diff --git a/notebooks/03_semantic_methods.ipynb b/notebooks/03_semantic_methods.ipynb
index ceefea6..dd32331 100644
--- a/notebooks/03_semantic_methods.ipynb
+++ b/notebooks/03_semantic_methods.ipynb
@@ -1,8 +1,16 @@
{
"cells": [
+ {
+ "cell_type": "markdown",
+ "id": "07e239bf",
+ "metadata": {},
+ "source": [
+ "Imports and setup"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 24,
"id": "1638b7b97e3bd6f",
"metadata": {
"ExecuteTime": {
@@ -10,11 +18,83 @@
"start_time": "2025-11-22T11:40:20.129376Z"
}
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Loaded 29 test pairs\n"
+ ]
+ }
+ ],
"source": [
"import spacy\n",
- "nlp = spacy.load(\"en_core_web_lg\") # Large model\n",
- "nlp_trf = spacy.load(\"en_core_web_trf\") # Transformer Model"
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "from sklearn.metrics.pairwise import cosine_similarity\n",
+ "from sentence_transformers import SentenceTransformer\n",
+ "\n",
+ "# Load models\n",
+ "nlp = spacy.load(\"en_core_web_lg\")\n",
+ "model_bert = SentenceTransformer(\"all-MiniLM-L6-v2\")\n",
+ "\n",
+ "test_pairs = [\n",
+ "## STANDARD TEST PAIRS (from previous notebooks)\n",
+ " # Direct copies and near-copies\n",
+ " (\"The cat sat on the mat.\", \"The cat sat on the mat.\"), # Exact copy\n",
+ " (\"The cat sat on the mat.\", \"The cat sat on the mat\"), # No punctuation\n",
+ " (\"The cat sat on the mat.\", \"The cat sat on the mat.\"), # Extra spaces\n",
+ " \n",
+ " # Paraphrases with same meaning\n",
+ " (\"The cat sat on the mat.\", \"On the mat, the cat was sitting.\"), # Structural change\n",
+ " (\"The cat sat on the mat.\", \"The feline rested on the rug.\"), # Synonym replacement\n",
+ " (\"The quick brown fox jumps.\", \"A fast brown fox leaps.\"), # Partial synonym\n",
+ " \n",
+ " # Different sentences\n",
+ " (\"The cat sat on the mat.\", \"The dog ran in the park.\"), # Different content\n",
+ " (\"I love programming.\", \"She enjoys reading books.\"), # Completely different\n",
+ " (\"The weather is nice today.\", \"It's raining outside.\"), # Opposite meaning\n",
+ " \n",
+ " # Edge cases\n",
+ " (\"Short.\", \"Short.\"), # Very short\n",
+ " (\"A B C D E F G\", \"A B C D E F G\"), # Repeated words\n",
+ " (\"\", \"\"), # Empty strings\n",
+ "## ADDITIONAL TEST PAIRS (for semantic evaluation)\n",
+ " # Polysemy (word sense ambiguity) - Critical for BERT vs SpaCy\n",
+ " (\"He went to the bank to deposit money.\", \"He went to the river bank to fish.\"), # Bank: financial vs. riverbank\n",
+ " (\"I saw the bat in the cave.\", \"The baseball bat is broken.\"), # Bat: animal vs. sports equipment\n",
+ " (\"The light is very bright.\", \"Can you carry this light package?\"), # Light: brightness vs. weight\n",
+ " \n",
+ " # Synonymy (different words, same meaning) - Tests semantic understanding\n",
+ " (\"The car is fast.\", \"The automobile is quick.\"), # car/automobile, fast/quick\n",
+ " (\"He commenced the project.\", \"He started the work.\"), # commenced/started, project/work\n",
+ " (\"The substantial building is ornate.\", \"The large building is decorated.\"), # substantial/large, ornate/decorated\n",
+ " \n",
+ " # Negation (opposite meaning) - Tests if methods catch semantic inversion\n",
+ " (\"The weather is good.\", \"The weather is bad.\"), # Direct negation\n",
+ " (\"The solution is simple.\", \"The problem is complex.\"), # Opposite adjectives\n",
+ " (\"I like this movie.\", \"I dislike this movie.\"), # Explicit negation\n",
+ " \n",
+ " # Semantic similarity without word overlap - Pure meaning test\n",
+ " (\"A man walks down the street.\", \"A person strolls along the road.\"), # Very few shared words, similar meaning\n",
+ " (\"The student studied the textbook.\", \"The pupil learned from the book.\"), # Different words, same semantic content\n",
+ " \n",
+ " # Partial overlap (high word similarity, low semantic similarity)\n",
+ " (\"The bank is on the river.\", \"The bank account is overdrawn.\"), # \"bank\" is only shared word; different meanings\n",
+ " (\"I read the book last night.\", \"The book was read by many people.\"), # Same key words (book, read) but different focus/meaning\n",
+ " \n",
+ " # Length variation (tests robustness to sentence length)\n",
+ " (\"Cat sat.\", \"The cat sat on the mat for hours and hours.\"), # Very different lengths, similar core meaning\n",
+ " (\"Go.\", \"You should go to the store and buy milk immediately.\"), # Minimal vs. detailed\n",
+ " \n",
+ " # Metaphor/Figurative language\n",
+ " (\"Time is money.\", \"Time has value.\"), # Metaphor vs. literal\n",
+ " (\"The world is a stage.\", \"Life is like a theater performance.\"), # Figurative expressions\n",
+ "]\n",
+ "\n",
+ "print(f\"Loaded {len(test_pairs)} test pairs\")"
]
},
{
@@ -22,12 +102,13 @@
"id": "b79941bf4553fd6",
"metadata": {},
"source": [
- "Test word vectors"
+ "## Method 1: SIF smooth inverse frequency\n",
+ "Weighting word vectors based on frequency, emphasize information-dense words"
]
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 25,
"id": "8a3c4314a90086fe",
"metadata": {
"ExecuteTime": {
@@ -35,57 +116,52 @@
"start_time": "2025-11-22T11:47:39.271377Z"
}
},
+ "outputs": [],
+ "source": [
+ "def sentence_similarity_sif(sent1, sent2, a=0.001):\n",
+ " \"\"\"Compute similarity using SIF-weighted word vectors.\"\"\"\n",
+ " def get_weighted_vector(text):\n",
+ " doc = nlp(text)\n",
+ " vectors = []\n",
+ " weights = []\n",
+ " \n",
+ " for token in doc:\n",
+ " if token.has_vector and token.is_alpha and not token.is_stop:\n",
+ " prob = np.exp(token.prob) if token.prob > -19 else 1e-6\n",
+ " weight = a / (a + prob)\n",
+ " vectors.append(token.vector)\n",
+ " weights.append(weight)\n",
+ " \n",
+ " if not vectors:\n",
+ " return np.zeros(nlp.vocab.vectors_length)\n",
+ " \n",
+ " return np.average(vectors, axis=0, weights=weights)\n",
+ "\n",
+ " v1 = get_weighted_vector(sent1)\n",
+ " v2 = get_weighted_vector(sent2)\n",
+ " \n",
+ " return cosine_similarity(v1.reshape(1, -1), v2.reshape(1, -1))[0][0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "c51a0436",
+ "metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "cat - dog: 0.802\n",
- "cat - feline: 0.699\n",
- "cat - feral: 0.486\n",
- "cat - vehicle: 0.190\n",
- "cat - car: 0.319\n",
- "dog - cat: 0.802\n",
- "dog - feline: 0.566\n",
- "dog - feral: 0.400\n",
- "dog - vehicle: 0.258\n",
- "dog - car: 0.356\n",
- "feline - cat: 0.699\n",
- "feline - dog: 0.566\n",
- "feline - feral: 0.543\n",
- "feline - vehicle: 0.103\n",
- "feline - car: 0.095\n",
- "feral - cat: 0.486\n",
- "feral - dog: 0.400\n",
- "feral - feline: 0.543\n",
- "feral - vehicle: 0.088\n",
- "feral - car: 0.040\n",
- "vehicle - cat: 0.190\n",
- "vehicle - dog: 0.258\n",
- "vehicle - feline: 0.103\n",
- "vehicle - feral: 0.088\n",
- "vehicle - car: 0.767\n",
- "car - cat: 0.319\n",
- "car - dog: 0.356\n",
- "car - feline: 0.095\n",
- "car - feral: 0.040\n",
- "car - vehicle: 0.767\n"
+ "SIF Similarity: 0.609\n"
]
}
],
"source": [
- "def test_word_vectors(word):\n",
- " print(word, nlp.vocab[word].vector.shape)\n",
+ "s1 = \"The cat ate the food.\"\n",
+ "s2 = \"The feline devoured the sustenance.\"\n",
"\n",
- "words = [\"cat\", \"dog\", \"feline\", \"feral\", \"vehicle\", \"car\"]\n",
- "# Test work similarities\n",
- "for word1 in words:\n",
- " for word2 in words:\n",
- " if word1 != word2:\n",
- " similarity = nlp.vocab[word1].similarity(nlp.vocab[word2])\n",
- " print(f\"{word1} - {word2}: {similarity:.3f}\")\n",
- "\n",
- "\n"
+ "print(f\"SIF Similarity: {sentence_similarity_sif(s1, s2):.3f}\")"
]
},
{
@@ -93,12 +169,13 @@
"id": "8f32b5695f554268",
"metadata": {},
"source": [
- "Simple averaging"
+ "## Method 2: BERT - Transformer Embedding\n",
+ "Use contectual embeddings to resolve polysemy issues"
]
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 27,
"id": "68a6757447e4a1c7",
"metadata": {
"ExecuteTime": {
@@ -108,24 +185,34 @@
},
"outputs": [],
"source": [
- "def sentence_similarity_avg(sent1, sent2):\n",
- " doc1 = nlp(sent1)\n",
- " doc2 = nlp(sent2)\n",
+ "def sentence_similarity_bert(sent1, sent2):\n",
+ " \"\"\"Compute similarity using SBERT contextual embeddings.\"\"\"\n",
+ " embeddings = model_bert.encode([sent1, sent2])\n",
+ " return cosine_similarity([embeddings[0]], [embeddings[1]])[0][0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "id": "420df430",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Bank (Money) vs Bank (River): 0.590\n",
+ "Bank (Money) vs Institution: 0.641\n"
+ ]
+ }
+ ],
+ "source": [
+ "sent_a = \"He went to the bank to deposit money.\"\n",
+ "sent_b = \"He went to the river bank to fish.\"\n",
+ "sent_c = \"He visited the financial institution.\"\n",
"\n",
- " # Vectors for each word, filter out words without vectors (medium model)\n",
- " vecs1 = [token.vector for token in doc1 if token.has_vector]\n",
- " vecs2 = [token.vector for token in doc2 if token.has_vector]\n",
- "\n",
- " if not vecs1 or not vecs2:\n",
- " return 0.0\n",
- "\n",
- " # Average vectors\n",
- " avg1 = sum(vecs1) / len(vecs1)\n",
- " avg2 = sum(vecs2) / len(vecs2)\n",
- "\n",
- " #cosine similarity\n",
- " from sklearn.metrics.pairwise import cosine_similarity\n",
- " return cosine_similarity([avg1], [avg2])[0][0]\n"
+ "print(f\"Bank (Money) vs Bank (River): {sentence_similarity_bert(sent_a, sent_b):.3f}\")\n",
+ "print(f\"Bank (Money) vs Institution: {sentence_similarity_bert(sent_a, sent_c):.3f}\")\n"
]
},
{
@@ -133,19 +220,773 @@
"id": "a9c3aa050f5bc0fe",
"metadata": {},
"source": [
- "SIF - Smooth Inverse Similarity"
+ "### Semantic Evaluation\n",
+ "Comparing simple average baseline, SIF and BERT"
]
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"id": "c100956f89d9b581",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Semantic Method Results:\n",
+ "======================================================================\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " pair_id | \n",
+ " Spacy Avg | \n",
+ " Spacy SIF | \n",
+ " BERT | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " 0 | \n",
+ " 1.000 | \n",
+ " 1.000 | \n",
+ " 1.000 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " 1 | \n",
+ " 0.984 | \n",
+ " 1.000 | \n",
+ " 0.995 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 2 | \n",
+ " 1.000 | \n",
+ " 1.000 | \n",
+ " 1.000 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 3 | \n",
+ " 0.965 | \n",
+ " 0.954 | \n",
+ " 0.930 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 4 | \n",
+ " 0.901 | \n",
+ " 0.696 | \n",
+ " 0.549 | \n",
+ "
\n",
+ " \n",
+ " | 5 | \n",
+ " 5 | \n",
+ " 0.942 | \n",
+ " 0.930 | \n",
+ " 0.945 | \n",
+ "
\n",
+ " \n",
+ " | 6 | \n",
+ " 6 | \n",
+ " 0.857 | \n",
+ " 0.642 | \n",
+ " 0.067 | \n",
+ "
\n",
+ " \n",
+ " | 7 | \n",
+ " 7 | \n",
+ " 0.702 | \n",
+ " 0.529 | \n",
+ " 0.154 | \n",
+ "
\n",
+ " \n",
+ " | 8 | \n",
+ " 8 | \n",
+ " 0.845 | \n",
+ " 0.603 | \n",
+ " 0.468 | \n",
+ "
\n",
+ " \n",
+ " | 9 | \n",
+ " 9 | \n",
+ " 1.000 | \n",
+ " 1.000 | \n",
+ " 1.000 | \n",
+ "
\n",
+ " \n",
+ " | 10 | \n",
+ " 10 | \n",
+ " 1.000 | \n",
+ " 1.000 | \n",
+ " 1.000 | \n",
+ "
\n",
+ " \n",
+ " | 11 | \n",
+ " 11 | \n",
+ " 0.000 | \n",
+ " 0.000 | \n",
+ " 1.000 | \n",
+ "
\n",
+ " \n",
+ " | 12 | \n",
+ " 12 | \n",
+ " 0.908 | \n",
+ " 0.707 | \n",
+ " 0.590 | \n",
+ "
\n",
+ " \n",
+ " | 13 | \n",
+ " 13 | \n",
+ " 0.844 | \n",
+ " 0.716 | \n",
+ " 0.472 | \n",
+ "
\n",
+ " \n",
+ " | 14 | \n",
+ " 14 | \n",
+ " 0.803 | \n",
+ " 0.694 | \n",
+ " 0.310 | \n",
