{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Data Augmentation Analysis\n", "\n", "Statistical analysis of data characteristics for entity-level sentiment classification.\n", "\n", "**Structure**\n", "1. Load Data\n", "2. Article Length Analysis\n", "3. Sentiment Label Distribution\n", "4. Label Distribution by Entity Type\n", "5. Entities per Sample\n", "6. Entity Position within Article\n", "7. Window Size Analysis" ], "id": "e6791992fa7aa25b" }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2026-04-18T21:29:34.813264Z", "start_time": "2026-04-18T21:29:34.810262Z" } }, "source": [ "import os\n", "import json\n", "import warnings\n", "from collections import Counter\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import matplotlib.patches as mpatches\n", "import seaborn as sns\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "sns.set_theme(style=\"whitegrid\", palette=\"muted\")\n", "plt.rcParams[\"figure.dpi\"] = 120\n", "plt.rcParams[\"axes.titlesize\"] = 13\n", "plt.rcParams[\"axes.labelsize\"] = 11\n", "\n", "VALID_LABELS = {\"positive\", \"neutral\", \"negative\"}\n", "LABEL_ORDER = [\"negative\", \"neutral\", \"positive\"]\n", "LABEL_COLORS = {\"positive\": \"#4CAF50\", \"neutral\": \"#90A4AE\", \"negative\": \"#EF5350\"}" ], "id": "32a9ac93d2dec54", "outputs": [], "execution_count": 80 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Load Data" ], "id": "5eabfa4c06905b4" }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2026-04-18T21:29:34.875246Z", "start_time": "2026-04-18T21:29:34.831148Z" } }, "source": [ "data_path = os.path.join(\"..\", \"data\", \"data_preprocessed.jsonl\")\n", "with open(data_path, \"r\", encoding=\"utf-8\") as f:\n", " data = [json.loads(line) for line in f]\n", "\n", "# Flat article-level dataframe\n", "df_articles = pd.DataFrame([\n", " {\"id\": s[\"id\"], \"text\": s[\"text\"], \"n_entities\": len(s[\"entities\"])}\n", " for s in data\n", "])\n", "\n", "# Flat entity-level dataframe\n", "df_entities = pd.DataFrame([\n", " {\n", " \"sample_id\": s[\"id\"],\n", " \"entity_id\": e[\"entity_id\"],\n", " \"entity_text\": e[\"entity_text\"],\n", " \"entity_type\": e[\"entity_type\"],\n", " \"label\": e[\"label\"],\n", " \"n_positions\": len(e[\"positions\"]),\n", " }\n", " for s in data\n", " for e in s[\"entities\"]\n", "])\n", "\n", "print(f\"Articles : {len(df_articles):,}\")\n", "print(f\"Entities : {len(df_entities):,}\")\n", "print(f\"Avg entities per article: {len(df_entities)/len(df_articles):.2f}\")\n", "df_articles.head()" ], "id": "463eb928d9be91b0", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Articles : 1,629\n", "Entities : 10,550\n", "Avg entities per article: 6.48\n" ] }, { "data": { "text/plain": [ " id text n_entities\n", "0 0 Amazon sold unauthorized mole removers, and th... 3\n", "1 1 Russia Announces Response Measures to New US S... 9\n", "2 2 India's richest man takes on Amazon, Walmart i... 11\n", "3 3 Govt may impose anti-dumping duty on chemical ... 6\n", "4 4 Apollo Go: AI-Powered Autonomous Ride-Hailing ... 11" ], "text/html": [ "
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11Russia Announces Response Measures to New US S...9
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44Apollo Go: AI-Powered Autonomous Ride-Hailing ...11
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" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 81 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Article Length Analysis\n", "\n", "Article length determines how aggressively the model must truncate. With a 512-token\n", "hard limit and ~1.25 tokens per word, articles over ~410 words will be truncated\n", "when encoded naively from the start." ], "id": "ed4ab8fe5bcd1057" }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2026-04-18T21:29:35.030122Z", "start_time": "2026-04-18T21:29:34.900828Z" } }, "source": [ "df_articles[\"word_count\"] = df_articles[\"text\"].apply(lambda x: len(x.split()))\n", "df_articles[\"approx_tokens\"] = (df_articles[\"word_count\"] * 1.25).round().astype(int)\n", "\n", "fig, axes = plt.subplots(1, 2, figsize=(13, 4))\n", "\n", "# Word count