFTrkLLn4Ttj21Z4bM19rv58EdF8NUroDHvhv8mit1airh4W/BQxe1HDLf/HC48BVD5pIkSZI6xInmkiRJ6poqlsOiqeG1sftC0Qg45Ep4+JLwNa/+BSYcAsN2SF6PrdFQD9PuDa9tujsUb5rafiRJkiRFSspEc4DCiKB5+YKOHVeSpJ6gYgV8+RLMfglmvQRlc9t3nPGHwh7fgZG7JK63ri47D46+Fv57IMTrm9anPxYM0djm5NT31hMt+xzuPQuWftr8uoxsOOg3sPNF3i1TkiRJUocZNJckSVLXNPslIGJaztj9g8/bnhq82THjiaZr4vXw4EXBRJfsvGR12bKvXoVVC8Nrk05KbS+SJEmSmlUSETTv8ETzwmHh28sMmkuS1ERtFcx9E2a9GLxGuHha+4+VkRXcUXD3y2HQFglrsVsZvgPs+b3oO0Q+8SMYtWf09zNKjGn3wqPfbnk6f+FIOOGm4PdNkiRJkhLAoLkkSZK6plkvhm8vHg39Rgdfx2Jw+N9g7ltQtbLp2uWfwwu/hYN/l7Q2WzT17vDtmb1gy6NS24skSZKkZpVU1IZuL+5o0LwgIpjlRHNJkoI7Ai6aGoTKZ78UvNZXv6Zjx8zuDTucDbteEn1nEa23149gxlOw5KOmtTVl8MilcPoDTs9OhtoqeOonMOWmltdOOAyOuhry+yW9LUmSJEk9h0FzSZIkdT3xOMx6Ibw2dr/Gj/tuAof/Fe49O3z9m1cHL8CP2j2hLbZKTSV89kh4bcKhkFeU0nYkSZIkNa80YqJ5cX52xw4cFXBbtQjq6yDTl/IlST3Myi9h9tcTy798BapKEnPc/AGw80Ww03mGcdsiKweOuRau2wcaQi68m/UCTLkRdjw35a11aytmwT1nhQf8N5SRBQf8Gna91LC/JEmSpITz1WlJkiR1Pcumw6qF4bWNg+YAE4+Bzx6Dj+8L2SEOD10MF78OvfomtM0WzXgCalaH1yadlNpeJEmSJDWrpq6Bipr60FpRsiaaxxuCsHnRiI4dX5Kkzq5yJXz5chAsn/UilM5J7PGLR8Ful8G2p0F2XmKP3VMM3gr2/Sk8/z/h9ad/DmP2XX+3SXXMxw/AI5dDzarm1xUMhxNuhBGTU9OXJEmSpB7HoLkkSZK6nqhp5rFMGL1neO2wP8FXr8LqJU1rpXPgmV/AEX9LWIutMvWu8O15/WCzA1LbiyRJkqRmRU0zByjunaSJ5gDlCwyaS5K6n9pqmPdWECqf/RIsmgrEE3iCGAzdFsbsEwym2HR3yMhM4PF7qN2+DdOfgAXvNa3VVsBDl8DZj0NGRup76y7q1sDTP4N3r2957biD4Jh/O51fkiRJUlIZNJckSVLXExU0H74T5BaG1/L7wZFXwR0nhNen3AhbHJ66gPfqpdHPY6vjgtvRSpIkSeo0SiprI2vFHZ1onlsIOX3C73hUNr9jx5YkqTNoaIDF04JQ+ewXYe5bUFed2HMUjwomao/ZB0bvZfg2GTKz4Jhr4do9wn//5r4Bb/8Ldv1W6nvrDlbOhnvP/vrCi2bEMmH/XwTBf0P9kiRJkpLMoLkkSZK6ltpq+Or18NrY/Zrfd/xBsP2Z8P4t4fWHL4NL3oC84o712Bof3Qfx+vDaNicn//ySJEmS2qSkmYnmRfkdnGgei0HBMFg+o2mtfEHHji1JUjo01EPJV8EdBme/BLNfhqqViT1HXjGM3jsIlo/ZB/qNTuzxFW7AONj/V/D0T8Prz10RDPMYOCG1fXV1nz4MD18Ka8qbX9d3CBx/I2y6a2r6kiRJktTjGTSXJElS1zLvLairCq+1FDQHOOh3MOslKJvbtLZqITz5Yzj2ug612CrT7grf3m8sDNsh+eeXJEmS1CalEUHz/JxMemVldvwEhRFBcyeaS5I6s6oSWD4TVnwBy7/4+vPMYDJz/ZrEniuzF4zcBcZ+PbV88DZOc06XnS+C6Y/DnNea1urXwIMXwXnPBhPQ1by6Gnj2F/D2tS2vHbt/8Np17wHJ70uSJEmSvuZPdpIkSepaZr0Qvj23EIZu1/L+uQVw9NVw8xHh9Wl3w+aHw5ZHtr/HliydHn3700knBdMMJUmSJHUqJZW1oduL83MSc4KCYeHby5xoLklKs/paKJmzPky+/HNYMTP4unJ5Ek8cgyGTvp5Yvm8QMs/OS+L51GoZGcFrrNfsBrUVTesL34fX/w/2+mHqe+tKSubAvWcHv17NiWXAvj+DPb7vxRWSJEmSUs6guSRJkrqWqKD5mH1aPyFn9F6w88Xw9r/C6499F0buCn0GtqvFFk27O7o26cTknFOSJElSh5RETDQvys9OzAkKR4RvL3eiuSQpBeJxqFyxwVTyL9aHyUu+hIa61PRRNDIIlY/ZB0bvDb37p+a8arviUXDw7+Cx74TXX/oDjDs4uFhATU1/HB66GKrLml/XZxM47r8wes/U9CVJkiRJGzFoLkmSpK5j9VJY/FF4bex+bTvW/r+Emc8Gb5htrHJ58AbJSbclfrp4QwN8dG94bcQu0G90Ys8nSZIkKSFKkz3RvLALTDRvqIfF06BXAfQb492YJKkrqlsDK79sGiZf/jlUl6a+n9yiYCjEmH1g7L5QPNr/X7qSHc6Gzx6FWc83rTXUBkHqC16ArF4pb63Tqq+F534Nb17V8trRe8Nx10OfQUlvS5IkSZKiGDSXJElS1zH7pejamH3bdqycfDj6WrjhIIg3NK1Pfwym3QPbnNS247ZkzutQNi+8luhzSZIkSUqYkookTzQviAiaVy6H2irIzkvMedpr0TS482Qo/zr4PnY/OOa65N0JSpK6opWz4cM7YMH7Qcg2IxsysyEja/3njOzgrnxNamGPN1zX0n6ZjWsZmVA2PwiQL5+5PlheOif8tbBUycyBkbsEwfIx+8CQbYNe1TXFYnDUVXDNLuGTuZd8DC//IRj60RNVlULp3OD14NJ5wecvXwku3GtWDPb5Cez1Q/9+SJIkSUo7g+aSJEnqOmaGTMYB6L8ZFG/a9uON2Al2/w689tfw+hM/hFF7RE8WbI9pd4Vvz8yBicck7jySJEmSEqok6RPNh0fXyhdC/7GJOU97rFoMtxwFVSvXb5v1Atx6DJz9GOQVpa01SeoUSufCy38MQubx+nR30/lssjWM3ScYFDFy12AAhLqPgqFw6J/gwW+G11/7P5hwGAzfMbV9JVs8HtyBs2xe0zD52s9rytt+3N4DgynmY/ZJeMuSJEmS1B4GzSVJktQ1xONBkCHM2P3af9x9fgJfPBNM19nYmjJ45DI4/f7E3LK3tgo+fSS8Nv5gyCvu+DkkSZIkJUVpZfhE8+JkTzSHYCJtuoLmDQ3w0MWNQ+ZrLfkomHJ++gOGBiX1TOUL4dW/wJSbgwnmPVl2bxiwGfQfBwPGBYMh1n7O6Z3u7pRsk06Ezx4J7hK5sXgDPHghXPhq1/p+ob4OVi3cKDw+t/Hj+jWJPeeoPYOQed/BiT2uJEmSJHWAQXNJkiR1DUs+gYql4bWOBM2zesEx18J1+4a/ITjreZhyI+x4bvvPsdaMJ6On2Ew6qePHlyRJkpQ0JRFB86JETTTPyYe8fuGB7vIFiTlHe7x9bfRFvwBz34R7z4aTb4fMBIXuJamzW700mNL87n8THzTt1GJQNCIkTD4umGqdiEEN6ppiMTj8b8H3BZUrmtZXzIQXfgOH/G/KW4tUWx1czLdxeHzthPLyhSm8Q0EM9voB7P0TyDTCIUmSJKlz8acUSZIkdQ1RwYaMLBi1R8eOPXhr2OfH8MJvw+tP/zy4tW+/0R07z7S7w7fnFsG4gzp2bEmSJElJVVoZPqm2uHcCw9WFw8KD5mVpCpov/hie+1XL6754Gh7+Fhx9LWRkJL8vSUqXypXw+t/gnf9AbWW6u0meXgVfh8jHN55S3m8MZOeluzt1Vn0GBmHze84Ir791DUw4DEbvmdw+6mqgYhmsXhxcFLJ6SfB51eLg61WLgkB51FCTVMvvD8f+BzbbP92dSJIkSVIog+aSJEnqGqKC5iN2hl59O3783b8bTBxfMKVprbYCHroEzn68/aGJiuUw87nw2lbHBpPVJUmSJHVK8Xic0qrwoHnCJpoDFAyHxR813V4+P3HnaK3aKrj/fKgPn+TexLS7Ia8YDrnSibaSup+qUnjzanjrX1CzKt3dJEYsA4pHhU8n7zPIf8vVPlseCVufCB/dE15/+BK4+I22v54bj0N1Kaxasj44vnpJ4zD52lrYRXud1ag94djrgjsCSJIkSVInZdBckiRJnV9tFcx5I7w2dt/EnCMzK5i+9+89oa66aX3uG8HUnd0ubd/xP74fGurCa5NObt8xJUmSJKVEeXUd9Q3x0FpxIoPmhcPCt5elIWj+7K9g2Wdt2+fta4OpnHv/KDk9qbHKldBQD/n9ICMz3d1I3dOaVfDWtfDmP6G6rPX7FY+CLY8G4lBfBw21UF/79ecNH9dFb2+utnZ71GtNG8orbhomHzAeikdDVgL/D5PWOuyP8NWrweTwjZXOhWd+Dkf8PXhct6ZxcHzVRlPINwySt/bit84oIysIkxdtCoUjoGgEjDsYhm3vRR2SJEmSOj2D5pIkSer85rwB9WvCa2MTeEvRgeNh/1/B0z8Nrz//PzDuQBg4oe3HnnpX+PbiUTBictuPJ0mSJCllSiujg03F+dmJO1Hh8PDtZQsSd47W+PwZeOff0fXMXtE/o734uyDUOPmC5PQmqCqBp38eTJFvqIVNtoajroKh26a7M6n7qKmAd/4Dr/+9bdORC4bD3j+EbU+DzAT+/xAlHm8+kJ5bGFyMIqVSXjEc+U+4/fjw+pSbgtd7Vy8NppR3B1l5QXh8bYi8cAQUjVz/uO8QLwqTJEmS1GUZNJckSVLnN+uF8O15/WDINok9184XwfTHYc5rTWv1a+DBC+G854IJ6K21/AtY+H54bdJJTq2RJEmSOrmSytrIWlEiJ5oXRATNy1MYNF+9DB6+JLq+xRGw2+Vwy1FQWxm+5okfBiGzrSMCZmq/xR/D3adDyZfrty35CG46HM58CIbvmLbWpG6hthqm3Aiv/hUqlrZ+vz6DYa8fwPZnQlav5PW3sVgsCLSnItQutcW4A2H7s+D9m8Pryz9PbT8d1aswCIxvGB5f93kk9B7ga7ySJEmSui2D5pIkSer8ooLmY/ZJ/CSYjAw4+mr41+5Qs7ppfeEH8Npf23Yr+Gl3R9cmndT2HiVJkiSlVEnERPPMjBgFuQl8mb1wWPj2NeVQXQ65BYk7V5h4HB65FCqWhdf7DoEj/hFMxz3pVrjj5GBybtMDBRfp5hYGQTMlxrR74ZHLoK6qaa1mFdx6LJz1iJPNpfaoq4EPboFX/gKrFrZ+v/wBsOf3YMdzITsvef1JXdHBv4PZL0Lp3HR30rLeAzcKkY9sHCbPLUx3h5IkSZKUNgbNJUmS1LmVL4Kln4bXxu6XnHMWjwreCHn02+H1l/8A4w9u3TT1hobooPnwnaD/2Ha3KUmSJCk1SiOC5kV52cQSOb2yICJoDsFU82QHzd/7L3z+VHT9mGuDkDnAZgfAsf+G+84D4k3XNtTB3WfAmQ/DyJ2T0m6PUV8Lz/4S3rqm+XVryuDWo+Hsx2GTiSlpTery6utg6p3w8h+hrA1h2Lzi4O4Ok78Jvfokrz+pK+vVF466Bm4+PH09ZGRBn002+BgUfC4c9nWIfCQUDvdCEUmSJElqhkFzSZIkdW6zX4yujd03eefd/iz47DGY+WzTWkMdPHgRfPOllm+HPO+t6Kk9TjOXJEmSuoSSirCp3VCUn53YExUMBWKEBrfL5sOgLRJ7vg0tmwFP/7/o+m6XBXeV2tBWx0FVCTz+/fB96qrgjhPg7Cdg8FYJa7VHWbUE7jsH5rzeuvVVJXDzkXDOEzBwQnJ7k7qyhnr46D54+UpYObv1+/UqgF0vhV0uTv7FP1J3MHpP2PliePtfiT1ubhH0Hbw+OL5xmLzv4ODr3KLgDpaSJEmSpHYzaC5JkqTObdYL4dsHTAimzSRLLAZH/hOu2QWqS5vWl34KL/0vHPDr5o8z9a7w7RnZQShDkiRJUqcXNdG8OD8nsSfKzA6CUasWNa2VzU/suTZUtwbuPw/qqsPrg7eG/X4RXtvpfKgsgRd/G16vLoPbjoVzn4Z+oxPTb08x7x2458zwPw/NqVy+PmzuXbSkxhoa4NOH4KUrYfmM1u+X3Rt2uSgIma+9s4Ok1tn/l7BiZvhAjw1l5kCfDcLjfTeaQr6uNqjl4R+SJEmSpIQxaC5JkqTOq6EBZkVMNB+7X/LPXzAEDvszPHB+eP31v8P4Q6NvA19bDZ88FF4bd5BvTEqSJEldREll1ETzBAfNAQqGhQeLyxck/lxrvfAbWPxReC0rF477b/OBrr1+AFUr4a1rwuurl8CtRwdh876DO9xutxePw3v/hSd/Ag3hf/ZatHrx+rB58aaJ7U/qiuJxmPEEvPh7WPJx6/fLyoPJ58Pu34HeA5LWntSt5eTDKXfBx/fDko+A2Prp4xuGyXOLguEfkiRJkqROxaC5JEmSOq8lHwWT2MKkImgOsPXx8NkjwcfG4g3w0EVw0WuQ07tp/YunYU1Z+HEnnZjYPiVJkiQlTUnkRPPsxJ+scBgseK/p9rIkBc1nvQhv/DO6fvDvYOCE5o8Ri8FBv4PKlTAt4q5OJV/BrcfCOY9DXnG72+32aqvg8e/Dh7c3vy6WAfv9HOa+BV88E76mfD7cfEQQNk/mHcHSbelnwVT+fqMhtzDd3aizicdh5nPw4u9g4Qet3y8zB3Y8F/b4XhCEldQxmVmwzUnASenuRJIkSZLURgbNJUmS1HnNeiF8e2YOjNo9NT3EYnD4/8HcN6FiWdP6ytnw3BVw2B+b1qbeHX7MXoUw/pDE9ilJkiQpaUojJpoX907GRPOIQHDZvMSfq3IlPHRxdH38IbDjea07VkYGHHUVVJfB50+Gr1n6CdxxMpzxYDDdVI2VzIF7zoBFU5tfl9cPjr8Bxu4b3EnrzpNg9kvha0vnrJ9s3t2myZfOhYe/BV++EjzOyA5+TbY8CiYc5l3EBLNfhhd+C/Pfaf0+GVmw3RnBnRq68wUakiRJkiRJrZSR7gYkSZKkSFFB85G7hE8QT5beA+Dwv0XX3/l30zf1K1dGT5WbeDRk5yaoOUmSJEnJFjXRvChZE83DlCd4onk8Do9cBqsWhdd7D4Qjrwouvm2tzGw44UbYtJkLg+e9BfecCfXh4f0ea9YLcN3eLYfMh2wLF74cBKoh+Nny5Dub/zVfOSsIm68OuXi6q/rqNbhun/Uhc4CG2uDn8Ie/BX8eF0zQn3IzVKxIW5tKkzlvwk2Hwy1Htj5kHsuEbU+Hy6bAEX8zZC5JkiRJkvQ1g+aSJEnqnGoqgluAhxm7X2p7AdjicNjmlOj6w5cGk/vW+vj+4E3uMNucnNjeJEmSJCVV5ETz/CRMNI8KN5YvDMLhifLBrTD9sej60f+CPgPbftzsPDjlThi8dfSamc8Gk9QbGtp+/O4mHodX/wq3HQdVJc2v3fZ0OPdpKBrZeHtOPpx6NwyfHL3v8hlwy1HBRdFdWTwO717/9XNpJkDeUAeznodHLw9C57ccBf+fvfsOj6M81z9+b1XvxbJkuUjuFTAGU1xpppheEwIpEFI5ScgJJ8lJIeUckt+BJJAGCSEhCR1CjU0zNr0Z3LskN1lWsXrfMr8/VpYleVbSSruzKt/Pde0l7fu8M/OsMaBy7zMfPTiywvbozjACb9j4+2XSgyukvW/280CbNOdq6WsfSpf+TkqbGMkuAQAAAAAAhh2C5gAAABia9r4t+cynBkYlaC5JK+6UkoNMF6w7IL30vWPPNz1mvi91vJS/MPy9AQAAAIiYYBPN0yIx0Tw5SNDc29p7sDYUR4qkVf8VvH7KLdKUcwZ+/tgU6fqnpfSC4Hs2PyGtvj284fnhprVeeux66bU7JKOX0L3dJV30K+mS3wa/O1ZMknT9k1LuicHPU7FV+vulUkvtYLqOHm+79MI3pBdvCwTJ+8vwBe5C9sI3pLumBiZdf/hnqaE8Qo3CUm0N0gd/kn53SiBkHuzueGZmXip95T3pij9JGYURaxEAAAAAAGA4I2gOAACAoSnYLwbjM6UxvUzGi6S4VOnie4PXP/mHtHNVILRx8EPzPXOvkex8GQ4AAAAMF21en5rbfaa11IhMNA/y5lZJqjs4+PP7PNJTN0meJvN61gzpnDsGf53EbOkzz0hJY4Pv+eB+ae2dg7/WcFSxQ/rT8t6nyktSUq70uVXSyZ+XbLbe9x4N+Pf2PXPZxsD09LaG0HuOpsYK6W8rpfV/Hdx5DH9g0vWLt0l3TZMevEB6/36pviwsbcJCVXukVbdLd82Q/v1tqWpX/4+ddoF0y5vS1X+TsqdHrkcAAAAAAIARgIQLAAAAhqZgQfPCZdENak8+Szr5C8Hrz90qvf/H4PW514S/JwAAAAARU9vsCVpLi0TQPCE7MMHaTDiC5mvvlA59bF5zuKUr/iy54gZ/HUlKmyB95l9SXFrwPevulN6/LzzXGy62PSv9+SzpyO7e9004U7plnZS/oP/njk+XbnhGyuolPFv6kfTPq6X2IG82GGpKP5buXyodeC/MJzakfW9Lq/5TunuG9MB50nt/CM+/Z4gMv1/a9XLgzRK/nR/4+Ut7CG+amHy2dPMa6bpHpLFzI9cnAAAAAADACELQHAAAAENP3UGpaqd5rXC5tb2YOecnUtok81p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+            "text/plain": [
+              "<Figure size 3000x1500 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "import matplotlib.dates as mdates\n",
+        "\n",
+        "# Limit the data to September 2024\n",
+        "stories_filtered = df.filter(pl.col(\"time\") < pl.datetime(2024, 10, 1))\n",
