{ "cells": [ { "cell_type": "markdown", "id": "729fd9f6", "metadata": { "id": "729fd9f6" }, "source": [ "# EmpathRAG — DeBERTa NLI Safety Guardrail (Karthik)\n", "**MSML641 · NLP Course Project** \n", "Fine-tune DeBERTa-v3-base on suicide detection NLI pairs \n", "Target: Recall > 0.80, Precision > 0.65 on crisis class \n", "Runtime: **A100 GPU** (Runtime → Change runtime type → A100) \n", "Expected time: ~2 hours\n", "\n", "All NLI CSVs are already on Google Drive — no uploads needed.\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "03507ddc", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "03507ddc", "outputId": "6acb3822-6889-4191-fdf9-91490eb08da1" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "GPU : NVIDIA A100-SXM4-40GB\n", "VRAM : 42.4 GB\n", "Torch: 2.10.0+cu128\n", "✅ GPU ready.\n" ] } ], "source": [ "# STEP 1: Verify A100\n", "import torch\n", "assert torch.cuda.is_available(), \"Switch to A100 runtime before proceeding.\"\n", "print(f\"GPU : {torch.cuda.get_device_name(0)}\")\n", "print(f\"VRAM : {torch.cuda.get_device_properties(0).total_memory/1e9:.1f} GB\")\n", "print(f\"Torch: {torch.__version__}\")\n", "print(\"✅ GPU ready.\")\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "b2e57b19", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "b2e57b19", "outputId": "d9271edd-0146-4264-ec35-b09c7e49079d" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/84.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hevaluate : 0.4.6\n", "accelerate: 1.13.0\n", "✅ Packages ready.\n" ] } ], "source": [ "# STEP 2: Install packages\n", "# DeBERTa-v3 requires sentencepiece + protobuf for tokenizer\n", "!pip install -q \"accelerate>=1.0.0\" evaluate sentencepiece protobuf\n", "import evaluate, accelerate\n", "print(f\"evaluate : {evaluate.__version__}\")\n", "print(f\"accelerate: {accelerate.__version__}\")\n", "print(\"✅ Packages ready.\")\n" ] }, { "cell_type": "code", "source": [ "# STEP 3: Mount Drive and verify paths\n", "from google.colab import drive\n", "import os\n", "drive.mount(\"/content/drive\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "mYJlsMkSudM0", "outputId": "218b491c-a436-405d-b366-630f74cc89b2" }, "id": "mYJlsMkSudM0", "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mounted at /content/drive\n" ] } ] }, { "cell_type": "code", "source": [ "\n", "TRAIN_CSV = \"/content/drive/MyDrive/Mapped_Data/nli_train.csv\"\n", "VAL_CSV = \"/content/drive/MyDrive/Mapped_Data/nli_val.csv\"\n", "TEST_CSV = \"/content/drive/MyDrive/Mapped_Data/nli_test.csv\"\n", "SAVE_DIR = \"/content/drive/MyDrive/Mapped_Data/safety_guardrail\"\n" ], "metadata": { "id": "XmdwcYSD1Lrj" }, "id": "XmdwcYSD1Lrj", "execution_count": 4, "outputs": [] }, { "cell_type": "code", "source": [ "import pandas as pd\n", "\n", "# Hard assertion — these must match your known-good values exactly\n", "EXPECTED = {\"/content/drive/MyDrive/Mapped_Data/nli_train.csv\": 185659, \"/content/drive/MyDrive/Mapped_Data/nli_val.csv\": 23207, \"/content/drive/MyDrive/Mapped_Data/nli_test.csv\": 23208}\n", "for fname, expected_rows in EXPECTED.items():\n", " # Use pandas to correctly count rows, handling multiline strings\n", " actual = len(pd.read_csv(fname, usecols=[0]))\n", " assert actual == expected_rows, f\"WRONG FILE: {fname} has {actual} rows, expected {expected_rows}\"\n", " print(f\" {fname}: {actual:,} rows ✅\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "z73_icEc1NoU", "outputId": "6c9369d0-421d-4101-cbf6-9bdcb4fed43f" }, "id": "z73_icEc1NoU", "execution_count": 5, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " /content/drive/MyDrive/Mapped_Data/nli_train.csv: 185,659 rows ✅\n", " /content/drive/MyDrive/Mapped_Data/nli_val.csv: 23,207 rows ✅\n", " /content/drive/MyDrive/Mapped_Data/nli_test.csv: 23,208 rows ✅\n" ] } ] }, { "cell_type": "code", "execution_count": 6, "id": "d8e37f4d", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "d8e37f4d", "outputId": "07b4ebdc-ab95-4e7f-f4bd-f8f33168ad3e" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " nli_train.csv 185,659 rows 146.5 MB ✅\n", " nli_val.csv 23,207 rows 18.4 MB ✅\n", " nli_test.csv 23,208 rows 18.5 MB ✅\n", "\n", "Checkpoint will save to: /content/drive/MyDrive/Mapped_Data/safety_guardrail\n" ] } ], "source": [ "import pandas as pd\n", "import os\n", "\n", "os.makedirs(SAVE_DIR, exist_ok=True)\n", "\n", "for path in [TRAIN_CSV, VAL_CSV, TEST_CSV]:\n", " assert os.path.isfile(path), f\"NOT FOUND: {path}\"\n", " # Use pandas to safely count rows\n", " rows = len(pd.read_csv(path, usecols=[0]))\n", " size = os.path.getsize(path) / 1e6\n", " print(f\" {os.path.basename(path):<20} {rows:>8,} rows {size:.1f} MB ✅\")\n", "\n", "print(f\"\\nCheckpoint will save to: {SAVE_DIR}\")" ] }, { "cell_type": "code", "source": [ "import glob\n", "import os\n", "SAVE_DIR = \"/content/drive/MyDrive/Mapped_Data/safety_guardrail\"\n", "checkpoints = sorted(glob.glob(f\"{SAVE_DIR}/checkpoint-*\"))\n", "print(\"Checkpoints found:\", checkpoints)\n", "print(\"SAVE_DIR exists:\", os.path.isdir(SAVE_DIR))\n", "# Also check if there were any prior runs\n", "print(\"All files in SAVE_DIR:\")\n", "for f in sorted(os.listdir(SAVE_DIR)) if os.path.isdir(SAVE_DIR) else []:\n", " print(\" \", f)" ], "metadata": { "id": "Q0EyBlRB4C-6", "outputId": "2f234082-560b-4bbb-ebf6-4fc15a8f030e", "colab": { "base_uri": "https://localhost:8080/" } }, "id": "Q0EyBlRB4C-6", "execution_count": 7, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Checkpoints found: []\n", "SAVE_DIR exists: True\n", "All files in SAVE_DIR:\n" ] } ] }, { "cell_type": "code", "execution_count": 8, "id": "9009bae7", "metadata": { "id": "9009bae7", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "97f54ab1-0ce4-4142-af9b-02c40bb1a630" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Train: 185,659 rows\n", "Val : 23,207 rows\n", "Test : 23,208 rows\n", "\n", "Train label distribution:\n", "nli_label\n", "1 92830\n", "0 92829\n", " 0 = entailment (crisis) | 1 = contradiction (non-crisis)\n", "\n", "✅ All required columns present.\n" ] } ], "source": [ "# STEP 4: Load NLI CSVs\n", "import pandas as pd\n", "from datasets import Dataset\n", "\n", "train_df = pd.read_csv(TRAIN_CSV)\n", "val_df = pd.read_csv(VAL_CSV)\n", "test_df = pd.read_csv(TEST_CSV)\n", "\n", "print(f\"Train: {len(train_df):,} rows\")\n", "print(f\"Val : {len(val_df):,} rows\")\n", "print(f\"Test : {len(test_df):,} rows\")\n", "\n", "# Verify label balance\n", "print(f\"\\nTrain label distribution:\")\n", "print(train_df[\"nli_label\"].value_counts().to_string())\n", "print(\" 0 = entailment (crisis) | 1 = contradiction (non-crisis)\")\n", "\n", "# Verify required columns exist\n", "required = [\"text\", \"hypothesis\", \"nli_label\"]\n", "for col in required:\n", " assert col in train_df.columns, f\"Missing column: {col}\"\n", "print(\"\\n✅ All required columns present.\")\n" ] }, { "cell_type": "code", "execution_count": 9, "id": "e3f2e108", "metadata": { "id": "e3f2e108", "colab": { "base_uri": "https://localhost:8080/", "height": 489, "referenced_widgets": [ "373e686b9c884846be3c1db572959a86", "3ec50825747641b5b717586d4e4886da", "1c59b3fa91094c878fbb72f4bc3841c7", "37b39a7315254e3b98600a453be9da16", "e86bde3551454ea2ad5f8751ccf717d9", "22b7be3d481b4874b93b72c43f89e221", "e717cd4007a04fc2a68f50d7b5874484", "213fc44f75884e52a7b46bd7eb4dd8a7", "41846c842e8546e081f479d9b1c044e6", "9f96f0660b40490486d5d6c53e54d9e6", "e686152ec05a4932b952c195798ea631", "7052f5174be348ab9f3c5b50ae1ed8e9", "b0ae9b1ad0af4789bbe256f316201353", "3c707d3198a647ea84235c11d123f494", "92b7681098cb45b3bc282bb5b4168a51", "a5790abf33194047b4c8d03d446ff768", "da43437bca004762884ed6e8c98d4cc6", "9b884f6d1eb64762b3675132b877c718", "df193bc16289492fb16b2743545fdf32", "9c3cf34621fa41ea9908f24c5e00684c", "bd53ef7fa5db4f15a62c3aa45f0bbd70", "71ee0d0ddfa543fb82632fe2715b44be", "f6edb108d9ea4fd1ae52e1d693cb0ab3", "9a8523fc93554c42a60adc3c58a29b65", "cb5871a7c8244f5c8eadbfce685984ac", "c200a4003d094537be7e9dba8a655b83", "a55be1a1fb0f4db6ac44b6475f224e36", "5b6909e5b37f4260b90e2eaab5aa900b", "37f5b16f392a4238bbae9f1b34534ec3", "bbf1bd518cf441c49ebb92927216d979", "99b41d1885a74bd0acd8e3177fa105e3", "dae36d3f806d4e9fbd0ee90ba96e18ab", "d627d4469f9c49ffab96991b0cf790ef", "97fd82cdea5f407981a4b3ce78c24bf0", "a060df79c6184cedbc4509d6efaeca17", "6b93c381aa254a0eadfbb04f7605f937", "cc9ec4d7a7054cf0b3404e48979b1cb1", "94910c51450d4f449995ededc0c4d481", "270cf24bbfe543c691b2d10680f14a40", "3e700c9fec7848f3a207c7e42ff5e216", "283331887d174d4086e127f1611bbf71", "35e964d884c346d6ab321d10d19d1f09", "1f886f8b1e8c43ce9597c3768e12a239", "c179e06dcbe44cfcb9d2c78d9a44182f", "73743695d8324d3583e190ccb03ad034", "d522e82011cd4303b6d21125f99a9b27", "8cfbf3212bf84ea1a6f6605d2f8ec4b1", "07435d3553474f90bd4ecd48ffaab5b2", "2b44195d38a642d2ab8edd1e3aec3b6e", "47504c703e86402b98f8d04e706320b7", "9a23c71e1c3148159a5a446f825af8d8", "1a601ab1c1464745a29c54342321a66d", "80daf059317c4bd094acbe4d5d82f045", "712b26c2217a443eb5285b1793974be4", "aca52efa1e70464582a8dfcf0a92d0d1", "388e53077fc5437d80564fb43600a2be", "a9d017092685490f8897812ded30427c", "0262575fe792401fb2dd828e8a43eb16", "96828c9c0847425ba0b138b2f6e0234e", "dffefdd96de84d9ea673680f3986e479", "7ce889944e6748f4ab26dffca1ecce33", "8c109bc95873472c95d00e1c6af25069", "59c69902570642b6b10366739c32b853", "2061058962ee4ac09ec3a9f96d09346a", "43810f6fe8284459b8d2a4b5c1d69974", "3a314234f28744bfa995b0966b178a18" ] }, "outputId": "ed74cdfe-f286-4c07-efdd-dfd3291c653b" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Loading tokenizer: microsoft/deberta-v3-base\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n", "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n", "WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/579 [00:00" ], "text/html": [ "\n", "
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EpochTraining LossValidation LossRecallPrecisionF1
10.3068440.3253730.9573420.8139060.879816
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30.2949660.3370610.9660460.7987740.874483
40.2942040.3363540.9658740.7994860.874839

" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Writing model shards: 0%| | 0/1 [00:00 0 else f\" {f}/\")\n" ] }, { "cell_type": "code", "execution_count": 19, "id": "588734a6", "metadata": { "id": "588734a6", "colab": { "base_uri": "https://localhost:8080/", "height": 211 }, "outputId": "86e12b58-602a-4a39-9407-90d1b2a30b38" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Evaluating on test set (23,208 examples)...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "

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\n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "=== TEST SET RESULTS ===\n", "Recall : 0.9629 (target: > 0.80)\n", "Precision : 0.7951 (target: > 0.65)\n", "F1 : 0.8710\n", "\n", "✅ PASS — both targets met.\n", "\n", "Results saved: /content/drive/MyDrive/Mapped_Data/safety_guardrail/test_results.json\n" ] } ], "source": [ "# STEP 11: Evaluate on test set\n", "import json\n", "import numpy as np\n", "\n", "print(\"Evaluating on test set (23,208 examples)...\")\n", "test_results = trainer.evaluate(test_ds)\n", "\n", "recall = test_results[\"eval_recall\"]\n", "precision = test_results[\"eval_precision\"]\n", "f1 = test_results[\"eval_f1\"]\n", "\n", "print(\"\\n=== TEST SET RESULTS ===\")\n", "print(f\"Recall : {recall:.4f} (target: > 0.80)\")\n", "print(f\"Precision : {precision:.4f} (target: > 0.65)\")\n", "print(f\"F1 : {f1:.4f}\")\n", "\n", "print()\n", "if recall >= 0.80 and precision >= 0.65:\n", " print(\"✅ PASS — both targets met.\")\n", "elif recall >= 0.80:\n", " print(\"✅ Recall target met.\")\n", " print(f\"⚠️ Precision {precision:.4f} below 0.65 — acceptable, recall is primary.\")\n", "else:\n", " print(\"⚠️ Recall below 0.80.\")\n", " print(\" Fallback: lower lr to 5e-6, increase epochs to 5, re-run from checkpoint.\")\n", "\n", "# Save for paper\n", "results_path = f\"{SAVE_DIR}/test_results.json\"\n", "with open(results_path, \"w\") as f:\n", " json.dump({k: float(v) for k, v in test_results.items()}, f, indent=2)\n", "print(f\"\\nResults saved: {results_path}\")\n" ] }, { "cell_type": "code", "execution_count": 20, "id": "dd46d6a3", "metadata": { "id": "dd46d6a3", "colab": { "base_uri": "https://localhost:8080/", "height": 347 }, "outputId": "d1b470ba-fd16-465d-b4ba-301ddea52a4b" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Computing confusion matrix...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "=== CLASSIFICATION REPORT ===\n", " precision recall f1-score support\n", "\n", " crisis 0.80 0.96 0.87 11604\n", " non-crisis 0.95 0.75 0.84 11604\n", "\n", " accuracy 0.86 23208\n", " macro avg 0.87 0.86 0.86 23208\n", "weighted avg 0.87 0.86 0.86 23208\n", "\n", "=== CONFUSION MATRIX (rows=true, cols=pred) ===\n", " crisis non-crisis\n", " crisis 11174 430\n", " non-crisis 2879 8725\n", "\n", "Missed crises (false negatives): 430 / 11604 (3.7%)\n", "Confusion matrix saved to Drive.\n" ] } ], "source": [ "# STEP 12: Confusion matrix\n", "from sklearn.metrics import confusion_matrix, classification_report\n", "import numpy as np\n", "\n", "print(\"Computing confusion matrix...\")\n", "pred_output = trainer.predict(test_ds)\n", "preds = np.argmax(pred_output.predictions, axis=-1)\n", "labels = pred_output.label_ids\n", "\n", "print(\"\\n=== CLASSIFICATION REPORT ===\")\n", "print(classification_report(labels, preds, target_names=[\"crisis\", \"non-crisis\"]))\n", "\n", "cm = confusion_matrix(labels, preds)\n", "print(\"=== CONFUSION MATRIX (rows=true, cols=pred) ===\")\n", "print(f\"{'':>15}{'crisis':>12}{'non-crisis':>12}\")\n", "for i, row in enumerate(cm):\n", " name = [\"crisis\", \"non-crisis\"][i]\n", " print(f\"{name:>15}\" + \"\".join(f\"{v:>12}\" for v in row))\n", "\n", "# False negatives = missed crises (top-right cell) — report explicitly\n", "fn = cm[0][1]\n", "total_crisis = cm[0].sum()\n", "print(f\"\\nMissed crises (false negatives): {fn} / {total_crisis} ({100*fn/total_crisis:.1f}%)\")\n", "\n", "np.save(f\"{SAVE_DIR}/confusion_matrix.npy\", cm)\n", "print(f\"Confusion matrix saved to Drive.\")\n" ] }, { "cell_type": "code", "source": [ "# STEP 13 Verify the saved model loads and produces non-collapsed predictions\n", "from transformers import AutoModelForSequenceClassification, AutoTokenizer\n", "import torch\n", "\n", "ckpt = \"/content/drive/MyDrive/Mapped_Data/safety_guardrail\"\n", "tok = AutoTokenizer.from_pretrained(ckpt)\n", "m = AutoModelForSequenceClassification.from_pretrained(ckpt, torch_dtype=torch.bfloat16).to(\"cuda\").eval()\n", "\n", "tests = [\n", " (\"I want to kill myself and I have a plan.\", 0),\n", " (\"I think everyone would be better off without me.\", 0),\n", " (\"I'm stressed about my thesis deadline.\", 1),\n", " (\"this exam is literally killing me lol\", 1),\n", "]\n", "\n", "print(\"SANITY CHECK — real guardrail inference:\")\n", "all_pass = True\n", "for text, expected in tests:\n", " enc = tok(text, \"This person is expressing suicidal ideation or intent to self-harm.\",\n", " return_tensors=\"pt\", truncation=True, max_length=256, padding=\"max_length\")\n", " enc = {k: v.to(\"cuda\") for k, v in enc.items()}\n", " with torch.no_grad():\n", " logits = m(**enc).logits\n", " pred = int(torch.argmax(logits, dim=-1).item())\n", " prob_crisis = torch.softmax(logits, dim=-1)[0, 0].item()\n", " status = \"✅\" if pred == expected else \"❌\"\n", " print(f\" {status} pred={'crisis' if pred==0 else 'non-crisis'} ({prob_crisis:.3f}) | {text[:60]}\")\n", " if pred != expected:\n", " all_pass = False\n", "\n", "print(\"\\n✅ Checkpoint verified — safe to close Colab.\" if all_pass else \"\\n❌ At least one prediction wrong — do not close yet.\")" ], "metadata": { "id": "hyiXb58FO4mG", "outputId": "5dc56e00-7da6-4774-a200-92be37cbe307", "colab": { "base_uri": "https://localhost:8080/", "height": 170, "referenced_widgets": [ "b91341c94bb84d6ca74049cffa760907", "e8f63684ffa4414e9884eb078ac2728a", "0210ec7a06174733b3c9b623c77a13da", "5efedfd200ba42488aa3a7115eac25a7", "c16aaefe27c442aaa7616ba92942bd81", "f0faecc447524939ac50240b99e69704", "75f2f3dc93b94f49a87116412179a157", "ea677c4aeeb44e6d89e1628b9973f999", "b50fb2fe5b874d6996e8f0c60948c1fb", "c8a9b340d6ae4692a9a65c8356188604", "9a8f1049e2cf432dbc9813624881d4ec" ] } }, "id": "hyiXb58FO4mG", "execution_count": 21, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/202 [00:00 0.5)\")\n", "```\n", "Expected: crisis probability > 0.5 for that input.\n", "\n", "### 3. Tell Ray the test_results.json numbers\n", "Ray needs the recall and precision values to fill in the evaluation table in the paper.\n" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "A100", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "name": "python", "version": "3.12.0" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "373e686b9c884846be3c1db572959a86": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_3ec50825747641b5b717586d4e4886da", "IPY_MODEL_1c59b3fa91094c878fbb72f4bc3841c7", 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