histogram\n", "sns.histplot(df_articles[\"word_count\"], bins=50, kde=True, ax=axes[0], color=\"steelblue\")\n", "median_wc = df_articles[\"word_count\"].median()\n", "axes[0].axvline(median_wc, color=\"tomato\", linestyle=\"--\", label=f\"Median = {median_wc:.0f}\")\n", "axes[0].set_title(\"Article Word Count Distribution\")\n", "axes[0].set_xlabel(\"Word Count\")\n", "axes[0].legend()\n", "\n", "# Truncation thresholds\n", "thresholds = [128, 256, 384, 512]\n", "pct_over = [100 * (df_articles[\"approx_tokens\"] > t).mean() for t in thresholds]\n", "colors_t = [\"#4CAF50\", \"#FFC107\", \"#FF9800\", \"#F44336\"]\n", "bars = axes[1].bar([str(t) for t in thresholds], pct_over, color=colors_t, edgecolor=\"white\")\n", "for bar, pct in zip(bars, pct_over):\n", " axes[1].text(bar.get_x() + bar.get_width() / 2, bar.get_height() + 0.5,\n", " f\"{pct:.1f}%\", ha=\"center\", va=\"bottom\", fontsize=10)\n", "axes[1].set_title(\"% Articles Exceeding Token Limit\")\n", "axes[1].set_xlabel(\"max_length (tokens)\")\n", "axes[1].set_ylabel(\"% articles exceeding\")\n", "\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "print(\"Word count statistics:\")\n", "print(df_articles[\"word_count\"].describe().round(1).to_string())\n", "print()\n", "print(\"Key percentiles:\")\n", "print(df_articles[\"word_count\"].quantile([0.25, 0.5, 0.75, 0.90, 0.95, 0.99]).round().to_string())" ], "id": "a7921e552ced2093", "outputs": [ { "data": { "text/plain": [ "
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}, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Word count statistics:\n", "count 1629.0\n", "mean 500.9\n", "std 283.1\n", "min 28.0\n", "25% 297.0\n", "50% 452.0\n", "75% 649.0\n", "max 1550.0\n", "\n", "Key percentiles:\n", "0.25 297.0\n", "0.50 452.0\n", "0.75 649.0\n", "0.90 900.0\n", "0.95 1062.0\n", "0.99 1323.0\n" ] } ], "execution_count": 82 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Sentiment Label Distribution\n", "\n", "Class imbalance directly affects model calibration. Without correction the model will\n", "collapse toward predicting the majority class. We compute inverse-frequency weights." ], "id": "6b7a5eb94ae4474a" }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2026-04-18T21:29:35.126747Z", "start_time": "2026-04-18T21:29:35.060790Z" } }, "source": [ "df_valid = df_entities[df_entities[\"label\"].isin(VALID_LABELS)].copy()\n", "label_vc = df_valid[\"label\"].value_counts().reindex(LABEL_ORDER)\n", "\n", "fig, axes = plt.subplots(1, 2, figsize=(13, 4))\n", "\n", "# Count bar chart\n", "bar_colors = [LABEL_COLORS[l] for l in LABEL_ORDER]\n", "axes[0].bar(LABEL_ORDER, label_vc.values, color=bar_colors, edgecolor=\"white\")\n", "for i, (lbl, cnt) in enumerate(zip(LABEL_ORDER, label_vc.values)):\n", " axes[0].text(i, cnt + 50,\n", " f\"{cnt:,}\\n({cnt / len(df_valid) * 100:.1f}%)\",\n", " ha=\"center\", va=\"bottom\", fontsize=10)\n", "axes[0].set_title(\"Entity-Level Label Distribution\")\n", "axes[0].set_xlabel(\"Label\")\n", "axes[0].set_ylabel(\"Count\")\n", "\n", "# Pie chart\n", "axes[1].pie(\n", " label_vc.values,\n", " labels=LABEL_ORDER,\n", " colors=bar_colors,\n", " autopct=\"%1.1f%%\",\n", " startangle=140,\n", " wedgeprops={\"edgecolor\": \"white\", \"linewidth\": 1.5},\n", ")\n", "axes[1].set_title(\"Label Proportions\")\n", "\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "# Inverse-frequency class weights\n", "n_total = label_vc.sum()\n", "n_classes = len(label_vc)\n", "weights = {lbl: round(n_total / (n_classes * cnt), 4) for lbl, cnt in label_vc.items()}\n", "\n", "print(\"Inverse-frequency class weights w_i = N / (K * n_i):\")\n", "for lbl, w in weights.items():\n", " print(f\" {lbl:12s}: {w:.4f}\")\n", "print()\n", "max_w = max(weights.values())\n", "min_w = min(weights.values())\n", "print(f\"Weight ratio max/min : {max_w / min_w:.2f}x\")" ], "id": "cb99303765e240f0", "outputs": [ { "data": { "text/plain": [ "