+        "\n",
+        "stories_with_quarter = stories_filtered.with_columns(\n",
+        "    pl.col(\"time\").dt.truncate(\"1q\").alias(\"year_quarter\")\n",
+        ")\n",
+        "\n",
+        "stories_per_quarter = (\n",
+        "    stories_with_quarter.group_by(\"year_quarter\")\n",
+        "    .agg(pl.col(\"id\").count().alias(\"num_stories\"))\n",
+        "    .sort(\"year_quarter\")\n",
+        ")\n",
+        "\n",
+        "avg_score_per_quarter = (\n",
+        "    stories_with_quarter.group_by(\"year_quarter\")\n",
+        "    .agg(pl.col(\"score\").mean().alias(\"avg_score\"))\n",
+        "    .sort(\"year_quarter\")\n",
+        ")\n",
+        "\n",
+        "fig, ax1 = plt.subplots(figsize=(10, 5), dpi=300)  # Increased figure size and DPI\n",
+        "\n",
+        "# Plot number of stories\n",
+        "color = \"tab:blue\"\n",
+        "ax1.set_xlabel(\"Year\")\n",
+        "ax1.set_ylabel(\"Number of Stories\", color=color)\n",
+        "ax1.plot(\n",
+        "    stories_per_quarter[\"year_quarter\"], stories_per_quarter[\"num_stories\"], color=color\n",
+        ")\n",
+        "ax1.tick_params(axis=\"y\", labelcolor=color)\n",
+        "\n",
+        "# Create a second y-axis for average score\n",
+        "ax2 = ax1.twinx()\n",
+        "color = \"tab:orange\"\n",
+        "ax2.set_ylabel(\"Average Score\", color=color)\n",
+        "ax2.plot(\n",
+        "    avg_score_per_quarter[\"year_quarter\"],\n",
+        "    avg_score_per_quarter[\"avg_score\"],\n",
+        "    color=color,\n",
+        ")\n",
+        "ax2.tick_params(axis=\"y\", labelcolor=color)\n",
+        "\n",
+        "plt.title(\"Number of Stories and Average Score Over Time\")\n",
+        "\n",
+        "# Set x-axis to show only years\n",
+        "ax1.xaxis.set_major_locator(mdates.YearLocator())\n",
+        "ax1.xaxis.set_major_formatter(mdates.DateFormatter(\"%Y\"))\n",
+        "\n",
+        "# Rotate x-axis labels by 45 degrees\n",
+        "plt.xticks(rotation=45, ha=\"right\")\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div><style>\n",
+              ".dataframe > thead > tr,\n",
+              ".dataframe > tbody > tr {\n",
+              "  text-align: right;\n",
+              "  white-space: pre-wrap;\n",
+              "}\n",
+              "</style>\n",
+              "<small>shape: (331_929, 20)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>id</th><th>by</th><th>time</th><th>title</th><th>text</th><th>url</th><th>score</th><th>descendants</th><th>kids</th><th>deleted</th><th>dead</th><th>scraping_error</th><th>scraped_title</th><th>scraped_published_at</th><th>scraped_byline</th><th>scraped_body</th><th>scraped_at</th><th>scraped_language</th><th>split</th><th>date</th></tr><tr><td>i64</td><td>str</td><td>datetime[μs]</td><td>str</td><td>str</td><td>str</td><td>i64</td><td>i64</td><td>list[i64]</td><td>list[str]</td><td>bool</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>datetime[μs]</td><td>str</td><td>str</td><td>date</td></tr></thead><tbody><tr><td>10820498</td><td>&quot;trengrj&quot;</td><td>2016-01-01 00:16:04</td><td>&quot;Debian creator Ian Murdock die…</td><td>null</td><td>&quot;http://www.zdnet.com/article/d…</td><td>1</td><td>null</td><td>null</td><td>null</td><td>true</td><td>&quot;no_error&quot;</td><td>&quot;Debian Linux founder Ian Murdo…</td><td>null</td><td>&quot;Written by&quot;</td><td>&quot;UPDATED: I&#x27;d known Ian Murdock…</td><td>2024-11-08 20:50:45</td><td>&quot;en&quot;</td><td>&quot;train&quot;</td><td>2016-01-01</td></tr><tr><td>10820620</td><td>&quot;BuckRogers&quot;</td><td>2016-01-01 00:52:20</td><td>&quot;Where are we in the Python 3 t…</td><td>null</td><td>&quot;http://www.snarky.ca/the-stage…</td><td>4</td><td>0</td><td>null</td><td>null</td><td>null</td><td>&quot;no_error&quot;</td><td>&quot;Where are we in the Python 3 t…</td><td>&quot;2015-12-31T04:35:00.000Z&quot;</td><td>&quot;Brett Cannon&quot;</td><td>&quot;\n",
+              "\n",
+              "\n",
+              "\n",
+              "    \n",
+              "\n",
+              "        \n",
+              "           …</td><td>2024-11-08 12:09:11</td><td>&quot;en&quot;</td><td>&quot;train&quot;</td><td>2016-01-01</td></tr><tr><td>10820781</td><td>&quot;jonbaer&quot;</td><td>2016-01-01 01:54:49</td><td>&quot;Algorithms of the Mind – What …</td><td>null</td><td>&quot;https://medium.com/deep-learni…</td><td>3</td><td>0</td><td>null</td><td>null</td><td>null</td><td>&quot;no_error&quot;</td><td>&quot;Algorithms of the Mind - Deep …</td><td>&quot;2015-05-22T09:27:31.481Z&quot;</td><td>&quot;Christopher Nguyen&quot;</td><td>&quot;What Machine Learning Teaches …</td><td>2024-11-08 14:18:52</td><td>&quot;en&quot;</td><td>&quot;train&quot;</td><td>2016-01-01</td></tr><tr><td>10820785</td><td>&quot;jonbaer&quot;</td><td>2016-01-01 01:55:57</td><td>&quot;Demystifying Deep Reinforcemen…</td><td>null</td><td>&quot;http://www.nervanasys.com/demy…</td><td>3</td><td>0</td><td>null</td><td>null</td><td>null</td><td>&quot;no_error&quot;</td><td>&quot;KINGGACOR | Situs Slot Gacor H…</td><td>null</td><td>null</td><td>&quot;\n",
+              "  KINGGACOR | Situs Slot Gaco…</td><td>2024-11-08 08:38:29</td><td>&quot;id&quot;</td><td>&quot;train&quot;</td><td>2016-01-01</td></tr><tr><td>10820925</td><td>&quot;Someone&quot;</td><td>2016-01-01 02:51:11</td><td>&quot;Users No Longer Need to Jailbr…</td><td>null</td><td>&quot;http://www.darkreading.com/vul…</td><td>2</td><td>0</td><td>null</td><td>null</td><td>null</td><td>&quot;no_error&quot;</td><td>&quot;Users No Longer Need to Jailbr…</td><td>&quot;2015-12-29T17:00:00.000Z&quot;</td><td>&quot;Ericka Chickowski, Contributin…</td><td>&quot;Security practitioners who&#x27;ve …</td><td>2024-11-08 07:55:51</td><td>&quot;en&quot;</td><td>&quot;train&quot;</td><td>2016-01-01</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>42057787</td><td>&quot;robaato&quot;</td><td>2024-11-06 07:18:46</td><td>&quot;Early morning and evening acti…</td><td>null</td><td>&quot;https://www.theguardian.com/so…</td><td>2</td><td>0</td><td>null</td><td>null</td><td>null</td><td>&quot;no_error&quot;</td><td>&quot;Early morning and evening acti…</td><td>&quot;2024-11-06T05:00:04.000Z&quot;</td><td>&quot;Andrew Gregory&quot;</td><td>&quot;Being more active in the morni…</td><td>2024-11-08 06:04:44</td><td>&quot;en&quot;</td><td>&quot;test&quot;</td><td>2024-11-06</td></tr><tr><td>42061756</td><td>&quot;DocFeind&quot;</td><td>2024-11-06 13:12:33</td><td>&quot;X has always had a Nazi proble…</td><td>null</td><td>&quot;https://www.theatlantic.com/te…</td><td>3</td><td>0</td><td>null</td><td>null</td><td>null</td><td>&quot;no_error&quot;</td><td>&quot;X Is a White-Supremacist Site&quot;</td><td>&quot;2024-11-05T19:41:17Z&quot;</td><td>&quot;Charlie Warzel&quot;</td><td>&quot;Elon Musk has made one of Twit…</td><td>2024-11-07 22:12:22</td><td>&quot;en&quot;</td><td>&quot;test&quot;</td><td>2024-11-06</td></tr><tr><td>42063552</td><td>&quot;Olshansky&quot;</td><td>2024-11-06 15:13:49</td><td>&quot;Vibe Checks Are All You Need&quot;</td><td>null</td><td>&quot;https://olshansky.substack.com…</td><td>3</td><td>0</td><td>null</td><td>null</td><td>null</td><td>&quot;no_error&quot;</td><td>&quot;Vibe Checks Are All You Need&quot;</td><td>&quot;2024-05-29T20:49:40+00:00&quot;</td><td>&quot;Daniel Olshansky&quot;</td><td>&quot;I’ve been using ChatGPT since …</td><td>2024-11-08 14:57:45</td><td>&quot;en&quot;</td><td>&quot;test&quot;</td><td>2024-11-06</td></tr><tr><td>42068691</td><td>&quot;0xlogk&quot;</td><td>2024-11-06 20:18:46</td><td>&quot;Are You Playing to Play, or Pl…</td><td>null</td><td>&quot;https://commoncog.com/playing-…</td><td>3</td><td>0</td><td>null</td><td>null</td><td>null</td><td>&quot;no_error&quot;</td><td>&quot;Are You Playing to Play, or Pl…</td><td>&quot;2021-09-21T14:39:12.000Z&quot;</td><td>&quot;Cedric Chin&quot;</td><td>&quot;\n",
+              "      \n",
+              "      My friend Lesley…</td><td>2024-11-08 10:31:35</td><td>&quot;en&quot;</td><td>&quot;test&quot;</td><td>2024-11-06</td></tr><tr><td>42071435</td><td>&quot;pentagrama&quot;</td><td>2024-11-06 23:50:11</td><td>&quot;Gimp 3.0 RC1 Released&quot;</td><td>null</td><td>&quot;https://www.gimp.org/news/2024…</td><td>1</td><td>0</td><td>null</td><td>null</td><td>null</td><td>&quot;no_error&quot;</td><td>&quot;GIMP - GIMP 3.0 RC1 Released&quot;</td><td>&quot;2024-11-06T00:00:00+01:00&quot;</td><td>&quot;by                           G…</td><td>&quot;\n",
+              "                    We are ve…</td><td>2024-11-08 06:29:21</td><td>&quot;en&quot;</td><td>&quot;test&quot;</td><td>2024-11-06</td></tr></tbody></table></div>"
+            ],
+            "text/plain": [
+              "shape: (331_929, 20)\n",
+              "┌──────────┬────────────┬────────────┬────────────┬───┬────────────┬───────────┬───────┬───────────┐\n",
+              "│ id       ┆ by         ┆ time       ┆ title      ┆ … ┆ scraped_at ┆ scraped_l ┆ split ┆ date      │\n",
