diff --git a/.env.example b/.env.example
index fdaab21..ce497ec 100644
--- a/.env.example
+++ b/.env.example
@@ -1,2 +1,5 @@
 HF_TOKEN="your_huggingface_token"
-WANDB_API_KEY="your_wandb_api_key"
\ No newline at end of file
+WANDB_API_KEY="your_wandb_api_key"
+
+AWS_SECRET_ACCESS_KEY="your_aws_secret_access_key"
+AWS_ACCESS_KEY_ID="your_aws_access_key_id"
diff --git a/cache/cache.py b/cache/cache.py
new file mode 100644
index 0000000..a664b20
--- /dev/null
+++ b/cache/cache.py
@@ -0,0 +1,281 @@
+from abc import ABC, abstractmethod
+import sqlite3
+import pickle
+import hashlib
+import asyncio
+from typing import Optional, Callable, Tuple, Any
+import os
+
+BUST_CACHE = os.getenv("BUST_CACHE", "")
+CACHE_ONLY = os.getenv("CACHE_ONLY", "false").lower() != "false"
+PRINT_CACHE_MISSES = os.getenv("PRINT_CACHE_MISSES", "false").lower() != "false"
+
+bust_cache_ids = set(BUST_CACHE.split(","))
+
+if "all" in bust_cache_ids:
+    print("Busting all caches, good luck")
+elif bust_cache_ids != {""}:
+    print(f"Busting cache for {bust_cache_ids}")
+
+if CACHE_ONLY:
+    print("CACHE_ONLY is set to True. Cache will be used exclusively.")
+
+
+class CacheBackend(ABC):
+    @abstractmethod
+    async def setup(self) -> None:
+        pass
+
+    @abstractmethod
+    async def get(self, fn_id: str, arg_hash: str) -> Tuple[bool, Any]:
+        """Returns (cache_hit, result)"""
+        pass
+
+    @abstractmethod
+    async def set(self, fn_id: str, arg_hash: str, result: Any) -> None:
+        pass
+
+    @abstractmethod
+    async def delete(self, fn_id: str, arg_hash: str) -> None:
+        """Deletes a cache entry"""
+        pass
+
+    @abstractmethod
+    async def delete_all(self) -> None:
+        """Deletes all cache entries"""
+        pass
+
+    @abstractmethod
+    async def delete_by_fn_id(self, fn_id: str) -> None:
+        """Deletes all cache entries for a specific function ID"""
+        pass
+
+
+class SQLiteBackend(CacheBackend):
+    def __init__(self, db_path: str):
+        self.db_path = db_path
+
+    def _get_connection(self):
+        return sqlite3.connect(self.db_path)
+
+    async def setup(self):
+        with self._get_connection() as conn:
+            cursor = conn.cursor()
+            cursor.execute(
+                """
+                CREATE TABLE IF NOT EXISTS cache (
+                    fn_id TEXT,
+                    arg_hash TEXT,
+                    result BLOB,
+                    chunk_index INTEGER,
+                    is_chunked BOOLEAN,
+                    created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
+                    PRIMARY KEY (fn_id, arg_hash, chunk_index)
+                )
+            """
+            )
+            conn.commit()
+
+    async def get(self, fn_id: str, arg_hash: str) -> Tuple[bool, Any]:
+        with self._get_connection() as conn:
+            cursor = conn.cursor()
+            cursor.execute(
+                """
+                SELECT result, is_chunked, chunk_index FROM cache 
+                WHERE fn_id = ? AND arg_hash = ?