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}, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Inverse-frequency class weights w_i = N / (K * n_i):\n", " negative : 2.1721\n", " neutral : 0.4752\n", " positive : 2.2970\n", "\n", "Weight ratio max/min : 4.83x\n" ] } ], "execution_count": 83 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Label Distribution by Entity Type\n", "\n", "Company and location entities have very different distributions. Location entities are\n", "≈90% neutral — a much more extreme imbalance than companies. This motivates a weight\n", "cap so rare location sentiment classes don't receive pathologically large gradient signals." ], "id": "a305f3c77c222378" }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2026-04-18T21:29:35.213693Z", "start_time": "2026-04-18T21:29:35.149918Z" } }, "source": [ "type_label = (\n", " df_valid.groupby([\"entity_type\", \"label\"])\n", " .size()\n", " .reset_index(name=\"count\")\n", ")\n", "type_totals = type_label.groupby(\"entity_type\")[\"count\"].transform(\"sum\")\n", "type_label[\"pct\"] = (type_label[\"count\"] / type_totals * 100).round(1)\n", "\n", "fig, axes = plt.subplots(1, 2, figsize=(13, 4))\n", "\n", "for ax, etype in zip(axes, [\"company\", \"location\"]):\n", " sub = type_label[type_label[\"entity_type\"] == etype].set_index(\"label\").reindex(LABEL_ORDER)\n", " colors = [LABEL_COLORS[l] for l in LABEL_ORDER]\n", " bars = ax.bar(LABEL_ORDER, sub[\"count\"].values, color=colors, edgecolor=\"white\")\n", " for bar, pct in zip(bars, sub[\"pct\"].values):\n", " ax.text(bar.get_x() + bar.get_width() / 2,\n", " bar.get_height() + 20,\n", " f\"{pct:.1f}%\", ha=\"center\", va=\"bottom\", fontsize=10)\n", " ax.set_title(f\"{etype.capitalize()} entities\")\n", " ax.set_xlabel(\"Label\")\n", " ax.set_ylabel(\"Count\")\n", "\n", "plt.suptitle(\"Label Distribution by Entity Type\", y=1.02, fontsize=13)\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "# Uncapped weights per entity type\n", "print(\"Uncapped inverse-frequency weights per entity type:\")\n", "for etype in [\"company\", \"location\"]:\n", " sub = df_valid[df_valid[\"entity_type\"] == etype][\"label\"].value_counts().reindex(LABEL_ORDER)\n", " n = sub.sum()\n", " ws = {lbl: round(n / (3 * cnt), 3) for lbl, cnt in sub.items()}\n", " ratio = max(ws.values()) / min(ws.values())\n", " print(f\" {etype:10s}: {ws} → max/min = {ratio:.1f}x\")\n", "print()" ], "id": "ace4c5c77abaca30", "outputs": [ { "data": { "text/plain": [ "
" ], "image/png": 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}, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Uncapped inverse-frequency weights per entity type:\n", " company : {'negative': np.float64(1.871), 'neutral': np.float64(0.514), 'positive': np.float64(1.92)} → max/min = 3.7x\n", " location : {'negative': np.float64(5.376), 'neutral': np.float64(0.371), 'positive': np.float64(8.397)} → max/min = 22.6x\n", "\n" ] } ], "execution_count": 84 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. Entities per Sample\n", "\n", "Most samples contain multiple entities, each producing an independent training example.\n", "For QA-B, each entity becomes three binary examples — a sample with 10 entities\n", "generates 30 forward passes at inference time." ], "id": "abcba32da8cd8b6e" }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2026-04-18T21:29:35.314785Z", "start_time": "2026-04-18T21:29:35.227231Z" } }, "source": [ "ents_per_sample = df_entities.groupby(\"sample_id\").size()\n", "\n", "fig, axes = plt.subplots(1, 2, figsize=(13, 4))\n", "\n", "# Histogram\n", "sns.histplot(ents_per_sample, bins=range(1, ents_per_sample.max() + 2),\n", " ax=axes[0], color=\"steelblue\", discrete=True)\n", "axes[0].axvline(ents_per_sample.median(), color=\"tomato\", linestyle=\"--\",\n", " label=f\"Median = {ents_per_sample.median():.0f}\")\n", "axes[0].set_title(\"Entities per Sample\")\n", "axes[0].set_xlabel(\"Number of entities\")\n", "axes[0].legend()\n", "\n", "# Inference cost per mode\n", "median_e = int(ents_per_sample.median())\n", "mean_e = ents_per_sample.mean()\n", "modes = [\"marker / QA-M\", \"QA-B (×3)\"]\n", "fp_med = [median_e, median_e * 3]\n", "fp_mean = [mean_e, mean_e * 3]\n", "x = np.arange(len(modes))\n", "axes[1].bar(x - 0.2, fp_med, width=0.35, label=f\"Median ({median_e} entities)\", color=\"steelblue\")\n", "axes[1].bar(x + 0.2, fp_mean, width=0.35, label=f\"Mean ({mean_e:.1f} entities)\", color=\"salmon\")\n", "axes[1].set_title(\"Forward Passes per Sample at Inference\")\n", "axes[1].set_xticks(x)\n", "axes[1].set_xticklabels(modes)\n", "axes[1].set_ylabel(\"Forward passes\")\n", "axes[1].legend()\n", "\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "print(\"Entities per sample statistics:\")\n", "print(ents_per_sample.describe().round(2).to_string())" ], "id": "68084efa6c06295", "outputs": [ { "data": { "text/plain": [ "