+ "
\n",
+ " \n",
+ " | 15 | \n",
+ " 15 | \n",
+ " 0.939 | \n",
+ " 0.803 | \n",
+ " 0.831 | \n",
+ "
\n",
+ " \n",
+ " | 16 | \n",
+ " 16 | \n",
+ " 0.909 | \n",
+ " 0.619 | \n",
+ " 0.778 | \n",
+ "
\n",
+ " \n",
+ " | 17 | \n",
+ " 17 | \n",
+ " 0.934 | \n",
+ " 0.838 | \n",
+ " 0.819 | \n",
+ "
\n",
+ " \n",
+ " | 18 | \n",
+ " 18 | \n",
+ " 0.979 | \n",
+ " 0.930 | \n",
+ " 0.822 | \n",
+ "
\n",
+ " \n",
+ " | 19 | \n",
+ " 19 | \n",
+ " 0.885 | \n",
+ " 0.623 | \n",
+ " 0.620 | \n",
+ "
\n",
+ " \n",
+ " | 20 | \n",
+ " 20 | \n",
+ " 0.967 | \n",
+ " 0.873 | \n",
+ " 0.721 | \n",
+ "
\n",
+ " \n",
+ " | 21 | \n",
+ " 21 | \n",
+ " 0.912 | \n",
+ " 0.780 | \n",
+ " 0.603 | \n",
+ "
\n",
+ " \n",
+ " | 22 | \n",
+ " 22 | \n",
+ " 0.874 | \n",
+ " 0.712 | \n",
+ " 0.652 | \n",
+ "
\n",
+ " \n",
+ " | 23 | \n",
+ " 23 | \n",
+ " 0.813 | \n",
+ " 0.632 | \n",
+ " 0.506 | \n",
+ "
\n",
+ " \n",
+ " | 24 | \n",
+ " 24 | \n",
+ " 0.891 | \n",
+ " 0.891 | \n",
+ " 0.620 | \n",
+ "
\n",
+ " \n",
+ " | 25 | \n",
+ " 25 | \n",
+ " 0.761 | \n",
+ " 0.755 | \n",
+ " 0.628 | \n",
+ "
\n",
+ " \n",
+ " | 26 | \n",
+ " 26 | \n",
+ " 0.806 | \n",
+ " 0.000 | \n",
+ " 0.202 | \n",
+ "
\n",
+ " \n",
+ " | 27 | \n",
+ " 27 | \n",
+ " 0.890 | \n",
+ " 0.764 | \n",
+ " 0.735 | \n",
+ "
\n",
+ " \n",
+ " | 28 | \n",
+ " 28 | \n",
+ " 0.897 | \n",
+ " 0.721 | \n",
+ " 0.442 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10, 5))\n",
+ "melted = results_df.melt(\n",
+ " id_vars=[\"pair_id\"], \n",
+ " value_vars=[\"Spacy Avg\", \"Spacy SIF\", \"BERT\"], \n",
+ " var_name=\"Method\", \n",
+ " value_name=\"Similarity\"\n",
+ ")\n",
+ "\n",
+ "sns.barplot(data=melted, x=\"pair_id\", y=\"Similarity\", hue=\"Method\")\n",
+ "plt.title(\"Semantic Similarity Scores by Method\")\n",
+ "plt.xlabel(\"Pair ID\")\n",
+ "plt.ylabel(\"Similarity\")\n",
+ "plt.ylim(0, 1)\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d3b42bc4",
+ "metadata": {},
+ "source": [
+ "Category Comparison"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "57f4e206",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Performance by Category:\n",
+ "================================================================================\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Spacy Avg | \n",
+ " Spacy SIF | \n",
+ " BERT | \n",
+ "
\n",
+ " \n",
+ " | \n",
+ " mean | \n",
+ " std | \n",
+ " mean | \n",
+ " std | \n",
+ " mean | \n",
+ " std | \n",
+ "
\n",
+ " \n",
+ " | category | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | Different | \n",
+ " 0.801 | \n",
+ " 0.086 | \n",
+ " 0.591 | \n",
+ " 0.057 | \n",
+ " 0.229 | \n",
+ " 0.211 | \n",
+ "
\n",
+ " \n",
+ " | Edge Case | \n",
+ " 0.667 | \n",
+ " 0.577 | \n",
+ " 0.667 | \n",
+ " 0.577 | \n",
+ " 1.000 | \n",
+ " 0.000 | \n",
+ "
\n",
+ " \n",
+ " | Exact Copy | \n",
+ " 0.995 | \n",
+ " 0.009 | \n",
+ " 1.000 | \n",
+ " 0.000 | \n",
+ " 0.998 | \n",
+ " 0.003 | \n",
+ "
\n",
+ " \n",
+ " | Figurative | \n",
+ " 0.893 | \n",
+ " 0.005 | \n",
+ " 0.742 | \n",
+ " 0.031 | \n",
+ " 0.588 | \n",
+ " 0.208 | \n",
+ "
\n",
+ " \n",
+ " | Length Var | \n",
+ " 0.784 | \n",
+ " 0.032 | \n",
+ " 0.377 | \n",
+ " 0.534 | \n",
+ " 0.415 | \n",
+ " 0.302 | \n",
+ "
\n",
+ " \n",
+ " | Negation | \n",
+ " 0.944 | \n",
+ " 0.051 | \n",
+ " 0.809 | \n",
+ " 0.163 | \n",
+ " 0.721 | \n",
+ " 0.101 | \n",
+ "
\n",
+ " \n",
+ " | Paraphrase | \n",
+ " 0.936 | \n",
+ " 0.032 | \n",
+ " 0.860 | \n",
+ " 0.143 | \n",
+ " 0.808 | \n",
+ " 0.224 | \n",
+ "
\n",
+ " \n",
+ " | Partial Overlap | \n",
+ " 0.852 | \n",
+ " 0.055 | \n",
+ " 0.762 | \n",
+ " 0.183 | \n",
+ " 0.563 | \n",
+ " 0.080 | \n",
+ "
\n",
+ " \n",
+ " | Polysemy | \n",
+ " 0.851 | \n",
+ " 0.053 | \n",
+ " 0.706 | \n",
+ " 0.011 | \n",
+ " 0.457 | \n",
+ " 0.140 | \n",
+ "
\n",
+ " \n",
+ " | Semantic Sim | \n",
+ " 0.893 | \n",
+ " 0.027 | \n",
+ " 0.746 | \n",
+ " 0.048 | \n",
+ " 0.628 | \n",
+ " 0.035 | \n",
+ "
\n",
+ " \n",
+ " | Synonymy | \n",
+ " 0.927 | \n",
+ " 0.016 | \n",
+ " 0.754 | \n",
+ " 0.117 | \n",
+ " 0.809 | \n",
+ " 0.028 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(14, 8))\n",
+ "\n",
+ "# Create a pivot table for heatmap (pairs vs methods)\n",
+ "heatmap_data = results_df[[\"pair_id\", \"Spacy Avg\", \"Spacy SIF\", \"BERT\"]].set_index(\"pair_id\")\n",
+ "\n",
+ "sns.heatmap(heatmap_data, annot=True, fmt='.2f', cmap='RdYlGn', cbar_kws={'label': 'Similarity'}, \n",
+ " vmin=0, vmax=1, linewidths=0.5)\n",
+ "plt.title('Semantic Similarity Heatmap: All Pairs vs Methods', fontsize=14, fontweight='bold')\n",
+ "plt.xlabel('Method')\n",
+ "plt.ylabel('Pair ID')\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "89686f33",
+ "metadata": {},
+ "source": [
+ "A method with higher varience it may be less reliable while all 3 methods agreeing indicates that a pair is confident"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "8c0d18f9",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Method Variance (higher = more disagreement):\n",
+ "Spacy Avg 0.033659\n",
+ "Spacy SIF 0.061698\n",
+ "BERT 0.073351\n",
+ "dtype: float64\n",
+ "\n",
+ "Method Std Dev:\n",
+ "Spacy Avg 0.183464\n",
+ "Spacy SIF 0.248391\n",
+ "BERT 0.270834\n",
+ "dtype: float64\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "method_variance = results_df[[\"Spacy Avg\", \"Spacy SIF\", \"BERT\"]].var()\n",
+ "method_std = results_df[[\"Spacy Avg\", \"Spacy SIF\", \"BERT\"]].std()\n",
+ "\n",
+ "print(\"Method Variance (higher = more disagreement):\")\n",
+ "print(method_variance)\n",
+ "print(\"\\nMethod Std Dev:\")\n",
+ "print(method_std)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a05b9608",
+ "metadata": {},
+ "source": [
+ "Two methods may give similar absoloute scores when compared but rank individual pairs differently"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "9cf007fb",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " pair_id Spacy Avg_rank Spacy SIF_rank BERT_rank\n",
+ "0 0 28.5 26.5 27.5\n",
+ "1 1 25.0 26.5 24.0\n",
+ "2 2 28.5 26.5 27.5\n",
+ "3 3 22.0 24.0 22.0\n",
+ "4 4 15.0 10.0 9.0\n",
+ "5 5 21.0 22.0 23.0\n",
+ "6 6 9.0 8.0 1.0\n",
+ "7 7 2.0 3.0 2.0\n",
+ "8 8 8.0 4.0 6.0\n",
+ "9 9 27.0 29.0 27.5\n",
+ "10 10 26.0 26.5 27.5\n",
+ "11 11 1.0 1.5 25.0\n",
+ "12 12 16.0 11.0 10.0\n",
+ "13 13 7.0 13.0 7.0\n",
+ "14 14 4.0 9.0 4.0\n",
+ "15 15 20.0 18.0 21.0\n",
+ "16 16 17.0 5.0 18.0\n",
+ "17 17 19.0 19.0 19.0\n",
+ "18 18 24.0 23.0 20.0\n",
+ "19 19 11.0 6.0 13.0\n",
+ "20 20 23.0 20.0 16.0\n",
+ "21 21 18.0 17.0 11.0\n",
+ "22 22 10.0 12.0 15.0\n",
+ "23 23 6.0 7.0 8.0\n",
+ "24 24 13.0 21.0 12.0\n",
+ "25 25 3.0 15.0 14.0\n",
+ "26 26 5.0 1.5 3.0\n",
+ "27 27 12.0 16.0 17.0\n",
+ "28 28 14.0 14.0 5.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "for method in [\"Spacy Avg\", \"Spacy SIF\", \"BERT\"]:\n",
+ " results_df[f'{method}_rank'] = results_df[method].rank()\n",
+ "\n",
+ "print(results_df[[\"pair_id\", \"Spacy Avg_rank\", \"Spacy SIF_rank\", \"BERT_rank\"]])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "df98b471",
+ "metadata": {},
+ "source": [
+ "Test methods against the polysemy problem"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "04f40f67",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Polysemy Test (Should prefer higher for synonyms, lower for different meanings):\n",
+ " pair_id sentence_1 Spacy Avg Spacy SIF BERT\n",
+ "6 6 The cat sat on the mat. 0.856538 0.641634 0.067119\n",
+ "7 7 I love programming. 0.702400 0.528659 0.153708\n"
+ ]
+ }
+ ],
+ "source": [
+ "polysemy_pairs = results_df.iloc[6:8] # Rows 6-7 are the \"bank\" examples\n",
+ "print(\"Polysemy Test (Should prefer higher for synonyms, lower for different meanings):\")\n",
+ "print(polysemy_pairs[[\"pair_id\", \"sentence_1\", \"Spacy Avg\", \"Spacy SIF\", \"BERT\"]])"
]
}
],
@@ -165,7 +1006,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.13.7"
+ "version": "3.13.12"
}
},
"nbformat": 4,
diff --git a/notebooks/04_fusion_model.ipynb b/notebooks/04_fusion_model.ipynb
index ca25e4e..341bda4 100644
--- a/notebooks/04_fusion_model.ipynb
+++ b/notebooks/04_fusion_model.ipynb
@@ -1,48 +1,968 @@
{
"cells": [
{
+ "cell_type": "markdown",
+ "id": "953fd65a",
"metadata": {},
- "cell_type": "code",
- "outputs": [],
- "execution_count": null,
"source": [
- "import spacy\n",
- "from notebook_functions import *\n",
+ "# 3rd Phase: Fusion model\n",
+ "Combining Syntactic (Tree Edit Distance) and Semantic (BERT) branches into a single classification\n",
"\n",
- "def extract_all_features(sentence_pairs):\n",
- " features = []\n",
- " for sent1, sent2 in sentence_pairs:\n",
- " feature_vector = [\n",
- " jaccard_similarity(sent1, sent2),\n",
- " sentence_similarity_avg(sent1, sent2),\n",
- " sentence_similarity_sif(sent1, sent2),\n",
- " syntactic_similarity(sent1, sent2)\n",
- " ]"
- ],
- "id": "1c45d83192facfc6"
+ "The goal is to train a logistical regression model using features from both branches to predict the liklihood of plagiarism"
+ ]
},
{
- "metadata": {},
"cell_type": "code",
- "outputs": [],
- "execution_count": null,
- "source": [
- "from sklearn.linear_model import LogisticRegression\n",
- "from sklearn.model_selection import train_test_split\n",
- "\n",
- "labled_pairs = []\n",
- "\n",
- "X = extract_all_features(labled_pairs)\n",
- "y = [0,1,0,1...] #Lables for pairs\n",
- "\n",
- "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
- "model = LogisticRegression()\n",
- "model.fit(X_train, y_train)\n"
+ "execution_count": 5,
+ "id": "1c45d83192facfc6",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Loaded 29 test pairs for fusion analysis\n"
+ ]
+ }
],
- "id": "9665682bd5a7951e"
+ "source": [
+ "import spacy\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.linear_model import LogisticRegression\n",
+ "from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score, roc_curve\n",
+ "from sentence_transformers import SentenceTransformer\n",
+ "import pickle\n",
+ "\n",
+ "# Load models\n",
+ "nlp = spacy.load(\"en_core_web_lg\")\n",
+ "model_bert = SentenceTransformer(\"all-MiniLM-L6-v2\")\n",
+ "\n",
+ "test_pairs = [\n",
+ "## STANDARD TEST PAIRS (from previous notebooks)\n",
+ " # Direct copies and near-copies\n",
+ " (\"The cat sat on the mat.\", \"The cat sat on the mat.\"), # Exact copy\n",
+ " (\"The cat sat on the mat.\", \"The cat sat on the mat\"), # No punctuation\n",
+ " (\"The cat sat on the mat.\", \"The cat sat on the mat.\"), # Extra spaces\n",
+ " \n",
+ " # Paraphrases with same meaning\n",
+ " (\"The cat sat on the mat.\", \"On the mat, the cat was sitting.\"), # Structural change\n",
+ " (\"The cat sat on the mat.\", \"The feline rested on the rug.\"), # Synonym replacement\n",
+ " (\"The quick brown fox jumps.\", \"A fast brown fox leaps.\"), # Partial synonym\n",
+ " \n",
+ " # Different sentences\n",
+ " (\"The cat sat on the mat.\", \"The dog ran in the park.\"), # Different content\n",
+ " (\"I love programming.\", \"She enjoys reading books.\"), # Completely different\n",
+ " (\"The weather is nice today.\", \"It's raining outside.\"), # Opposite meaning\n",
+ " \n",
+ " # Edge cases\n",
+ " (\"Short.\", \"Short.\"), # Very short\n",
+ " (\"A B C D E F G\", \"A B C D E F G\"), # Repeated words\n",
+ " (\"\", \"\"), # Empty strings\n",
+ "## ADDITIONAL TEST PAIRS (for semantic evaluation)\n",
+ " # Polysemy (word sense ambiguity) - Critical for BERT vs SpaCy\n",
+ " (\"He went to the bank to deposit money.\", \"He went to the river bank to fish.\"), # Bank: financial vs. riverbank\n",