+                ORDER BY chunk_index
+            """,
+                (fn_id, arg_hash),
+            )
+            rows = cursor.fetchall()
+
+            if not rows:
+                return False, None
+
+            if not rows[0][1]:  # not chunked
+                return True, pickle.loads(rows[0][0])
+
+            # Combine chunks
+            result_data = b"".join(row[0] for row in rows)
+            return True, pickle.loads(result_data)
+
+    async def set(self, fn_id: str, arg_hash: str, result: Any) -> None:
+        pickled_data = pickle.dumps(result)
+        chunk_size = 1024 * 1024  # 1MB chunks
+        is_chunked = len(pickled_data) > chunk_size
+
+        with self._get_connection() as conn:
+            cursor = conn.cursor()
+            cursor.execute(
+                "DELETE FROM cache WHERE fn_id = ? AND arg_hash = ?",
+                (fn_id, arg_hash),
+            )
+
+            if not is_chunked:
+                cursor.execute(
+                    """
+                    INSERT INTO cache (fn_id, arg_hash, result, chunk_index, is_chunked)
+                    VALUES (?, ?, ?, 0, 0)
+                """,
+                    (fn_id, arg_hash, pickled_data),
+                )
+            else:
+                chunks = [
+                    pickled_data[i : i + chunk_size]
+                    for i in range(0, len(pickled_data), chunk_size)
+                ]
+                for i, chunk in enumerate(chunks):
+                    cursor.execute(
+                        """
+                        INSERT INTO cache (fn_id, arg_hash, result, chunk_index, is_chunked)
+                        VALUES (?, ?, ?, ?, 1)
+                    """,
+                        (fn_id, arg_hash, chunk, i),
+                    )
+
+            conn.commit()
+
+    async def delete(self, fn_id: str, arg_hash: str) -> None:
+        with self._get_connection() as conn:
+            cursor = conn.cursor()
+            cursor.execute(
+                "DELETE FROM cache WHERE fn_id = ? AND arg_hash = ?",
+                (fn_id, arg_hash),
+            )
+            conn.commit()
+
+    async def delete_all(self) -> None:
+        with self._get_connection() as conn:
+            cursor = conn.cursor()
+            cursor.execute("DELETE FROM cache")
+            conn.commit()
+
+    async def delete_by_fn_id(self, fn_id: str) -> None:
+        with self._get_connection() as conn:
+            cursor = conn.cursor()
+            cursor.execute("DELETE FROM cache WHERE fn_id = ?", (fn_id,))
+            conn.commit()
+
+
+class Cache:
+    def __init__(self, backend: CacheBackend):
+        self.backend = backend
+        self._setup_done = False
+
+    async def ensure_setup(self):
+        if not self._setup_done:
+            await self.backend.setup()
+            self._setup_done = True
+
+    def cache(
+        self,
+        id: Optional[str] = None,
+        hash_func: Optional[Callable[..., str]] = None,
+        debug: bool = False,
+    ):
+        def decorator(func):
+            if not asyncio.iscoroutinefunction(func):
+                raise ValueError(
+                    "Cache decorator can only be used with async functions"
+                )
+
+            async def async_wrapper(*args, **kwargs):
+                await self.ensure_setup()
+                fn_id = id if id else func.__name__
+
+                try:
+                    if hash_func:
+                        arg_hash = hashlib.sha256(
+                            hash_func(*args, **kwargs).encode()
+                        ).hexdigest()
+                    else:
+                        arg_hash = hashlib.sha256(
+                            pickle.dumps((args, kwargs))
+                        ).hexdigest()
+                except Exception as e:
+                    print(
+                        f"Error computing arg_hash for fn_id={fn_id} with inputs args={args}, kwargs={kwargs}"
+                    )
+                    raise e
+
+                if debug:
+                    print(f"Debug: fn_id={fn_id}, arg_hash={arg_hash}")
+
+                should_bust_cache = fn_id in bust_cache_ids or "all" in bust_cache_ids
+                if debug:
+                    print(f"should_bust_cache={should_bust_cache}")
+
+                if not should_bust_cache:
+                    cache_hit, cached_result = await self.backend.get(fn_id, arg_hash)
+                    if cache_hit:
+                        return cached_result
+
+                if CACHE_ONLY:
+                    raise Exception(
+                        f"Cache miss for fn_id={fn_id} with arg_hash={arg_hash}. CACHE_ONLY is set to True."
+                    )
+
+                if PRINT_CACHE_MISSES:
+                    print(
+                        f"Cache miss for fn_id={fn_id} with arg_hash={arg_hash}. Executing function."