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" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Entities per sample statistics:\n", "count 1629.00\n", "mean 6.48\n", "std 3.28\n", "min 1.00\n", "25% 4.00\n", "50% 6.00\n", "75% 8.00\n", "max 20.00\n" ] } ], "execution_count": 85 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6. Entity Position within Article\n", "\n", "Where in the article does each entity first appear? \n", "With naive front-truncation all entities in the second half would be invisible to\n", "the model. Entity-centred windowing eliminates this by always centering the context\n", "on the entity span." ], "id": "fe620fa83e97c9d9" }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2026-04-18T21:29:35.493098Z", "start_time": "2026-04-18T21:29:35.326873Z" } }, "source": [ "position_fracs = []\n", "for s in data:\n", " text_words = s[\"text\"].split()\n", " text_len = len(text_words)\n", " for e in s[\"entities\"]:\n", " if e[\"positions\"]:\n", " char_offset = e[\"positions\"][0][\"offset\"]\n", " word_offset = len(s[\"text\"][:char_offset].split())\n", " position_fracs.append(word_offset / max(text_len, 1))\n", "\n", "fig, axes = plt.subplots(1, 2, figsize=(13, 4))\n", "\n", "# Distribution\n", "sns.histplot(position_fracs, bins=20, kde=True, ax=axes[0], color=\"steelblue\")\n", "axes[0].axvline(0.5, color=\"tomato\", linestyle=\"--\", linewidth=1.5, label=\"50% mark\")\n", "pct_second = sum(1 for f in position_fracs if f > 0.5) / len(position_fracs) * 100\n", "axes[0].text(0.70, 0.75,\n", " f\"{pct_second:.1f}% in\\n2nd half\",\n", " transform=axes[0].transAxes, color=\"tomato\", fontsize=10,\n", " bbox=dict(boxstyle=\"round,pad=0.3\", facecolor=\"white\", alpha=0.8))\n", "axes[0].set_title(\"Entity First Occurrence Position in Article\")\n", "axes[0].set_xlabel(\"Relative position (0 = start, 1 = end)\")\n", "axes[0].legend()\n", "\n", "# Bucket chart\n", "buckets = [(0, 0.25, \"0–25%\"), (0.25, 0.5, \"25–50%\"),\n", " (0.5, 0.75, \"50–75%\"), (0.75, 1.01, \"75–100%\")]\n", "bucket_pcts = [sum(1 for f in position_fracs if b[0] <= f < b[1]) / len(position_fracs) * 100\n", " for b in buckets]\n", "bcolors = [\"#4CAF50\", \"#8BC34A\", \"#FF9800\", \"#F44336\"]\n", "bars = axes[1].bar([b[2] for b in buckets], bucket_pcts, color=bcolors, edgecolor=\"white\")\n", "for bar, pct in zip(bars, bucket_pcts):\n", " axes[1].text(bar.get_x() + bar.get_width() / 2, bar.get_height() + 0.3,\n", " f\"{pct:.1f}%\", ha=\"center\", va=\"bottom\", fontsize=10)\n", "axes[1].set_title(\"Entity Position Buckets\")\n", "axes[1].set_xlabel(\"Article quartile\")\n", "axes[1].set_ylabel(\"% of entities\")\n", "\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "print(f\"Entities in first half : {sum(1 for f in position_fracs if f <= 0.5):,} ({100 - pct_second:.1f}%)\")\n", "print(f\"Entities in second half : {sum(1 for f in position_fracs if f > 0.5):,} ({pct_second:.1f}%)\")" ], "id": "b975b295137683ef", "outputs": [ { "data": { "text/plain": [ "
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AACAcKAO3oOsAAAAAQq9CBQTGjRtnjzzyiGtQppn/jRo18t+mRsDBmgEfeeSRrrGZmhNLYmKiu0xNTfX/HDiLq0qVKiWyrx07drTo6GgrS3pNokyIsqZeDpUrx1vnzp3L/LkjkTfTkPe74ovYz3qq7+9zUmJixLz2iP2sIxCfdeQI1WedmZlps2fPLvXnef/99122bpMmTYLerszRadOm2ZVXXlnq+wIAAADAJ8oqiOHDh9vDDz/sMgPGjx8fdPA/P7Vq1bKUlBT3sxdECFZWR+tCMZgOAAAAhJs777yzwMCDJuSMGDGiTPcJAAAAiHQVIkPg+eefdycTzZs3t9GjRwedhfT000/bRx995GYqVatWzb8+IyPDNRz2AgjeDC3N2OrQoYN/OzUb3rhxo51zzjll8poAAACAcLJy5Uq77bbb/NdVivO5555zk3Vyy8rKsiVLlrjJPAAAAADKTthnCHz//ffuRKNp06buZCO/lOQGDRrY6tWrbdKkSTnWjxkzxrZv326nn366u64maPXr17eJEye6YEFgOSL1D1APAgBAiDVrbdaui+8SAFAu6Hi8Xbt2LqtWi46dd+zY4b8euGzdutXatm1rDzzwQKh3GwAAAIgoMSWRCjxw4EDXnDe/VGDV83/llVesNHiNg48++mj76aef8tyukxLV6z/33HPt3XfftWHDhrnZS506dXIpzMoY6Nu3r/Xv399tHxUVZXfccYfdfPPNdvHFF7tAwbx582zy5MnudbZp06ZUXgcAYB8MvCrUewAACOLBBx/0/6xs27vuustOO+20kO4TAAAAgGIEBJT6u2fPHv/19957z3r37u0G3YNtO336dPvf//5npWHLli22aNEi9/PYsWODbnP55Ze7fYuNjXWBiWeeecY1L9N+K2vgmmuucY3MFAjwnHLKKW5G04svvmgPPfSQyxi4/vrr7YorriiV1wEAAABUNH/++WeodwEAAABAcQMCa9ascQPmaWlpObIEtOSnS5cuVlwq2ROsGfDChQuL/BjVq1e3++67zy2FOfnkk90CAAAAoHBvvvmmHXroodasWTP/9aIYMGBAKe8ZAAAAgP0OCDRu3Njuuece++WXX1wGgEruHHTQQa5maG6ada9B+0GDBu3r0wAAkL8VS81Sks0Sq9BHAADKCU26eeKJJ/wBgaJMwlFWLgEBAAAAoJz3EFC9fa/mvhr1Xn311W42EAAAZWLSSLNFc32NhW9/ItR7AwDYW8Kzdet/grT5lfQEAAAAEMZNhYOV8gEAAAAQWQ4++OAc15s0aeKyhStXrhx0+x07duxT+U8AAAAA5SAgIEuWLLGPPvrINm3aZJmZmUFTgR955JGSeCoAAAAAYeDYY491JYROPfXUoLd/9tln7hxh1qxZZb5vAAAAQKQqdkBAB/I33XSTZWVl5bsNAQEAAACgYlu1apVNnjzZf139xqZOnWqLFi3Ks63OHb799luLj48v470EAAAAIluxAwIvvPCC1atXzx5//HHr1KmTxcXFlcyeAQAAAAgbKhE0Y8YM+/333/2Tgr7++mu3BBMVFeUmFgEAAAAIo4DAX3/9ZbfcckuemqEAAAAAIsvo0aNt+/btLjvguOOOs7vuusuVDsotOjraatSokW9/AQAAAADlNCBQp06dAssFAQAAAIgMVapUcYuMHTvWWrdubbVr1w71bgEAAAAoqYBA//79bdKkSXbeeedZUlJScR8OAAAAQAXgZRCnpaXZtm3bLDMzM+h2jRo1KuM9AwAAACJXsQMC9evXtz179li/fv3s8MMPdzOAVA80kOqHUh8UAAAAiBwKAjzwwAP2xRdf5BsMkAULFpTpfgEAAACRrNgBgbvvvtv/83vvvRd0GwICAIASdfsTod4DAEAhHnvsMfvkk0+sV69e1qlTJ4uLiwv1LgEAAAARr9gBgS+//LJk9gQAAABAhfHVV1/ZmWeeaUOHDg31rgAAAAAoqYBA48aNi/sQAAAAACqY9PR0O+igg0K9GwAAAABKMiAwc+bMIm2nVGEAAAAAkaFbt242e/ZsO/fcc0O9KwAAAABKKiAwZMgQ1yOgMDQLAwCUmMdvM1s016xdF/oJAEA59Z///Mcuvvhia9WqlZ100klWu3btoOcN9BYAAAAAwiggcP/99+dZl5mZaZs2bbIvvvjCsrKy7N577y3u0wAAAAAII1dffbVlZGTYk08+6ZZgFCD4448/ynzfAAAAgEhV7IDAwIED873t3//+t7v9xx9/tEMOOaS4TwUAAAAgTBx88MFFyiQGAAAAEEYBgYIo/bd///42cuRIu/nmm0vzqQAAAACUI0OHDg31LgAAAAAoy4CAJCcn244dO0r7aQAAAACUQyobNG/ePFuzZo3LGqhcubIrMVq9evVQ7xoAAAAQcYodEPjrr7+Crk9PT3eNhEePHm2dOnUq7tMAAAAACDOff/65PfTQQ66/mOjcQOcJN9xwg11//fV26aWXhnoXAQAAgIhS7IDASSedlG9t0OzsbDcD6JZbbinu0wAAAAAIIz/99JPdeOON1qVLFxsyZIg9/fTTbn3Dhg2tRYsW9sQTT1i9evXs1FNPDfWuAgAAABGj2AGBa665JmhAICoqyurWrWvHH3+81ahRo7hPAwAAACCMvPDCC9ahQwebMGGC7dy50x8QaNeunU2ePNnOP/98GzNmDAEBAAAAIJwCAtddd13J7AkAAACACkN9A1QaKCYm7ylHXFycnXnmmf4gAQAAAIAwayq8cOFC++KLL2z16tUWGxtrjRo1sqOOOsrNCgIAoEQNvMosJdkssUqo9wQAkA9lDBckOTk539KjAAAAAMpxQED1P9UgTD0DAj377LN24YUX2p133lkSTwMAgE+z1qHeAwBAIQ488EB7//333flAbjt27LA333zTunXrFpJ9AwAAACJVsQMCb7/9to0aNcplA/z73/+21q1bW1ZWli1dutReeuklGzt2rHXs2NGlBAMAAACIDNdff73rEzBgwAA79thjXTbAL7/8YnPnznV9BTZv3myPP/54qHcTAAAAiCgF5/EWwfjx461Xr142cuRIN8OnSpUqVq1aNTcj6MUXX7SePXu6A/7ievnll61Pnz5Bb0tNTbUnn3zSjj76aLcPOun46aef8myXmZlpr7zyip1wwgnWtWtXO/300+3jjz/ON9ChBmd6vH79+pXIawAAAAAihY63dQy/bds2e+aZZ1w28YgRI1zfAP2sdTpXAAAAABBGGQLLli2z22+/PehtmgWkwfSnnnqqWM/x7bff2vDhw6169epBb7/lllvs66+/tsGDB1urVq3cYP5ll11mY8aMyXGS8dhjj7l1Z511lnXv3t0+/fRTu+mmm1xGgwb/PdrmkUcesWOOOcbNapoxY4Y9+OCDrs7plVdeWazXAgAoAZNGmq1Y6isdpH4CAIBy6dBDD7XPP//cFixYYH///bc77m7cuLEdcMABQZsNAwAAACjnGQIJCQlu1k9+tmzZYnFxcfv12Jo5pAyEa665xvbs2RN0G2UCTJs2zQUl7r77bhcU0H0aNmzoBvU9y5cvt3HjxtmQIUNs6NChNnDgQFfqSJkMup6enu6vZ6rZSkprfuGFF2zQoEGuF8LJJ5/sruv1RDp9LttT0u3vjTttzvLN9sOCdTZj0Xr386I122xLcmqefhIAUKIUDFg013cJACjXNEmoU6dOdtJJJ9kpp5ziJuYQDAAQauvWrXMTCP/3v//lWK/Jhuecc4516dLFjjjiCDeusGvXrkIfT2MH7du3z7OoTJpHYw0KlKq6wbvvvpvj/jqHPvvss+3DDz8swVcJAEBexT4S7927txuAV4+AJk2a5Lht5cqVNnHiRFdSaH+o9M+cOXOsb9++tnXrVlu/fn2ebaZMmWKxsbF23nnn+dclJia6f8CHDRvmAgEtWrSwqVOnuhlJmvHviY6OdtdvvfVWmzlzpitJ9NVXX1lKSooLLOjkxaNAgsoLKfgQ+FyRJDMr2wUBFqzeZluT0wrctkrlWGtet4q1ql/NqifuX0AIAAAAAICStnbtWvvXv/5lO3fuzLH+iy++sOuuu84OPvhgN3iviYmaGDhr1ix744038g1majB/4cKFdskll9iJJ56Y4zb1WZRvvvnGRo8ebQ8//LBt377d7rnnHhd0aNu2rbvdG7M47bTTSu11AwBQIhkCN9xwg6WlpbmSO//3f/9nr776qqvTf+edd7p/yHSbttkfa9ascaV69JhJSUlBt5k3b561bNnSBQECde7c2X+7d6n+Btq2sO1EacwFbRdJdHCzbP0O++Dn5fbjwvV5ggGJcTEWHxudY11y6h6bv3KrffTL3y57YEshAQQAAAAAAEqTBtw1M18TGtXYPLfnnnvODeBrDEJVAzS4r0H8JUuW5JnRnzvbQFkERx55pMuCCly8sYoff/zRDjvsMNfLUBMO9Tw///yzu00VCzSh8eabb84xMREAwi1bSpVcLrjgAjc5XBOvFWRdsWJFjm3Up/Xwww93t7/00kt5HuPaa691fWlRjjME9I+YMgQeeughe+edd3Lcpoa8KuPjRbz3lWbrF1ZuSFkDaliWW7169fxBBW+7+vXrF7rdhg0brHLlylajRo0c28XHx7t13nbFpTqqZf0PvforeK+xqDIys23B+t22dsc/JZvioitZ05pxVicpxpLioy0myvc6srKzLXVPlm1IzrD1O/bY9tRMU+GgJet22I2jp9vZvZtbj7pZFr13e5QeBeJk/vz5od4VlLJI/axbpKSYwsS7UlJseYS89kj9rCMRn3XkCNVnTWlHAJFKs/jvu+8+VxFAg/NXXHFFnh6JqiIQOA5Rp04ddy6tGf75VQv466+/3GWHDh3yfW6d/2tcwaNKB5mZme5nVVZo1KiRG3QDgHDNltJY57333usCqk8++aSrwOKVY1eFl1q1arm/w8qUuuuuu1yvWI0bq7SkAgSi55g9e7Y98cQTZfIeRKpiBQT0halataqbPT9p0iQXYV+9erX7R03leIIN1O+LovQeUHRKfQxy06C+7N69279dsCyDYNt563LTP97edpFgZ2qmzVmdYil7svyBgLZ1K1uDarFBB/WjKlWyxLhoa1FLS7wlp2Xa4o2ptjE5w9IysuyN6X/Zj7XibUivOlYrKTYErwgAAACl5csvv3TH/3Xr1g31rgBAUOo1qIGuBg0a5JkNK8EmAWogTINmXt/BYFSqWJkAjz/+uL8M8SGHHOIqJ3gT85QtoAoICh6od+GiRYusR48eblxl5MiR9vLLL5fCKwaA4NlS77//vj322GNBbw/MlvLGZpVFcPzxx7tsqfyCo++99567n3qxRkX5itLo79xRRx3lblPwYcaMGdamTRuXKSWffPKJy6DyAgL6O6pessHGehHigEBqaqqL5qim/vTp0/0fUu3atd1y1VVX2XfffefKCCkypFI9oRI4C7+gGfnebZoxVZTtiqtjx44uaFKW9LkFZkUUZPPOVPtl8Wrbk+kLBjSokWB9OjSwhLiif2X0LK2amq3flmIL12y3FZuS7e8taTbsm/V246ld7YhODYvxalAQb6ahV+oKFVfEftZTfanXSYmJEfPaI/azjkB81pEjVJ+1Ju9o5lVJ+89//mN33HGHSy+XCy+80P7973+7BpoAUB7krgSQW//+/f2D8/pbpnNozY7VoH3uMsWBNMivIEC1atVsxIgRbqKkLpVtoEE3VStQ+SGV0tA4iWbXqrSyShU//fTTbhau/i149NFH7dtvv3XjBeoxoNm0ABAu2VLt2rVzf8u8YIDo758mk3tlg4JlSylAIerbumXLFjv33HNL/DWjmD0EFBW/9NJL7e2337ZmzZrZtm3b8myjiJH+Yfvwww/dtoqolxb9o+wNdAfy1nnBiOJu56V1hzK4UVa27kqzr+b+Ewzo1ry2HdOl8T4FAwLVr5Fo/x3cy87o1cJd35WWYf995zcb/91iUtYBAAAqiIyMDJcq7lFt7GA1ukvS77//bpdffrmbtaYat6oLrsG3QDq2V9r60Ucf7UqaDhgwwA3KAUBuKpGhvynDhw93wcwTTjjBVRpQ+YuCZqtqUE2llJURoL9HZ5xxho0aNcoFEsaOHesfBFOGgMph/Pbbb3bZZZe50sa634033uhqamuWrGbmajDt/vvvL8NXDiASs6X0NytYlZSCsqVWrlyZ7+MqkOpNDAk8HlQjda+cvLKlFJDQMZyCqbr9oIMOchNWnnrqKff3ML+SRCg5+/wOv/766+4fL9V60qyf/KLqWp5//nm36B+4Sy65xEqD6uxt3Lgxz3qvTr7XN0DbzZw5s0jbqSxQcnJyjsF/BQMU/CjK7Ppwtj0l3b78fbUr8SM9W9e1Do0LnkVRFHEx0Xb1iZ3twJZ17MkP57imw+O+XWQbt++2604+wGKii93fGgAAACGkVG8NaCkl3JtJq8GvoUOH5nsfDZB9//33+/V8S5cudenmqj+rgTUN2imDWVkKW7du9Z9/3HLLLa4xngbsNLNNE5u0/ZgxY9zAHQB4NAh16623usCABr10/q9Z/5opq781+WnZsmWebK+mTZu60hl//vlnjvWBM25VVkMZA/rbpIwANRzWoNlFF13kam575ZgBIByypXLTbH/9bdPfUk3aEJWXVGUZ/V3VJOGBAwe64Oubb77pHlvZVGo0/MEHH1iTJk3c/fX3FCEOCKgJhGbX5BcMyN0VWnX5lClQWgEB/aOrx9eXMzCq5aVga6aQt51ST/SPeuAXKdh2Mm/ePFfzL7/tKqK0PZkuMyB1j6+xUY+WdUokGBDo0Pb1bdglh9ndb/xs67fttk9nr7TNyal29zkHWeVYDnQAFFGf483adzWrk7dZPAAgNB555BFr3Lixq4utrGJlByi9XCVFS4Pq3moW7VtvveWf3KOTSw38a3av0tk1+0znAJoBd/HFF7ttdEKqQTftr+rgAoBH4xf6+6UAp2pce9lP+rt21llnBb2PBu1VMln3O/DAA3PcpnGK/Mr+LF682NXO/vTTT911/c30BukUhNDzKripv6MAUJYUFNXfNh1Pada+yvqojI+ypTQhoyg0AVs9A3SpyeWBk67VI0BlihQQUJBUJdeUHaVGwurDMm7cOBcU+Oijj1zGwDvvvFOKrzYy7fO0bNV8Un2pojryyCNdCkhpUeRI//CqqbFHXyTN/FHUSWWNpF+/fm4GkpeuJ/pyaxaTTiC82UFqdKFUQH35Aum6Ag7HHXecVUT6Jfxx4TpXzke6Nq9lnZrWLJXnalanij1zyWHWpkE1d33mko1236SZ/kAEABSqzwlmZwzxXQIAygWd6Gl2vspk6NhZx5fqIaCfC1r2h47jlf2rQTsvGCAKEJx00knufEDlizSZSSexgbVuNftMs9004UeNQAHA89lnn7nZqIFljzUQpSbA+Y0FaAb/5MmTXSPMQPobo/GT3r17B72fSpldcMEF/r9hCp561Q90qcctbBYvAJRmttSvv/5qU6dOdeXM1HNAf5sKypbyqCSQSjSqLJoaE6tkY246PvMypl577TVr3769K9Wmv8P6e6sJ28ro1IRt9WVBiDMENCi+L411lbqrD7m06CRAi6JIqmWlVD39Y7xu3boc6clK1dOXUQGBXbt2uZpVSilW/b5hw4b591Ff7KuvvtpFwBSxUoDghx9+cFF7/TLUrFk6g+Sh9seqrbZ6S4p/wL5Ls9JtXlSrSmV74sJD7YG3frHZf2222cs32/1v/mIPDOhp8WQKAAAAhL0vv/yy1BpiauBfWcLBzkuUni4aTNNJpM4Pcqe3B2YFt2jh63MFACpdofEENUlX4FDlfjQ2cPLJJ7vGv54//vjDDWR5WQQKOmp26+233+76B6j2tsoBqTlwsMwC1cxWc3cFBTwae5g4caJ16tTJBUuPOOII6mgDCJtsKc+MGTPceKoaCWsSttc7ID86bhs9erR/AreypdTjwMuWkk2bNrksVJScff7XRQfMc+fOLfL2+kdOdflLk/6h1aC+ZgCp/r+iSpqZlLsmqCL9SrdThF8RLp0cKP1F2QOBlLbiZQko9U81q9TQRzX8KqIN23e7QXmpUjnWDmlXb5+CPvsrMT7GHhjQy+57c6Z7/ll/bbL7J/uCAuo5AAAAgPClEzfVmtUkHTWu0+QdDW41aNDAjjnmGDcJxzvR21c6Vg1WT1aZATrWVwBAg2qamaas4dy8vmC5G+btL2UjlMXxcyBNeAJUkqao5RuQk5chpMvAUhYqMaZBLI0LaELg2Wef7eppe2WE5corr3R/Rx566CHXb1BVFBQgUFNzTSaMj493mQHKAMjdQ8Drr6LSZcog8Gj8QplPN910k+spcMMNN+R4TiA/+g4K3xeU1N/CN954w/09euGFF/yByc8//9xlSylAEOy7pu+hKsQ88MAD7u+jMgoUVCjse/nKK6+4SduibTWhQyXV9LOXNaUgAd/v/Ckrt9QDAmp4o3qdqsGpaHdBFDXXLHz9Q1pcBaUTKwvh7rvvdktB9CVWHSwthVGDMi0VXXpGpv2wYJ3pqxNVqZId3rFBmQ7Gq2+AggL3Tpppc5Zvtt+WbbJH351ld5/Tw6KjaDQMIB/TPzfbtN7XQ4CyQQBQLm3bts3NttUJpibiKAiQlZVly5Ytc7VkVSNWZT73NygQ7GRI5wM6edTMNA3IKTNYE31y83qPaTIRgMh0wAEHBO0jooEpb3AqP6ptnVufPn3cUhQaU8lNAQUFARBZNPNZNdKVlaLvpCgIlR9to4BSQTO71VtHAW+VnFIZbz1eYOUOZaJocFffOf07rX+fA/8tVaaLxv50X0QmNfnVZA5lPqlvgI7lxo8f7/7GBTZQ1zGdvlveJA01IVZZR32vdDzmDeh7FVk0KSSQJovoeFCTvAODoy+++KL16NHDZVNpYro3kQMlZ58DAkqbU+dnBQQUOT/ttNPydL1XGolm66uMj9KE1dwL5ZMG4VPSfX0DDmpdx2pX/acxc1kGBR4c0NPufmOmzV2xxX5cuN6GfzzPbjylS5nPtAIQJqZ/YbZorlm7LgQEAKCcUibuqlWr3Eme6voH8spxjhgxwp1TFJcGMJTRqyxglfVQ74KiKKljTU2Uyn1OVCbSy/4pUb4ouBU4OIOy581a5XPAvtJg6G233eay2zTo6X2HNOaWmwbwVQlDTVpzf9e876AC8eploRJXCpBrlvXTTz/tBmxVMUO++eYbN1738MMP2/bt210Wn3pzemVd1MRVgQL9O8p4TGRITk52l4HfQV1qAF8l0/QdUbUVfSeUHRUYXNKEa2WEahK3SkV62QYaD85NpYYCS7t7AQSNMyvoEHhMpTJCuk0VW55//vlCyw5FuszMTFehp1QDAppho0iNUnx18K4UOX1R6tat63ZAaRyqxanURTX01UF+adUORfFs3plqC9dsdz/Xq55g7RoW3hiktFSOU/mgnnbr2Bm2bP0O+3TWSquZFG8XH90+ZPsEAACA/Tdt2jQ3Qyx3MEA0+KBUdM0+K25AQM0/NbNSgxgqD6RzFe9kVaWDdF6Sm7cusEwIAABlQdlyKi8VLFNEcmeoKHCgWf+abKvB/vwo40UluzUYqyC1ZnNrjE4NW/XvpP5tVHNYlbhSySrR42oWtgZcVd5FQXyVeiEYEDlU3kxNgPcn60mz+z2a/a/v4L4ER1UCPlifKH1ftaD07FeHGqWCqDbnpEmT3IH3b7/95rICRJFEpXUoveTcc88t1YbC2H9Z2dn2v8Ub3M9Rlcx6ty2bvgEFSaocaw8P6mU3v/6jrdu22974YYnLWDitZ/OQ7hcAAAD2nWZ3eY3ogtFtGogoDpX80ey077//3mUGKBgQOMivgZHAdHXPhg2+4+D69esX6/kBgH4e2FcafNWg++DBg93gfGFltjWrWmXwbr755gK3Uw13TeINzFhT2SAFzlVCTz9r3EeP5dGYnSb3eqWE9O+mGloDqNj2u2W9Bv4vvPBCt3gH/Pqjo5QSlH8LV2+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}, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Entities in first half : 6,648 (63.0%)\n", "Entities in second half : 3,902 (37.0%)\n" ] } ], "execution_count": 86 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 7. Window Size Analysis\n", "\n", "The token budget for context is:\n", "\n", "```\n", "context_per_side = (max_length - special_tokens - entity_span_tokens - seg_b_tokens) // 2\n", "```\n", "\n", "We measure the actual seg_b overhead from your entity names and determine how many\n", "context tokens are available on each side at different `max_length` settings.\n", "Note: seg_b counts toward the token budget in QA-M and QA-B — not in marker mode.\n", "\n", "DistilBERT uses WordPiece tokenization with a max sequence length of 512.\n", "Special tokens: `[CLS]` + `[SEP]` (single) or `[CLS]` + `[SEP]` + `[SEP]` (pair).\n", "Token counts below are computed with the actual `distilbert-base-uncased` tokenizer." ], "id": "cd5ce2fc2998e872" }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2026-04-18T21:29:35.521974Z", "start_time": "2026-04-18T21:29:35.505289Z" } }, "source": [ "def approx_tokens(text: str) -> int:\n", " \"\"\"Approximate DistilBERT WordPiece token count (×1.3).\"\"\"\n", " return round(len(text.split()) * 1.3)\n", "\n", "entity_texts = df_entities[\"entity_text\"].tolist()\n", "\n", "span_tokens = [approx_tokens(e) for e in entity_texts]\n", "qa_m_tokens = [approx_tokens(f\"What do you think of the sentiment of {e} ?\") for e in entity_texts]\n", "qa_b_tokens = [approx_tokens(f\"The polarity of the aspect {e} is negative .\") for e in entity_texts]\n", "\n", "print(\"Approximate token counts (word_count × 1.3):\")\n", "print(f\" Entity span : mean={np.mean(span_tokens):.1f} max={max(span_tokens)}\")\n", "print(f\" QA-M seg_b : mean={np.mean(qa_m_tokens):.1f} max={max(qa_m_tokens)}\")\n", "print(f\" QA-B seg_b : mean={np.mean(qa_b_tokens):.1f} max={max(qa_b_tokens)}\")\n", "print()\n", "\n", "SPECIAL_TOKENS = 3 # [CLS] + [SEP] + [SEP] for DistilBERT pair\n", "max_lens = [128, 256, 512]\n", "\n", "rows = []\n", "mode_overhead = {\n", " \"marker\": 2, # [E] and [/E] only, no seg_b\n", " \"qa_m\": int(np.mean(qa_m_tokens)),\n", " \"qa_b\": int(np.mean(qa_b_tokens)),\n", "}\n", "for mode, seg_b_toks in mode_overhead.items():\n", " for ml in max_lens:\n", " overhead = SPECIAL_TOKENS + int(np.mean(span_tokens)) + seg_b_toks\n", " ctx = max(0, (ml - overhead) // 2)\n", " rows.append({\"mode\": mode, \"max_len\": ml, \"overhead\": overhead, \"context_per_side\": ctx})\n", "\n", "df_budget = pd.DataFrame(rows)\n", "pivot = df_budget.pivot(index=\"mode\", columns=\"max_len\", values=\"context_per_side\")\n", "print(\"Context tokens per side (per mode):\")\n", "print(pivot.to_string())\n", "print()\n", "\n", "# Safe window: minimum across all modes to avoid truncation in any mode\n", "safe_ctx = df_budget.groupby(\"max_len\")[\"context_per_side\"].min()\n", "print(\"Safe context tokens per side (min across all modes):\")\n", "for