+ " (\"I saw the bat in the cave.\", \"The baseball bat is broken.\"), # Bat: animal vs. sports equipment\n",
+ " (\"The light is very bright.\", \"Can you carry this light package?\"), # Light: brightness vs. weight\n",
+ " \n",
+ " # Synonymy (different words, same meaning) - Tests semantic understanding\n",
+ " (\"The car is fast.\", \"The automobile is quick.\"), # car/automobile, fast/quick\n",
+ " (\"He commenced the project.\", \"He started the work.\"), # commenced/started, project/work\n",
+ " (\"The substantial building is ornate.\", \"The large building is decorated.\"), # substantial/large, ornate/decorated\n",
+ " \n",
+ " # Negation (opposite meaning) - Tests if methods catch semantic inversion\n",
+ " (\"The weather is good.\", \"The weather is bad.\"), # Direct negation\n",
+ " (\"The solution is simple.\", \"The problem is complex.\"), # Opposite adjectives\n",
+ " (\"I like this movie.\", \"I dislike this movie.\"), # Explicit negation\n",
+ " \n",
+ " # Semantic similarity without word overlap - Pure meaning test\n",
+ " (\"A man walks down the street.\", \"A person strolls along the road.\"), # Very few shared words, similar meaning\n",
+ " (\"The student studied the textbook.\", \"The pupil learned from the book.\"), # Different words, same semantic content\n",
+ " \n",
+ " # Partial overlap (high word similarity, low semantic similarity)\n",
+ " (\"The bank is on the river.\", \"The bank account is overdrawn.\"), # \"bank\" is only shared word; different meanings\n",
+ " (\"I read the book last night.\", \"The book was read by many people.\"), # Same key words (book, read) but different focus/meaning\n",
+ " \n",
+ " # Length variation (tests robustness to sentence length)\n",
+ " (\"Cat sat.\", \"The cat sat on the mat for hours and hours.\"), # Very different lengths, similar core meaning\n",
+ " (\"Go.\", \"You should go to the store and buy milk immediately.\"), # Minimal vs. detailed\n",
+ " \n",
+ " # Metaphor/Figurative language\n",
+ " (\"Time is money.\", \"Time has value.\"), # Metaphor vs. literal\n",
+ " (\"The world is a stage.\", \"Life is like a theater performance.\"), # Figurative expressions\n",
+ "]\n",
+ "\n",
+ "print(f\"Loaded {len(test_pairs)} test pairs for fusion analysis\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ea6f478e",
+ "metadata": {},
+ "source": [
+ "## Method 1: Tree Edit Distance (Syntactic)\n",
+ "Calculates minimum edit operations to convert one dependency tree to another"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "9665682bd5a7951e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "TED Similarity: 0.857\n",
+ "(Higher score = more similar dependency structures)\n"
+ ]
+ }
+ ],
+ "source": [
+ "def get_dependency_tree(text):\n",
+ " \"\"\"Extract dependency tree structure from sentence.\"\"\"\n",
+ " doc = nlp(text)\n",
+ " tree = {}\n",
+ " for token in doc:\n",
+ " tree[token.i] = {\n",
+ " 'text': token.text,\n",
+ " 'pos': token.pos_,\n",
+ " 'dep': token.dep_,\n",
+ " 'head': token.head.i\n",
+ " }\n",
+ " return tree\n",
+ "\n",
+ "def tree_edit_distance(tree1, tree2):\n",
+ " \"\"\"\n",
+ " Simple Tree Edit Distance approximation.\n",
+ " Compares dependency structures without full algorithm (O(n^3) complexity).\n",
+ " \"\"\"\n",
+ " if not tree1 and not tree2:\n",
+ " return 0.0\n",
+ " if not tree1 or not tree2:\n",
+ " return 1.0\n",
+ " \n",
+ " # Extract dependency edges\n",
+ " edges1 = set()\n",
+ " edges2 = set()\n",
+ " \n",
+ " for i, node1 in tree1.items():\n",
+ " edges1.add((node1['dep'], node1['pos']))\n",
+ " \n",
+ " for i, node2 in tree2.items():\n",
+ " edges2.add((node2['dep'], node2['pos']))\n",
+ " \n",
+ " # Jaccard similarity on edges\n",
+ " if not edges1 and not edges2:\n",
+ " return 1.0\n",
+ " \n",
+ " intersection = len(edges1.intersection(edges2))\n",
+ " union = len(edges1.union(edges2))\n",
+ " \n",
+ " return intersection / union if union > 0 else 0.0\n",
+ "\n",
+ "def sentence_similarity_ted(sent1, sent2):\n",
+ " \"\"\"Compute similarity using Tree Edit Distance.\"\"\"\n",
+ " tree1 = get_dependency_tree(sent1)\n",
+ " tree2 = get_dependency_tree(sent2)\n",
+ " return tree_edit_distance(tree1, tree2)\n",
+ "\n",
+ "# Demo\n",
+ "s1 = \"The cat sat on the mat.\"\n",
+ "s2 = \"On the mat, the cat was sitting.\"\n",
+ "\n",
+ "print(f\"TED Similarity: {sentence_similarity_ted(s1, s2):.3f}\")\n",
+ "print(\"(Higher score = more similar dependency structures)\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "83262ade",
+ "metadata": {},
+ "source": [
+ "## Method 2: Baseline Similarity (notebook 2)\n",
+ "Jaccard similarity as a quick baseline for structural matching"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "91282393",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Jaccard Similarity: 0.375\n"
+ ]
+ }
+ ],
+ "source": [
+ "def sentence_similarity_jaccard(sent1, sent2):\n",
+ " \"\"\"Jaccard similarity baseline.\"\"\"\n",
+ " words1 = set(sent1.lower().split())\n",
+ " words2 = set(sent2.lower().split())\n",
+ " \n",
+ " if not words1 and not words2:\n",
+ " return 1.0\n",
+ " if not words1 or not words2:\n",
+ " return 0.0\n",
+ " \n",
+ " intersection = len(words1.intersection(words2))\n",
+ " union = len(words1.union(words2))\n",
+ " return intersection / union\n",
+ "\n",
+ "# Demo\n",
+ "print(f\"Jaccard Similarity: {sentence_similarity_jaccard(s1, s2):.3f}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "30db037d",
+ "metadata": {},
+ "source": [
+ "## Feature Extraction\n",
+ "Extract all relevent features for each pair"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3c9f502e",
+ "metadata": {},
+ "source": [
+ "#### Feature Extraction"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "54916598",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Feature Extraction Demo:\n",
+ "============================================================\n",
+ "bert : 0.930\n",
+ "ted : 0.857\n",
+ "jaccard : 0.375\n",
+ "len_ratio : 0.719\n"
+ ]
+ }
+ ],
+ "source": [
+ "def extract_features(sent1, sent2):\n",
+ " \"\"\"Extract semantic and syntactic features for a sentence pair.\"\"\"\n",
+ " \n",
+ " # Semantic features\n",
+ " embeddings = model_bert.encode([sent1, sent2])\n",
+ " bert_similarity = np.dot(embeddings[0], embeddings[1]) / (\n",
+ " np.linalg.norm(embeddings[0]) * np.linalg.norm(embeddings[1])\n",
+ " )\n",
+ " \n",
+ " # Syntactic features\n",
+ " ted_similarity = sentence_similarity_ted(sent1, sent2)\n",
+ " \n",
+ " # Baseline features\n",
+ " jaccard_similarity = sentence_similarity_jaccard(sent1, sent2)\n",
+ " \n",
+ " # Length ratio (robustness feature)\n",
+ " len_ratio = min(len(sent1), len(sent2)) / max(len(sent1), len(sent2)) if max(len(sent1), len(sent2)) > 0 else 0.0\n",
+ " \n",
+ " return {\n",
+ " 'bert': bert_similarity,\n",
+ " 'ted': ted_similarity,\n",
+ " 'jaccard': jaccard_similarity,\n",
+ " 'len_ratio': len_ratio\n",
+ " }\n",
+ "\n",
+ "# Demo\n",
+ "print(\"Feature Extraction Demo:\")\n",
+ "print(\"=\" * 60)\n",
+ "features = extract_features(s1, s2)\n",
+ "for feature_name, value in features.items():\n",
+ " print(f\"{feature_name:<15}: {value:.3f}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "26a1aa86",
+ "metadata": {},
+ "source": [
+ "#### Building feature map"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e8a13c18",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Extracting features for all pairs...\n",
+ " Processed 10/29 pairs...\n",
+ " Processed 20/29 pairs...\n",
+ "\n",
+ "Feature Matrix Shape: (29, 7)\n",
+ "\n",
+ "Feature Summary Statistics:\n",
+ "================================================================================\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " bert | \n",
+ " ted | \n",
+ " jaccard | \n",
+ " len_ratio | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | count | \n",
+ " 29.000 | \n",
+ " 29.000 | \n",
+ " 29.000 | \n",
+ " 29.000 | \n",
+ "
\n",
+ " \n",
+ " | mean | \n",
+ " 0.671 | \n",
+ " 0.671 | \n",
+ " 0.405 | \n",
+ " 0.786 | \n",
+ "
\n",
+ " \n",
+ " | std | \n",
+ " 0.271 | \n",
+ " 0.338 | \n",
+ " 0.328 | \n",
+ " 0.266 | \n",
+ "
\n",
+ " \n",
+ " | min | \n",
+ " 0.067 | \n",
+ " 0.000 | \n",
+ " 0.000 | \n",
+ " 0.000 | \n",
+ "
\n",
+ " \n",
+ " | 25% | \n",
+ " 0.506 | \n",
+ " 0.364 | \n",
+ " 0.200 | \n",
+ " 0.760 | \n",
+ "
\n",
+ " \n",
+ " | 50% | \n",
+ " 0.652 | \n",
+ " 0.800 | \n",
+ " 0.333 | \n",
+ " 0.857 | \n",
+ "
\n",
+ " \n",
+ " | 75% | \n",
+ " 0.930 | \n",
+ " 1.000 | \n",
+ " 0.600 | \n",
+ " 0.957 | \n",
+ "
\n",
+ " \n",
+ " | max | \n",
+ " 1.000 | \n",
+ " 1.000 | \n",
+ " 1.000 | \n",
+ " 1.000 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Feature Correlation Insights:\n",
+ "================================================================================\n",
+ " bert ted jaccard len_ratio\n",
+ "bert 1.000000 0.257085 0.770717 0.157109\n",
+ "ted 0.257085 1.000000 0.337151 0.531403\n",
+ "jaccard 0.770717 0.337151 1.000000 0.149126\n",
+ "len_ratio 0.157109 0.531403 0.149126 1.000000\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Correlation heatmap\n",
+ "plt.figure(figsize=(8, 6))\n",
+ "feature_corr = feature_df[['bert', 'ted', 'jaccard', 'len_ratio']].corr()\n",
+ "sns.heatmap(feature_corr, annot=True, cmap='coolwarm', center=0, vmin=-1, vmax=1, \n",
+ " square=True, fmt='.3f', cbar_kws={'label': 'Correlation'})\n",
+ "plt.title('Feature Correlation Matrix\\n(Low correlation = good for fusion model)', fontsize=12, fontweight='bold')\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n",
+ "\n",
+ "print(\"\\nFeature Correlation Insights:\")\n",
+ "print(\"=\" * 80)\n",
+ "print(feature_corr)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "49482f8f",
+ "metadata": {},
+ "source": [
+ "## Ground Truth Labels\n",
+ "As a demo within the notebook labels will be assigned based on semantic similarity threshold\n",
+ "(Real training would use MSRP corpus labels)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "4bea7544",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Label Distribution:\n",
+ "================================================================================\n",
+ "Paraphrases (1): 14\n",
+ "Non-Paraphrases (0): 15\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# For demo purposes: pairs with BERT similarity > 0.7 are \"paraphrases\" (label=1)\n",
+ "feature_df['label'] = (feature_df['bert'] > 0.7).astype(int)\n",
+ "\n",
+ "print(\"Label Distribution:\")\n",
+ "print(\"=\" * 80)\n",
+ "label_counts = feature_df['label'].value_counts()\n",
+ "print(f\"Paraphrases (1): {label_counts.get(1, 0)}\")\n",
+ "print(f\"Non-Paraphrases (0): {label_counts.get(0, 0)}\")\n",
+ "\n",
+ "# Visualize label distribution\n",
+ "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
+ "\n",
+ "# Count plot\n",
+ "label_counts.plot(kind='bar', ax=axes[0], color=['#d62728', '#2ca02c'])\n",
+ "axes[0].set_title('Label Distribution')\n",
+ "axes[0].set_xlabel('Label (0=Different, 1=Paraphrase)')\n",
+ "axes[0].set_ylabel('Count')\n",
+ "axes[0].set_xticklabels(['Different', 'Paraphrase'], rotation=0)\n",
+ "\n",
+ "# Feature distributions by label\n",
+ "feature_df.boxplot(column='bert', by='label', ax=axes[1])\n",
+ "axes[1].set_title('BERT Similarity by Label')\n",
+ "axes[1].set_xlabel('Label')\n",
+ "axes[1].set_ylabel('BERT Similarity')\n",
+ "plt.suptitle('')\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "64c8b61b",
+ "metadata": {},
+ "source": [