+                    )
+
+                result = await func(*args, **kwargs)
+                await self.backend.set(fn_id, arg_hash, result)
+                return result
+
+            async def bust_cache(*args, **kwargs):
+                await self.ensure_setup()
+                fn_id = id if id else func.__name__
+
+                if not args and not kwargs:
+                    await self.backend.delete_by_fn_id(fn_id)
+                    return
+
+                try:
+                    if hash_func:
+                        arg_hash = hashlib.sha256(
+                            hash_func(*args, **kwargs).encode()
+                        ).hexdigest()
+                    else:
+                        arg_hash = hashlib.sha256(
+                            pickle.dumps((args, kwargs))
+                        ).hexdigest()
+                    await self.backend.delete(fn_id, arg_hash)
+                except Exception as e:
+                    print(
+                        f"Error computing arg_hash for fn_id={fn_id} with inputs args={args}, kwargs={kwargs}"
+                    )
+                    raise e
+
+            async def read_cache(*args, **kwargs):
+                await self.ensure_setup()
+                fn_id = id if id else func.__name__
+
+                try:
+                    if hash_func:
+                        arg_hash = hashlib.sha256(
+                            hash_func(*args, **kwargs).encode()
+                        ).hexdigest()
+                    else:
+                        arg_hash = hashlib.sha256(
+                            pickle.dumps((args, kwargs))
+                        ).hexdigest()
+                except Exception as e:
+                    print(
+                        f"Error computing arg_hash for fn_id={fn_id} with inputs args={args}, kwargs={kwargs}"
+                    )
+                    raise e
+
+                return await self.backend.get(fn_id, arg_hash)
+
+            async_wrapper.bust_cache = bust_cache
+            async_wrapper.read_cache = read_cache
+            return async_wrapper
+
+        return decorator
+
+    async def bust_all(self) -> None:
+        """Busts the entire cache"""
+        await self.ensure_setup()
+        await self.backend.delete_all()
diff --git a/cache/test_cache.py b/cache/test_cache.py
new file mode 100644
index 0000000..b61fc0f
--- /dev/null
+++ b/cache/test_cache.py
@@ -0,0 +1,136 @@
+import pytest
+import asyncio
+import os
+from cache import Cache, SQLiteBackend
+
+
+# Test fixtures
+@pytest.fixture
+def cache():
+    # Use temporary test database
+    db_path = "test_cache.db"
+    backend = SQLiteBackend(db_path)
+    cache = Cache(backend)
+
+    yield cache
+
+    # Cleanup
+    asyncio.run(cache.bust_all())
+    if os.path.exists(db_path):
+        os.remove(db_path)
+
+
+# Test functions to be cached
+async def async_add(a: int, b: int) -> int:
+    return a + b
+
+
+async def async_multiply(a: int, b: int) -> int:
+    return a * b
+
+
+@pytest.mark.asyncio
+async def test_async_cache_hit(cache):
+    cached_add = cache.cache()(async_add)
+
+    # First call - should miss cache
+    result1 = await cached_add(2, 3)
+    assert result1 == 5
+
+    # Verify cache hit
+    cache_hit, cached_result = await cached_add.read_cache(2, 3)
+    assert cache_hit
+    assert cached_result == 5
+
+    # Verify cached result is returned quickly
+    result2 = await cached_add(2, 3)
+    assert result2 == 5
+
+
+@pytest.mark.asyncio
+async def test_custom_cache_id(cache):
+    cached_add = cache.cache(id="custom_add")(async_add)
+
+    # Initial call and verify result is cached
+    result = await cached_add(2, 3)
+    assert result == 5
+    cache_hit, cached_result = await cached_add.read_cache(2, 3)
+    assert cache_hit
+    assert cached_result == 5
+
+    # Bust cache and verify it's gone
+    await cached_add.bust_cache()
+    cache_hit, _ = await cached_add.read_cache(2, 3)
+    assert not cache_hit
+
+
+@pytest.mark.asyncio
+async def test_custom_hash_function(cache):
+    def hash_func(a, b):
+        return f"{a}+{b}"
+
+    cached_add = cache.cache(hash_func=hash_func)(async_add)
+
+    # Initial call and verify result is cached
+    result = await cached_add(2, 3)
+    assert result == 5
+    cache_hit, cached_result = await cached_add.read_cache(2, 3)
+    assert cache_hit
+    assert cached_result == 5
+
+
+@pytest.mark.asyncio
+async def test_bust_specific_args(cache):
+    cached_add = cache.cache()(async_add)
+
+    # Cache multiple different calls
+    result1 = await cached_add(2, 3)
+    result2 = await cached_add(4, 5)
+    assert result1 == 5
+    assert result2 == 9
+
+    # Bust specific args and verify only that entry is removed
+    await cached_add.bust_cache(2, 3)
+
+    cache_hit1, _ = await cached_add.read_cache(2, 3)
+    cache_hit2, cached_result2 = await cached_add.read_cache(4, 5)
+    assert not cache_hit1  # Should be busted
+    assert cache_hit2  # Should still exist
+    assert cached_result2 == 9
+
+
+@pytest.mark.asyncio
+async def test_bust_entire_function(cache):
+    cached_add = cache.cache()(async_add)
+    cached_multiply = cache.cache()(async_multiply)
+
+    await cached_add(2, 3)
+    await cached_multiply(4, 5)
+
+    await cached_add.bust_cache()
+
+    # Verify first cache is cleared but second remains
+    add_hit, _ = await cached_add.read_cache(2, 3)
+    mult_hit, mult_result = await cached_multiply.read_cache(4, 5)
+    assert not add_hit
+    assert mult_hit
+    assert mult_result == 20
+
+
+@pytest.mark.asyncio
+async def test_bust_entire_cache(cache):
+    cached_add1 = cache.cache()(async_add)
+    cached_add2 = cache.cache()(async_add)
+
+    # Cache results for both functions
+    await cached_add1(2, 3)
+    await cached_add2(4, 5)
+
+    # Bust entire cache
+    await cache.bust_all()
+
+    # Verify all caches are cleared
+    add1_hit, _ = await cached_add1.read_cache(2, 3)
+    add2_hit, _ = await cached_add2.read_cache(4, 5)
+    assert not add1_hit
+    assert not add2_hit
diff --git a/pyproject.toml b/pyproject.toml
index 08604ed..76f6609 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,23 +5,27 @@ description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.11"
 dependencies = [
-    "bitsandbytes>=0.44.1",
+    # "bitsandbytes>=0.44.1",
     "datasets>=3.0.1",
     "dicttoxml>=1.7.16",
-    "flash-attn==2.6.3",
+    # "flash-attn==2.6.3",
     "hf-transfer>=0.1.8",
     "ipykernel>=6.29.5",
     "ipywidgets>=8.1.5",
     "jupyter>=1.1.1",
-    "liger-kernel>=0.3.1",
+    # "liger-kernel>=0.3.1",
     "matplotlib>=3.9.2",
+    "numpy>=1.26.4",
     "peft>=0.13.2",
+    "plotly>=5.24.1",
     "polars>=1.9.0",
+    "pytest-asyncio>=0.24.0",
+    "pytest>=8.3.3",
     "python-dotenv>=1.0.1",
     "schedulefree>=1.2.7",
     "scikit-learn>=1.5.2",
     "seaborn>=0.13.2",
-    "sglang[all]>=0.3.3.post1",
+    # "sglang[all]>=0.3.3.post1",
     "torch==2.4.0",
     "tqdm>=4.66.5",
     "transformers>=4.45.2",
@@ -31,4 +35,3 @@ dependencies = [
 
 [tool.uv.sources]
 transformers = { git = "https://github.com/huggingface/transformers" }
-sglang = { path = "deps/sglang/python", editable = true }
diff --git a/scraped-stories/prepare-data.ipynb b/scraped-stories/prepare-data.ipynb
new file mode 100644
index 0000000..e8328b7
--- /dev/null
+++ b/scraped-stories/prepare-data.ipynb
@@ -0,0 +1,655 @@
+{
+ "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": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cb28a7521fb548a29e983b3e2c20507b",
+       "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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+      "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": 99,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def serialize_story(story, include_datetime=True):\n",
+    "    time_format = \"%A %I:%M %p, %Y-%m-%d\" if include_datetime else \"%Y-%m-%d\"\n",
+    "\n",
+    "    return f\"\"\"URL: {story[\"url\"]}\n",
+    "Author: {story[\"scraped_byline\"]}\n",
+    "HN Submitter: {story[\"by\"]}\n",
+    "Contents:\n",
+    "{story[\"scraped_body\"].strip()}\n",
+    "\n",
+    "Published: {story[\"time\"].strftime(time_format)}\n",
+    "HN Title: {story[\"title\"]}\"\"\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "relevant_cols = pl.struct(\n",
+    "    [\n",
+    "        \"url\",\n",
+    "        \"scraped_byline\",\n",
+    "        \"by\",\n",
+    "        \"scraped_body\",\n",
+    "        \"time\",\n",
+    "        \"title\",\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "# Create two versions of the dataset with different serialization formats\n",
+    "df_with_datetime = df.with_columns(\n",
+    "    relevant_cols.map_elements(\n",