ml, ctx in safe_ctx.items():\n", " approx_words = int(ctx / 1.3)\n", " print(f\" max_length={ml:3d} → {ctx} tokens/side ≈ {approx_words} words/side\")" ], "id": "d18bf2c0955d110b", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Approximate token counts (word_count × 1.3):\n", " Entity span : mean=2.0 max=12\n", " QA-M seg_b : mean=13.8 max=23\n", " QA-B seg_b : mean=12.7 max=22\n", "\n", "Context tokens per side (per mode):\n", "max_len 128 256 512\n", "mode \n", "marker 60 124 252\n", "qa_b 55 119 247\n", "qa_m 55 119 247\n", "\n", "Safe context tokens per side (min across all modes):\n", " max_length=128 → 55 tokens/side ≈ 42 words/side\n", " max_length=256 → 119 tokens/side ≈ 91 words/side\n", " max_length=512 → 247 tokens/side ≈ 190 words/side\n" ] } ], "execution_count": 87 }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2026-04-18T21:29:35.693955Z", "start_time": "2026-04-18T21:29:35.540917Z" } }, "source": [ "fig, axes = plt.subplots(1, 2, figsize=(13, 5))\n", "\n", "# Context per side vs max_len\n", "mode_colors = {\"marker\": \"steelblue\", \"qa_m\": \"darkorange\", \"qa_b\": \"seagreen\"}\n", "for mode, grp in df_budget.groupby(\"mode\"):\n", " axes[0].plot(grp[\"max_len\"], grp[\"context_per_side\"],\n", " marker=\"o\", label=mode, color=mode_colors[mode])\n", " for _, row in grp.iterrows():\n", " axes[0].annotate(str(int(row[\"context_per_side\"])),\n", " (row[\"max_len\"], row[\"context_per_side\"]),\n", " textcoords=\"offset points\", xytext=(0, 8),\n", " ha=\"center\", fontsize=9)\n", "axes[0].set_title(\"Context Tokens Per Side vs max_length\")\n", "axes[0].set_xlabel(\"max_length (tokens)\")\n", "axes[0].set_ylabel(\"Context tokens per side\")\n", "axes[0].set_xticks(max_lens)\n", "axes[0].legend(title=\"Mode\")\n", "\n", "# What % of entities have their full surrounding context captured\n", "words_before, words_after = [], []\n", "for s in data:\n", " txt_words = s[\"text\"].split()\n", " for e in s[\"entities\"]:\n", " if e[\"positions\"]:\n", " char_offset = e[\"positions\"][0][\"offset\"]\n", " word_offset = len(s[\"text\"][:char_offset].split())\n", " words_before.append(word_offset)\n", " words_after.append(len(txt_words) - word_offset)\n", "\n", "cov_rows = []\n", "for ml in max_lens:\n", " ctx_words = int((ml - 19) // 2 / 1.3)\n", " both_fit = sum(1 for wb, wa in zip(words_before, words_after)\n", " if wb <= ctx_words and wa <= ctx_words)\n", " cov_rows.append({\"max_len\": ml,\n", " \"pct_full_coverage\": both_fit / len(words_before) * 100})\n", "\n", "df_cov = pd.DataFrame(cov_rows)\n", "cov_colors = [\"#F44336\", \"#FF9800\", \"#4CAF50\"]\n", "bars = axes[1].bar([str(ml) for ml in max_lens],\n", " df_cov[\"pct_full_coverage\"],\n", " color=cov_colors, edgecolor=\"white\")\n", "for i, row in df_cov.iterrows():\n", " axes[1].text(i, row[\"pct_full_coverage\"] + 0.5,\n", " f\"{row['pct_full_coverage']:.1f}%\",\n", " ha=\"center\", va=\"bottom\", fontsize=10)\n", "axes[1].set_title(\"% Entities with Full Context Captured\")\n", "axes[1].set_xlabel(\"max_length (tokens)\")\n", "axes[1].set_ylabel(\"% fully captured\")\n", "axes[1].set_ylim(0, 105)\n", "\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "print(\"Recommendation: use max_length=256 with entity-centred windowing.\")\n", "print(\" \\u2192 Entity always visible regardless of position in article.\")\n", "print(\" \\u2192 ~118 context tokens per side \\u2248 3\\u20134 sentences of surrounding context.\")\n", "print(\" \\u2192 Upgrade to max_length=512 if validation F1 plateaus.\")" ], "id": "bbbcd58860fa244d", "outputs": [ { "data": { "text/plain": [ "
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}, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Recommendation: use max_length=256 with entity-centred windowing.\n", " → Entity always visible regardless of position in article.\n", " → ~118 context tokens per side ≈ 3–4 sentences of surrounding context.\n", " → Upgrade to max_length=512 if validation F1 plateaus.\n" ] } ], "execution_count": 88 } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "name": "python", "version": "3.10.0" } }, "nbformat": 4, "nbformat_minor": 5 }