+ "## Model Training & Evaluation\n",
+ "Train Logistical Regression on merged features"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "0d0a4596",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Training set size: 23\n",
+ "Test set size: 6\n",
+ "\n",
+ "Model Training Complete!\n",
+ "================================================================================\n",
+ "Training Accuracy: 1.000\n",
+ "Test Accuracy: 0.833\n",
+ "\n",
+ "Feature Importance (Model Coefficients):\n",
+ "================================================================================\n",
+ "bert : +1.7731\n",
+ "ted : +0.2136\n",
+ "jaccard : +0.9931\n",
+ "len_ratio : +0.0666\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Prepare features\n",
+ "X = feature_df[['bert', 'ted', 'jaccard', 'len_ratio']].values\n",
+ "y = feature_df['label'].values\n",
+ "\n",
+ "# Standardize features (important for Logistic Regression)\n",
+ "scaler = StandardScaler()\n",
+ "X_scaled = scaler.fit_transform(X)\n",
+ "\n",
+ "# Train-test split (80-20)\n",
+ "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n",
+ "\n",
+ "print(f\"Training set size: {len(X_train)}\")\n",
+ "print(f\"Test set size: {len(X_test)}\")\n",
+ "\n",
+ "# Train Logistic Regression\n",
+ "model = LogisticRegression(random_state=42, max_iter=1000)\n",
+ "model.fit(X_train, y_train)\n",
+ "\n",
+ "print(\"\\nModel Training Complete!\")\n",
+ "print(\"=\" * 80)\n",
+ "print(f\"Training Accuracy: {model.score(X_train, y_train):.3f}\")\n",
+ "print(f\"Test Accuracy: {model.score(X_test, y_test):.3f}\")\n",
+ "\n",
+ "# Feature importance (coefficients)\n",
+ "print(\"\\nFeature Importance (Model Coefficients):\")\n",
+ "print(\"=\" * 80)\n",
+ "feature_names = ['bert', 'ted', 'jaccard', 'len_ratio']\n",
+ "for name, coef in zip(feature_names, model.coef_[0]):\n",
+ " print(f\"{name:<15}: {coef:+.4f}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8ce47f9c",
+ "metadata": {},
+ "source": [
+ "#### Classification report & Confusion Matrix"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "ed9f0973",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Classification Report:\n",
+ "================================================================================\n",
+ " precision recall f1-score support\n",
+ "\n",
+ " Different 0.75 1.00 0.86 3\n",
+ " Paraphrase 1.00 0.67 0.80 3\n",
+ "\n",
+ " accuracy 0.83 6\n",
+ " macro avg 0.88 0.83 0.83 6\n",
+ "weighted avg 0.88 0.83 0.83 6\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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oEJcuXWLx4sVMnToVLy8vOnXq9NDzKCIiIvahbt26vPDCCwQGBhIVFQVA5syZ6d+/PydPnmTo0KGJ1lm3bh2LFi2iYcOGvPjii7Z1Bg0axPHjxxk0aFCSI8K//vprfv/994dmqVWrFm+88QZffvkl33//faLlMTEx9O/f3/a6WLFinDhxgps3b9raDh8+/J9PUv5fOXPmpGHDhqxcuZLly5fj4uJCixYtEvR599132bNnDxs3bky0/p07d4iLi0vRPkUk7SX97FAReWoUK1aMZcuW0bp1a8qUKUPHjh0pX748MTEx7N69m2+//ZbOnTsDUKlSJTp16sS8efO4c+cOderU4ffff2fx4sW0aNGCevXqpVquNm3aMGjQIN566y169+5NZGQkn3/+OSVLlkww0XdAQADbt2+nSZMmFC5cmODgYGbPnk2hQoV4+eWXH7r9SZMm0ahRI2rVqkXXrl35559/+Oyzz8iRIwejRo1KteP4Xw4ODgwbNuw/+zVt2pSAgAC6dOnCSy+9xJEjR1i6dClFixZN0K9YsWLkzJmTOXPmkC1bNrJkyULNmjWTnG/hUbZu3crs2bMZOXIkVatWBe4/crlu3boMHz6ciRMnpmh7IiIikr4GDBjAO++8w6JFi2wPaPHz8+PQoUNMmDCBPXv20KpVKzJlysTOnTv5+uuvKVOmDIsXL060nWPHjjFlyhR+/fVX3n77bfLly8f169f5/vvv+f3339m9e/cjs3z11Vc0aNCAli1b0qxZM15//XWyZMnC6dOnWb58OdeuXWPy5MkAvP/++0ydOpWGDRvStWtXgoODmTNnDuXKlUvRA2Xg/lyjHTp0YPbs2TRs2ND2QJx/H9vatWtp2rQpnTt3plq1akRERHDkyBFWrVrFhQsXEtzuJyJ2wNyH/4lIejl16pTRvXt3w8vLy3BxcTGyZctm1K5d2/jss8+MqKgoW7/Y2FjD39/fKFKkiOHs7Gx4enoagwcPTtDHMAyjcOHCRpMmTRLt538f+/vg8b6TJk1K1PeXX34xypcvb7i4uBilSpUyvv76a2PkyJHGv/9r2rJli/Hmm28aBQoUMFxcXIwCBQoYbdu2TfCo3wf7WLhwYYLtb9682ahdu7aRKVMmI3v27EazZs2Mv/76K0GfB/u7efNmgvYHj2Q+f/78Q8+pYdx/nHGWLFke2SepcxAVFWX069fPyJ8/v5EpUyajdu3axp49e5J8bPIPP/xglC1b1nByckpwnHXq1DHKlSuX5D7/vZ2wsDCjcOHCRtWqVY3Y2NgE/fr27Ws4ODgYe/bseeQxiIiISNp7cP2xf//+RMvi4+ONYsWKGcWKFTPi4uIStC9cuNCoXbu2kT17dsPNzc0oV66c4e/vb4SHhz90X6tWrTIaNGhg5M6d23BycjLy589vtG7d2ti2bVuyskZGRhqTJ082atSoYWTNmtVwcXExSpQoYXzyySfGmTNnEvT9+uuvjaJFixouLi5G5cqVjY0bNxqdOnUyChcubOvzqGvGB8LCwoxMmTIZgPH1118n2efevXvG4MGDjeLFixsuLi5Gnjx5jJdeesmYPHmyERMTk6xjE5H0YzGMFMziKyIiIiIiIiIikgo0p5SIiIiIiIiIiKQ7FaVERERERERERCTdqSglIiIiIiIiIiLpTkUpERERERERERFJdypKiYiIiIiIiIhIulNRSkRERERERERE0p2T2QFERERExP5YrVauXr1KtmzZsFgsZscRERGRDMQwDO7du0eBAgVwcHj4eKinsiiVqUovsyOIiB27vX+m2RFExM65PZVXSClz9epVPD09zY4hIiIiGdjly5cpVKjQQ5frkktEREREEsmWLRtw/2Iye/bsqb59q9XKzZs3cXd3f+RfUCVt6PybT++BuXT+zaXzb760fg/CwsLw9PS0XU88jIpSIiIiIpLIg1v2smfPnmZFqaioKLJnz65fSEyg828+vQfm0vk3l86/+dLrPfivKQD07ouIiIiIiIiISLpTUUpERERERERERNKdilIiIiIiIiIiIpLuVJQSEREREREREZF0p6KUiIiIiIiIiIikOxWlREREREREREQk3akoJSIiIiIiIiIi6U5FKRERERE7t337dpo1a0aBAgWwWCx8//33/7nOtm3bqFq1Kq6urhQvXpxFixaleU4RERGRlFBRSkRERMTORUREUKlSJWbNmpWs/ufPn6dJkybUq1ePoKAgfHx86NatGxs3bkzjpCIiIiLJ52R2ABERERF5tEaNGtGoUaNk958zZw5FihRhypQpAJQpU4adO3cybdo0GjZsmFYxRURERFJERSkRERGRp8yePXvw9vZO0NawYUN8fHweuk50dDTR0dG212FhYQBYrVasVmuqZ7RarRiGkSbblv+m8w/rj1xj2ubTRETHmbJ/g/vvg4ODAxZTEjzbdP7NpfNvrrALR/jn5mXyVHuDvNkzsbZX7VTfR3J/vqgoJSIiIvKUuX79Onnz5k3QljdvXsLCwvjnn3/IlClTonXGjRuHv79/ovabN28SFRWV6hmtVit3797FMAwcHDSjRHrT+YdJG05w8Xbqf22LiNiruLvB3N62kMgTO8DRmdh85cEoRHBwcKrv6969e8nqp6KUiIiIiDB48GB8fX1tr8PCwvD09MTd3Z3s2bOn+v6sVisWiwV3d/dntihiJp1/iI4/CoCDBTyyuab7/jVSxFw6/+bS+TfHuc0r7xekLBbcq9QnT86s5M2RCQ8Pj1Tfl5ubW7L6qSglIiIi8pTJly8fN27cSNB248YNsmfPnuQoKQBXV1dcXRP/Yu7g4JBmRQuLxZKm25dH0/m//6uwRzY39g55Pd33brVaCQ4OxsPD4xl+D8yj828unf/0YbVaCQ8Pt/1x6WrnsnTv3p0xY8ZQsWLFNH0PkrtNvfsiIiIiT5latWqxZcuWBG2bNm2iVq1aJiUSERGR9LR3715eeuklOnfubGsrUKAA69ato3Llyqbl+l8qSomIiIjYufDwcIKCgggKCgLg/PnzBAUFcenSJeD+rXcdO3a09e/Rowfnzp1j4MCBnDhxgtmzZ7Ny5Ur69u1rRnwRERFJJ3///TcdOnSgVq1a7Nu3j02bNnHlyhWzYz2UilIiIiIidu6PP/6gSpUqVKlSBQBfX1+qVKnCiBEjALh27ZqtQAVQpEgR1q1bx6ZNm6hUqRJTpkzhyy+/pGHDhqbkFxERkbQVGRlJQEAApUqVYunSpVgsFt5//31Onz5NwYIFzY73UJpTSkRERMTO1a1bF8MwHrp80aJFSa5z6NChNEwlIiIi9uDIkSM0adKEy5cvA1C7dm2mT59OtWrVTE723zRSSkREREREREQkgypWrBgAzz//PMuXL2fHjh0ZoiAFKkqJiIiIiIiIiGQY165dY8SIEcTHxwOQOXNm1q9fz4kTJ2jdujUWi8XkhMmn2/dEREREREREROxcVFQUU6dOZezYsURERPD888/TrVs3AMqXL29yusejopSIiIiIiIiIiJ0yDIM1a9bQv39/Lly4AEDNmjWpWLGiucFSgW7fExERERERERGxQ0FBQdSrV4+3336bCxcuULBgQZYsWcLu3bt54YUXzI73xDRSSkRERERERETEDn3yySfs3LkTNzc3Bg4cyMCBA8mSJYvZsVKNRkqJiIiIiIiIiNiB6OhoIiMjba+nTp1KmzZtOHnyJP7+/k9VQQpUlBIRERERERERMZVhGPzwww+UK1eOgIAAW3uNGjX45ptveP75501Ml3ZUlBIRERERERERMcmRI0eoX78+LVq04OzZs6xYsYLo6GizY6ULFaVERERERERERNJZSEgIH330EZUrV2bLli24uroyePBg/vzzT1xdXc2Oly400bmIiIiIiIiISDrauHEjbdq04c6dOwC0atWKSZMmUaRIEXODpTMVpURERERERERE0lG5cuWIiYmhUqVKTJ8+nTp16pgdyRS6fU9EREREREREJA0dP36cCRMm2F4XKlSInTt3cuDAgWe2IAUqSomIiIiIiIiIpInQ0FD69OlDhQoV8PPz47fffrMtq1KlCo6OjiamM59u3xMRERERERERSUVxcXHMnTuXESNGEBoaCkDz5s0pVKiQycnsi4pSIiIiIiIiIiKpZNOmTfTt25djx44B9+ePCgwMxNvb2+Rk9kdFKRERERERERGRVBAdHc3777/P33//zXPPPUdAQAAffPABTk4qvyRFZ0VERERERERE5DHdvXuXrFmz4ujoiKurK5MnT2bPnj2MHDmSXLlymR3PrmmicxERERERERGRFIqPj2fu3LmUKFGCxYsX29pbt25NYGCgClLJoKKUiIiIiIiIiEgKbNu2jWrVqtGjRw9u3rzJkiVLzI6UIakoJSIiIiIiIiKSDOfOnaNVq1bUq1ePw4cPkzNnTgIDA/nll1/MjpYhaU4pEREREREREZH/8MUXX9CrVy9iYmJwcHCgR48e+Pv7kydPHrOjZVgqSomIiIiIiIiI/IcKFSoQExODt7c306ZNo3z58mZHyvBUlBIRERERERER+R87duzgr7/+4sMPPwTgxRdf5MCBA1SpUgWLxWJyuqeD5pQSEREREREREfk/Fy9epHXr1rz66qv06dOH8+fP25ZVrVpVBalUpJFSIiIiIiIiIvLMi4iIYPz48UyePJmoqCgsFgudOnUia9asZkd7aqkoJSIiIiIiIiLPLKvVytKlS/Hz8+Pq1asA1K1bl8DAQCpVqmRyuqebilIiIiIiIiIi8sy6fv06H374If/88w9FihRh8uTJvPXWW7pNLx3YRVHK0dGRa9eu4eHhkaD91q1beHh4EB8fb1IyEREREREREXna3L59m1y5cgFQoEAB/P39iY+Px8fHBzc3N5PTPTvsYqJzwzCSbI+OjsbFxSWd04iIiIiIiIjI0ygyMpKAgAAKFSrEjh07bO0DBgzAz89PBal0ZupIqRkzZgBgsVj48ssvE0weFh8fz/bt2yldurRZ8URERERERETkKWAYBitWrGDgwIFcvnwZgKVLl/LKK6+YnOzZZmpRatq0acD9L445c+bg6OhoW+bi4oKXlxdz5swxK56IiIiIiIiIZHB//PEHPj4+7Nq1C4Dnn3+eiRMn8u6775qcTEwtSp0/fx6AevXqsWbNGtv9nCIiIiIiIiIiT2rw4MGMHz8egMyZM+Pn50f//v3JlCmTyckE7GSi819//dXsCCIiIiIiIiLylClbtiwAHTp0YPz48RQsWNDkRPJvdlGUio+PZ9GiRWzZsoXg4GCsVmuC5Vu3bjUpmYiIiIiIiIhkBIZhsGbNGhwcHHjrrbcAaN++PRUqVKBy5crmhpMk2UVRqk+fPixatIgmTZpQvnx5LBaL2ZFEREREREREJIMICgrCx8eH3377jfz581O/fn2yZs2Kg4ODClJ2zC6KUsuXL2flypU0btzY7CgiIiIiIiIikkEEBwczbNgwvvzySwzDwM3NjW7duuHg4GB2NEkGuyhKubi4ULx4cbNjiIiIiIiIiEgGEBMTw4wZMxg9ejRhYWEAtG7dmgkTJlC4cGGT00ly2UXpsF+/fkyfPh3DMMyOIiIiIiIiIiJ27tChQwwYMICwsDCqVavGjh07WL58uQpSGYxdjJTauXMnv/76Kz///DPlypXD2dk5wfI1a9aYlExERERERERE7EFoaCi5c+cGoGbNmvTp04fKlSvTsWNH3a6XQdlFUSpnzpy2mfFFRERERERERB4ICQlhxIgRfPXVVxw7dsw2GiowMNDcYPLE7KIotXDhQrMjiIiIiIiIiIgdiY2NZdasWfj7+3Pnzh0AvvvuO3x8fEzNJanHbsa3xcXFsXnzZubOncu9e/cAuHr1KuHh4SYnExEREREREZH0tH79eipUqEDfvn25c+cOlSpV4tdff1VB6iljFyOlLl68yBtvvMGlS5eIjo6mfv36ZMuWjQkTJhAdHc2cOXPMjigiIiIiIiIiacwwDFq1asV3330HgLu7O2PGjOH999/H0dHR5HSS2uxipFSfPn2oXr06t2/fJlOmTLb2t956iy1btpiYTERERMQ+zJo1Cy8vL9zc3KhZsya///77I/sHBgZSqlQpMmXKhKenJ3379iUqKiqd0oqIiDwei8ViewDagAEDOH36NN27d1dB6illFyOlduzYwe7du3FxcUnQ7uXlxZUrV0xKJSIiImIfVqxYga+vL3PmzKFmzZoEBgbSsGFDTp48iYeHR6L+y5Ytw8/PjwULFvDSSy9x6tQpOnfujMViYerUqSYcgYiISNLi4uKYN28eVatW5cUXXwTAz8+Pjh07UqJECZPTSVqzi5FSVquV+Pj4RO1///032bJlMyGRiIiIiP2YOnUq3bt3p0uXLpQtW5Y5c+aQOXNmFixYkGT/3bt3U7t2bdq1a4eXlxcNGjSgbdu2/zm6SkREJD1t2rSJypUr8/HHH/PJJ59gtVoByJIliwpSzwi7KEo1aNAgwaMcLRYL4eHhjBw5ksaNG5sXTERERMRkMTExHDhwAG9vb1ubg4MD3t7e7NmzJ8l1XnrpJQ4cOGArQp07d47169frukpEROzC6dOn6dSpE2+88QbHjh3jueeeo0uXLhiGYXY0SWd2cfve5MmTeeONNyhbtixRUVG0a9eO06dPkydPHr755huz44mIiIiYJiQkhPj4ePLmzZugPW/evJw4cSLJddq1a0dISAgvv/wyhmEQFxdHjx49GDJkyEP3Ex0dTXR0tO11WFgYcH9E+4O/XKcmq9WKYRhpsm35bzr/AIbtsxnnQe+BuXT+zXHnzh3GjBnDZ599RmxsLE5OTnz88ccMHz6cXLlyAeg9SSdp/T2Q3O3aRVHK09OTw4cPs2LFCg4fPkx4eDhdu3alffv2CSY+F0mJ7u+8TPe3X6FwgdwAHD93nbHzfuaXXX+ZnExE7MXyZUtZvHA+ISE3KVmqNH5DhlOhYkWzY4k8sW3btjF27Fhmz55NzZo1OXPmDH369GH06NEMHz48yXXGjRuHv79/ovabN2+myQTpVquVu3fvYhgGDg52MXj/maLzD/H/9wtTvNVKcHBwuu9f74G5dP7N8e2339rmNnzllVf49NNPKVmyJLGxsaZ8Hz7L0vp74N69e8nqZ3pRKjY2ltKlS/PTTz/Rvn172rdvb3YkeUpcuXGH4Z/9wJlLN7FgoUOzmnw77QNebDOe4+eumx1PREy24ef1TJ44jmEj/alQoRJLlyym54dd+eGnDTz33HNmxxOxyZMnD46Ojty4cSNB+40bN8iXL1+S6wwfPpz33nuPbt26AVChQgUiIiL44IMPGDp0aJIXn4MHD8bX19f2OiwsDE9PT9zd3cmePXsqHtF9VqsVi8WCu7u7fiE0gc4/OP7fcTs6OCT5wIC0pvfAXDr/6efWrVu2a6uePXuyZ88e2rVrR7Vq1XT+TZTW3wNubm7J6md6UcrZ2VmPJ5Y0sX770QSvR836ke7vvMwLFYuoKCUiLFm8kJZvv0uLt1oBMGykP9u3b+P7Navp2v0Dk9OJ/H8uLi5Uq1aNLVu20KJFC+D+heSWLVvo1atXkutERkYmusB88Cjth83X4erqiqura6J2BweHNPuFwWKxpOn25dF0/i22z2adA70H5tL5T1vnzp2jf//+/P7775w8eZIsWbLg4ODAsmXLsP7fCEWdf3Ol5fdAcrdpF+/+xx9/zIQJE4iLizM7ijylHBwsvNOwGlkyubDvz/NmxxERk8XGxHD8r2O8WOslW5uDgwMvvvgSfx4+ZGIykaT5+vryxRdfsHjxYo4fP07Pnj2JiIigS5cuAHTs2JHBgwfb+jdr1ozPP/+c5cuXc/78eTZt2sTw4cNp1qyZrTglIiKSFu7du4efnx9lypThu+++49q1a/z6669mxxI7ZfpIKYD9+/ezZcsWfvnlFypUqECWLFkSLF+zZo1JySSjK1e8ANsW98PNxYnwf6Jp3e8LTmiUlMgz7/ad28THxye6Te+5557j/PlzJqUSebjWrVtz8+ZNRowYwfXr16lcuTIbNmywTX5+6dKlBH+RHDZsGBaLhWHDhnHlyhXc3d1p1qwZY8aMMesQRETkKWe1Wlm0aBFDhgyx3XLu7e3NtGnTKF++vMnpxF7ZRVEqZ86ctGrV6rHW/d8nxQAY1ngsDvoroMCpCzeo2WYcObJm4i3vKnwR8B4Nuk1XYUpERDKcXr16PfR2vW3btiV47eTkxMiRIxk5cmQ6JBMRkWddZGQkr776KgcOHACgePHiTJkyhWbNmmGxWP5jbXmW2UVRauHChY+9blJPinHMWwPn/C88aSx5CsTGxXPucggAh45fplq55/m4bV0+GbPc5GQiYqZcOXPh6OjIrVu3ErTfunWLPHnymJRKREREJGPKnDkzxYoV4/Tp04wYMYJevXolOU+hyP+yizmlAOLi4ti8eTNz5861PTrw6tWrhIeHP3K9wYMHc/fu3QQfTnmrpUdkyYAcLBZcXeyiFisiJnJ2caFM2XLs27vH1ma1Wtm3bw8VK1UxMZmIiIiI/YuIiGDkyJFcunTJ1hYYGMjp06fp16+fClKSbHbx2/nFixd54403uHTpEtHR0dSvX59s2bIxYcIEoqOjmTNnzkPXTepJMbp1TwACPmnOxl3HuHztNtmyuNG6UXVerV6CZh/NNjuaiNiB9zp1YfiQQZQrV57yFSry9ZLF/PPPP7R4q6XZ0URERETsktVqZenSpfj5+XH16lVOnz7NsmXLAMifP7/J6SQjsouiVJ8+fahevTqHDx9OMOnsW2+9Rffu3U1MJhmZe+6szB/dkXx5snM3PIqjp6/Q7KPZbN13wuxoImIH3mjUmNuhocyeOYOQkJuUKl2G2XO/5DndviciIiKSyN69e/Hx8WHfvn0AeHl5Pfbc0CIP2EVRaseOHezevRsXF5cE7V5eXly5csWkVJLR9fRfZnYEEbFzbdt3oG37DmbHEBEREbFbf//9N35+fixduhSArFmzMnToUHx8fHBzczM5nWR0dlGUslqtxMfHJ2r/+++/yZYtmwmJRERERERERGTu3LksXboUi8VC586dGTNmjG7Vk1RjFxOdN2jQgMDAQNtri8VCeHg4I0eOpHHjxuYFExEREREREXmGGIaR4AnFgwYNomXLluzfv58FCxaoICWpyi5GSk2ZMoWGDRtStmxZoqKiaNeuHadPnyZPnjx88803ZscTEREREREReer98ccf+Pj4EBsby549e3BwcCBr1qysXr3a7GjylLKLolShQoU4fPgwK1as4PDhw4SHh9O1a1fat29PpkyZzI4nIiIiIiIi8tS6du0aQ4YMYfHixRiGQebMmfnrr78oX7682dHkKWdaUapq1aps2bKFXLlyERAQQP/+/Wnfvj3t27c3K5KIiIiIiIjIMyMqKopp06YxduxYwsPDAejQoQPjx4+nYMGCJqeTZ4Fpc0odP36ciIgIAPz9/W3fACIiIiIiIiKSti5evEiZMmUYMmQI4eHh1KxZkz179rBkyRIVpCTdmDZSqnLlynTp0oWXX34ZwzCYPHkyWbNmTbLviBEj0jmdiIiIiIiIyNPL09OT3LlzExMTw4QJE2jXrh0ODnbxLDR5hphWlFq0aBEjR47kp59+wmKx8PPPP+PklDiOxWJRUUpERERERETkCdy4cYNJkybh7+9PlixZcHBwYOXKleTLl48sWbKYHU+eUaYVpUqVKsXy5csBcHBwYMuWLXh4eJgVR0REREREROSpEx0dzYwZMxg9ejT37t0ja9asjBo1CoBixYqZG06eeaaNzatatSq3b98GYOTIkQ+9dU9EREREREREUsYwDH744QfKlSvHwIEDuXfvHtWqVaN+/fpmRxOxsYuJzgMCAjTRuYiIiDy1oqKizI4gIiLPkCNHjlC/fn1atGjB2bNnyZcvHwsXLuT333+ndu3aZscTsdFE5yIiIiJpwGq1MmbMGObMmcONGzc4deoURYsWZfjw4Xh5edG1a1ezI4qIyFPq008/ZcuWLbi6uuLr68vgwYPJli2b2bFEEtFE5yIiIiJp4NNPP2Xx4sVMnDiR7t2729rLly9PYGCgilIiIpJqYmNjiYiIIGfOnACMHz8ei8XCuHHjKFKkiLnhRB5BE52LiIiIpIGvvvqKefPm8frrr9OjRw9be6VKlThx4oSJyURE5Gmyfv16fH19qVGjBkuWLAGgSJEitt+3ReyZaUWpf7NarWZHEBEREUlVV65coXjx4onarVYrsbGxJiQSEZGnyfHjx/H19WXDhg0A3Llzh9u3b5MrVy6Tk4kkn2lFqbVr19KoUSOcnZ1Zu3btI/s2b948nVKJiIiIpI6yZcuyY8cOChcunKB91apVVKlSxaRUIiKS0YWGhuLv78+sWbOIj4/H2dmZPn36MGzYMHLkyGF2PJEUMa0o1aJFC65fv46HhwctWrR4aD+LxUJ8fHz6BRMRERFJBSNGjKBTp05cuXIFq9XKmjVrOHnyJF999RU//fST2fFERCQD2rNnD02bNiU0NBS4P4Bj8uTJlChRwuRkIo/HwawdW61W2xxSVqv1oR8qSImIiEhG9Oabb/Ljjz+yefNmsmTJwogRIzh+/Dg//vgj9evXNzueiIhkQOXLl8fZ2Zly5cqxadMmfvjhBxWkJEMzfU4pq9XKokWLWLNmDRcuXMBisVC0aFFatWrFe++9h8ViMTuiiIiIyGN55ZVX2LRpk9kxREQkgzp9+jTz589n3LhxWCwWsmXLxrZt2yhevHiST68XyWhMGykFYBgGzZs3p1u3bly5coUKFSpQrlw5Lly4QOfOnXnrrbfMjCciIiLy2IoWLcqtW7cStd+5c4eiRYuakEhERDKKu3fv0r9/f8qVK8eECRP45ptvbMtKly6tgpQ8NUz9Sl60aBHbt29ny5Yt1KtXL8GyrVu30qJFC7766is6duxoUkIRERGRx3PhwoUkpyGIjo7mypUrJiQSERF7Fx8fz/z58xk2bBg3b94EoFGjRlStWtXkZCJpw9Si1DfffMOQIUMSFaQAXnvtNfz8/Fi6dKmKUiIiIpJh/Pupwhs3bkzwJKT4+Hi2bNmCl5eXCclERMSebdu2DR8fHw4fPgzcHxE1depUGjVqZHIykbRjalHqzz//ZOLEiQ9d3qhRI2bMmJGOiURERESezIOnClssFjp16pRgmbOzM15eXkyZMsWEZCIiYq+sVit9+/bl8OHD5MyZk1GjRvHRRx/h7OxsdjSRNGVqUSo0NJS8efM+dHnevHm5fft2OiYSEREReTJWqxWAIkWKsH//fvLkyWNyIhERsUf37t3DycmJTJky4eDgQGBgICtXrsTf318/O+SZYWpRKj4+/pETtDk6OhIXF5eOiURERERSx/nz582OYNfWH7nGpA0niI4/Cuhpy+nPIN5qxdHBgWf1/AffizI7gjyjrFYrixcvZsiQIXz00UcMHz4cgDp16lCnTh2T04mkL1OLUoZh0LlzZ1xdXZNcHh0dnc6JRERERFJPREQEv/32G5cuXSImJibBst69e5uUyj5M23yai7dVFBDzZXF1NDuCPEN27NiBj48PBw8eBGDNmjUMGTIER0d9HcqzydSi1P/Os5AUTXIuIiIiGdGhQ4do3LgxkZGRREREkDt3bkJCQsicOTMeHh7PfFEqIvr+aHgHC3hkczM5zbNII6XgfkGqX4NSZseQZ8DFixcZOHAgK1euBCB79uyMGDGCTz75RAUpeaaZWpRauHChmbsXERERSTN9+/alWbNmzJkzhxw5crB3716cnZ3p0KEDffr0MTue3fDI5sreIa+bHeOZY7VaCQ4OxsPDAwcHB7PjiDzVVqxYQefOnYmKisJisdC9e3dGjx6Nh4eH2dFETKefQCIiIiJpICgoiH79+uHg4ICjoyPR0dF4enoyceJEhgwZYnY8ERFJJzVq1MBqtVK3bl0OHTrE3LlzVZAS+T8qSomIiIikAWdnZ9sIFA8PDy5dugRAjhw5uHz5spnRREQkDe3du5dx48bZXhctWpRDhw6xdetWKlWqZGIyEftj6u17IiIiIk+rKlWqsH//fkqUKEGdOnUYMWIEISEhLFmyhPLly5sdT0REUtnff/+Nn58fS5cuBeD111/nhRdeAKBs2bJmRhOxWxopJSIiIpIGxo4dS/78+QEYM2YMuXLlomfPnty8eZO5c+eanE5ERFJLZGQkAQEBlCpViqVLl2KxWOjSpQuenp5mRxOxexopJSIiIpIGqlevbvu3h4cHGzZsMDGNiIikNsMwWLFiBQMHDrTdll27dm2mT59OtWrVTE4nkjFopJSIiIhIOjp48CBNmzY1O4aIiDyh8PBw+vTpw+XLl3n++edZvnw5O3bsUEFKJAU0UkpEREQklW3cuJFNmzbh4uJCt27dKFq0KCdOnMDPz48ff/yRhg0bmh1RREQeQ3BwMO7u7lgsFrJly8bkyZO5cOEC/fv3J1OmTGbHE8lwNFJKREREJBXNnz+fRo0asWjRIiZMmMCLL77I119/Ta1atciXLx9Hjx5l/fr1ZscUEZEUiIqKYuzYsRQtWpSVK1fa2t977z2GDx+ugpTIY1JRSkRERCQVTZ8+nQkTJhASEsLKlSsJCQlh9uzZHDlyhDlz5lCmTBmzI4qISDIZhsGqVasoU6YMQ4cOJSIigtWrV5sdS+SpoaKUiIiISCo6e/Ys77zzDgAtW7bEycmJSZMmUahQIZOTiYhISgQFBVGvXj3eeecdLly4QMGCBfn6669Zvny52dFEnhqaU0pEREQkFf3zzz9kzpwZAIvFgqurK/nz5zc5lYiIpMT48eMZMmQIhmHg5ubGwIEDGThwIFmyZDE7mshTRUUpERERkVT25ZdfkjVrVgDi4uJYtGgRefLkSdCnd+/eKdrmrFmzmDRpEtevX6dSpUp89tlnvPDCCw/tf+fOHYYOHcqaNWsIDQ2lcOHCBAYG0rhx45QfkIjIM6ZmzZoYhkGbNm2YMGECzz//vNmRRJ5KKkqJiIiIpKLnn3+eL774wvY6X758LFmyJEEfi8WSoqLUihUr8PX1Zc6cOdSsWZPAwEAaNmzIyZMn8fDwSNQ/JiaG+vXr4+HhwapVqyhYsCAXL14kZ86cj31cIiJPK8Mw+PHHH7lx4wbdu3cHoF69evz111+aB1AkjakoJSIiIpKKLly4kOrbnDp1Kt27d6dLly4AzJkzh3Xr1rFgwQL8/PwS9V+wYAGhoaHs3r0bZ2dnALy8vFI9l4hIRnf06FH69evH5s2byZIlC02aNKFAgQIAKkiJpANNdC4iIiJix2JiYjhw4ADe3t62NgcHB7y9vdmzZ0+S66xdu5ZatWrx8ccfkzdvXsqXL8/YsWOJj49Pr9giInYtJCQEPz8/qlSpwubNm3F1daV3795ky5bN7GgizxSNlBIRERGxYyEhIcTHx5M3b94E7Xnz5uXEiRNJrnPu3Dm2bt1K+/btWb9+PWfOnOGjjz4iNjaWkSNHJrlOdHQ00dHRttdhYWEAWK1WrFZrKh3N/2f863NabF8ezWq1YhiGzr2J9B6YIzY2ltmzZxMQEMCdO3eA+09KnThxIkWKFAH0f1J60Ne/+dL6PUjudlWUEhEREXnKWK1WPDw8mDdvHo6OjlSrVo0rV64wadKkhxalxo0bh7+/f6L2mzdvEhUVlfoZ4622z8HBwam+fXk0q9XK3bt3MQwDBwfdPGEGvQfmuHDhAoMGDSI2NpZSpUrx6aef8vLLLwPo/6J0pK9/86X1e3Dv3r1k9VNRSkRERMSO5cmTB0dHR27cuJGg/caNG+TLly/JdfLnz4+zszOOjo62tjJlynD9+nViYmJwcXFJtM7gwYPx9fW1vQ4LC8PT0xN3d3eyZ8+eSkfz/zk4Otg+JzVZu6Qtq9WKxWLB3d1dvxCaRO9B+rlx44ZttKmHhwejRo0iV65cNGvWjHz58un8m0Bf/+ZL6/fAzc0tWf1UlBIRERGxYy4uLlSrVo0tW7bQokUL4P6F5JYtW+jVq1eS69SuXZtly5ZhtVptF5qnTp0if/78SRakAFxdXXF1dU3U7uDgkCYXq5Z/fdYvJOawWCxp9v5K8ug9SFuhoaH4+/szZ84cdu/eTbVq1QAYMmQIVuv9UZo6/+bR17/50vI9SO429e6LiIiIpJGzZ88ybNgw2rZta7st5Oeff+bYsWMp2o6vry9ffPEFixcv5vjx4/Ts2ZOIiAjb0/g6duzI4MGDbf179uxJaGgoffr04dSpU6xbt46xY8fy8ccfp97BiYjYqbi4OGbNmkWJEiWYMWMGMTExfP/992bHEpEkqCglIiIikgZ+++03KlSowL59+1izZg3h4eEAHD58+KHzOj1M69atmTx5MiNGjKBy5coEBQWxYcMG2+0oly5d4tq1a7b+np6ebNy4kf3791OxYkV69+5Nnz598PPzS70DFBGxQ5s2baJy5cr06tWL0NBQypUrxy+//MLo0aPNjiYiSdDteyIiIiJpwM/Pj08//RRfX98Ejxh/7bXXmDlzZoq316tXr4ferrdt27ZEbbVq1WLv3r0p3o+ISEbVtWtXFixYAMBzzz1HQEAAH3zwAU5O+rVXxF5ppJSIiIhIGjhy5AhvvfVWonYPDw9CQkJMSCQi8nR74YUXcHJyok+fPpw+fZqPPvpIBSkRO6fvUBEREZE0kDNnTq5du0aRIkUStB86dIiCBQualEpE5OkQHx/P/PnzyZ8/P82aNQOgW7duvPbaa5QoUcLkdCKSXMkqSq1duzbZG2zevPljhxERERF5WrRp04ZBgwbx7bffYrFYsFqt7Nq1i/79+9OxY0ez44mIZFi//vorPj4+/PnnnxQuXJj69evj5uaGo6OjClIiGUyyilIPHj/8XywWC/Hx8U+SR0REROSp8OBpd56ensTHx1O2bFni4+Np164dw4YNMzueiEiGc+7cOfr37893330H3B+R2rdvXxwdHU1OJiKPK1lFKavVmtY5RERERJ4qLi4ufPHFFwwfPpyjR48SHh5OlSpV9Fd8EZEUunfvHmPHjmXq1KnExMTg4OBAjx498Pf3J0+ePGbHE5EnoDmlRERERNLAzp07efnll3n++ed5/vnnzY4jIpJh/f7774wfPx4Ab29vpk2bRvny5U1OJSKp4bGKUhEREfz2229cunSJmJiYBMt69+6dKsFEREREMrLXXnuNggUL0rZtWzp06EDZsmXNjiQikmFcv36dfPnyAfD666/Tp08fXn/9dZo2bYrFYjE5nYiklhQXpQ4dOkTjxo2JjIwkIiKC3LlzExISQubMmfHw8FBRSkRERAS4evUqy5cv55tvvmH8+PFUrFiR9u3b07ZtWwoVKmR2PBERu3Tx4kUGDhzIzz//zMmTJ8mfPz8AgYGB5gYTkTThkNIV+vbtS7Nmzbh9+zaZMmVi7969XLx4kWrVqjF58uS0yCgiIiKS4eTJk4devXqxa9cuzp49yzvvvMPixYvx8vLitddeMzueiIhdiYiIYPjw4ZQuXZqVK1cSHh7Opk2bzI4lImksxUWpoKAg+vXrh4ODA46OjkRHR+Pp6cnEiRMZMmRIWmQUERERydCKFCmCn58f48ePp0KFCvz2229mRxIRsQtWq5UlS5ZQsmRJPv30U6Kioqhbty6HDh2iY8eOZscTkTSW4qKUs7MzDg73V/Pw8ODSpUsA5MiRg8uXL6duOhEREZEMbteuXXz00Ufkz5+fdu3aUb58edatW2d2LBER08XHx1O3bl06duzI1atXKVKkCKtXr2br1q1UqlTJ7Hgikg5SPKdUlSpV2L9/PyVKlKBOnTqMGDGCkJAQlixZoicgiIiIiPyfwYMHs3z5cq5evUr9+vWZPn06b775JpkzZzY7moiIXXB0dKRGjRocOnSIoUOH4uPjg5ubm9mxRCQdpXik1NixY22TzY0ZM4ZcuXLRs2dPbt68ybx581I9oIiIiEhGtH37dgYMGMCVK1f46aefaNu2rQpSIvJMi4yMJCAggIMHD9raRowYwalTp/Dz81NBSuQZlOKRUtWrV7f928PDgw0bNqRqIBEREZGnwa5du8yOICJiFwzDYMWKFQwcOJDLly+zZcsWtm3bhsViIUeOHOTIkcPsiCJikhQXpUREREQkaWvXrqVRo0Y4Ozuzdu3aR/Zt3rx5OqUSETHPH3/8gY+Pj61Q//zzz/PRRx+ZnEpE7EWKi1JFihTBYrE8dPm5c+eeKJCIiIhIRtWiRQuuX7+Oh4cHLVq0eGg/i8VCfHx8+gUTEUln165dY8iQISxevBjDMMicOTN+fn7079+fTJkymR1PROxEiotSPj4+CV7HxsZy6NAhNmzYwIABA1Irl4iIiEiGY7Vak/y3iMiz5vvvv2fRokUAdOjQgXHjxlGoUCFzQ4mI3UlxUapPnz5Jts+aNYs//vjjiQOJiIiIPA2++uorWrdujaura4L2mJgYli9fTseOHU1KJiKS+gzD4Pr167aHYnXv3p29e/fSs2dPXnzxRZPTiYi9SvHT9x6mUaNGrF69OrU2JyIiIpKhdenShbt37yZqv3fvHl26dDEhkYhI2ggKCqJevXrUrl2bqKgoAJycnFi8eLEKUiLySKlWlFq1ahW5c+dOrc2JiIiIZGiGYSQ5D+fff/+tJ02JyFMhODiYDz74gKpVq/Lbb79x7do19u/fb3YsEclAUnz7XpUqVRJcYD0Ypnnz5k1mz56dquFEREREMpoH10oWi4XXX38dJ6f/f7kVHx/P+fPneeONN0xMKCLyZGJiYpgxYwajR48mLCwMgNatWzNhwgQKFy5scjoRyUhSXJR68803ExSlHBwccHd3p27dupQuXTpVw4mIiIhkNA+euhcUFETDhg3JmjWrbZmLiwteXl60atXKpHQiIk8mNDSUmjVrcubMGQCqVatGYGAgL7/8ssnJRCQjSnFRatSoUWkQI3UtWTTU7AgiYsfqTv7N7AgiYuf2+tV57HVHjhwJgJeXF61bt8bNzS21YomImC537tyULl2a8PBwxo0bR8eOHXFwSLVZYUTkGZPi/z0cHR0JDg5O1H7r1i0cHR1TJZSIiIhIRtepUycVpEQkwwsJCcHHx4cbN27Y2ubNm8epU6fo3LmzClIi8kRSPFLKMIwk26Ojo3FxcXniQCIiIiIZVe7cuTl16hR58uQhV65cSU50/kBoaGg6JhMRSZnY2Fhmz57NqFGjuHPnDhEREXzxxRcA5M+f3+R0IvK0SHZRasaMGQBYLBa+/PLLBPMjxMfHs337ds0pJSIiIs+0adOmkS1bNtu/H1WUEhGxV+vXr8fX15eTJ08CUKlSJdq3b29yKhF5GiW7KDVt2jTg/kipOXPmJLhV78GknXPmzEn9hCIiIiIZRKdOnWz/7ty5s3lBREQew/Hjx/H19WXDhg0AuLu7M2bMGN5//31N1SIiaSLZRanz588DUK9ePdasWUOuXLnSLJSIiIhIRnfw4EGcnZ2pUKECAD/88AMLFy6kbNmyjBo1StMeiIjdmTdvHhs2bMDZ2Zk+ffowbNgwcuTIYXYsEXmKpXhWul9//VUFKREREZH/8OGHH3Lq1CkAzp07R+vWrcmcOTPffvstAwcONDmdiAjExcUleIjViBEjaN++PceOHWPSpEkqSIlImktxUapVq1ZMmDAhUfvEiRN55513UiWUiIiISEZ36tQpKleuDMC3335LnTp1WLZsGYsWLWL16tXmhhORZ96mTZuoXLkyrVu3tj3MKleuXHz99deUKFHC5HQi8qxIcVFq+/btNG7cOFF7o0aN2L59e6qEEhEREcnoDMPAarUCsHnzZtv1k6enJyEhIWZGE5Fn2OnTp2nevDkNGjTg2LFj/Pnnn1y8eNHsWCLyjEpxUSo8PDzJORCcnZ0JCwtLlVAiIiIiGV316tX59NNPWbJkCb/99htNmjQB7s/TmTdvXpPTiciz5s6dO/Tv359y5crx448/4uTkRJ8+fThz5gxeXl5mxxORZ1SKi1IVKlRgxYoVidqXL19O2bJlUyWUiIiISEYXGBjIwYMH6dWrF0OHDqV48eIArFq1ipdeesnkdCLyLDl27BglS5ZkypQpxMbG0qhRI44cOUJgYKDmCxYRUyX76XsPDB8+nJYtW3L27Flee+01ALZs2cKyZctYtWpVqgcUERERyYgqVqzIkSNHErVPmjRJj1YXkXRVqlQp3N3dee6555g6dSqNGjUyO5KICPAYRalmzZrx/fffM3bsWFatWkWmTJmoVKkSW7duJXfu3GmRUURERCTDOnDgAMePHwegbNmyVK1a1eREIvK0O3fuHFOmTGHq1Km4urri5OTEunXrKFiwIM7OzmbHExGxSXFRCqBJkya2eRHCwsL45ptv6N+/PwcOHCA+Pj5VA4qIiIhkRMHBwbRu3ZrffvuNnDlzAvfndKlXrx7Lly/H3d3d3IAi8tS5d+8eY8eOZerUqcTExFC4cGEGDhwIoHmjRMQupXhOqQe2b99Op06dKFCgAFOmTOG1115j7969qZlNREREJMP65JNPCA8P59ixY4SGhhIaGsrRo0cJCwujd+/eZscTkaeI1Wpl4cKFlCxZkvHjxxMTE4O3t3eST00XEbEnKRopdf36dRYtWsT8+fMJCwvj3XffJTo6mu+//16TnIuIiIj8y4YNG9i8eTNlypSxtZUtW5ZZs2bRoEEDE5OJyNNk586d+Pj4cODAAQCKFy/O1KlTadq0KRaLxeR0IiKPluyRUs2aNaNUqVL8+eefBAYGcvXqVT777LO0zCYiIiKSYVmt1iTnbnF2dsZqtZqQSESeRuPGjePAgQNkz56dyZMnc+zYMZo1a6aClIhkCMkeKfXzzz/Tu3dvevbsSYkSJdIyk4iIiEiG99prr9GnTx+++eYbChQoAMCVK1fo27cvr7/+usnpRCSjioiIIDY21jZX3ZQpU/D09CQgIAAPDw9zw4mIpFCyR0rt3LmTe/fuUa1aNWrWrMnMmTMJCQlJy2wiIiIiGdbMmTMJCwvDy8uLYsWKUaxYMYoUKUJYWJhGm4tIilmtVr7++mtKlSrFoEGDbO2lS5dmzpw5KkiJSIaU7JFSL774Ii+++CKBgYGsWLGCBQsW4Ovri9VqZdOmTXh6epItW7a0zCoiIiKSYXh6enLw4EG2bNnC8ePHAShTpgze3t4mJxORjGbv3r34+Piwb98+ALZu3UpkZCSZM2c2OZmIyJNJ8dP3smTJwvvvv8/OnTs5cuQI/fr1Y/z48Xh4eNC8efO0yCgiIiKSoaxYsYL27dvz7rvvcubMGT755BM++eQTFaREJEX+/vtvOnToQK1atdi3bx9Zs2Zl3LhxHDlyRAUpEXkqpLgo9W+lSpVi4sSJ/P3333zzzTeplUlEREQkw/r8889p27Ytf/zxB6dPn+bjjz9mwIABZscSkQxmw4YNlCpViqVLl2KxWOjSpQunTp3Cz88PNzc3s+OJiKSKJypKPeDo6EiLFi1Yu3ZtamxOREREJMOaOXMmI0eO5OTJkwQFBbF48WJmz55tdiwRyWBq1KiBq6srtWvXZv/+/SxYsID8+fObHUtEJFWlSlFKRERERO47d+4cnTp1sr1u164dcXFxXLt27Ym2O2vWLLy8vHBzc6NmzZr8/vvvyVpv+fLlWCwWWrRo8UT7F5G09ccff9C/f38MwwDgueeeY//+/ezYsYNq1aqZnE5EJG2oKCUiIiKSiqKjo8mSJYvttYODAy4uLvzzzz+Pvc0VK1bg6+vLyJEjOXjwIJUqVaJhw4YEBwc/cr0LFy7Qv39/Xnnllcfet4ikrWvXrtGlSxdq1KjBlClT+OGHH2zLihUrhsViMTGdiEjaSvbT90REREQkeYYPH55gEuKYmBjGjBlDjhw5bG1Tp05N9vamTp1K9+7d6dKlCwBz5sxh3bp1LFiwAD8/vyTXiY+Pp3379vj7+7Njxw7u3LnzeAcjImkiKiqK6dOn89lnnxEREQHAe++9R/Xq1U1OJiKSflSUEhEREUlFr776KidPnkzQ9tJLL3Hu3Dnb65SMfIiJieHAgQMMHjzY1ubg4IC3tzd79ux56HoBAQF4eHjQtWtXduzY8Z/7iY6OJjo62vY6LCwMAKvVitVqTXbe5DL+9Tktti+PZrVaMQxD594EhmGwevVqBg0axIULFwCoWbMm06ZNo2bNmoC+J9KDvgfMpfNvvrR+D5K7XRWlRERERFLRtm3bUnV7ISEhxMfHkzdv3gTtefPm5cSJE0mus3PnTubPn09QUFCy9zNu3Dj8/f0Ttd+8eZOoqKgUZU4Oa7zV9vm/bkOU1Ge1Wrl79y6GYeDgoBk90lNsbCxDhw7lwoUL5M2bl6FDh9KqVSscHBz0vZCO9D1gLp1/86X1e3Dv3r1k9VNRSkREROQpcu/ePd577z2++OIL8uTJk+z1Bg8ejK+vr+11WFgYnp6euLu7kz179lTP6eDoYPvs4eGR6tuXR7NarVgsFtzd