+    "        lambda x: serialize_story(x, include_datetime=True), return_dtype=pl.Utf8\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/stories_train_model_v13.py b/stories_train_model_v13.py
new file mode 100644
index 0000000..70bb03e
--- /dev/null
+++ b/stories_train_model_v13.py
@@ -0,0 +1,144 @@
+import torch
+from datasets import load_dataset, Dataset
+from transformers import (
+    AutoModelForSequenceClassification,
+    AutoTokenizer,
+    Trainer,
+    TrainingArguments,
+)
+from peft.tuners.lora import LoraConfig
+from peft.mapping import get_peft_model
+import wandb
+from dotenv import load_dotenv
+import polars as pl
+from utils import stories_dataset
+from liger_kernel.transformers import _apply_liger_kernel_to_instance
+from training_helpers import (
+    compute_metrics,
+    run_final_inference_and_report_metrics,
+    MandT,
+)
+
+load_dotenv("/workspace/.env")
+
+# Configuration
+base_model = "unsloth/Meta-Llama-3.1-8B"
+run_name = __file__.split("/")[-1].replace(".py", "")
+output_dir = f"./models/{run_name}"
+num_epochs = 1
+batch_size = 32
+gradient_accumulation_steps = 1
+learning_rate = 2e-4
+max_length = 4096
+
+# Initialize wandb
+wandb.init(project="hn_stories_model_training", name=run_name)
+
+
+def create_dataset(split, num_rows, tokenizer):
+    stories = stories_dataset()
+    stories = stories.filter(pl.col("split") == split).head(num_rows)
+
+    stories = stories.with_columns(
+        [
+            pl.col("serialized").alias("text"),
+            pl.col("log_score").alias("label"),
+        ]
+    )
+
+    stories = stories.with_columns(
+        [
+            pl.col("text")
+            .map_elements(
+                lambda x: tokenizer(x)["input_ids"], return_dtype=pl.List(pl.Int64)
+            )
+            .alias("input_ids"),
+        ]
+    ).select(["input_ids", "label"])
+    return Dataset.from_polars(stories)
+
+
+print("Loading tokenizer and model...")
+tokenizer = AutoTokenizer.from_pretrained(
+    base_model,
+    truncation=True,
+    padding=True,
+    max_length=max_length,
+)
+
+model = AutoModelForSequenceClassification.from_pretrained(
+    base_model,
+    num_labels=1,  # Regression task
+    device_map="auto",
+    attn_implementation="flash_attention_2",
+    torch_dtype=torch.bfloat16,
+)
+_apply_liger_kernel_to_instance(model=model)
+
+model.gradient_checkpointing_enable(
+    gradient_checkpointing_kwargs={"use_reentrant": True}
+)
+
+
+model.config.pad_token_id = tokenizer.pad_token_id
+tokenizer.padding_side = "right"
+
+print("Configuring LoRA...")
+model = get_peft_model(
+    model,
+    LoraConfig(
+        task_type="SEQ_CLS",
+        r=8,
+        lora_alpha=16,
+        lora_dropout=0,
+    ),
+)
+
+print("Loading dataset...")
+train_stories = create_dataset("train", 1000000, tokenizer)
+validation_stories = create_dataset("val", 1000, tokenizer)
+
+
+# Configure training arguments
+training_args = TrainingArguments(
+    output_dir=output_dir,
+    num_train_epochs=num_epochs,
+    per_device_train_batch_size=batch_size,
+    per_device_eval_batch_size=batch_size,
+    learning_rate=learning_rate,
+    weight_decay=0,
+    evaluation_strategy="steps",
+    eval_steps=0.05,
+    logging_steps=100,
+    save_strategy="steps",
+    save_steps=1000,
+    report_to="wandb",
+    no_cuda=False,
+    bf16=True,
+    warmup_steps=100,
+    gradient_accumulation_steps=gradient_accumulation_steps,
+)
+
+
+print("Initializing Trainer...")
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=train_stories,
+    eval_dataset=validation_stories,
+    tokenizer=tokenizer,
+    compute_metrics=compute_metrics,
+)
+
+print("Starting model training...")
+trainer.train()
+
+print("Saving final model...")
+trainer.save_model(output_dir)
+tokenizer.save_pretrained(output_dir)
+
+# Add the final inference and metrics reporting
+print("Running final inference and reporting metrics...")
+metrics = run_final_inference_and_report_metrics(MandT(model, tokenizer), output_dir)
+
+print("Stories model training complete")
diff --git a/uv.lock b/uv.lock
index bf95973..0ad8d59 100644
--- a/uv.lock
+++ b/uv.lock
@@ -115,34 +115,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/76/ac/a7305707cb852b7e16ff80eaf5692309bde30e2b1100a1fcacdc8f731d97/aiosignal-1.3.1-py3-none-any.whl", hash = "sha256:f8376fb07dd1e86a584e4fcdec80b36b7f81aac666ebc724e2c090300dd83b17", size = 7617 },
 ]
 
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