+              "│ ---      ┆ ---        ┆ ---        ┆ ---        ┆   ┆ ---        ┆ anguage   ┆ ---   ┆ ---       │\n",
+              "│ i64      ┆ str        ┆ datetime[μ ┆ str        ┆   ┆ datetime[μ ┆ ---       ┆ str   ┆ date      │\n",
+              "│          ┆            ┆ s]         ┆            ┆   ┆ s]         ┆ str       ┆       ┆           │\n",
+              "╞══════════╪════════════╪════════════╪════════════╪═══╪════════════╪═══════════╪═══════╪═══════════╡\n",
+              "│ 10820498 ┆ trengrj    ┆ 2016-01-01 ┆ Debian     ┆ … ┆ 2024-11-08 ┆ en        ┆ train ┆ 2016-01-0 │\n",
+              "│          ┆            ┆ 00:16:04   ┆ creator    ┆   ┆ 20:50:45   ┆           ┆       ┆ 1         │\n",
+              "│          ┆            ┆            ┆ Ian        ┆   ┆            ┆           ┆       ┆           │\n",
+              "│          ┆            ┆            ┆ Murdock    ┆   ┆            ┆           ┆       ┆           │\n",
+              "│          ┆            ┆            ┆ die…       ┆   ┆            ┆           ┆       ┆           │\n",
+              "│ 10820620 ┆ BuckRogers ┆ 2016-01-01 ┆ Where are  ┆ … ┆ 2024-11-08 ┆ en        ┆ train ┆ 2016-01-0 │\n",
+              "│          ┆            ┆ 00:52:20   ┆ we in the  ┆   ┆ 12:09:11   ┆           ┆       ┆ 1         │\n",
+              "│          ┆            ┆            ┆ Python 3   ┆   ┆            ┆           ┆       ┆           │\n",
+              "│          ┆            ┆            ┆ t…         ┆   ┆            ┆           ┆       ┆           │\n",
+              "│ 10820781 ┆ jonbaer    ┆ 2016-01-01 ┆ Algorithms ┆ … ┆ 2024-11-08 ┆ en        ┆ train ┆ 2016-01-0 │\n",
+              "│          ┆            ┆ 01:54:49   ┆ of the     ┆   ┆ 14:18:52   ┆           ┆       ┆ 1         │\n",
+              "│          ┆            ┆            ┆ Mind –     ┆   ┆            ┆           ┆       ┆           │\n",
+              "│          ┆            ┆            ┆ What …     ┆   ┆            ┆           ┆       ┆           │\n",
+              "│ 10820785 ┆ jonbaer    ┆ 2016-01-01 ┆ Demystifyi ┆ … ┆ 2024-11-08 ┆ id        ┆ train ┆ 2016-01-0 │\n",
+              "│          ┆            ┆ 01:55:57   ┆ ng Deep    ┆   ┆ 08:38:29   ┆           ┆       ┆ 1         │\n",
+              "│          ┆            ┆            ┆ Reinforcem ┆   ┆            ┆           ┆       ┆           │\n",
+              "│          ┆            ┆            ┆ en…        ┆   ┆            ┆           ┆       ┆           │\n",
+              "│ 10820925 ┆ Someone    ┆ 2016-01-01 ┆ Users No   ┆ … ┆ 2024-11-08 ┆ en        ┆ train ┆ 2016-01-0 │\n",
+              "│          ┆            ┆ 02:51:11   ┆ Longer     ┆   ┆ 07:55:51   ┆           ┆       ┆ 1         │\n",
+              "│          ┆            ┆            ┆ Need to    ┆   ┆            ┆           ┆       ┆           │\n",
+              "│          ┆            ┆            ┆ Jailbr…    ┆   ┆            ┆           ┆       ┆           │\n",
+              "│ …        ┆ …          ┆ …          ┆ …          ┆ … ┆ …          ┆ …         ┆ …     ┆ …         │\n",
+              "│ 42057787 ┆ robaato    ┆ 2024-11-06 ┆ Early      ┆ … ┆ 2024-11-08 ┆ en        ┆ test  ┆ 2024-11-0 │\n",
+              "│          ┆            ┆ 07:18:46   ┆ morning    ┆   ┆ 06:04:44   ┆           ┆       ┆ 6         │\n",
+              "│          ┆            ┆            ┆ and        ┆   ┆            ┆           ┆       ┆           │\n",
+              "│          ┆            ┆            ┆ evening    ┆   ┆            ┆           ┆       ┆           │\n",
+              "│          ┆            ┆            ┆ acti…      ┆   ┆            ┆           ┆       ┆           │\n",
+              "│ 42061756 ┆ DocFeind   ┆ 2024-11-06 ┆ X has      ┆ … ┆ 2024-11-07 ┆ en        ┆ test  ┆ 2024-11-0 │\n",
+              "│          ┆            ┆ 13:12:33   ┆ always had ┆   ┆ 22:12:22   ┆           ┆       ┆ 6         │\n",
+              "│          ┆            ┆            ┆ a Nazi     ┆   ┆            ┆           ┆       ┆           │\n",
+              "│          ┆            ┆            ┆ proble…    ┆   ┆            ┆           ┆       ┆           │\n",
+              "│ 42063552 ┆ Olshansky  ┆ 2024-11-06 ┆ Vibe       ┆ … ┆ 2024-11-08 ┆ en        ┆ test  ┆ 2024-11-0 │\n",
+              "│          ┆            ┆ 15:13:49   ┆ Checks Are ┆   ┆ 14:57:45   ┆           ┆       ┆ 6         │\n",
+              "│          ┆            ┆            ┆ All You    ┆   ┆            ┆           ┆       ┆           │\n",
+              "│          ┆            ┆            ┆ Need       ┆   ┆            ┆           ┆       ┆           │\n",
+              "│ 42068691 ┆ 0xlogk     ┆ 2024-11-06 ┆ Are You    ┆ … ┆ 2024-11-08 ┆ en        ┆ test  ┆ 2024-11-0 │\n",
+              "│          ┆            ┆ 20:18:46   ┆ Playing to ┆   ┆ 10:31:35   ┆           ┆       ┆ 6         │\n",
+              "│          ┆            ┆            ┆ Play, or   ┆   ┆            ┆           ┆       ┆           │\n",
+              "│          ┆            ┆            ┆ Pl…        ┆   ┆            ┆           ┆       ┆           │\n",
+              "│ 42071435 ┆ pentagrama ┆ 2024-11-06 ┆ Gimp 3.0   ┆ … ┆ 2024-11-08 ┆ en        ┆ test  ┆ 2024-11-0 │\n",
+              "│          ┆            ┆ 23:50:11   ┆ RC1        ┆   ┆ 06:29:21   ┆           ┆       ┆ 6         │\n",
+              "│          ┆            ┆            ┆ Released   ┆   ┆            ┆           ┆       ┆           │\n",
+              "└──────────┴────────────┴────────────┴────────────┴───┴────────────┴───────────┴───────┴───────────┘"
+            ]
+          },
+          "execution_count": 33,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df = df.filter(pl.col(\"time\") > pl.datetime(2016, 1, 1))\n",
+        "\n",
+        "df"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 73,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "starting\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div><style>\n",
+              ".dataframe > thead > tr,\n",
+              ".dataframe > tbody > tr {\n",
+              "  text-align: right;\n",
+              "  white-space: pre-wrap;\n",
+              "}\n",
+              "</style>\n",
+              "<small>shape: (1, 21)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>id</th><th>by</th><th>time</th><th>title</th><th>text</th><th>url</th><th>score</th><th>descendants</th><th>kids</th><th>deleted</th><th>dead</th><th>scraping_error</th><th>scraped_title</th><th>scraped_published_at</th><th>scraped_byline</th><th>scraped_body</th><th>scraped_at</th><th>scraped_language</th><th>split</th><th>date</th><th>serialized</th></tr><tr><td>i64</td><td>str</td><td>datetime[μs]</td><td>str</td><td>str</td><td>str</td><td>i64</td><td>i64</td><td>list[i64]</td><td>list[str]</td><td>bool</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>datetime[μs]</td><td>str</td><td>str</td><td>date</td><td>str</td></tr></thead><tbody><tr><td>38731083</td><td>&quot;TaurenHunter&quot;</td><td>2023-12-22 04:56:40</td><td>&quot;A startup tested if ChatGPT an…</td><td>null</td><td>&quot;https://www.cnbc.com/2023/12/1…</td><td>5</td><td>3</td><td>[38731703]</td><td>null</td><td>null</td><td>&quot;no_error&quot;</td><td>&quot;GPT and other AI models can&#x27;t …</td><td>&quot;2023-12-19T12:49:12+0000&quot;</td><td>&quot;Kif Leswing&quot;</td><td>&quot;Patronus AI co-founders Anand …</td><td>2024-11-08 09:38:56</td><td>&quot;en&quot;</td><td>&quot;train&quot;</td><td>2023-12-22</td><td>null</td></tr></tbody></table></div>"
+            ],
+            "text/plain": [
+              "shape: (1, 21)\n",
+              "┌──────────┬────────────┬────────────┬────────────┬───┬────────────┬───────┬───────────┬───────────┐\n",
+              "│ id       ┆ by         ┆ time       ┆ title      ┆ … ┆ scraped_la ┆ split ┆ date      ┆ serialize │\n",
+              "│ ---      ┆ ---        ┆ ---        ┆ ---        ┆   ┆ nguage     ┆ ---   ┆ ---       ┆ d         │\n",
+              "│ i64      ┆ str        ┆ datetime[μ ┆ str        ┆   ┆ ---        ┆ str   ┆ date      ┆ ---       │\n",
+              "│          ┆            ┆ s]         ┆            ┆   ┆ str        ┆       ┆           ┆ str       │\n",
+              "╞══════════╪════════════╪════════════╪════════════╪═══╪════════════╪═══════╪═══════════╪═══════════╡\n",
+              "│ 38731083 ┆ TaurenHunt ┆ 2023-12-22 ┆ A startup  ┆ … ┆ en         ┆ train ┆ 2023-12-2 ┆ null      │\n",
+              "│          ┆ er         ┆ 04:56:40   ┆ tested if  ┆   ┆            ┆       ┆ 2         ┆           │\n",
+              "│          ┆            ┆            ┆ ChatGPT    ┆   ┆            ┆       ┆           ┆           │\n",
+              "│          ┆            ┆            ┆ an…        ┆   ┆            ┆       ┆           ┆           │\n",
+              "└──────────┴────────────┴────────────┴────────────┴───┴────────────┴───────┴───────────┴───────────┘"
+            ]
+          },
+          "execution_count": 73,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "def serialize_story(story, include_datetime=True):\n",
+        "    print(\"starting\")\n",
+        "    time_format = \"%A %I:%M %p, %Y-%m-%d\" if include_datetime else \"%Y-%m-%d\"\n",
+        "\n",
+        "    string = f\"\"\"URL: {story[\"url\"]}\"\"\"\n",
+        "    print(string)\n",
+        "    return string\n",
+        "\n",
+        "\n",
+        "test_df.with_columns(\n",
+        "    pl.struct([\"title\", \"by\", \"time\", \"scraped_body\"])\n",
+        "    .map_elements(\n",
+        "        lambda x: serialize_story(x, include_datetime=True), return_dtype=pl.Utf8\n",
+        "    )\n",
+        "    .alias(\"serialized\")\n",
+        ")\n",
+        "# print(serialize_story(df.sample(1).row(0, named=True)))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 58,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'title': 'A startup tested if ChatGPT and other AI could understand SEC filings', 'by': 'TaurenHunter', 'time': datetime.datetime(2023, 12, 22, 4, 56, 40), 'scraped_body': 'Patronus AI co-founders Anand Kannappan and Rebecca QianPatronus AILarge language models, similar to the one at the heart of ChatGPT, frequently fail to answer questions derived from Securities and Exchange Commission filings, researchers from a startup called Patronus AI found.Even the best-performing artificial intelligence model configuration they tested, OpenAI\\'s GPT-4-Turbo, when armed with the ability to read nearly an entire filing alongside the question, only got 79% of answers right on Patronus AI\\'s new test, the company\\'s founders told CNBC.Oftentimes, the so-called large language models would refuse to answer, or would \"hallucinate\" figures and facts that weren\\'t in the SEC filings.\"That type of performance rate is just absolutely unacceptable,\" Patronus AI co-founder Anand Kannappan said. \"It has to be much much higher for it to really work in an automated and production-ready way.\"The findings highlight some of the challenges facing AI models as big companies, especially in regulated industries like finance, seek to incorporate cutting-edge technology into their operations, whether for customer service or research.The ability to extract important numbers quickly and perform analysis on financial narratives has been seen as one of the most promising applications for chatbots since ChatGPT was released late last year. SEC filings are filled with important data, and if a bot could accurately summarize them or quickly answer questions about what\\'s in them, it could give the user a leg up in the competitive financial industry.In the past year, Bloomberg LP developed its own AI model for financial data, business school professors researched whether ChatGPT can parse financial headlines, and JPMorgan is working on an AI-powered automated investing tool, CNBC previously reported. Generative AI could boost the banking industry by trillions of dollars per year, a recent McKinsey forecast said.But GPT\\'s entry into the industry hasn\\'t been smooth. When Microsoft first launched its Bing Chat using OpenAI\\'s GPT, one of its primary examples was using the chatbot to quickly summarize an earnings press release. Observers quickly realized that the numbers in Microsoft\\'s example were off, and some numbers were entirely made up.\\'Vibe checks\\'Part of the challenge when incorporating LLMs into actual products, say the Patronus AI co-founders, is that LLMs are nondeterministic — they\\'re not guaranteed to produce the same output every time for the same input. That means that companies will need to do more rigorous testing to make sure they\\'re operating correctly, not going off-topic, and providing reliable results.The founders met at Facebook parent company Meta, where they worked on AI problems related to understanding how models come up with their answers and making them more \"responsible.\" They founded Patronus AI, which has received seed funding from Lightspeed Venture Partners, to automate LLM testing with software, so companies can feel comfortable that their AI bots won\\'t surprise customers or workers with off-topic or wrong answers.\"Right now evaluation is largely manual. It feels like just testing by inspection,\" Patronus AI co-founder Rebecca Qian said. \"One company told us it was \\'vibe checks.\\'\"Patronus AI worked to write a set of more than 10,000 questions and answers drawn from SEC filings from major publicly traded companies, which it calls FinanceBench. The dataset includes the correct answers, and also where exactly in any given filing to find them. Not all of the answers can be pulled directly from the text, and some questions require light math or reasoning.Qian and Kannappan say it\\'s a test that gives a \"minimum performance standard\" for language AI in the financial sector.Here\\'s some examples of questions in the dataset, provided by Patronus AI:Has CVS Health paid dividends to common shareholders in Q2 of FY2022?Did AMD report customer concentration in FY22?What is Coca Cola\\'s FY2021 COGS % margin? Calculate what was asked by utilizing the line items clearly shown in the income statement.How the AI models did on the testPatronus AI tested four language models: OpenAI\\'s GPT-4 and GPT-4-Turbo, Anthropic\\'s Claude 2 and Meta\\'s Llama 2, using a subset of 150 of the questions it had produced.It also tested different configurations and prompts, such as one setting where the OpenAI models were given the exact relevant source text in the question, which it called \"Oracle\" mode. In other tests, the models were told where the underlying SEC documents would be stored, or given \"long context,\" which meant including nearly an entire SEC filing alongside the question in the prompt.GPT-4-Turbo failed at the startup\\'s \"closed book\" test, where it wasn\\'t given access to any SEC source document. It failed to answer 88% of the 150 questions it was asked, and only produced a correct answer 14 times.It was able to improve significantly when given access to the underlying filings. In \"Oracle\" mode, where it was pointed to the exact text for the answer, GPT-4-Turbo answered the question correctly 85% of the time, but still produced an incorrect answer 15% of the time.But that\\'s an unrealistic test because it requires human input to find the exact pertinent place in the filing — the exact task that many hope that language models can address.Llama 2, an open-source AI model developed by Meta, had some of the worst \"hallucinations,\" producing wrong answers as much as 70% of the time, and correct answers only 19% of the time, when given access to an array of underlying documents.Anthropic\\'s Claude 2 performed well when given \"long context,\" where nearly the entire relevant SEC filing was included along with the question. It could answer 75% of the questions it was posed, gave the wrong answer for 21%, and failed to answer only 3%. GPT-4-Turbo also did well with long context, answering 79% of the questions correctly, and giving the wrong answer for 17% of them.After running the tests, the co-founders were surprised about how poorly the models did — even when they were pointed to where the answers were.\"One surprising thing was just how often models refused to answer,\" said Qian. \"The refusal rate is really high, even when the answer is within the context and a human would be able to answer it.\"Even when the models performed well, though, they just weren\\'t good enough, Patronus AI found.\"There just is no margin for error that\\'s acceptable, because, especially in regulated industries, even if the model gets the answer wrong 1 out of 20 times, that\\'s still not high enough accuracy,\" Qian said.But the Patronus AI co-founders believe there\\'s huge potential for language models like GPT to help people in the finance industry — whether that\\'s analysts, or investors — if AI continues to improve.\"We definitely think that the results can be pretty promising,\" said Kannappan. \"Models will continue to get better over time. We\\'re very hopeful that in the long term, a lot of this can be automated. But today, you will definitely need to have at least a human in the loop to help support and guide whatever workflow you have.\"An OpenAI representative pointed to the company\\'s usage guidelines, which prohibit offering tailored financial advice using an OpenAI model without a qualified person reviewing the information, and require anyone using an OpenAI model in the financial industry to provide a disclaimer informing them that AI is being used and its limitations. OpenAI\\'s usage policies also say that OpenAI\\'s models are not fine-tuned to provide financial advice.Meta did not immediately return a request for comment, and Anthropic didn\\'t immediately have a comment.Don\\'t miss these stories from CNBC PRO:Five stocks to buy before the year-end, according to the prosMorgan Stanley fund manager names 4 top stocks to buy \\'on the cheap\\'JPMorgan picks China stocks to buy now. Alibaba\\'s not on the listAnalysts love this self-driving car tech stock and give it over 400% upside'}\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div><style>\n",
+              ".dataframe > thead > tr,\n",
+              ".dataframe > tbody > tr {\n",
+              "  text-align: right;\n",
+              "  white-space: pre-wrap;\n",
+              "}\n",
+              "</style>\n",
+              "<small>shape: (1, 21)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>id</th><th>by</th><th>time</th><th>title</th><th>text</th><th>url</th><th>score</th><th>descendants</th><th>kids</th><th>deleted</th><th>dead</th><th>scraping_error</th><th>scraped_title</th><th>scraped_published_at</th><th>scraped_byline</th><th>scraped_body</th><th>scraped_at</th><th>scraped_language</th><th>split</th><th>date</th><th>serialized</th></tr><tr><td>i64</td><td>str</td><td>datetime[μs]</td><td>str</td><td>str</td><td>str</td><td>i64</td><td>i64</td><td>list[i64]</td><td>list[str]</td><td>bool</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>datetime[μs]</td><td>str</td><td>str</td><td>date</td><td>str</td></tr></thead><tbody><tr><td>38731083</td><td>&quot;TaurenHunter&quot;</td><td>2023-12-22 04:56:40</td><td>&quot;A startup tested if ChatGPT an…</td><td>null</td><td>&quot;https://www.cnbc.com/2023/12/1…</td><td>5</td><td>3</td><td>[38731703]</td><td>null</td><td>null</td><td>&quot;no_error&quot;</td><td>&quot;GPT and other AI models can&#x27;t …</td><td>&quot;2023-12-19T12:49:12+0000&quot;</td><td>&quot;Kif Leswing&quot;</td><td>&quot;Patronus AI co-founders Anand …</td><td>2024-11-08 09:38:56</td><td>&quot;en&quot;</td><td>&quot;train&quot;</td><td>2023-12-22</td><td>null</td></tr></tbody></table></div>"
+            ],
+            "text/plain": [
+              "shape: (1, 21)\n",
+              "┌──────────┬────────────┬────────────┬────────────┬───┬────────────┬───────┬───────────┬───────────┐\n",
+              "│ id       ┆ by         ┆ time       ┆ title      ┆ … ┆ scraped_la ┆ split ┆ date      ┆ serialize │\n",
+              "│ ---      ┆ ---        ┆ ---        ┆ ---        ┆   ┆ nguage     ┆ ---   ┆ ---       ┆ d         │\n",
+              "│ i64      ┆ str        ┆ datetime[μ ┆ str        ┆   ┆ ---        ┆ str   ┆ date      ┆ ---       │\n",
+              "│          ┆            ┆ s]         ┆            ┆   ┆ str        ┆       ┆           ┆ str       │\n",
+              "╞══════════╪════════════╪════════════╪════════════╪═══╪════════════╪═══════╪═══════════╪═══════════╡\n",
+              "│ 38731083 ┆ TaurenHunt ┆ 2023-12-22 ┆ A startup  ┆ … ┆ en         ┆ train ┆ 2023-12-2 ┆ null      │\n",
+              "│          ┆ er         ┆ 04:56:40   ┆ tested if  ┆   ┆            ┆       ┆ 2         ┆           │\n",
+              "│          ┆            ┆            ┆ ChatGPT    ┆   ┆            ┆       ┆           ┆           │\n",
+              "│          ┆            ┆            ┆ an…        ┆   ┆            ┆       ┆           ┆           │\n",
+              "└──────────┴────────────┴────────────┴────────────┴───┴────────────┴───────┴───────────┴───────────┘"
+            ]
+          },
+          "execution_count": 58,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 56,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'title': 'A startup tested if ChatGPT and other AI could understand SEC filings', 'by': 'TaurenHunter', 'time': datetime.datetime(2023, 12, 22, 4, 56, 40), 'scraped_body': 'Patronus AI co-founders Anand Kannappan and Rebecca QianPatronus AILarge language models, similar to the one at the heart of ChatGPT, frequently fail to answer questions derived from Securities and Exchange Commission filings, researchers from a startup called Patronus AI found.Even the best-performing artificial intelligence model configuration they tested, OpenAI\\'s GPT-4-Turbo, when armed with the ability to read nearly an entire filing alongside the question, only got 79% of answers right on Patronus AI\\'s new test, the company\\'s founders told CNBC.Oftentimes, the so-called large language models would refuse to answer, or would \"hallucinate\" figures and facts that weren\\'t in the SEC filings.\"That type of performance rate is just absolutely unacceptable,\" Patronus AI co-founder Anand Kannappan said. \"It has to be much much higher for it to really work in an automated and production-ready way.\"The findings highlight some of the challenges facing AI models as big companies, especially in regulated industries like finance, seek to incorporate cutting-edge technology into their operations, whether for customer service or research.The ability to extract important numbers quickly and perform analysis on financial narratives has been seen as one of the most promising applications for chatbots since ChatGPT was released late last year. SEC filings are filled with important data, and if a bot could accurately summarize them or quickly answer questions about what\\'s in them, it could give the user a leg up in the competitive financial industry.In the past year, Bloomberg LP developed its own AI model for financial data, business school professors researched whether ChatGPT can parse financial headlines, and JPMorgan is working on an AI-powered automated investing tool, CNBC previously reported. Generative AI could boost the banking industry by trillions of dollars per year, a recent McKinsey forecast said.But GPT\\'s entry into the industry hasn\\'t been smooth. When Microsoft first launched its Bing Chat using OpenAI\\'s GPT, one of its primary examples was using the chatbot to quickly summarize an earnings press release. Observers quickly realized that the numbers in Microsoft\\'s example were off, and some numbers were entirely made up.\\'Vibe checks\\'Part of the challenge when incorporating LLMs into actual products, say the Patronus AI co-founders, is that LLMs are nondeterministic — they\\'re not guaranteed to produce the same output every time for the same input. That means that companies will need to do more rigorous testing to make sure they\\'re operating correctly, not going off-topic, and providing reliable results.The founders met at Facebook parent company Meta, where they worked on AI problems related to understanding how models come up with their answers and making them more \"responsible.\" They founded Patronus AI, which has received seed funding from Lightspeed Venture Partners, to automate LLM testing with software, so companies can feel comfortable that their AI bots won\\'t surprise customers or workers with off-topic or wrong answers.\"Right now evaluation is largely manual. It feels like just testing by inspection,\" Patronus AI co-founder Rebecca Qian said. \"One company told us it was \\'vibe checks.\\'\"Patronus AI worked to write a set of more than 10,000 questions and answers drawn from SEC filings from major publicly traded companies, which it calls FinanceBench. The dataset includes the correct answers, and also where exactly in any given filing to find them. Not all of the answers can be pulled directly from the text, and some questions require light math or reasoning.Qian and Kannappan say it\\'s a test that gives a \"minimum performance standard\" for language AI in the financial sector.Here\\'s some examples of questions in the dataset, provided by Patronus AI:Has CVS Health paid dividends to common shareholders in Q2 of FY2022?Did AMD report customer concentration in FY22?What is Coca Cola\\'s FY2021 COGS % margin? Calculate what was asked by utilizing the line items clearly shown in the income statement.How the AI models did on the testPatronus AI tested four language models: OpenAI\\'s GPT-4 and GPT-4-Turbo, Anthropic\\'s Claude 2 and Meta\\'s Llama 2, using a subset of 150 of the questions it had produced.It also tested different configurations and prompts, such as one setting where the OpenAI models were given the exact relevant source text in the question, which it called \"Oracle\" mode. In other tests, the models were told where the underlying SEC documents would be stored, or given \"long context,\" which meant including nearly an entire SEC filing alongside the question in the prompt.GPT-4-Turbo failed at the startup\\'s \"closed book\" test, where it wasn\\'t given access to any SEC source document. It failed to answer 88% of the 150 questions it was asked, and only produced a correct answer 14 times.It was able to improve significantly when given access to the underlying filings. In \"Oracle\" mode, where it was pointed to the exact text for the answer, GPT-4-Turbo answered the question correctly 85% of the time, but still produced an incorrect answer 15% of the time.But that\\'s an unrealistic test because it requires human input to find the exact pertinent place in the filing — the exact task that many hope that language models can address.Llama 2, an open-source AI model developed by Meta, had some of the worst \"hallucinations,\" producing wrong answers as much as 70% of the time, and correct answers only 19% of the time, when given access to an array of underlying documents.Anthropic\\'s Claude 2 performed well when given \"long context,\" where nearly the entire relevant SEC filing was included along with the question. It could answer 75% of the questions it was posed, gave the wrong answer for 21%, and failed to answer only 3%. GPT-4-Turbo also did well with long context, answering 79% of the questions correctly, and giving the wrong answer for 17% of them.After running the tests, the co-founders were surprised about how poorly the models did — even when they were pointed to where the answers were.\"One surprising thing was just how often models refused to answer,\" said Qian. \"The refusal rate is really high, even when the answer is within the context and a human would be able to answer it.\"Even when the models performed well, though, they just weren\\'t good enough, Patronus AI found.\"There just is no margin for error that\\'s acceptable, because, especially in regulated industries, even if the model gets the answer wrong 1 out of 20 times, that\\'s still not high enough accuracy,\" Qian said.But the Patronus AI co-founders believe there\\'s huge potential for language models like GPT to help people in the finance industry — whether that\\'s analysts, or investors — if AI continues to improve.\"We definitely think that the results can be pretty promising,\" said Kannappan. \"Models will continue to get better over time. We\\'re very hopeful that in the long term, a lot of this can be automated. But today, you will definitely need to have at least a human in the loop to help support and guide whatever workflow you have.\"An OpenAI representative pointed to the company\\'s usage guidelines, which prohibit offering tailored financial advice using an OpenAI model without a qualified person reviewing the information, and require anyone using an OpenAI model in the financial industry to provide a disclaimer informing them that AI is being used and its limitations. OpenAI\\'s usage policies also say that OpenAI\\'s models are not fine-tuned to provide financial advice.Meta did not immediately return a request for comment, and Anthropic didn\\'t immediately have a comment.Don\\'t miss these stories from CNBC PRO:Five stocks to buy before the year-end, according to the prosMorgan Stanley fund manager names 4 top stocks to buy \\'on the cheap\\'JPMorgan picks China stocks to buy now. Alibaba\\'s not on the listAnalysts love this self-driving car tech stock and give it over 400% upside'}\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div><style>\n",
+              ".dataframe > thead > tr,\n",
+              ".dataframe > tbody > tr {\n",
+              "  text-align: right;\n",
+              "  white-space: pre-wrap;\n",
+              "}\n",
+              "</style>\n",
+              "<small>shape: (1, 21)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>id</th><th>by</th><th>time</th><th>title</th><th>text</th><th>url</th><th>score</th><th>descendants</th><th>kids</th><th>deleted</th><th>dead</th><th>scraping_error</th><th>scraped_title</th><th>scraped_published_at</th><th>scraped_byline</th><th>scraped_body</th><th>scraped_at</th><th>scraped_language</th><th>split</th><th>date</th><th>serialized</th></tr><tr><td>i64</td><td>str</td><td>datetime[μs]</td><td>str</td><td>str</td><td>str</td><td>i64</td><td>i64</td><td>list[i64]</td><td>list[str]</td><td>bool</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>datetime[μs]</td><td>str</td><td>str</td><td>date</td><td>str</td></tr></thead><tbody><tr><td>38731083</td><td>&quot;TaurenHunter&quot;</td><td>2023-12-22 04:56:40</td><td>&quot;A startup tested if ChatGPT an…</td><td>null</td><td>&quot;https://www.cnbc.com/2023/12/1…</td><td>5</td><td>3</td><td>[38731703]</td><td>null</td><td>null</td><td>&quot;no_error&quot;</td><td>&quot;GPT and other AI models can&#x27;t …</td><td>&quot;2023-12-19T12:49:12+0000&quot;</td><td>&quot;Kif Leswing&quot;</td><td>&quot;Patronus AI co-founders Anand …</td><td>2024-11-08 09:38:56</td><td>&quot;en&quot;</td><td>&quot;train&quot;</td><td>2023-12-22</td><td>null</td></tr></tbody></table></div>"
+            ],
+            "text/plain": [
+              "shape: (1, 21)\n",
+              "┌──────────┬────────────┬────────────┬────────────┬───┬────────────┬───────┬───────────┬───────────┐\n",
+              "│ id       ┆ by         ┆ time       ┆ title      ┆ … ┆ scraped_la ┆ split ┆ date      ┆ serialize │\n",
+              "│ ---      ┆ ---        ┆ ---        ┆ ---        ┆   ┆ nguage     ┆ ---   ┆ ---       ┆ d         │\n",
+              "│ i64      ┆ str        ┆ datetime[μ ┆ str        ┆   ┆ ---        ┆ str   ┆ date      ┆ ---       │\n",
+              "│          ┆            ┆ s]         ┆            ┆   ┆ str        ┆       ┆           ┆ str       │\n",
+              "╞══════════╪════════════╪════════════╪════════════╪═══╪════════════╪═══════╪═══════════╪═══════════╡\n",
+              "│ 38731083 ┆ TaurenHunt ┆ 2023-12-22 ┆ A startup  ┆ … ┆ en         ┆ train ┆ 2023-12-2 ┆ null      │\n",
+              "│          ┆ er         ┆ 04:56:40   ┆ tested if  ┆   ┆            ┆       ┆ 2         ┆           │\n",
+              "│          ┆            ┆            ┆ ChatGPT    ┆   ┆            ┆       ┆           ┆           │\n",
+              "│          ┆            ┆            ┆ an…        ┆   ┆            ┆       ┆           ┆           │\n",
+              "└──────────┴────────────┴────────────┴────────────┴───┴────────────┴───────┴───────────┴───────────┘"
+            ]
+          },
+          "execution_count": 56,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "test_df = df.sample(1)\n",
+        "\n",
+        "# Create two versions of the dataset with different serialization formats\n",
+        "df_with_datetime = test_df.with_columns(\n",
+        "    pl.struct([\"title\", \"by\", \"time\", \"scraped_body\"])\n",
+        "    .map_elements(\n",
+        "        lambda x: serialize_story(x, include_datetime=True), return_dtype=pl.Utf8\n",
+        "    )\n",
+        "    .alias(\"serialized\")\n",
+        ")\n",
+        "\n",
+        "df_with_datetime\n",
+        "\n",
+        "# df_with_date = test_df.with_columns(\n",
+        "#     pl.struct([\"title\", \"by\", \"time\", \"scraped_body\"])\n",
+        "#     .map_elements(\n",
+        "#         lambda x: serialize_story(x, include_datetime=False), return_dtype=pl.Utf8\n",
+        "#     )\n",
+        "#     .alias(\"serialized\")\n",
+        "# )\n",
+        "\n",
+        "# # Save both versions\n",
+        "# df_with_datetime.write_parquet(\"./data/stories_with_datetime.parquet\")\n",
+        "# df_with_date.write_parquet(\"./data/stories_with_date.parquet\")\n",
+        "\n",
+        "# print(\"Datetime version example:\")\n",
+        "# print(df_with_datetime[\"serialized\"][0])\n",
+        "# print(\"\\nDate-only version example:\")\n",
+        "# print(df_with_date[\"serialized\"][0])\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.1"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}
diff --git a/explore.ipynb b/explore.ipynb
index f915ff5..e69de29 100644
--- a/explore.ipynb
+++ b/explore.ipynb
@@ -1,100 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loading dataset...\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['chosen', 'rejected', 'chosen_prompt', 'rejected_prompt'],\n",
-       "    num_rows: 90000\n",
-       "})"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from datasets import load_from_disk, Dataset\n",
-    "from transformers import (\n",
-    "    AutoModelForSequenceClassification,\n",
-    "    AutoTokenizer,\n",
-    ")\n",
-    "from trl import RewardTrainer, RewardConfig\n",
-    "from peft.tuners.lora import LoraConfig\n",
-    "from peft.mapping import get_peft_model\n",
-    "import wandb\n",
-    "\n",
-    "# Configuration\n",
-    "model_name = \"unsloth/Llama-3.2-3B\"\n",
-    "dataset_path = \"./data/sample_pairs\"\n",
-    "output_dir = \"./reward_model_output\"\n",
-    "num_epochs = 1\n",
-    "batch_size = 8\n",
-    "learning_rate = 5e-5\n",
-    "max_length = 4096\n",
-    "\n",
-    "print(\"Loading dataset...\")\n",
-    "dataset: Dataset = load_from_disk(dataset_path)[\"train\"]\n",
-    "\n",
-    "dataset\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Dataset({\n",
-       "    features: ['_data_files', '_fingerprint', '_format_columns', '_format_kwargs', '_format_type', '_output_all_columns', '_split'],\n",
-       "    num_rows: 1\n",
-       "})"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dataset"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/pyproject.toml b/pyproject.toml
index 78c3859..16f38f4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -10,6 +10,8 @@ dependencies = [
     # "bitsandbytes>=0.44.1",
     "datasets>=3.0.1",
     "dicttoxml>=1.7.16",
+    "docker>=7.1.0",
+    "google-api-python-client>=2.152.0",
     # "flash-attn==2.6.3",
     "hf-transfer>=0.1.8",
     "ipykernel>=6.29.5",
@@ -30,6 +32,7 @@ dependencies = [
     "schedulefree>=1.2.7",
     "scikit-learn>=1.5.2",
     "seaborn>=0.13.2",
+    "skypilot[gcp,runpod]>=0.7.0",
     # "sglang[all]>=0.3.3.post1",
     "torch==2.4.0",
     "tqdm>=4.66.5",
diff --git a/scraped_stories/rm/model1.py b/scraped_stories/rm/model1.py
index 42b11f3..a97be21 100644
--- a/scraped_stories/rm/model1.py
+++ b/scraped_stories/rm/model1.py
@@ -29,7 +29,7 @@
         )
     },
 )
-async def main():
+def main():
     import os
     import torch
     from datasets import Dataset
diff --git a/scraped_stories/rm/update_image.py b/scraped_stories/rm/update_image.py
new file mode 100644
index 0000000..650cc67
--- /dev/null
+++ b/scraped_stories/rm/update_image.py
@@ -0,0 +1,35 @@
+import io
+import subprocess
+import tempfile
+import os
+import hashlib
+
+
+def update_image():
+    # Create hash of dockerfile contents
+    dockerfile_hash = hashlib.sha256(dockerfile.encode()).hexdigest()[:12]
+    image_name = f"ghcr.io/openpipe/scraped-stories-rm:{dockerfile_hash}"
+
+    print(f"Building image {image_name}")
+
+    # Create temporary directory and write dockerfile there
+    with tempfile.TemporaryDirectory() as tmpdir:
+        dockerfile_path = os.path.join(tmpdir, "Dockerfile")
+        with open(dockerfile_path, "w") as f:
+            f.write(dockerfile)
+
+        subprocess.run(
+            ["docker", "build", "--platform", "linux/amd64", "-t", image_name, tmpdir],
+            check=True,
+        )
+
+    print(f"Pushing image {image_name}")
+    subprocess.run(["docker", "push", image_name], check=True)
+
+    print(f"Image {image_name} updated")
+
+    return image_name
+
+
+if __name__ == "__main__":
+    update_image()
diff --git a/scraped_stories/run_skypilot.py b/scraped_stories/run_skypilot.py
new file mode 100644
index 0000000..b995d4c
--- /dev/null
+++ b/scraped_stories/run_skypilot.py
@@ -0,0 +1,32 @@
+import sky
+import dotenv
+import os
+from typing import Callable
+
+dotenv.load_dotenv()
+
+
+def run_skypilot(script_path: str, env_vars: dict[str, str] | None = None):
+    env_vars = env_vars or dotenv.dotenv_values()  # type: ignore
+
+    s3_bucket_name = os.getenv("REMOTE_BUCKET")
+    task = sky.Task(
+        run="pwd && ls",
+        envs=env_vars,
+        workdir=os.path.dirname(os.path.abspath(__file__)),
+    )
+
+    # task.set_resources(sky.Resources(cloud=sky.RunPod(), accelerators="H100:1"))
+    task.set_resources(sky.Resources(cloud=sky.RunPod()))
+
+    # task.set_storage_mounts({"/remote": sky.Storage(source=f"s3://{s3_bucket_name}")})
+
+    sky.launch(task, cluster_name="scraped-stories-rm", down=True)
+
+
+def is_local():
+    return "SKYPILOT_TASK_ID" not in os.environ
+
+
+if __name__ == "__main__":
+    run_skypilot("scraped_stories/rm/model1.py")
diff --git a/training_helpers.py b/training_helpers.py
index e4dbffa..061d8f6 100644
--- a/training_helpers.py
+++ b/training_helpers.py
@@ -4,8 +4,17 @@
 from scipy.stats import pearsonr
 from sklearn.metrics import root_mean_squared_error
 import wandb
-from inference import run_inference_transformers, ModelOrPath, MandT, load_model
-from utils import stories_dataset, calculate_metrics_by_split
+from .inference import run_inference_transformers, ModelOrPath, MandT, load_model
+import math
+import logging
+from datasets import Dataset
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format="[%(asctime)s %(levelname)s] %(message)s",
+    datefmt="%Y-%m-%d %H:%M:%S",
+)
 
 
 def compute_metrics(eval_pred):
@@ -27,7 +36,7 @@ def compute_metrics(eval_pred):
 
 
 def run_final_inference_and_report_metrics(
-    model_or_path: ModelOrPath, output_dir: Optional[str] = None, dataset=None
+    model_or_path: ModelOrPath, dataset=None, output_dir: Optional[str] = None
 ):
     if output_dir is None:
         if not isinstance(model_or_path, str):
@@ -39,7 +48,7 @@ def run_final_inference_and_report_metrics(
     predictions_path = f"{output_dir}/dataset_predictions.parquet"
 
     if dataset is None:
-        dataset = stories_dataset()
+        raise ValueError("dataset is required")
 
     # Check if predictions file already exists
     try:
@@ -68,16 +77,83 @@ def run_final_inference_and_report_metrics(
 
     print(metrics)
 
-    # Log metrics to wandb if it's being used
-    if wandb.run is not None:
-        for row in metrics.iter_rows(named=True):
-            split = row["split"]
-            wandb.summary.update(
-                {
-                    f"final/{split}/baseline_rmse": row["baseline_rmse"],
-                    f"final/{split}/model_rmse": row["model_rmse"],
-                    f"final/{split}/correlation": row["model_correlation"],
-                }
-            )
+    for row in metrics.iter_rows(named=True):
+        split = row["split"]
+        wandb.summary.update(
+            {
+                f"final/{split}/baseline_rmse": row["baseline_rmse"],
+                f"final/{split}/model_rmse": row["model_rmse"],
+                f"final/{split}/correlation": row["model_correlation"],
+            }
+        )
 
     return metrics
+
+
+def calculate_metrics_by_split(df: pl.DataFrame) -> pl.DataFrame:
+    """
+    Calculate correlation and RMSE metrics for each split in the dataset.
+
+    Args:
+        df: DataFrame with log_score, predictions and split columns
+
+    Returns:
+        DataFrame with metrics for each split
+    """
+    metrics = []
+
+    for split in df["split"].unique():
+        split_df = df.filter(pl.col("split") == split)
+
+        # Calculate baseline (mean) metrics
+        average_score = split_df["log_score"].mean()
+        rmse_baseline = math.sqrt(
+            (split_df["log_score"] - average_score).pow(2).sum() / len(split_df)
+        )
+
+        # Calculate model metrics
+        rmse_model = math.sqrt(
+            (split_df["log_score"] - split_df["predictions"]).pow(2).sum()
+            / len(split_df)
+        )
+        correlation_model = split_df.select(pl.corr("log_score", "predictions"))[
+            "log_score"
+        ][0]
+
+        metrics.append(
+            {
+                "split": split,
+                "baseline_rmse": rmse_baseline,
+                "model_rmse": rmse_model,
+                "model_correlation": correlation_model,
+                "num_rows": len(split_df),
+            }
+        )
+
+    return pl.DataFrame(metrics)
+
+
+def create_dataset(
+    df: pl.DataFrame,
+    split: str,
+    num_rows: int,
+    tokenizer,
+    max_len: int,
+    n_proc: int = 4,
+):
+    df = df.with_columns(pl.col("score").log().alias("log_score"))
+    df = df.filter(pl.col("split") == split).head(num_rows)
+    df = df.with_columns(
+        [
+            pl.col("serialized").alias("text"),
+            pl.col("log_score").alias("label"),
+        ]
+    )
+    dataset = Dataset.from_polars(df.select(["text", "label"]))
+
+    def tokenize_function(examples):
+        return tokenizer(examples["text"], truncation=False)
+
+    dataset = dataset.map(tokenize_function, batched=True, num_proc=n_proc)
+    dataset = dataset.filter(lambda example: len(example["input_ids"]) <= max_len)
+    return dataset
diff --git a/utils.py b/utils.py
index 16c0eea..d7b397e 100644
--- a/utils.py
+++ b/utils.py
@@ -247,6 +247,65 @@ def with_story_info(comments_df: pl.DataFrame) -> pl.DataFrame:
     )
 
 
+@cache_dataframe("./data/stories_dataset.parquet")
+def stories_dataset() -> pl.DataFrame:
+    stories = full_dataset().filter(
+        (pl.col("type") == "story")
+        & pl.col("time").is_not_null()
+        & pl.col("text").is_not_null()
+        & pl.col("url").is_null()
+        & pl.col("deleted").is_null()
+        & pl.col("dead").is_null()
+    )
+
+    # There's a weird discontinuity in late 2015, just ignore it
+    stories = stories.filter(pl.col("time") >= pl.datetime(2016, 1, 1))
+
+    # Add a log score, it's a very skewed distribution
+    stories = stories.with_columns(pl.col("score").log().alias("log_score"))
+
+    progress_bar = tqdm.tqdm(total=len(stories), desc="Serializing stories")
+
+    def serialize_story(story):
+        progress_bar.update(1)
+        escaped_story = html.unescape(story["text"]).replace("<p>", "\n\n")
+        return f"""
+{story["title"]}
+{story["by"]}, {story["time"].strftime("%Y-%m-%d")}
+
+{escaped_story}
+"""
+
+    stories = stories.with_columns(
+        pl.struct(["title", "by", "time", "text"])
+        .map_elements(serialize_story, return_dtype=pl.Utf8)
+        .alias("serialized")
+    )
+
+    progress_bar.close()
+
+    stories = stories.sample(fraction=1, shuffle=True, seed=42)
+
+    split_assignments = np.random.choice(
+        ["train", "test", "val"], size=len(stories), p=[0.8, 0.1, 0.1]
+    )
+
+    stories = stories.with_columns(pl.Series("split", split_assignments))
+
+    return stories.select(
+        "id",
+        "title",
+        "by",
+        "text",
+        "score",
+        "descendants",
+        "time",
+        "log_score",
+        "serialized",
+        "split",
+    )
+
+
 def calculate_metrics_by_split(df: pl.DataFrame) -> pl.DataFrame:
     """
     Calculate correlation and RMSE metrics for each split in the dataset.
diff --git a/uv.lock b/uv.lock
index 1ebe271..aedd1ef 100644
--- a/uv.lock
+++ b/uv.lock
@@ -66,6 +66,18 @@ boto3 = [
     { name = "boto3" },
 ]
 
+[[package]]
+name = "aiodns"
+version = "3.2.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pycares" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/e7/84/41a6a2765abc124563f5380e76b9b24118977729e25a84112f8dfb2b33dc/aiodns-3.2.0.tar.gz", hash = "sha256:62869b23409349c21b072883ec8998316b234c9a9e36675756e8e317e8768f72", size = 7823 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/15/14/13c65b1bd59f7e707e0cc0964fbab45c003f90292ed267d159eeeeaa2224/aiodns-3.2.0-py3-none-any.whl", hash = "sha256:e443c0c27b07da3174a109fd9e736d69058d808f144d3c9d56dbd1776964c5f5", size = 5735 },
+]
+
 [[package]]
 name = "aiofiles"
 version = "24.1.0"
@@ -145,6 +157,25 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/ae/63/3e1aee3e554263f3f1011cca50d78a4894ae16ce99bf78101ac3a2f0ef74/aiohttp-3.10.10-cp313-cp313-win_amd64.whl", hash = "sha256:486f7aabfa292719a2753c016cc3a8f8172965cabb3ea2e7f7436c7f5a22a151", size = 376785 },
 ]
 
+[package.optional-dependencies]
+speedups = [
+    { name = "aiodns", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
+    { name = "brotli", marker = "platform_python_implementation == 'CPython'" },
+    { name = "brotlicffi", marker = "platform_python_implementation != 'CPython'" },
+]
+
+[[package]]
+name = "aiohttp-retry"
+version = "2.9.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "aiohttp" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/9d/61/ebda4d8e3d8cfa1fd3db0fb428db2dd7461d5742cea35178277ad180b033/aiohttp_retry-2.9.1.tar.gz", hash = "sha256:8eb75e904ed4ee5c2ec242fefe85bf04240f685391c4879d8f541d6028ff01f1", size = 13608 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1a/99/84ba7273339d0f3dfa57901b846489d2e5c2cd731470167757f1935fffbd/aiohttp_retry-2.9.1-py3-none-any.whl", hash = "sha256:66d2759d1921838256a05a3f80ad7e724936f083e35be5abb5e16eed6be6dc54", size = 9981 },
+]
+
 [[package]]
 name = "aioitertools"
 version = "0.12.0"
@@ -294,6 +325,45 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/ed/20/bc79bc575ba2e2a7f70e8a1155618bb1301eaa5132a8271373a6903f73f8/babel-2.16.0-py3-none-any.whl", hash = "sha256:368b5b98b37c06b7daf6696391c3240c938b37767d4584413e8438c5c435fa8b", size = 9587599 },
 ]
 
+[[package]]
+name = "backoff"
+version = "2.2.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/47/d7/5bbeb12c44d7c4f2fb5b56abce497eb5ed9f34d85701de869acedd602619/backoff-2.2.1.tar.gz", hash = "sha256:03f829f5bb1923180821643f8753b0502c3b682293992485b0eef2807afa5cba", size = 17001 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/df/73/b6e24bd22e6720ca8ee9a85a0c4a2971af8497d8f3193fa05390cbd46e09/backoff-2.2.1-py3-none-any.whl", hash = "sha256:63579f9a0628e06278f7e47b7d7d5b6ce20dc65c5e96a6f3ca99a6adca0396e8", size = 15148 },
+]
+
+[[package]]
+name = "bcrypt"
+version = "4.2.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/e4/7e/d95e7d96d4828e965891af92e43b52a4cd3395dc1c1ef4ee62748d0471d0/bcrypt-4.2.0.tar.gz", hash = "sha256:cf69eaf5185fd58f268f805b505ce31f9b9fc2d64b376642164e9244540c1221", size = 24294 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a9/81/4e8f5bc0cd947e91fb720e1737371922854da47a94bc9630454e7b2845f8/bcrypt-4.2.0-cp37-abi3-macosx_10_12_universal2.whl", hash = "sha256:096a15d26ed6ce37a14c1ac1e48119660f21b24cba457f160a4b830f3fe6b5cb", size = 471568 },
+    { url = "https://files.pythonhosted.org/packages/05/d2/1be1e16aedec04bcf8d0156e01b987d16a2063d38e64c3f28030a3427d61/bcrypt-4.2.0-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c02d944ca89d9b1922ceb8a46460dd17df1ba37ab66feac4870f6862a1533c00", size = 277372 },
+    { url = "https://files.pythonhosted.org/packages/e3/96/7a654027638ad9b7589effb6db77eb63eba64319dfeaf9c0f4ca953e5f76/bcrypt-4.2.0-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1d84cf6d877918620b687b8fd1bf7781d11e8a0998f576c7aa939776b512b98d", size = 273488 },
+    { url = "https://files.pythonhosted.org/packages/46/54/dc7b58abeb4a3d95bab653405935e27ba32f21b812d8ff38f271fb6f7f55/bcrypt-4.2.0-cp37-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:1bb429fedbe0249465cdd85a58e8376f31bb315e484f16e68ca4c786dcc04291", size = 277759 },
+    { url = "https://files.pythonhosted.org/packages/ac/be/da233c5f11fce3f8adec05e8e532b299b64833cc962f49331cdd0e614fa9/bcrypt-4.2.0-cp37-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:655ea221910bcac76ea08aaa76df427ef8625f92e55a8ee44fbf7753dbabb328", size = 273796 },
+    { url = "https://files.pythonhosted.org/packages/b0/b8/8b4add88d55a263cf1c6b8cf66c735280954a04223fcd2880120cc767ac3/bcrypt-4.2.0-cp37-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:1ee38e858bf5d0287c39b7a1fc59eec64bbf880c7d504d3a06a96c16e14058e7", size = 311082 },
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+    { url = "https://files.pythonhosted.org/packages/00/03/2af7c45034aba6002d4f2b728c1a385676b4eab7d764410e34fd768009f2/bcrypt-4.2.0-cp37-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:3698393a1b1f1fd5714524193849d0c6d524d33523acca37cd28f02899285060", size = 325185 },
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+    { url = "https://files.pythonhosted.org/packages/cc/14/b9ff8e0218bee95e517b70e91130effb4511e8827ac1ab00b4e30943a3f6/bcrypt-4.2.0-cp39-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:8d7bb9c42801035e61c109c345a28ed7e84426ae4865511eb82e913df18f58c2", size = 277934 },
+    { url = "https://files.pythonhosted.org/packages/3e/d0/31938bb697600a04864246acde4918c4190a938f891fd11883eaaf41327a/bcrypt-4.2.0-cp39-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:3d3a6d28cb2305b43feac298774b997e372e56c7c7afd90a12b3dc49b189151c", size = 273804 },
+    { url = "https://files.pythonhosted.org/packages/e7/c3/dae866739989e3f04ae304e1201932571708cb292a28b2f1b93283e2dcd8/bcrypt-4.2.0-cp39-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:9c1c4ad86351339c5f320ca372dfba6cb6beb25e8efc659bedd918d921956bae", size = 311275 },
+    { url = "https://files.pythonhosted.org/packages/5d/2c/019bc2c63c6125ddf0483ee7d914a405860327767d437913942b476e9c9b/bcrypt-4.2.0-cp39-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:27fe0f57bb5573104b5a6de5e4153c60814c711b29364c10a75a54bb6d7ff48d", size = 306355 },
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+    { url = "https://files.pythonhosted.org/packages/24/55/1a7127faf4576138bb278b91e9c75307490178979d69c8e6e273f74b974f/bcrypt-4.2.0-cp39-abi3-win32.whl", hash = "sha256:77800b7147c9dc905db1cba26abe31e504d8247ac73580b4aa179f98e6608f34", size = 155857 },
+    { url = "https://files.pythonhosted.org/packages/1c/2a/c74052e54162ec639266d91539cca7cbf3d1d3b8b36afbfeaee0ea6a1702/bcrypt-4.2.0-cp39-abi3-win_amd64.whl", hash = "sha256:61ed14326ee023917ecd093ee6ef422a72f3aec6f07e21ea5f10622b735538a9", size = 151717 },
+]
+
 [[package]]
 name = "beautifulsoup4"
 version = "4.12.3"
@@ -314,6 +384,8 @@ dependencies = [
     { name = "aioboto3" },
     { name = "datasets" },
     { name = "dicttoxml" },
+    { name = "docker" },
+    { name = "google-api-python-client" },
     { name = "hf-transfer" },
     { name = "ipykernel" },
     { name = "ipywidgets" },
@@ -332,6 +404,7 @@ dependencies = [
     { name = "schedulefree" },
     { name = "scikit-learn" },
     { name = "seaborn" },
+    { name = "skypilot", extra = ["gcp", "runpod"] },
     { name = "torch" },
     { name = "tqdm" },
     { name = "transformers" },
@@ -344,6 +417,8 @@ requires-dist = [
     { name = "aioboto3", specifier = ">=13.2.0" },
     { name = "datasets", specifier = ">=3.0.1" },
     { name = "dicttoxml", specifier = ">=1.7.16" },
+    { name = "docker", specifier = ">=7.1.0" },
+    { name = "google-api-python-client", specifier = ">=2.152.0" },
     { name = "hf-transfer", specifier = ">=0.1.8" },
     { name = "ipykernel", specifier = ">=6.29.5" },
     { name = "ipywidgets", specifier = ">=8.1.5" },
@@ -362,6 +437,7 @@ requires-dist = [
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     { name = "tqdm", specifier = ">=4.66.5" },
     { name = "transformers", git = "https://github.com/huggingface/transformers" },
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