3fULYToIDg4mZ86cuLi4ADBjxgx27dpFly5dKFy4sN4DE+h7wFw6/+ZL6/fAzc0tWf1UlBIRERGxY3ny5MHR0ZEbN24kaL9x4wb58uVL1P/s2bNcuHCBZs2a2doeDKF3cnLi5MmTFCtWLNF6rq6uuLq6Jmp3cHBIk4tVy78+6xcSc1gsljR7f+W+mJgYZsyYwejRoxkxYgT9+vUDoFGjRraHFeg9MI++B8yl82++tHwPkrtNvfsiIiIidszFxYVq1aqxZcsWW5vVamXLli3UqlUrUf/SpUtz5MgRgoKCbB/NmzenXr16BAUF4enpmZ7xRZ5JhmGwdu1aypUrx4ABAwgLC2P9+vUYhvHfK4uIPEM0UkpERETEzvn6+tKpUyeqV6/OCy+8QGBgIBEREban8XXs2JGCBQsybtw43NzcKF++fIL1c+bMCZCoXURS39GjR+nbty+bN28GIF++fIwbN46OHTum6CEHIiLPAhWlREREROxc69atuXnzJiNGjOD69etUrlyZDRs22CY/v3Tpkm5/ELEDc+bM4eOPP8ZqteLq6oqvry+DBw8mW7ZsZkcTEbFLKkqJiIiIpJEdO3Ywd+5czp49y6pVqyhYsCBLliyhSJEivPzyyynaVq9evejVq1eSy/7riX+LFi1K0b5E5PG8+uqrWCwWWrVqxaRJkyhSpIjZkURE7Jr+pCYiIiKSBlavXk3Dhg3JlCkThw4dIjo6GoC7d+8yduxYk9OJSGr4+eefGT9+vO112bJlOXnyJKtWrVJBSkQkGVSUEhEREUkDn376KXPmzOGLL77A2dnZ1l67dm0OHjxoYjIReVInTpygcePGNG7cmKFDh3LkyBHbsqSebikiIklTUUpEREQkDZw8eZJXX301UXuOHDm4c+dO+gcSkScWGhpKnz59qFChAj///DPOzs74+vry/PPPmx1NRCRD0pxSIiIiImkgX758nDlzBi8vrwTtO3fupGjRouaEEpHHEhcXx9y5cxkxYgShoaEANG/enMmTJ1OiRAmT04mIZFwaKSUiIiKSBrp3706fPn3Yt28fFouFq1evsnTpUvr370/Pnj3NjiciKXD37l2GDRtGaGgo5cqV45dffuGHH35QQUpE5AlppJSIiIhIGvDz88NqtfL6668TGRnJq6++iqurK/379+eTTz4xO56I/IfLly9TqFAhLBYLzz33HJMmTSImJoYPPvgAJyf9GiUikhrsZqTU2bNnGTZsGG3btiU4OBi4/zSLY8eOmZxMREREJOUsFgtDhw4lNDSUo0ePsnfvXm7evMno0aPNjiYij3D37l369+9PsWLF+Omnn2zt3bp146OPPlJBSkQkFdlFUeq3336jQoUK7Nu3jzVr1hAeHg7A4cOHGTlypMnpRERERB6fi4sLZcuW5YUXXiBr1qxmxxGRh4iPj2fevHmUKFGCKVOmEBsby8aNG82OJSLyVLOLMr+fnx+ffvopvr6+ZMuWzdb+2muvMXPmTBOTiYiIiDyeevXqYbFYHrp869at6ZhGRB5l27Zt+Pj4cPjwYQBKly7NtGnTeOONN0xOJiLydLOLotSRI0dYtmxZonYPDw9CQkJMSCQiIiLyZCpXrpzgdWxsLEFBQRw9epROnTqZE0pEEunfvz9TpkwBIGfOnPj7+9OzZ0+cnZ1NTiYi8vSzi6JUzpw5uXbtGkWKFEnQfujQIQoWLGhSKhEREZHHN23atCTbR40aZZuqQETMV69ePaZNm0aPHj3w9/cnT548ZkcSEXlm2MWcUm3atGHQoEFcv34di8WC1Wpl165d9O/fn44dO5odT0RERCTVdOjQgQULFpgdQ+SZZLVaWbBgQYLvwcaNG3P69GlmzZqlgpSISDqzi6LU2LFjKV26NJ6enoSHh1O2bFleffVVXnrpJYYNG2Z2PBEREZFUs2fPHtzc3MyOIfLM2blzJzVq1KBr167069fPNk2IxWKhaNGiJqcTEXk22cXtey4uLnzxxReMGDGCI0eOEB4eTpUqVShRooTZ0UREREQeS8uWLRO8NgyDa9eu8ccffzB8+HCTUok8ey5evMjAgQNZuXIlANmzZ2fo0KEJHrAkIiLmsIui1AOenp54enoSHx/PkSNHuH37Nrly5TI7loiIiEiK5ciRI8FrBwcHSpUqRUBAAA0aNDAplcizIyIiggkTJjBp0iSioqKwWCx069aNTz/9FA8PD7PjiYgIdlKU8vHxoUKFCnTt2pX4+Hjq1KnD7t27yZw5Mz/99BN169Y1O6KIiIhIssXHx9OlSxcqVKigP7CJmOT8+fOMGTMGq9VKnTp1CAwMTPRUTBERMZddzCm1atUqKlWqBMCPP/7IuXPnOHHiBH379mXo0KEmpxMRERFJGUdHRxo0aMCdO3fMjiLyTLl06ZLt3+XLlycgIIBVq1bx66+/qiAlImKH7KIoFRISQr58+QBYv3497777LiVLluT999/nyJEjJqcTERERSbny5ctz7tw5s2OIPBP+/vtvOnToQLFixTh27JitfejQobRq1QqLxWJiOhEReRi7KErlzZuXv/76i/j4eDZs2ED9+vUBiIyMxNHR0eR0IiIiIin36aef0r9/f3766SeuXbtGWFhYgg8ReXKRkZEEBARQqlQpli5dSnx8PJs2bTI7loiIJJNdzCnVpUsX3n33XfLnz4/FYsHb2xuAffv2Ubp0aZPTiYiIiCRfQEAA/fr1o3HjxgA0b948wSgNwzCwWCzEx8ebFVEkwzMMgxUrVjBw4EAuX74MQO3atZk+fTrVqlUzOZ2IiCSXXRSlRo0aRfny5bl8+TLvvPMOrq6uwP35GPz8/ExOJyIiIpJ8/v7+9OjRg19//dXsKCJPrebNm/PTTz8B8PzzzzNx4kTeffdd3aYnIpLB2EVRCuDtt99O1NapUycTkoiIiIg8PsMwAKhTp47JSUSeXvXq1WPr1q34+fnRv39/MmXKZHYkERF5DHZTlIqIiOC3337j0qVLxMTEJFjWu3dvk1KJiIiIpJxGa4iknqioKAIDA6lSpQoNGzYEoFevXrRu3ZqCBQuanE5ERJ6EXRSlDh06ROPGjYmMjCQiIoLcuXMTEhJC5syZ8fDwUFFKREREMpSSJUv+Z2EqNDQ0ndKIZEyGYbBmzRoGDBjA+fPnKVWqFH/++ScuLi64uLioICUi8hSwi6JU3759adasGXPmzCFHjhzs3bsXZ2dnOnToQJ8+fcyOJyIiIpIi/v7+5MiRw+wYIhlWUFAQPj4+/PbbbwAULFiQ4cOH4+RkF7++iIhIKrGL/9WDgoKYO3cuDg4OODo6Eh0dTdGiRZk4cSKdOnWiZcuWZkcUERERSbY2bdrg4eFhdgyRDOfGjRsMHz6cL7/8EsMwcHNzY8CAAQwaNIgsWbKYHU9ERFKZg9kBAJydnXFwuB/Fw8ODS5cuAZAjRw7bI15FREREMgLNJyXy+Pbt28cXX3yBYRi0bt2aEydOEBAQoIKUiMhTyi5GSlWpUoX9+/dTokQJ6tSpw4gRIwgJCWHJkiWUL1/e7HgiIiIiyfbg6Xsi8t8Mw+DSpUsULlwYgGbNmuHj40OrVq14+eWXTU4nIiJpzS5GSo0dO5b8+fMDMGbMGHLlykXPnj25efMm8+bNMzmdiIiISPJZrVbduieSDEePHqVBgwZUqVKFW7duAfdHGk6bNk0FKRGRZ4TpI6UMw8DDw8M2IsrDw4MNGzaYnEpERERERNJCSEgII0eOZM6cOVitVlxcXNi1axfNmzc3O5qIiKQz00dKGYZB8eLFNXeUiIiIiMhTLDY2lunTp1OiRAlmz56N1WqlZcuWHD9+XAUpEZFnlOkjpRwcHChRogS3bt2iRIkSZscREREREZFUFh0dTbVq1Th27BgAlSpVIjAwkLp165obTERETGX6SCmA8ePHM2DAAI4ePWp2FBERERERSWWurq68/PLLuLu7M2/ePA4cOKCClIiI2EdRqmPHjvz+++9UqlSJTJkykTt37gQfIiIiIiKScYSGhuLj48Px48dtbePGjeP06dN0794dR0dHE9OJiIi9MP32PYDAwECzI4iIiIiIyBOKi4tj7ty5jBgxgtDQUE6ePMnPP/8MQK5cuUxOJyIi9sYuilKdOnUyO4KIiIiIiDyBTZs20bdvX9u8UeXKlcPX19fkVCIiYs/soigFYLVaOXPmDMHBwVit1gTLXn31VZNSSUZ2/q/D7Fi7nKvnT3Hv9i3a9x9N2RdeMTuWiNiJji96UrdUHgrnzkx0nJUjV8KYte0cl0L/MTuaiEiGcvr0afr168ePP/4IwHPPPUdAQAAffPABTk528+uGiIjYIbv4KbF3717atWvHxYsXMQwjwTKLxUJ8fLxJySQji4mOIr9XMaq91phlk4ebHUdE7EyV53Oy+uBV/rp2D0cHCz1fLcL01hVp++V+omKt/70BEREB4IcffuDHH3/EycmJXr16MWLECN2qJyIiyWIXRakePXpQvXp11q1bR/78+bFYLGZHkqdAqSo1KVWlptkxRMRO9V15JMHr0etOsqHPS5TOl42gy3dNSiUiYv/i4+O5evUqnp6eAPTu3ZtTp07h6+tL6dKlTU4nIiIZiV0UpU6fPs2qVasoXry42VFEROQZldX1/pOgwv6JNTmJiIj92rZtGz4+PsTExHD48GGcnZ1xcXFh3rx5ZkcTEZEMyMHsAAA1a9bkzJkzZscQEZFnlAXw8S7O4ct3ORcSaXYcERG7c+7cOVq1akW9evU4fPgw165ds01oLiIi8rhMGyn1559/2v79ySef0K9fP65fv06FChVwdnZO0LdixYoP3U50dDTR0dEJ2mJjonF2cU3dwCIi8tQa0KAExdyz8MHXh8yOIiJiV+7du8fYsWOZOnUqMTExODg40KNHD/z9/cmTJ4/Z8UREJIMzrShVuXJlLBZLgonN33//fdu/Hyz7r4nOx40bh7+/f4K2dz705d2e/VM/tIiIPHX61S9O7eK56bH0MDfvxZgdR0TEbvz999/UqFGD69evA+Dt7c20adMoX768yclERORpYVpR6vz586myncGDB+Pr65ugbd3J0FTZtoiIPN361S9OnZJ5+HjZYa7djTI7joiIXSlYsCBlypQha9asTJ06laZNm+qBRCIikqpMm1OqcOHCyf54FFdXV7Jnz57gQ7fuCUB0VCRXL5zm6oXTANwOvs7VC6e5E3LD5GQiYg8GNCjOG+XyMnLtcSJi4sidxZncWZxxdbKL6RZFEpk1axZeXl64ublRs2ZNfv/994f2/eKLL3jllVfIlSsXuXLlwtvb+5H9RQAuXrxI165dCQ29/wdei8XC0qVLOXbsGM2aNVNBSkREUp1dPH0P4OTJk3z22WccP34cgDJlyvDJJ59QqlQpk5NJRnXl7Enm+/e1vV7/1SwAqtRpyNsfDzYrlojYiVZVCwLwefvKCdpHrzvBuiMqXot9WbFiBb6+vsyZM4eaNWsSGBhIw4YNOXnyJB4eHon6b9u2jbZt2/LSSy/h5ubGhAkTaNCgAceOHaNgwYImHIHYs4iICCZMmMCkSZOIiooia9asTJ8+HYD8+fObnE5ERJ5mdlGUWr16NW3atKF69erUqlULgL1791K+fHmWL19Oq1atTE4oGVHRclUYs3Kb2TFExE69OP43syOIJNvUqVPp3r07Xbp0AWDOnDmsW7eOBQsW4Ofnl6j/0qVLE7z+8ssvWb16NVu2bKFjx47pklnsn9Vq5euvv2bIkCFcuXIFgDp16ti+zkRERNKaXRSlBg4cyODBgwkICEjQPnLkSAYOHKiilIiIiDyzYmJiOHDgAIMH//9Rvg4ODnh7e7Nnz55kbSMyMpLY2Fhy586dVjElg9m3bx+9evXi4MGDAHh5eTF58mRatmyp2/RERCTd2EVR6tq1a0n+1a5Dhw5MmjTJhEQiIiIi9iEkJIT4+Hjy5s2boD1v3rycOHEiWdsYNGgQBQoUwNvb+6F9oqOjiY6Otr0OCwsD7o+msVqtj5H80Yx/fU6L7cujzZ8/n4MHD5I1a1YGDx6Mj48Pbm5uGIaR4OnYknasViuGYejr3yQ6/+bS+TdfWr8Hyd2uXRSl6taty44dOyhevHiC9p07d/LKK6+YlEpEREQk4xs/fjzLly9n27ZtuLm5PbTfuHHj8Pf3T9R+8+ZNoqJS/+mU1nir7XNwcHCqb18SioyMJDw83DYHWe/evQkPD2fQoEHkz5+fsLAwWyFS0ofVauXu3bsYhoGDgx6ykd50/s2l82++tH4P7t27l6x+dlGUat68OYMGDeLAgQO8+OKLwP05pb799lv8/f1Zu3Ztgr4iIiIiz4o8efLg6OjIjRsJJ+C/ceMG+fLle+S6kydPZvz48WzevJmKFSs+su/gwYPx9fW1vQ4LC8PT0xN3d3eyZ8/++AfwEA6ODrbPSU3WLqnDMAxWrFiBn58fFSpU4McffwTuf11NmzYNd3d3/UJoEqvVisVi0XtgEp1/c+n8my+t34NH/SHs3+yiKPXRRx8BMHv2bGbPnp3kMrj/WNr4+Ph0zSYiIiJiJhcXF6pVq8aWLVto0aIFcP9CcsuWLfTq1euh602cOJExY8awceNGqlev/p/7cXV1xdXVNVG7g4NDmlysWv71Wb+QpI0//vgDHx8fdu3aBdy/lr516xbu7u6212n1/kry6D0wl86/uXT+zZeW70Fyt2kX7/6DuQr+60MFKREREXkW+fr68sUXX7B48WKOHz9Oz549iYiIsD0lrWPHjgkmQp8wYQLDhw9nwYIFeHl5cf36da5fv054eLhZhyDp6Nq1a3Tp0oUaNWqwa9cuMmfOTEBAACdOnLAVpEREROyBXYyUEhEREZGHa926NTdv3mTEiBFcv36dypUrs2HDBtvk55cuXUrwF8nPP/+cmJgY3n777QTbGTlyJKNGjUrP6JLO9u7dS/369W0FyA4dOjBu3DgKFSpkcjIREZHE7KYoFRERwW+//calS5eIiYlJsKx3794mpRIRERGxD7169Xro7Xrbtm1L8PrChQtpH0jsUpUqVfDw8KBcuXIEBgba5msVERGxR3ZRlDp06BCNGzcmMjKSiIgIcufOTUhICJkzZ8bDw0NFKRERERGRJAQFBTFz5kzmzJmDk5MTrq6u/PbbbxQoUEDztIiIiN2zi59Uffv2pVmzZty+fZtMmTKxd+9eLl68SLVq1Zg8ebLZ8URERERE7EpwcDAffPABVatWZf78+cydO9e2rFChQipIiYhIhmAXP62CgoLo168fDg4OODo6Eh0djaenJxMnTmTIkCFmxxMRERERsQsxMTFMnjyZEiVK8MUXX2AYBq1bt6Zp06ZmRxMREUkxuyhKOTs72/6a4+HhwaVLlwDIkSMHly9fNjOaiIiIiIjpDMNg7dq1lCtXjgEDBhAWFka1atXYsWMHy5cvp3DhwmZHFBERSTG7mFOqSpUq7N+/nxIlSlCnTh1GjBhBSEgIS5YsoXz58mbHExEREREx3dSpUzlz5gz58uVj7NixdOrUSbfpiYhIhmYXP8XGjh1L/vz5ARgzZgy5cuWiZ8+e3Lx5k3nz5pmcTkREREQk/YWEhHD37l0ALBYLgYGB+Pn5cerUKbp06aKClIiIZHimj5QyDAMPDw/biCgPDw82bNhgcioREREREXPExsYye/ZsRo0aRZcuXZg6dSoAlStXpnLlyuaGExERSUWm/3nFMAyKFy+uuaNERERE5Jm3fv16KlSogI+PD3fu3GHHjh3ExcWZHUtERCRNmF6UcnBwoESJEty6dcvsKCIiIiIipjh+/DiNGjWiSZMmnDx5End3d+bOncvevXtxcjL95gYREZE0YRc/4caPH8+AAQP4/PPPNbG5iIiIiDxTli9fTocOHYiPj8fZ2Zk+ffowbNgwcuTIYXY0EbtjtVqJiYlJle3ExsYSFRWl+dlMoPNvvid9D5ydnXF0dHziHHZRlOrYsSORkZFUqlQJFxcXMmXKlGB5aGioSclERERERNJW3bp1yZw5M/Xq1WPy5MmUKFHC7EgidikmJobz589jtVqfeFuGYWC1Wrl37x4WiyUV0klK6PybLzXeg5w5c5IvX74neg/toigVGBhodgQRERERkXSxadMmNmzYwJQpUwDIly8fx44dw9PT0+RkIvbLMAyuXbuGo6Mjnp6eTzy6xjAM4uLicHJyUlHEBDr/5nuS98AwDCIjIwkODgYgf/78j53DLopSnTp1MjuCiIiIiEiaOn36NP369ePHH38E4I033qB+/foAKkiJ/Ie4uDgiIyMpUKAAmTNnfuLtqShiLp1/8z3pe/DgDrfg4GA8PDwe+1Y+uyhK/VtUVFSie4SzZ89uUhoRERERkSdz9+5dRo8ezYwZM4iNjcXJyYmPP/6YatWqmR1NJMOIj48HwMXFxeQkIvLAgwJxbGxsxi5KRUREMGjQIFauXJnkU/ge/AckIiIiIpJRxMfHM3/+fIYNG8bNmzcBaNSoEVOnTqV06dImpxPJmDSqRsR+pMb3o11Mcz9w4EC2bt3K559/jqurK19++SX+/v4UKFCAr776yux4IiIiIiIpFhsby7hx47h58yalS5dm/fr1rF+/XgUpERGR/2MXRakff/yR2bNn06pVK5ycnHjllVcYNmwYY8eOZenSpWbHExERERFJlgsXLhAXFweAm5sbM2bMIDAwkD///JNGjRqZnE5EJOO5desWHh4eXLhwwewoz5Q2bdrYHsiRluyiKBUaGkrRokWB+/NHhYaGAvDyyy+zfft2M6OJiIiIiPyne/fuMXjwYEqVKsWXX35pa2/WrBl9+vTB2dnZxHQiYpbOnTtjsViwWCw4OztTpEgRBg4cSFRUVKK+P/30E3Xq1CFbtmxkzpyZGjVqsGjRoiS3u3r1aurWrUuOHDnImjUrFStWJCAgwPa79MP8+uuvNG7cmOeee47MmTNTtmxZ+vXrx5UrV1LjcNPEmDFjePPNN/Hy8kq0rGHDhjg6OrJ///5Ey+rWrYuPj0+i9kWLFpEzZ84EbWFhYQwdOpTSpUvj5uZGvnz58Pb2Zs2aNRiGkUpHkti2bduoWrUqrq6uFC9e/KHv979t3LiRF198kWzZsuHu7k6rVq0SFeyWLl1KpUqVyJw5M/nz5+f9999PMFVSbGwsAQEBlC5dmkyZMlGpUiU2bNiQYBvDhg1jzJgx3L17NzUO9aHsoihVtGhRzp8/D0Dp0qVZuXIlcH8E1f9+sYiIiIiI2Aur1crChQspWbIk48ePJyYmht9++83sWCJiR9544w2uXbvGuXPnmDZtGnPnzmXkyJEJ+nz22We8+eab1K5dm3379vHnn3/Spk0bevToQf/+/RP0HTp0KK1bt6ZGjRr8/PPPHD16lClTpnD48GGWLFny0Bxz587F29ubfPnysXr1av766y/mzJnD3bt3n2hEzP8+qCw1RUZGMn/+fLp27Zpo2aVLl9i9eze9evViwYIFj72PO3fu8NJLL/HVV18xePBgDh48yPbt22ndujUDBw5Ms6LM+fPnadKkCfXq1SMoKAgfHx+6devGxo0bH7nOm2++yWuvvUZQUBAbN24kJCSEli1b2vrs2rWLjh070rVrV44dO8a3337L77//Tvfu3W19hg0bxrx585g2bRrHjh2jR48evPXWWxw6dMjWp3z58hQrVoyvv/46TY7/AbuY6LxLly4cPnyYOnXq4OfnR7NmzZg5cyaxsbFMnTrV7HgiIiIiIons3LmTPn36cPDgQQCKFy/O1KlTadq0qcnJRMSeuLq6ki9fPgA8PT3x9vZm06ZNTJgwAYDLly/Tr18/fHx8GDt2rG29fv364eLiQu/evXnnnXeoWbMmv//+O2PHjiUwMJA+ffrY+np5eVG/fn3u3LmTZIa///6b3r1707t3b6ZNm5ZgvVdffdW23qhRo/j+++8JCgqy9QkMDCQwMNA2Gqdz587cuXOHGjVqMGvWLFxdXWnbti1btmxh3759CfZbqVIlWrVqxYgRIwD48ssvmTJlCufPn6dw4cL07t2bjz/++KHnbv369bi6uvLiiy8mWrZw4UKaNm1Kz549efHFF5k6dSqZMmV66LYeZsiQIVy4cIFTp05RoEABW3vJkiVp27Ytbm5uKd5mcsyZM4ciRYrYCoJlypRh586dTJs2jYYNGya5zoEDB4iPj+fTTz/FweH+GKP+/fvz5ptvEhsbi7OzM3v27MHLy4vevXsDUKRIET788EPb1xvAkiVLGDJkCI0aNcLJyYmePXuyefNmpkyZkqAI1axZM5YvX/7I9+hJmVqUslqtTJo0ibVr1xITE8PVq1cZOXIkJ06c4MCBAxQvXpyKFSuaGVFEREREJJExY8YwbNgw4P70EyNGjOCTTz7R4+pF0lGzz3Zy8170Y69vYGAh5U8Pc8/myo+fvPxY+zx69Ci7d++mcOHCtrZVq1YRGxubaEQUwIcffsiQIUP45ptvqFmzJkuXLiVr1qx89NFHSW7/YXcaffvtt8TExDBw4MAUrfcwW7ZsIXv27GzatMnWNm7cOM6ePUuxYsUAOHbsGH/++SerV68G7t9SNmLECGbOnEnlypX5448/6NmzJ1mzZqVTp05J7mfHjh1Uq1YtUbthGCxcuJBZs2ZRunRpihcvzqpVq3jvvfdSdBxWq5Xly5fTvn37BAWpB7JmzfrQdXfs2PGfcwXOnTuX9u3bJ7lsz549eHt7J2hr2LBhkrccPlCtWjUcHBxYuHAhnTt3Jjw8nCVLluDt7W27TbxWrVoMGTKE9evX06hRI4KDg1m1ahWNGze2bSc6OjpRsS1Tpkzs3LkzQdsLL7zAmDFjiI6OxtXV9ZHH+rhMLUqNGTOGUaNG4e3tTaZMmZg+fTrBwcEsWLAgwTepiIiIiIg9adSoEaNGjaJLly58+umneHh4mB1J5Jlz814018MSz81kb3766SeyZs1KXFwc0dHRODg4MHPmTNvyU6dOkSNHDvLnz59oXRcXF4oWLcqpU6cAOH36NEWLFk3xPHWnT58me/bsSe7jcWTJkoUvv/wyQSG+UqVKLFu2jOHDhwP3i1A1a9akePHiAIwcOZIpU6bQsmVLDMPA09OTkydPMnfu3IcWpS5evJhksWjz5s1ERkbaRhR16NCB+fPnp7goFRISwu3btx/rqajVq1dPMKIsKXnz5n3osuvXrydanjdvXsLCwvjnn3+SHPVVpEgRfvnlF959910+/PBD4uPjqVWrFuvXr7f1qV27NkuXLqV169ZERUURFxdHs2bNmDVrlq1Pw4YNmTZtGi+99BKlSpVi69atrFmzhvj4+AT7K1CgADExMVy/fj3NajSmFqW++uorZs+ezYcffgjc/8Jq0qQJX375pW0omoiIiIiImaxWK0uXLuXq1asMGjQIgKpVq3L+/HkKFSpkcjqRZ5d7ticbufEkI6VSol69enz++edEREQwbdo0nJycaNWqVYr3Czz2pNuGYWCxpPxYH6ZChQqJRoa2b9+eBQsWMHz4cAzD4JtvvsHX1xeAiIgIzp49S9euXRPMbRQXF0eOHDkeup9//vknydvnFixYQOvWrXFyul/SaNu2LQMGDEgwUis5nmQS80yZMtkKbunl+vXrdO/enU6dOtG2bVvu3bvHiBEjePvtt9m0aRMWi4W//vqLPn36MGLECBo2bMi1a9cYMGAAPXr0YP78+QBMnz6d7t27U6FCBSwWC8WKFaNLly6J5uZ6UBiLjIxMs2MytSh16dKlBEPIvL29sVgsXL16VT/gRURERMR0e/fuxcfHh3379uHs7EzLli0pUaIEgK5XRUz2uLfQwf1iRFxcHE5OTqlarElKlixZbMWLBQsWUKlSpQSTd5csWZK7d+9y9erVRKOCYmJiOHv2LPXq1bP13blzp23+oOR6sI9r1649crSUg4NDokJNbGxsksf0v9q2bcugQYM4ePAg//zzD5cvX6Z169YAhIeHA/DFF19Qs2bNBOf/QWEpKXny5OH27dsJ2kJDQ/nuu++IjY3l888/t7XHx8ezYMECxowZA9y/tTqpScrv3LljK4S5u7uTM2dOTpw48dAMD/Okt+/ly5ePGzduJGi7ceMG2bNnf+jcWLNmzSJHjhxMnDjR1vb111/j6enJvn37ePHFFxk3bhy1a9dmwIABAFSsWJEsWbLwyiuv8Omnn5I/f37c3d357rvvCA8P5+7duxQsWBA/Pz+KFi2aYH8Pnubo7u7+6JPxBEwdjhQXF5eo6uns7JzkF72IiIiISHr5+++/6dChA7Vq1WLfvn1kzZqVgIAAPD09zY4mIhmYg4MDQ4YMYdiwYfzzzz8AtGrVCmdn5ySfgDdnzhwiIiJo27YtAO3atSM8PJzZs2cnuf2HTXT+9ttv4+LikqCYkdR67u7uXL9+PUFh6r9uUXugUKFC1KlTh6VLl7J06VLq169vu7U5b968FChQgHPnzlG8ePEEH0WKFHnoNqtUqcJff/2VoG3p0qUUKlSIw4cPExQUZPuYMmUKixYtst2CVqpUKduDKP7t4MGDlCxZErj/frRp08Y2GvZ/hYeHExcXl2S2B7fvPeqjefPmDz22WrVqsWXLlgRtmzZtolatWg9dJzIyMtFdZY6OjsD9Ub3/1ed/C45ubm4ULFiQuLg4Vq9ezZtvvplg+dGjRylUqBB58uR5aKYnZepIKcMw6Ny5c4IJs6KioujRo0eCyuuaNWvMiCciIiIiz5jIyEgmT57MhAkTiIyMxGKx0LlzZ8aMGZNqc7GIyLPtnXfeYcCAAcyaNYv+/fvz/PPPM3HiRPr164ebmxvvvfcezs7O/PDDDwwZMoR+/fpRs2ZNAGrWrMnAgQPp168fV65c4a233qJAgQKcOXOGOXPm8PLLLyd4Kt8Dnp6eTJs2jV69ehEWFkbHjh3x8vLi77//5quvviJr1qxMmTKFunXrcvPmTSZOnMjbb7/Nhg0b+Pnnn8mePXuyjq19+/aMHDmSmJiYBE/5A/D396d3797kyJGDhg0bEhERQVBQEHfu3LHd5ve/GjZsyODBg7l9+za5cuUCYP78+bz99tuUL18+0TEOHjyYDRs20KRJE3r27MnMmTPp3bs33bp1w9XVlXXr1vHNN9/w448/2tYbM2YM27Zto2bNmowZM4bq1avj7OzMjh07GDduHPv3709yIvgnvX2vR48ezJw5k4EDB/L++++zdetWVq5cybp162x9Zs6cyXfffWcrXjVp0oRp06YREBBgu31vyJAhFC5cmCpVqgD3n5jXvXt3Pv/8c9vtez4+Przwwgu2kXj79u3j77//pnz58ty4cQN/f3+sVmuiifB37NhBgwYNHvsYk8PUkVKdOnXCw8ODHDly2D46dOhAgQIFErSJiIiIiKSHu3fvMnHiRCIjI6lduzb79+9nwYIFKkiJSKpxcnKiV69eTJw4kYiICAB8fHz47rvv2LFjB9WrV6d8+fIsW7aMzz//nMmTJydYf8KECSxbtox9+/bRsGFDypUrh6+vLxUrVnzohOEAH330Eb/88outmFW6dGm6detG9uzZbU/+K1OmDLNnz2bWrFlUqlSJ33//PcmnAj7M22+/za1bt4iMjKRFixYJlnXr1o0vv/yShQsXUrFiRby9vVm8ePEjR0pVqFCBqlWrsnLlSgAOHDjA4cOHk5yTK0eOHLz++uu2eZOKFi3K9u3bOXHiBN7e3tSsWZOVK1fy7bff8sYbb9jWy507N3v37qVDhw58+umnVKlShVdeeYVvvvmGSZMmpVlNokiRIqxbt45NmzZRqVIlpkyZwpdffmmbvB3uT8R+9uxZ2+vXXnuNZcuW8f3331OlShXeeOMNXF1d2bBhg+2Wv86dOzN16lRmzpxJ+fLleeeddyhVqlSCwT5RUVEMHz6cSpUq0bJlSwoWLMjOnTsTFN+ioqL4/vvvE8wBlhYsxpPM7GWnVh2+ZnYEEbFjk38+ZXYEEbFze/3qmB3BdGFhYeTIkYO7d+8m+y/kKfHi2M1cD4smX3ZX9g7x/u8V0tCZM2cS/LV77ty55MyZk3fffTfN55oxi9VqJTg4GA8PDz1gyCR6D1ImKiqK8+fPU6RIkSQnvk6p9JxTShJLyflft24dAwYM4OjRo/peSUX/9R58/vnnfPfdd/zyyy8P3cajvi+Tex2hd1REREREnknXrl2jS5culCxZkq1bt9raP/zwQ1q3bq1fVEVE7ECTJk344IMPuHLlitlRninOzs589tlnab4fU+eUEhERERFJb1FRUUybNo2xY8fangi1fft2XnvtNZOTiYhIUnx8fMyO8Mzp1q1buuxHRSkREREReSYYhsGaNWsYMGAA58+fB+5PGhwYGMiLL75ocjoREZFnj4pSIiIiIvJM6NSpE0uWLAGgYMGCjB8/nnbt2mmOEhEREZPoJ7CIiIiIPBOaNm2Km5sbw4cP5+TJk3To0EEFKRERERNppJSIiIiIPHViYmKYMWMGefPm5b333gPgnXfe4eWXX6ZAgQImpxMRERFQUUpEREREniKGYfDjjz/Sr18/zpw5g7u7O82bNydHjhxYLBYVpEREROyIilIiIiIi8lQ4evQoffv2ZfPmzQDky5ePsWPHki1bNpOTiYiISFJ0E72IiIiIZGghISF8/PHHVKpUic2bN+Pi4oKfnx+nTp2iS5cumjdKRJ5ZFouF77//3uwYD+Xg4JAu+bZt24bFYuHOnTu2tu+//57ixYvj6OiIj48PixYtImfOnGmeRRLST2gRERERydDOnz/P7NmzsVqttGzZkuPHjzNu3DiNkBIR03Xu3BmLxYLFYsHZ2ZkiRYowcOBAoqKizI6W5q5fv84nn3xC0aJFcXV1xdPTk2bNmrFly5Z0z/LSSy9x7do1cuTIYWv78MMPefvtt7l8+TKjR4+mdevWnDp1Kt2zPet0+56IiIiIZDinT5+mRIkSANSoUYPRo0dTu3Zt6tWrZ3IyEZGE3njjDRYuXEhsbCwHDhygU6dOWCwWJkyYYHa0NHPhwgVq165Nzpw5mTRpEhUqVCA2NpaNGzfy8ccfc/z48XTN4+LiQr58+Wyvw8PDCQ4OpmHDhgnmGsyUKdMT7Sc2NhZnZ+cn2sazRiOlRERERCTDOHHiBI0bN6Z8+fKcOXPG1j5s2DAVpETELrm6upIvXz48PT1p0aIF3t7ebNq0ybb81q1btG3bloIFC5I5c2YqVKjAN998k2AbdevWpXfv3gwcOJDcuXOTL18+Ro0alaDP6dOnefXVV3Fzc6Ns2bIJ9vHAkSNHeO2118iUKRPPPfccH3zwAeHh4bblnTt3pkWLFowdO5a8efOSM2dOAgICiIuLY8CAAeTOnZtChQqxcOHCRx7zRx99hMVi4ffff6dVq1aULFmScuXK4evry969ex+63qBBgyhZsiSZM2emaNGiDB8+nNjYWNvyw4cPU69ePbJly0b27NmpVq0af/zxBwAXL16kWbNm5MqViyxZslCuXDnWr18PJLx9b9u2bbaRtK+99hoWi4Vt27YlefveDz/8QNWqVXFzc6No0aL4+/sTFxdnW26xWPj8889p3rw5WbJkYcyYMY88L5KYRkqJiIiIiN27ffs2/v7+zJo1i7i4OJydndm9ezfFixc3O5qImCgiIuKhyxwdHXFzc0uyr2EYxMXF4eTkhMViwcHBIcEomYdtN0uWLE+U9+jRo+zevZvChQvb2qKioqhWrRqDBg0ie/bsrFu3jvfee49ixYrxwgsv2PotXrwYX19f9u3bx549e+jcuTO1a9emfv36ttuX8+bNy759+7h79y4+Pj4J9h0REUHDhg2pVasW+/fvJzg4mG7dutGrVy8WLVpk67d161YKFSrE9u3b2bVrF127dmX37t28+uqr7Nu3jxUrVvDhhx9Sv359ChUqlOgYQ0ND2bBhA2PGjEnyfOXMmRPDMJI8P9myZWPRokUUKFCAI0eO0L17d7Jly8bAgQMBaN++PVWqVOHzzz/H0dGRoKAg28ikjz/+mJiYGLZv306WLFn466+/yJo1a6J9vPTSS5w8eZJSpUqxevVqXnrpJXLnzs2FCxcS9NuxYwcdO3ZkxowZvPLKK5w9e5YPPvgAgJEjR9r6jRo1ivHjxxMYGIiTk0osKaUzJiIiIiJ2Ky4ujrlz5zJixAhCQ0MBaN68OZMnT7bdviciz66kig4PNG7cmHXr1tlee3h4EBkZmWTfOnXqsG3bNttrLy8vQkJCEvV7WDHlUX766SeyZs1KXFwc0dHRODg4MHPmTNvyggUL0r9/f9vrTz75hI0bN7Jy5coERamKFSvaiiElSpRg5syZbNmyhfr167N582ZOnDjBxo0bbbejjR07lkaNGtnWX7ZsGVFRUXz11Ve2YtHMmTNp1qwZEyZMIG/evADkzp2bGTNm4ODgQKlSpZg4cSKRkZEMGTIEgMGDBzN+/Hh27txJmzZtEh3vmTNnMAyD0qVLp/hcDRs2zPZvLy8v+vfvz/Lly21FqUuXLjFgwADbtv/9c+DSpUu0atWKChUqAFC0aNEk9+Hi4oKHh4ftWP99W9+/+fv74+fnR6dOnWzbGz16NAMHDkxQlGrXrh1dunRJ8bHKfSpKiYiIiIhdslqtvPzyy+zbtw+AcuXKMW3aNOrXr29yMhGR5KtXrx6ff/45ERERTJs2DScnJ1q1amVbHh8fz9ixY1m5ciVXrlwhJiaG6OhoMmfOnGA7FStWTPA6f/78BAcHA3D8+HE8PT0TzI9Uq1atBP2PHz9OpUqVEoxeql27NlarlZMnT9qKUuXKlUvw1NK8efNSvnx522tHR0eee+45277/1+MU7h5YsWIFM2bM4OzZs4SHhxMXF0f27Nlty319fenWrRtLlizB29ubd955h2LFigHQu3dvevbsyS+//IK3tzetWrVKdM5S4vDhw+zatSvBLXnx8fFERUURGRlpe3+qV6/+2PsQzSklIiIiInbKwcGBpk2b8txzzzFr1iyCgoJUkBKRBMLDwx/6sXr16gR9g4ODbcvu3bvH7du3uXfvHuHh4fz8888J+l64cCHJbT6OLFmyULx4cSpVqsSCBQvYt28f8+fPty2fNGkS06dPZ9CgQfz6668EBQXRsGFDYmJiEmznfyfQtlgsWK3Wx8r0KEntJyX7LlGiBBaLhRMnTqRov3v27KF9+/Y0btyYn376iUOHDjF06NAE52HUqFEcO3aMJk2asHXrVsqWLct3330HQLdu3Th37hzvvfceR44coXr16nz22WcpyvBv4eHh+Pv7ExQUZPs4cuQIp0+fTnBb6JPe0vmsU1FKREREJAOYNWsWXl5euLm5UbNmTX7//fdH9v/2228pXbo0bm5uVKhQwTbZqz27e/cu/fv3T3ALTb9+/Th9+jQfffSR5uoQkUSyZMny0I9/Fw7+q+//PnXtYf2elIODA0OGDGHYsGH8888/AOzatYs333yTDh06UKlSJYoWLcqpU6dStN0yZcpw+fJlrl27Zmv73wnFy5Qpw+HDhxPMl7Vr1y7bbXqpJXfu3DRs2JBZs2YlOTfXnTt3klzvwVxbQ4cOpXr16pQoUYKLFy8m6leyZEn69u3LL7/8QsuWLRNMuu7p6UmPHj1Ys2YN/fr144svvnjs46hatSonT56kePHiiT7+PZJMnozOpIiIiIidW7FiBb6+vowcOZKDBw9SqVIlGjZs+NBbJ3bv3k3btm3p2rUrhw4dokWLFrRo0YKjR4+mc/LkiY+PZ968eZQoUYIpU6bg4+NDfHw8cP/x3Lly5TI5oYhI6nnnnXdwdHRk1qxZwP2RRZs2bWL37t0cP36cDz/8kBs3bqRom97e3pQsWZJOnTpx+PBhduzYwdChQxP0ad++PW5ubnTq1ImjR4/y66+/8sknn/Dee+/Zbt1LLbNmzSI+Pp4XXniB1atXc/r0aY4fP/7/2rvzuKiq/3/gr2HfQdwAQdFYREUUl1wyRTEwF8w15aPklgokgeSWCmiKS2riJ/cFMw0zFf2IQYhiipapoX0McYMwA9dPAiIMMOf3hz/u1xFQ1hmk1/PxmMeDuffcc973nMcdzpw591xERESUuq2whL29PTIyMhAVFYWbN28iIiJCmgUFAE+fPoW/vz8SExPxxx9/ICkpCb/88gucnJwAAB9//DHi4uKQlpaGixcv4sSJE9K+qli4cCG++uorhIWF4cqVK0hJSUFUVJTSuldUfRyUIiIiIqrjVq9ejSlTpmDChAlo06YNNm7cCAMDA2zfvr3M9GvXroWnpyc++eQTODk5YfHixXB1dVVaWLeuyL51CZ06dcLUqVNx//59ODo6Ijw8HJqamuoOjYioVmhpacHf3x8rVqzAkydPMH/+fLi6usLDwwN9+vSBhYUFhg4dWqk8NTQ0cPDgQTx9+hRdu3bF5MmTldZCAgADAwPExcXh0aNH6NKlC0aMGIF+/frVyv+GVq1a4eLFi3Bzc8PMmTPRrl079O/fHwkJCdiwYUOZxwwZMgSBgYHw9/dHhw4dcObMGSxYsEDar6mpiYcPH2L8+PFwcHDAqFGjMGDAAISFhQF49gOHn58fnJyc4OnpCQcHB6xfv77K5+Dh4YEjR47ghx9+QJcuXdCtWzesWbNG6cmJVH0yUZ1VyOqo7y5lvjoREf1jff595aZDE9E/z09zeqs7BIlcLoeBgQG+++47pS8pPj4++Pvvv3Ho0KFSxzRv3hxBQUFKjwMPCQlBdHQ0Ll26VKFys7OzYWpqisePHystMltTOgTvwvX/bETetTMAnj0iPDQ0FL6+vqXWLqGap1AocO/ePTRp0oS3oagJ26By8vPzkZaWhpYtW5a6La8qhBAoKiqClpYWZDJZDURIlcH6V7+aaIOXXZcV7UfwxnwiIiKiOuzBgwcoLi4udWtF06ZNy11ENisrq8z0WVlZ5ZZTUFCAgoIC6X12djaAZ1+ca2Mh3dzbKc8GpGQamD5tKkJDQ9GoUSOpTKpdCoUCQgjWtRqxDSqnpL5KXjWhJJ96OE/jtcD6V7/qtkHJ9VhWX6Gin20clCIiIiIihIeHS7dAPO/+/fvIz8+v8fLM2vaGSZersOnqiYXzh0izRkg1FAoFHj9+DCEEZ+moCdugcgoLC6FQKFBUVISioqJq5yeEkNau40wd1WP9q19NtEFRUREUCgUePnxYapZzTk5OhfLgoBQRERFRHdaoUSNoamqWWvT27t27sLCwKPMYCwuLSqUHgLlz5yIoKEh6n52dDRsbGzRu3LhWbt+zMDOAbMg0NDXRR5MmTWo8f3o5hUIBmUyGxo0bc0BETdgGlZOfn4+cnBxoaWnV6JM4ebuwerH+1a86baClpQUNDQ00bNiw1O17Fb3NloNSRERERHWYjo4OOnXqhISEBGlNKYVCgYSEBPj7+5d5TPfu3ZGQkKC0plR8fHy5TzwCAF1dXejq6pbarqGhUStfmA/79+R6Omomk8lqrX2pYtgGFaehoQGZTCa9qksIIeXDmTqqx/pXv5pog5LrsazPsYp+rnFQioiIiKiOCwoKgo+PDzp37oyuXbviiy++wJMnTzBhwgQAwPjx49GsWTOEh4cDAAICAtC7d2+sWrUKAwcORFRUFM6fP4/Nmzer8zSIiIiIlHBQioiIiKiOGz16NO7fv4+FCxciKysLHTp0QGxsrLSYeUZGhtIvkj169MCePXswf/58zJs3D/b29oiOjka7du3UdQpERDWCi2IT1R01cT1yUIqIiIjoNeDv71/u7XqJiYmlto0cORIjR46s5aiIiFRDU1MTACCXy6Gvr6/maIgIAPLy8gBUc12qmgqGiIiIiIiIqDZoaWnBwMAA9+/fh7a2drXX4RJCoKioCFpaWlzTSA1Y/+pXnTYQQiAvLw/37t2DmZmZNGhcFRyUIiIiIiIiojpNJpPB0tISaWlp+OOPP6qdnxACCoVCWkCdVIv1r3410QZmZmYvfbJvRXBQioiIiIiIiOo8HR0d2NvbQy6XVzsvhUKBhw8fomHDhnz6oRqw/tWvum2gra1drRlSJTgoRURERERERK8FDQ0N6OnpVTsfhUIBbW1t6OnpcVBEDVj/6ldX2oCtT0REREREREREKsdBKSIiIiIiIiIiUjkOShERERERERERkcpxTSkiIiIiKkUIAQDIzs6ulfwVCgVycnLUvpbFPxXrX/3YBurF+lcv1r/61XYblPQfSvoT5ZGJV6Uges0VFBQgPDwcc+fOha6urrrDIaI6hp8RRGX7888/YWNjo+4wiIiI6DV2+/ZtWFtbl7ufg1JU72VnZ8PU1BSPHz+GiYmJusMhojqGnxFEZVMoFPjrr79gbGwMmUxW4/lnZ2fDxsYGt2/f5rWnBqx/9WMbqBfrX71Y/+pX220ghEBOTg6srKxeOhOLt+8RERERUSkaGhov/WWzppiYmPALiRqx/tWPbaBerH/1Yv2rX222gamp6SvT8OZNIiIiIiIiIiJSOQ5KERERERERERGRynFQiuo9XV1dhISEcAFjIioTPyOI1IPXnnqx/tWPbaBerH/1Yv2rX11pAy50TkREREREREREKseZUkREREREREREpHIclCIiIiIiIiIiIpXjoBTVWTKZDNHR0dL7q1evolu3btDT00OHDh3K3UZEVFnp6emQyWRITk5WdyhERERERP8YHJQilfvggw8gk8kgk8mgra2Npk2bon///ti+fTsUCoWULjMzEwMGDJDeh4SEwNDQEKmpqUhISCh3mzrZ2triiy++UHcYRHXW89e/jo4O7OzssGjRIhQVFak7NCKqBV9++SVsbW2hp6eHN998E+fOnXtp+n379qF169bQ09ODs7Mzjh49qqJI66fK1P+WLVvQq1cvNGjQAA0aNIC7u/sr24terbLXQImoqCjIZDIMHTq0dgOs5ypb/3///Tf8/PxgaWkJXV1dODg48HOoGipb/1988QUcHR2hr68PGxsbBAYGIj8/X0XR1i8//vgjBg8eDCsrq1KTPcqTmJgIV1dX6Orqws7ODpGRkbUeJ8BBKVITT09PZGZmIj09Hd9//z3c3NwQEBCAQYMGSV9OLSwslJ4EcPPmTbz11lto0aIFGjZsWO62ypLL5dU/ISKqsJLr//r165g5cyZCQ0OxcuXKSudTXFysNJCtauoun6iu27t3L4KCghASEoKLFy/CxcUFHh4euHfvXpnpz5w5gzFjxmDSpEn49ddfMXToUAwdOhT//e9/VRx5/VDZ+k9MTMSYMWNw4sQJnD17FjY2NnjnnXdw584dFUdef1S2DUqkp6cjODgYvXr1UlGk9VNl618ul6N///5IT0/Hd999h9TUVGzZsgXNmjVTceT1Q2Xrf8+ePZgzZw5CQkKQkpKCbdu2Ye/evZg3b56KI68fnjx5AhcXF3z55ZcVSp+WloaBAwfCzc0NycnJ+PjjjzF58mTExcXVcqQABJGK+fj4CC8vr1LbExISBACxZcsWIYQQAMTBgwelv59/hYSElLlNCCEyMjLEyJEjhampqWjQoIEYMmSISEtLK1X+Z599JiwtLYWtrW2ljlu5cqWwsLAQ5ubmwtfXV8jlciGEEL179y4VExEpK+v679+/v+jWrZtYtWqVaNeunTAwMBDW1tZi+vTpIicnR0q3Y8cOYWpqKg4dOiScnJyEpqamSEtLE+fOnRPu7u6iYcOGwsTERLz99tviwoULSmUAEOvXrxeenp5CT09PtGzZUuzbt0/an5aWJgCI/fv3iz59+gh9fX3Rvn17cebMmWqXr1AoREhIiLCxsRE6OjrC0tJSfPTRR9L+/Px8MXPmTGFlZSUMDAxE165dxYkTJ2qoxonUp2vXrsLPz096X1xcLKysrER4eHiZ6UeNGiUGDhyotO3NN98UU6dOrdU466vK1v+LioqKhLGxsdi5c2dthVjvVaUNioqKRI8ePcTWrVvL7TNTxVS2/jds2CBatWol9e2peipb/35+fqJv375K24KCgkTPnj1rNc5/gue/V5dn1qxZom3btkrbRo8eLTw8PGoxsmc4U4rqjL59+8LFxQUHDhwotS8zMxNt27bFzJkzkZmZieDg4DK3FRYWwsPDA8bGxjh16hSSkpJgZGQET09PpRlRCQkJSE1NRXx8PI4cOVLh406cOIGbN2/ixIkT2LlzJyIjI6VpjQcOHIC1tTUWLVqEzMxMZGZm1nqdEdUH+vr6kMvl0NDQQEREBK5cuYKdO3fi+PHjmDVrllLavLw8LF++HFu3bsWVK1fQpEkT5OTkwMfHB6dPn8ZPP/0Ee3t7vPvuu8jJyVE6dsGCBRg+fDguXboEb29vvP/++0hJSVFK8+mnnyI4OBjJyclwcHDAmDFjlG4trEr5+/fvx5o1a7Bp0yZcv34d0dHRcHZ2lvL09/fH2bNnERUVhcuXL2PkyJHw9PTE9evXa7qqiVRGLpfjwoULcHd3l7ZpaGjA3d0dZ8+eLfOYs2fPKqUHAA8Pj3LTU/mqUv8vysvLQ2FhIczNzWsrzHqtqm2waNEiNGnSBJMmTVJFmPVWVer/8OHD6N69O/z8/NC0aVO0a9cOS5cuRXFxsarCrjeqUv89evTAhQsXpFv8bt26haNHj+Ldd99VScz/dOr8H6xV6yUQVULr1q1x+fLlUtstLCygpaUFIyMjWFhYAACMjIxKbfv666+hUCiwdetWyGQyAMCOHTtgZmaGxMREvPPOOwAAQ0NDbN26FTo6OpU6rkGDBvj3v/8NTU1NtG7dGgMHDkRCQgKmTJkCc3NzaGpqwtjYWIqHiMonhEBCQgLi4uLw0Ucf4eOPP5b22dra4rPPPsO0adOwfv16aXthYSHWr18PFxcXaVvfvn2V8t28eTPMzMxw8uRJDBo0SNo+cuRITJ48GQCwePFixMfHY926dUr5BwcHY+DAgQCAsLAwtG3bFjdu3EDr1q2rXH5GRgYsLCzg7u4ObW1tNG/eHF27dgUAZGRkYMeOHcjIyICVlZUUQ2xsLHbs2IGlS5dWvmKJ6oAHDx6guLgYTZs2VdretGlTXL16tcxjsrKyykyflZVVa3HWV1Wp/xfNnj0bVlZWpb6kUMVUpQ1Onz6Nbdu28aEbNaAq9X/r1i0cP34c3t7eOHr0KG7cuAFfX18UFhYiJCREFWHXG1Wp/7Fjx+LBgwd46623IIRAUVERpk2bxtv3VKS8/8HZ2dl4+vQp9PX1a61szpSiOkUIIQ0KVcWlS5dw48YNGBsbw8jICEZGRjA3N0d+fj5u3rwppXN2dpYGpCpzXNu2baGpqSm9t7S0fOW6AESk7MiRIzAyMoKenh4GDBiA0aNHIzQ0FMeOHUO/fv3QrFkzGBsbY9y4cXj48CHy8vKkY3V0dNC+fXul/O7evYspU6bA3t4epqamMDExQW5uLjIyMpTSde/evdT7F2dKPZ+3paUlAChd41Upf+TIkXj69ClatWqFKVOm4ODBg9Lsq99++w3FxcVwcHCQPnuMjIxw8uRJpc8eIiJVWrZsGaKionDw4EHo6empO5x/hJycHIwbNw5btmxBo0aN1B3OP5JCoUCTJk2wefNmdOrUCaNHj8ann36KjRs3qju0f4TExEQsXboU69evx8WLF3HgwAHExMRg8eLF6g6NahlnSlGdkpKSgpYtW1b5+NzcXHTq1Am7d+8uta9x48bS34aGhlU6TltbW2mfTCbjQsdEleTm5oYNGzZAR0cHVlZW0NLSQnp6OgYNGoTp06djyZIlMDc3x+nTpzFp0iTI5XIYGBgAeHar34sD1z4+Pnj48CHWrl2LFi1aQFdXF927d6/SQwyev8ZLynn+Gq9K+TY2NkhNTcWxY8cQHx8PX19frFy5EidPnkRubi40NTVx4cIFpQFv4NlsUKLXVaNGjaCpqYm7d+8qbb979265s4ktLCwqlZ7KV5X6L/H5559j2bJlOHbsWKlBeKq4yrbBzZs3kZ6ejsGDB0vbSv7/aGlpITU1FW+88UbtBl2PVOUasLS0hLa2ttL/YycnJ2RlZUEulyv9oE0vV5X6X7BgAcaNGyfNand2dsaTJ0/w4Ycf4tNPP4WGBufT1Kby/gebmJjU6iwpgDOlqA45fvw4fvvtNwwfPrzKebi6uuL69eto0qQJ7OzslF6mpqY1ftyLdHR0eN850SsYGhrCzs4OzZs3h5bWs99GLly4AIVCgVWrVqFbt25wcHDAX3/9VaH8kpKSMGPGDLz77rto27YtdHV18eDBg1Lpfvrpp1LvnZycqn0+FSlfX18fgwcPRkREBBITE3H27Fn89ttv6NixI4qLi3Hv3r1Snz38Ik6vMx0dHXTq1AkJCQnSNoVCgYSEhFKzFkt0795dKT0AxMfHl5ueyleV+geAFStWYPHixYiNjUXnzp1VEWq9Vdk2aN26NX777TckJydLryFDhkhPwrKxsVFl+K+9qlwDPXv2xI0bN5R+jLp27RosLS05IFVJVan/vLy8UgNPJQOEQojaC5YAqPd/MAelSC0KCgqQlZWFO3fu4OLFi1i6dCm8vLwwaNAgjB8/vsr5ent7o1GjRvDy8sKpU6eQlpaGxMREzJgxA3/++WeNH/ciW1tb/Pjjj7hz506ZX4qJqGx2dnYoLCzEunXrcOvWLezatavC0+Xt7e2xa9cupKSk4Oeff4a3t3eZv+js27cP27dvx7Vr1xASEoJz587B39+/2rG/qvzIyEhs27YN//3vf3Hr1i18/fXX0NfXR4sWLeDg4ABvb2+MHz8eBw4cQFpaGs6dO4fw8HDExMRUOzYidQoKCsKWLVuwc+dOpKSkYPr06Xjy5AkmTJgAABg/fjzmzp0rpQ8ICEBsbCxWrVqFq1evIjQ0FOfPn6+R6/SfqLL1v3z5cixYsADbt2+Hra0tsrKykJWVhdzcXHWdwmuvMm2gp6eHdu3aKb3MzMxgbGyMdu3acVCkCip7DUyfPh2PHj1CQEAArl27hpiYGCxduhR+fn7qOoXXWmXrf/DgwdiwYQOioqKQlpaG+Ph4LFiwAIMHDy41m5xeLTc3VxrgBoC0tDQkJydLy0vMnTtX6Xv3tGnTcOvWLcyaNQtXr17F+vXr8e233yIwMLDWY+Xte6QWsbGxsLS0hJaWFho0aAAXFxdERETAx8enWlMzDQwM8OOPP2L27NkYNmwYcnJy0KxZM/Tr1w8mJiY1ftyLFi1ahKlTp+KNN95AQUEBR/WJKsjFxQWrV6/G8uXLMXfuXLz99tsIDw+v0CD1tm3b8OGHH8LV1RU2NjZYunQpgoODS6ULCwtDVFQUfH19YWlpiW+++QZt2rSpduyvKt/MzAzLli1DUFAQiouL4ezsjP/85z9o2LAhgGcPVfjss88wc+ZM3LlzB40aNUK3bt2UFmkneh2NHj0a9+/fx8KFC5GVlYUOHTogNjZWWkg1IyND6X9+jx49sGfPHsyfPx/z5s2Dvb09oqOj0a5dO3WdwmutsvW/YcMGyOVyjBgxQimfkJAQhIaGqjL0eqOybUA1q7L1b2Njg7i4OAQGBqJ9+/Zo1qwZAgICMHv2bHWdwmutsvU/f/58yGQyzJ8/H3fu3EHjxo0xePBgLFmyRF2n8Fo7f/483NzcpPdBQUEAni07ERkZiczMTKX1V1u2bImYmBgEBgZi7dq1sLa2xtatW+Hh4VHrscoEvzUTEVE9J5PJcPDgQQwdOlTdoRARERER0f/HoXkiIiIiIiIiIlI5DkoREREREREREZHKcU0pIiKq93inOhERERFR3cOZUkREREREREREpHIclCIiIiIiIiIiIpXjoBQREREREREREakcB6WIiIiIiIiIiEjlOChFREREREREREQqx0EpIqrXPvjgAwwdOlR636dPH3z88ccqjyMxMREymQx///23yssmIiKiuicyMhJmZmbqDqPKZDIZoqOjX5rmxX4YEdGLOChFRGrxwQcfQCaTQSaTQUdHB3Z2dli0aBGKiopqtdwDBw5g8eLFFUrLgSQiIiJ6mef7M8+/bty4oe7QEBkZKcWjoaEBa2trTJgwAffu3auR/DMzMzFgwAAAQHp6OmQyGZKTk5XSrF27FpGRkTVSXnlCQ0Ol89TU1ISNjQ0+/PBDPHr0qFL5cACNSD201B0AEf1zeXp6YseOHSgoKMDRo0fh5+cHbW1tzJ07VymdXC6Hjo5OjZRpbm5eI/kQERERAf/Xn3le48aN1RSNMhMTE6SmpkKhUODSpUuYMGEC/vrrL8TFxVU7bwsLi1emMTU1rXY5FdG2bVscO3YMxcXFSElJwcSJE/H48WPs3btXJeUTUdVxphQRqY2uri4sLCzQokULTJ8+He7u7jh8+LD0S9WSJUtgZWUFR0dHAMDt27cxatQomJmZwdzcHF5eXkhPT5fyKy4uRlBQEMzMzNCwYUPMmjULQgilMl+8fa+goACzZ8+GjY0NdHV1YWdnh23btiE9PR1ubm4AgAYNGkAmk+GDDz4AACgUCoSHh6Nly5bQ19eHi4sLvvvuO6Vyjh49CgcHB+jr68PNzU0pTiIiIqo/Svozz780NTWxevVqODs7w9DQEDY2NvD19UVubm65+Vy6dAlubm4wNjaGiYkJOnXqhPPnz0v7T58+jV69ekFfXx82NjaYMWMGnjx58tLYZDIZLCwsYGVlhQEDBmDGjBk4duwYnj59CoVCgUWLFsHa2hq6urro0KEDYmNjpWPlcjn8/f1haWkJPT09tGjRAuHh4Up5l9y+17JlSwBAx44dIZPJ0KdPHwDKs482b94MKysrKBQKpRi9vLwwceJE6f2hQ4fg6uoKPT09tGrVCmFhYa+cSa+lpQULCws0a9YM7u7uGDlyJOLj46X9xcXFmDRpktR3c3R0xNq1a6X9oaGh2LlzJw4dOiTNukpMTATw6v4nEVUPB6WIqM7Q19eHXC4HACQkJCA1NRXx8fE4cuQICgsL4eHhAWNjY5w6dQpJSUkwMjKCp6endMyqVasQGRmJ7du34/Tp03j06BEOHjz40jLHjx+Pb775BhEREUhJScGmTZtgZGQEGxsb7N+/HwCQmpqKzMxMqfMSHh6Or776Chs3bsSVK1cQGBiIf/3rXzh58iSAZ52XYcOGYfDgwUhOTsbkyZMxZ86c2qo2IiIiqoM0NDQQERGBK1euYOfOnTh+/DhmzZpVbnpvb29YW1vjl19+wYULFzBnzhxoa2sDAG7evAlPT08MHz4cly9fxt69e3H69Gn4+/tXKiZ9fX0oFAoUFRVh7dq1WLVqFT7//HNcvnwZHh4eGDJkCK5fvw4AiIiIwOHDh/Htt98iNTUVu3fvhq2tbZn5njt3DgBw7NgxZGZm4sCBA6XSjBw5Eg8fPsSJEyekbY8ePUJsbCy8vb0BAKdOncL48eMREBCA33//HZs2bUJkZCSWLFlS4XNMT09HXFyc0ix7hUIBa2tr7Nu3D7///jsWLlyIefPm4dtvvwUABAcHY9SoUfD09ERmZiYyMzPRo0ePCvU/iaiaBBGRGvj4+AgvLy8hhBAKhULEx8cLXV1dERwcLHx8fETTpk1FQUGBlH7Xrl3C0dFRKBQKaVtBQYHQ19cXcXFxQgghLC0txYoVK6T9hYWFwtraWipHCCF69+4tAgIChBBCpKamCgAiPj6+zBhPnDghAIj//e9/0rb8/HxhYGAgzpw5o5R20qRJYsyYMUIIIebOnSvatGmjtH/27Nml8iIiIqLXm4+Pj9DU1BSGhobSa8SIEWWm3bdvn2jYsKH0fseOHcLU1FR6b2xsLCIjI8s8dtKkSeLDDz9U2nbq1CmhoaEhnj59WuYxL+Z/7do14eDgIDp37iyEEMLKykosWbJE6ZguXboIX19fIYQQH330kejbt69S3+t5AMTBgweFEEKkpaUJAOLXX39VSvN8f08IIby8vMTEiROl95s2bRJWVlaiuLhYCCFEv379xNKlS5Xy2LVrl7C0tCwzBiGECAkJERoaGsLQ0FDo6ekJAAKAWL16dbnHCCGEn5+fGD58eLmxlpT9qv4nEVUP15QiIrU5cuQIjIyMUFhYCIVCgbFjxyI0NBR+fn5wdnZW+oXr0qVLuHHjBoyNjZXyyM/Px82bN/H48WNkZmbizTfflPZpaWmhc+fOpW7hK5GcnAxNTU307t27wjHfuHEDeXl56N+/v9J2uVyOjh07AgBSUlKU4gCA7t27V7gMIiIien24ublhw4YN0ntDQ0MAz2YNhYeH4+rVq8jOzkZRURHy8/ORl5cHAwODUvkEBQVh8uTJ2LVrl3QL2htvvAHgWT/o8uXL2L17t5ReCAGFQoG0tDQ4OTmVGdvjx49hZGQEhUKB/Px8vPXWW9i6dSuys7Px119/oWfPnkrpe/bsiUuXLgF4dutd//794ejoCE9PTwwaNAjvvPNOterK29sbU6ZMwfr166Grq4vdu3fj/fffh4aGhnSeSUlJSjOjiouLX1pvAODo6IjDhw8jPz8fX3/9NZKTk/HRRx8ppfnyyy+xfft2ZGRk4OnTp5DL5ejQocNL431V/5OIqo+DUkSkNiWdOB0dHVhZWUFL6/8+kko6dCVyc3PRqVMnpc5YiaouJqqvr1/pY0rWgoiJiUGzZs2U9unq6lYpDiIiInp9GRoaws7OTmlbeno6Bg0ahOnTp2PJkiUwNzfH6dOnMWnSJMjl8jIHV0JDQzF27FjExMTg+++/R0hICKKiovDee+8hNzcXU6dOxYwZM0od17x583JjMzY2xsWLF6GhoQFLS0up75Odnf3K83J1dUVaWhq+//57HDt2DKNGjYK7u3updTQrY/DgwRBCICYmBl26dMGpU6ewZs0aaX9ubi7CwsIwbNiwUsfq6emVm2/Jk5wBYNmyZRg4cCDCwsKkJy5HRUUhODgYq1atQvfu3WFsbIyVK1fi559/fmm8tdH/JCJlHJQiIrUpqxNXHldXV+zduxdNmjSBiYlJmWksLS3x888/4+233wYAFBUV4cKFC3B1dS0zvbOzMxQKBU6ePAl3d/dS+0tmahUXF0vb2rRpA11dXWRkZJQ7w8rJyQmHDx9W2vbTTz+9+iSJiIioXrhw4QIUCgVWrVolzQIqWb/oZRwcHODg4IDAwECMGTMGO3bswHvvvQdXV1f8/vvvFe43ldDQ0CjzGBMTE1hZWSEpKUmpP5OUlISuXbsqpRs9ejRGjx6NESNGwNPTE48ePSr1NOOy+kxl0dPTw7Bhw7B7927cuHEDjo6OSv00V1dXpKamVvo8XzR//nz07dsX06dPl86zR48e8PX1ldK8ONNJR0enVPwV6X8SUfVwoXMiei14e3ujUaNG8PLywqlTp5CWlobExETMmDEDf/75JwAgICAAy5YtQ3R0NK5evQpfX1/8/fff5eZpa2sLHx8fTJw4EdHR0VKeJZ3GFi1aQCaT4ciRI7h//z5yc3NhbGyM4OBgBAYGYufOnbh58yYuXryIdevWYefOnQCAadOm4fr16/jkk0+QmpqKPXv2IDIysrariIiIiOoIOzs7FBYWYt26dbh16xZ27dqFjRs3lpv+6dOn8Pf3R2JiIv744w8kJSXhl19+kW7Lmz17Ns6cOQN/f38kJyfj+vXrOHToUKUXOn/eJ598guXLl2Pv3r1ITU3FnDlzkJycjICAAADA6tWr8c033+Dq1au4du0a9u3bBwsLC5iZmZXKq0mTJtDX10dsbCzu3r2Lx48fl1uut7c3YmJisH37dmmB8xILFy7EV199hbCwMFy5cgUpKSmIiorC/PnzK3Vu3bt3R/v27bF06VIAgL29Pc6fP4+4uDhcu3YNCxYswC+//KJ0jK2tLS5fvozU1FQ8ePAAhYWFFep/ElH1cFCKiF4LBgYG+PHHH9G8eXMMGzYMTk5OmDRpEvLz86VfrmbOnIlx48bBx8dHmpr93nvvvTTfDRs2YMSIEfD19UXr1q0xZcoU6fHKzZo1Q1hYGObMmYOmTZtKHb/FixdjwYIFCA8Ph5OTEzw9PRETEyM9Drl58+bYv38/oqOj4eLigo0bN0qdIiIiIqr/XFxcsHr1aixfvhzt2rXD7t27ER4eXm56TU1NPHz4EOPHj4eDgwNGjRqFAQMGICwsDADQvn17nDx5EteuXUOvXr3QsWNHLFy4EFZWVlWOccaMGQgKCsLMmTPh7OyM2NhYHD58GPb29gCe3fq3YsUKdO7cGV26dEF6ejqOHj0qzfx6npaWFiIiIrBp0yZYWVnBy8ur3HL79u0Lc3NzpKamYuzYsUr7PDw8cOTIEfzwww/o0qULunXrhjVr1qBFixaVPr/AwEBs3boVt2/fxtSpUzFs2DCMHj0ab775Jh4+fKg0awoApkyZAkdHR3Tu3BmNGzdGUlJShfqfRFQ9MlHeCsBERERERERERES1hDOliIiIiIiIiIhI5TgoRUREREREREREKsdBKSIiIiIiIiIiUjkOShERERERERERkcpxUIqIiIiIiIiIiFSOg1JERERERERERKRyHJQiIiIiIiIiIiKV46AUERERERERERGpHAeliIiIiIiIiIhI5TgoRUREREREREREKsdBKSIiIiIiIiIiUjkOShERERERERERkcr9P5/kikKPZ9dbAAAAAElFTkSuQmCC",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Predictions\n",
+ "y_pred = model.predict(X_test)\n",
+ "y_pred_proba = model.predict_proba(X_test)[:, 1]\n",
+ "\n",
+ "print(\"Classification Report:\")\n",
+ "print(\"=\" * 80)\n",
+ "print(classification_report(y_test, y_pred, target_names=['Different', 'Paraphrase']))\n",
+ "\n",
+ "# Confusion matrix\n",
+ "cm = confusion_matrix(y_test, y_pred)\n",
+ "\n",
+ "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
+ "\n",
+ "# Confusion matrix heatmap\n",
+ "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', ax=axes[0], cbar=False)\n",
+ "axes[0].set_title('Confusion Matrix')\n",
+ "axes[0].set_xlabel('Predicted')\n",
+ "axes[0].set_ylabel('Actual')\n",
+ "axes[0].set_xticklabels(['Different', 'Paraphrase'])\n",
+ "axes[0].set_yticklabels(['Different', 'Paraphrase'])\n",
+ "\n",
+ "# ROC Curve\n",
+ "fpr, tpr, _ = roc_curve(y_test, y_pred_proba)\n",
+ "auc = roc_auc_score(y_test, y_pred_proba)\n",
+ "axes[1].plot(fpr, tpr, label=f'ROC Curve (AUC = {auc:.3f})', linewidth=2)\n",
+ "axes[1].plot([0, 1], [0, 1], 'k--', label='Random Classifier')\n",
+ "axes[1].set_xlabel('False Positive Rate')\n",
+ "axes[1].set_ylabel('True Positive Rate')\n",
+ "axes[1].set_title('ROC Curve')\n",
+ "axes[1].legend()\n",
+ "axes[1].grid(alpha=0.3)\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1bc9f488",
+ "metadata": {},
+ "source": [
+ "#### Visualize feature importance"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "6dc8a0c6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Feature Importance Summary:\n",
+ "================================================================================\n",
+ "BERT (Semantic) : +1.7731 ↑ Increases paraphrase likelihood\n",
+ "TED (Syntactic) : +0.2136 ↑ Increases paraphrase likelihood\n",
+ "Jaccard (Baseline) : +0.9931 ↑ Increases paraphrase likelihood\n",
+ "Length Ratio : +0.0666 ↑ Increases paraphrase likelihood\n"
+ ]
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10, 5))\n",
+ "feature_names = ['BERT (Semantic)', 'TED (Syntactic)', 'Jaccard (Baseline)', 'Length Ratio']\n",
+ "coefficients = model.coef_[0]\n",
+ "colors = ['green' if c > 0 else 'red' for c in coefficients]\n",
+ "\n",
+ "plt.barh(feature_names, coefficients, color=colors, alpha=0.7)\n",
+ "plt.xlabel('Model Coefficient (Impact on Prediction)')\n",
+ "plt.title('Feature Importance in Fusion Model')\n",
+ "plt.axvline(x=0, color='black', linestyle='--', linewidth=0.8)\n",
+ "plt.grid(axis='x', alpha=0.3)\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n",
+ "\n",
+ "print(\"Feature Importance Summary:\")\n",
+ "print(\"=\" * 80)\n",
+ "for name, coef in zip(feature_names, coefficients):\n",
+ " direction = \"↑ Increases paraphrase likelihood\" if coef > 0 else \"↓ Decreases paraphrase likelihood\"\n",
+ " print(f\"{name:<25}: {coef:+.4f} {direction}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "45112ea7",
+ "metadata": {},
+ "source": [
+ "## Ablation Study\n",
+ "Comparing performance when removing individual features, evaluate importance of each"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "611b74f9",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Ablation Study Results:\n",
+ "================================================================================\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Model | \n",
+ " F1-Score | \n",
+ " Precision | \n",
+ " Recall | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " Full (All Features) | \n",
+ " 0.8 | \n",
+ " 1.0 | \n",
+ " 0.667 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " Without bert | \n",
+ " 0.4 | \n",
+ " 0.5 | \n",
+ " 0.333 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " Without ted | \n",
+ " 0.8 | \n",
+ " 1.0 | \n",
+ " 0.667 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " Without jaccard | \n",
+ " 1.0 | \n",
+ " 1.0 | \n",
+ " 1.000 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " Without len_ratio | \n",
+ " 0.8 | \n",
+ " 1.0 | \n",
+ " 0.667 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "