diff --git "a/notebooks/colab_deberta_guardrail.ipynb" "b/notebooks/colab_deberta_guardrail.ipynb"
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+{
+  "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": [
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+          ]
+        },
+        "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<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "373e686b9c884846be3c1db572959a86"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "tokenizer_config.json:   0%|          | 0.00/52.0 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "7052f5174be348ab9f3c5b50ae1ed8e9"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "spm.model:   0%|          | 0.00/2.46M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "f6edb108d9ea4fd1ae52e1d693cb0ab3"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Tokenizing train:   0%|          | 0/185659 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "97fd82cdea5f407981a4b3ce78c24bf0"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Tokenizing val:   0%|          | 0/23207 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "73743695d8324d3583e190ccb03ad034"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Tokenizing test:   0%|          | 0/23208 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "388e53077fc5437d80564fb43600a2be"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Tokenizer output keys: ['input_ids', 'token_type_ids', 'attention_mask']\n",
+            "set_format columns: ['input_ids', 'token_type_ids', 'attention_mask', 'labels']\n",
+            "\n",
+            "Tokenization complete.\n",
+            "  Train: 185,659  |  Val: 23,207  |  Test: 23,208\n",
+            "✅ set_format correct.\n"
+          ]
+        }
+      ],
+      "source": [
+        "# STEP 5: Tokenize\n",
+        "from transformers import AutoTokenizer\n",
+        "from datasets import Dataset\n",
+        "\n",
+        "MODEL = \"microsoft/deberta-v3-base\"\n",
+        "print(f\"Loading tokenizer: {MODEL}\")\n",
+        "tokenizer = AutoTokenizer.from_pretrained(MODEL)\n",
+        "\n",
+        "def tokenize(batch):\n",
+        "    return tokenizer(\n",
+        "        batch[\"text\"],\n",
+        "        batch[\"hypothesis\"],\n",
+        "        truncation=True,\n",
+        "        max_length=256,\n",
+        "        padding=\"max_length\",\n",
+        "    )\n",
+        "\n",
+        "train_ds = Dataset.from_pandas(train_df).map(tokenize, batched=True, desc=\"Tokenizing train\")\n",
+        "val_ds   = Dataset.from_pandas(val_df).map(tokenize, batched=True, desc=\"Tokenizing val\")\n",
+        "test_ds  = Dataset.from_pandas(test_df).map(tokenize, batched=True, desc=\"Tokenizing test\")\n",
+        "\n",
+        "train_ds = train_ds.rename_column(\"nli_label\", \"labels\")\n",
+        "val_ds   = val_ds.rename_column(\"nli_label\", \"labels\")\n",
+        "test_ds  = test_ds.rename_column(\"nli_label\", \"labels\")\n",
+        "\n",
+        "# Detect what the tokenizer actually produces and set_format accordingly\n",
+        "sample_keys = list(tokenizer(\"test premise\", \"test hypothesis\", truncation=True, max_length=256, padding=\"max_length\").keys())\n",
+        "print(f\"Tokenizer output keys: {sample_keys}\")\n",
+        "\n",
+        "format_cols = [k for k in sample_keys if k in [\"input_ids\", \"attention_mask\", \"token_type_ids\"]] + [\"labels\"]\n",
+        "print(f\"set_format columns: {format_cols}\")\n",
+        "\n",
+        "train_ds.set_format(type=\"torch\", columns=format_cols)\n",
+        "val_ds.set_format(type=\"torch\",   columns=format_cols)\n",
+        "test_ds.set_format(type=\"torch\",  columns=format_cols)\n",
+        "\n",
+        "print(f\"\\nTokenization complete.\")\n",
+        "print(f\"  Train: {len(train_ds):,}  |  Val: {len(val_ds):,}  |  Test: {len(test_ds):,}\")\n",
+        "print(\"✅ set_format correct.\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "id": "04ae6c04",
+      "metadata": {
+        "id": "04ae6c04",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 634,
+          "referenced_widgets": [
+            "09b407811a794dc38fdc3326f5e993f6",
+            "14c45ae1d01b4bb78994fda97a23f914",
+            "9f440a34e7824b7bab5e7b6b013f4414",
+            "acf245fef3a34be0a21b672b43c869c7",
+            "b72893b2ceb948e091e49c227428c822",
+            "cf2ad5aa71994f0da34369990fa56193",
+            "c6350860be9e430abbc0914790753504",
+            "31028bcb605648ed89eebacc53f487f5",
+            "87327531f22941a3b41d449951042a57",
+            "55ae39eee821498680bf1f87c794da95",
+            "7fd4484b851c4a78bccefcc1abc6066f",
+            "e75b01e67555409ba6b51ed79ae3c12c",
+            "b89b6152749b4ec08525d6abf36e3926",
+            "a680a80837354f28ac853eb2f2ab3272",
+            "c4a08c0d253f4bddb53990e11ae9ce03",
+            "7fa133568504455487817968a2687236",
+            "1242a5cf4af441e3b3399b0739524365",
+            "f4a5820bb67244edb1213f5d1fb1bbd8",
+            "b75b176721844abb8a99b259f845157c",
+            "6f40518a3319417c88babb73f6ccb904",
+            "93aae2ca3e6e4482b4684565af162960",
+            "120ff70271b44f1d9ea07c319142a11c",
+            "84f894c86133434882ebf8cca5f67ef2",
+            "1a42ce7b75684be18aad461c9fd341cd",
+            "8a1cd85cf29649b8a5cab91b21e78170",
+            "d90578be497c4936b8008ccb4b9d8c55",
+            "d09c260c0d5d48cb93acb9229b87b04d",
+            "1d31590a17054c01a53758a8c67be7f9",
+            "28ff9226286a4c5ca37eca7424ce67bc",
+            "67715c17828e413a9b1220fc6f8c525c",
+            "b56619f248a7427da169045e3f263ca5",
+            "6908f94a33724ec3a21d627ffe198d3e",
+            "120ee587d1a949c6b02a51e147d1d636"
+          ]
+        },
+        "outputId": "ff1f5b20-2c41-49c2-8cc1-a5efbfcda4d8"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "`torch_dtype` is deprecated! Use `dtype` instead!\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "pytorch_model.bin:   0%|          | 0.00/371M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "09b407811a794dc38fdc3326f5e993f6"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Loading weights:   0%|          | 0/198 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "e75b01e67555409ba6b51ed79ae3c12c"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "DebertaV2ForSequenceClassification LOAD REPORT from: microsoft/deberta-v3-base\n",
+            "Key                                     | Status     | \n",
+            "----------------------------------------+------------+-\n",
+            "mask_predictions.classifier.bias        | UNEXPECTED | \n",
+            "mask_predictions.LayerNorm.weight       | UNEXPECTED | \n",
+            "mask_predictions.dense.bias             | UNEXPECTED | \n",
+            "mask_predictions.LayerNorm.bias         | UNEXPECTED | \n",
+            "lm_predictions.lm_head.bias             | UNEXPECTED | \n",
+            "mask_predictions.classifier.weight      | UNEXPECTED | \n",
+            "lm_predictions.lm_head.LayerNorm.weight | UNEXPECTED | \n",
+            "lm_predictions.lm_head.dense.weight     | UNEXPECTED | \n",
+            "lm_predictions.lm_head.dense.bias       | UNEXPECTED | \n",
+            "mask_predictions.dense.weight           | UNEXPECTED | \n",
+            "lm_predictions.lm_head.LayerNorm.bias   | UNEXPECTED | \n",
+            "pooler.dense.bias                       | MISSING    | \n",
+            "classifier.bias                         | MISSING    | \n",
+            "pooler.dense.weight                     | MISSING    | \n",
+            "classifier.weight                       | MISSING    | \n",
+            "\n",
+            "Notes:\n",
+            "- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n",
+            "- MISSING\t:those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "model.safetensors:   0%|          | 0.00/371M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "84f894c86133434882ebf8cca5f67ef2"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Model dtype:      torch.bfloat16\n",
+            "Classifier dtype: torch.bfloat16\n",
+            "Device:           cuda:0\n",
+            "Model parameters: 184,423,682\n",
+            "Trainable params: 184,423,682\n",
+            "\n",
+            "✅ DeBERTa-v3-base loaded as bf16 on CUDA.\n"
+          ]
+        }
+      ],
+      "source": [
+        "# STEP 6: Load DeBERTa-v3-base\n",
+        "from transformers import AutoModelForSequenceClassification\n",
+        "import torch\n",
+        "\n",
+        "# 2 labels: 0 = entailment (crisis), 1 = contradiction (non-crisis)\n",
+        "id2label = {0: \"crisis\", 1: \"non-crisis\"}\n",
+        "label2id = {\"crisis\": 0, \"non-crisis\": 1}\n",
+        "\n",
+        "model = AutoModelForSequenceClassification.from_pretrained(\n",
+        "    MODEL,\n",
+        "    num_labels=2,\n",
+        "    id2label=id2label,\n",
+        "    label2id=label2id,\n",
+        "    torch_dtype=torch.bfloat16,  # load directly as bf16 — matches A100 training dtype\n",
+        ")\n",
+        "model = model.to(\"cuda\")\n",
+        "\n",
+        "# Verify — both must say torch.bfloat16\n",
+        "print(f\"Model dtype:      {next(model.parameters()).dtype}\")\n",
+        "print(f\"Classifier dtype: {model.classifier.weight.dtype}\")\n",
+        "print(f\"Device:           {next(model.parameters()).device}\")\n",
+        "print(f\"Model parameters: {sum(p.numel() for p in model.parameters()):,}\")\n",
+        "print(f\"Trainable params: {sum(p.numel() for p in model.parameters() if p.requires_grad):,}\")\n",
+        "print(\"\\n✅ DeBERTa-v3-base loaded as bf16 on CUDA.\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "abe5a660",
+      "metadata": {
+        "id": "abe5a660",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 148,
+          "referenced_widgets": [
+            "07f660fdd4b1455a9d1a206b188d9c06",
+            "becf913ac2f1400d8cc626926aa37e61",
+            "4e93fa8637d74d4999d15000477f009e",
+            "0e1e6faf86064b11ae704cf0935e6801",
+            "33a78aa2af1640579332fbc7aa030a30",
+            "2611b177858f4bf49a5e6c9fa2013367",
+            "e910d11aedc94c27881836810826f6e6",
+            "b6b3599e39434b678a6cabfc4f01ffbf",
+            "8e7b16e9c4914caba1fd18a908be74f6",
+            "0853a735e4194cbf893df9f7680f57a7",
+            "f269ffac04714f808e3f649c5963cbd9",
+            "9cff7d5bae8c428c973bc70024a86874",
+            "314e6f994a534e63939088f897709293",
+            "0138097d37f84e0995b97c2cf55ff431",
+            "1cb061b50d594f068d8924263e8cec1d",
+            "b96a9f653d5243c6881d8c937969d843",
+            "477128a7e9784f29b7839157d3088f84",
+            "8e82e458cf2b4c57b9c8cf3cccbca6c8",
+            "8b070309311a41b5b525f426818fcc23",
+            "c22d74aaa36e4bdaa8e4f7ab5f074e26",
+            "229c7d1b47dd46988169a7cfc0f69dc9",
+            "75d33522621c4d53b7b44fd5047fee85",
+            "99286ab6ef7c45c98dfe12b9bd366dd8",
+            "22b091bf81b84a929294bd78e09759b6",
+            "92b90b2a974143a9b1b11e34d6321b61",
+            "b01ef6756f7347f8a780c67cb2f57bc2",
+            "096346caa0694eb9b75d055d97b0a70a",
+            "53f6f36d3de64f4d9d7fe68165036ac0",
+            "2534cc4f0d3e489aba5c9ac0400fadc9",
+            "044de25109b743299eeb98574c56fe7f",
+            "c311a811226c47e28b118dcff4a0b411",
+            "754887541339410bb91310ea73cef699",
+            "77e33887812d4301832ed115989af0a0"
+          ]
+        },
+        "outputId": "c7c9a5fd-140d-4f06-dee6-c1fb14d4b8b7"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading builder script: 0.00B [00:00, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "07f660fdd4b1455a9d1a206b188d9c06"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading builder script: 0.00B [00:00, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "9cff7d5bae8c428c973bc70024a86874"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Downloading builder script: 0.00B [00:00, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "99286ab6ef7c45c98dfe12b9bd366dd8"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Metrics: recall + precision + F1 on crisis class (label 0).\n",
+            "Primary metric for best-model selection: recall\n"
+          ]
+        }
+      ],
+      "source": [
+        "# STEP 7: Metrics\n",
+        "# Primary metric is RECALL on crisis class (label 0)\n",
+        "# A missed crisis is the worst possible outcome — recall is paramount\n",
+        "import evaluate\n",
+        "import numpy as np\n",
+        "\n",
+        "f1_m        = evaluate.load(\"f1\")\n",
+        "recall_m    = evaluate.load(\"recall\")\n",
+        "precision_m = evaluate.load(\"precision\")\n",
+        "\n",
+        "def compute_metrics(eval_pred):\n",
+        "    logits, labels = eval_pred\n",
+        "    preds = np.argmax(logits, axis=-1)\n",
+        "    # pos_label=0 because crisis = label 0\n",
+        "    return {\n",
+        "        \"recall\":    recall_m.compute(predictions=preds,    references=labels, pos_label=0)[\"recall\"],\n",
+        "        \"precision\": precision_m.compute(predictions=preds, references=labels, pos_label=0)[\"precision\"],\n",
+        "        \"f1\":        f1_m.compute(predictions=preds,        references=labels, pos_label=0)[\"f1\"],\n",
+        "    }\n",
+        "\n",
+        "print(\"Metrics: recall + precision + F1 on crisis class (label 0).\")\n",
+        "print(\"Primary metric for best-model selection: recall\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# import torch\n",
+        "# import numpy as np\n",
+        "\n",
+        "# # Take 4 samples from val set — 2 crisis (label 0), 2 non-crisis (label 1)\n",
+        "# crisis_indices     = [i for i, x in enumerate(val_ds) if x[\"labels\"].item() == 0][:2]\n",
+        "# noncrisis_indices  = [i for i, x in enumerate(val_ds) if x[\"labels\"].item() == 1][:2]\n",
+        "# sample_indices = crisis_indices + noncrisis_indices\n",
+        "\n",
+        "# model.eval()\n",
+        "# with torch.no_grad():\n",
+        "#     for idx in sample_indices:\n",
+        "#         item = val_ds[idx]\n",
+        "#         input_ids      = item[\"input_ids\"].unsqueeze(0).to(model.device)\n",
+        "#         attention_mask = item[\"attention_mask\"].unsqueeze(0).to(model.device)\n",
+        "#         token_type_ids = item[\"token_type_ids\"].unsqueeze(0).to(model.device)\n",
+        "#         true_label     = item[\"labels\"].item()\n",
+        "\n",
+        "#         logits = model(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids).logits\n",
+        "#         probs  = torch.softmax(logits, dim=-1).squeeze().tolist()\n",
+        "#         pred   = int(torch.argmax(logits, dim=-1).item())\n",
+        "\n",
+        "#         print(f\"true={true_label} | pred={pred} | p(crisis)={probs[0]:.4f} | p(non-crisis)={probs[1]:.4f} | logits={[round(x,3) for x in logits.squeeze().tolist()]}\")"
+      ],
+      "metadata": {
+        "id": "kZgaRYac948W"
+      },
+      "id": "kZgaRYac948W",
+      "execution_count": 12,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# import torch\n",
+        "\n",
+        "# idx = crisis_indices[0]\n",
+        "# item = val_ds[idx]\n",
+        "\n",
+        "# print(\"input_ids shape:      \", item[\"input_ids\"].shape)\n",
+        "# print(\"attention_mask shape: \", item[\"attention_mask\"].shape)\n",
+        "# print(\"token_type_ids shape: \", item[\"token_type_ids\"].shape)\n",
+        "# print(\"labels:               \", item[\"labels\"])\n",
+        "\n",
+        "# print(\"\\nFirst 20 input_ids:      \", item[\"input_ids\"][:20].tolist())\n",
+        "# print(\"Last 20 input_ids:       \", item[\"input_ids\"][-20:].tolist())\n",
+        "# print(\"attention_mask sum:      \", item[\"attention_mask\"].sum().item(), \"(non-padding tokens)\")\n",
+        "# print(\"token_type_ids unique:   \", item[\"token_type_ids\"].unique().tolist())\n",
+        "\n",
+        "# # Check a second sample is actually different\n",
+        "# idx2 = noncrisis_indices[0]\n",
+        "# item2 = val_ds[idx2]\n",
+        "# print(\"\\nSample 2 first 20 input_ids:\", item2[\"input_ids\"][:20].tolist())\n",
+        "# print(\"Are input_ids identical between samples?\", item[\"input_ids\"].equal(item2[\"input_ids\"]))"
+      ],
+      "metadata": {
+        "id": "xkQRRhIV-cAU"
+      },
+      "id": "xkQRRhIV-cAU",
+      "execution_count": 13,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# # Check which parameters actually have gradients and which are frozen\n",
+        "# print(\"=== GRADIENT STATUS ===\")\n",
+        "# frozen, trainable, zero_grad = [], [], []\n",
+        "# for name, param in model.named_parameters():\n",
+        "#     if not param.requires_grad:\n",
+        "#         frozen.append(name)\n",
+        "#     elif param.grad is None:\n",
+        "#         zero_grad.append(name)\n",
+        "#     else:\n",
+        "#         trainable.append(name)\n",
+        "\n",
+        "# print(f\"Frozen (requires_grad=False): {len(frozen)}\")\n",
+        "# print(f\"Trainable with grad:          {len(trainable)}\")\n",
+        "# print(f\"Trainable but grad is None:   {len(zero_grad)}\")\n",
+        "\n",
+        "# # Check classifier weights specifically\n",
+        "# print(\"\\n=== CLASSIFIER WEIGHTS ===\")\n",
+        "# print(\"classifier.weight:\", model.classifier.weight.data)\n",
+        "# print(\"classifier.bias:  \", model.classifier.bias.data)\n",
+        "\n",
+        "# # Check if encoder is actually processing input differently per sample\n",
+        "# # by hooking into the last encoder layer output\n",
+        "# hook_output = []\n",
+        "# def hook_fn(module, input, output):\n",
+        "#     hook_output.append(output[0].detach().cpu())\n",
+        "\n",
+        "# hook = model.deberta.encoder.layer[-1].register_forward_hook(hook_fn)\n",
+        "\n",
+        "# model.eval()\n",
+        "# with torch.no_grad():\n",
+        "#     for idx in crisis_indices[:2]:\n",
+        "#         item = val_ds[idx]\n",
+        "#         model(\n",
+        "#             input_ids=item[\"input_ids\"].unsqueeze(0).to(model.device),\n",
+        "#             attention_mask=item[\"attention_mask\"].unsqueeze(0).to(model.device),\n",
+        "#             token_type_ids=item[\"token_type_ids\"].unsqueeze(0).to(model.device)\n",
+        "#         )\n",
+        "\n",
+        "# hook.remove()\n",
+        "\n",
+        "# print(\"\\n=== LAST ENCODER LAYER OUTPUT ===\")\n",
+        "# print(\"Sample 1 [CLS] vector (first 8 dims):\", hook_output[0][0, 0, :8].tolist())\n",
+        "# print(\"Sample 2 [CLS] vector (first 8 dims):\", hook_output[1][0, 0, :8].tolist())\n",
+        "# print(\"Are encoder outputs identical?\", hook_output[0].equal(hook_output[1]))"
+      ],
+      "metadata": {
+        "id": "Pg6o4rnO-22H"
+      },
+      "id": "Pg6o4rnO-22H",
+      "execution_count": 14,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# # Check actual model dtype and device\n",
+        "# print(\"Model dtype:\", next(model.parameters()).dtype)\n",
+        "# print(\"Model device:\", next(model.parameters()).device)\n",
+        "\n",
+        "# # Check what bf16 actually does here\n",
+        "# print(\"bf16 flag in training_args:\", training_args.bf16)\n",
+        "# print(\"fp16 flag in training_args:\", training_args.fp16)\n",
+        "\n",
+        "# # Force model to bfloat16 explicitly before training\n",
+        "# model = model.to(torch.bfloat16)\n",
+        "# print(\"Model dtype after cast:\", next(model.parameters()).dtype)\n",
+        "\n",
+        "# # Verify classifier is now bfloat16\n",
+        "# print(\"Classifier weight dtype:\", model.classifier.weight.dtype)\n",
+        "# print(\"Classifier bias dtype:  \", model.classifier.bias.dtype)"
+      ],
+      "metadata": {
+        "id": "0gOIiHkF_bky"
+      },
+      "id": "0gOIiHkF_bky",
+      "execution_count": 15,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "id": "d1cb3fe2",
+      "metadata": {
+        "id": "d1cb3fe2",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 121
+        },
+        "outputId": "13149749-e6eb-4f41-f729-c6912818506a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "warmup_ratio is deprecated and will be removed in v5.2. Use `warmup_steps` instead.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Starting fresh.\n",
+            "Epochs       : 4\n",
+            "Train batch  : 32\n",
+            "Learning rate: 1e-05\n",
+            "Best metric  : recall\n"
+          ]
+        }
+      ],
+      "source": [
+        "# STEP 8: Training configuration\n",
+        "import glob\n",
+        "from transformers import TrainingArguments\n",
+        "\n",
+        "# Detect existing checkpoints for resume\n",
+        "existing = sorted(glob.glob(f\"{SAVE_DIR}/checkpoint-*\"))\n",
+        "RESUME_FROM = None\n",
+        "print(f\"Resume from: {RESUME_FROM}\" if RESUME_FROM else \"Starting fresh.\")\n",
+        "\n",
+        "training_args = TrainingArguments(\n",
+        "    output_dir=SAVE_DIR,\n",
+        "\n",
+        "    # Schedule — lower LR than RoBERTa because full fine-tune (no LoRA)\n",
+        "    num_train_epochs=4,\n",
+        "    per_device_train_batch_size=32,     # DeBERTa-v3 is larger — 32 is safe on A100\n",
+        "    per_device_eval_batch_size=64,\n",
+        "    learning_rate=1e-5,\n",
+        "    warmup_ratio=0.1,\n",
+        "    weight_decay=0.01,\n",
+        "\n",
+        "    # Checkpointing\n",
+        "    eval_strategy=\"epoch\",\n",
+        "    save_strategy=\"epoch\",\n",
+        "    load_best_model_at_end=True,\n",
+        "    metric_for_best_model=\"recall\",     # recall is primary — never miss a crisis\n",
+        "    greater_is_better=True,\n",
+        "    save_total_limit=3,\n",
+        "\n",
+        "    # Speed: Switched to bf16 because DeBERTa-v3 has issues with fp16 gradient scaling, and A100 supports bf16 natively.\n",
+        "    bf16=True,\n",
+        "    dataloader_num_workers=4,\n",
+        "\n",
+        "    # Logging\n",
+        "    logging_steps=200,\n",
+        "    report_to=\"none\",\n",
+        ")\n",
+        "\n",
+        "print(f\"Epochs       : {training_args.num_train_epochs}\")\n",
+        "print(f\"Train batch  : {training_args.per_device_train_batch_size}\")\n",
+        "print(f\"Learning rate: {training_args.learning_rate}\")\n",
+        "print(f\"Best metric  : {training_args.metric_for_best_model}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "id": "4a9f6c15",
+      "metadata": {
+        "id": "4a9f6c15",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 453,
+          "referenced_widgets": [
+            "e4b80c2927a2436cbac0301a4b1b542a",
+            "0c315f6e2fea4ffd98f478de52cd28b1",
+            "5cc5d1cce94c4b95b6a3ad4a3a318478",
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+            "0833aa972d694bf59f720c812e7cef44",
+            "eaa2e5061caf4e3ca84a5d6ee628bf72",
+            "85e133b7562f444abc2d61da9d2fcd8e",
+            "39f8e4b8713d48fda18de5ea35ca52ff",
+            "c5b6b5f6e7bd43b89fb3e54d1aaf24fe",
+            "ce47af4eb8c541fcaa5e58033c3df514",
+            "0718bd2c93c346bebbd6d1e193920cda",
+            "fcb5de4827f64b5fa0298924db1fb608",
+            "49a661f9da1941819b2ecb1a6abf33c0",
+            "7c58a6f7ebcf46d98d77dceede7a7874",
+            "7e49cf769d794ce6bb61733b5d4585ac",
+            "9951096824274227b8784d7706d8f5b4",
+            "32cf19f6365b44a8a9db7240b2918a04",
+            "3f5705bddf324ce6a5e4adeac7583cdb",
+            "4cdf4f9bfb87405cb4b70be64bf55860",
+            "bd6ed756effb43a984d0119fcfb2fef0",
+            "0c0c685c3ec241ada8ddc5e1c31ebfef",
+            "ad454cb7b2664f93afd319042e7bb54a",
+            "1a482ecb20a843f7a1e6e93967c5ef4a",
+            "8977624ca540485898b9d57912dac538",
+            "4bf95a9b72364c7d8aa6e446bc1acaa3",
+            "1600560cf89d4d3099833306900b6bb6",
+            "9c66e2b6493f443dbb03095ac627a7fc",
+            "975d25af70dc47c49d0e1f9562b23026",
+            "2af4b0dd13724b9dbb8d3d57fbb96a38",
+            "de60a06ed98c4a5dbbdb452dbd952059",
+            "07f43023f8514daaab144c88f5af96cc",
+            "01a0da6d753d4e9e8529317dfa367e7a",
+            "6f6c05fa687b442086a2a33b38b33cc5",
+            "3e70f81cccba419e9a6ed31ca8224883",
+            "1eb36cba8937443d81492bab2e213c36",
+            "ce3215220f664c7bbec9428f499d3ff9",
+            "2d6228cb265943f09c12978e975c1abb",
+            "d11b595a1eb340858a103d44abb24e91",
+            "af5d41cdc406493aba68876d1e3c12cb",
+            "acf5024cb2f34967bd83e0f3e9c705b7",
+            "f6a85dcaa827418f9d450c6d40660062",
+            "b1f6c063a419464aadfcf707259469a8",
+            "e413d67d7fde4db0a98ba6c769c5b80d",
+            "749eb630478f4b7786a8d37b480f9e1b"
+          ]
+        },
+        "outputId": "00e71f3e-bcd4-40fe-8277-d6304c66775e"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Starting DeBERTa training...\n",
+            "------------------------------------------------------------\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "    <div>\n",
+              "      \n",
+              "      <progress value='23208' max='23208' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+              "      [23208/23208 56:38, Epoch 4/4]\n",
+              "    </div>\n",
+              "    <table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              " <tr style=\"text-align: left;\">\n",
+              "      <th>Epoch</th>\n",
+              "      <th>Training Loss</th>\n",
+              "      <th>Validation Loss</th>\n",
+              "      <th>Recall</th>\n",
+              "      <th>Precision</th>\n",
+              "      <th>F1</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <td>1</td>\n",
+              "      <td>0.306844</td>\n",
+              "      <td>0.325373</td>\n",
+              "      <td>0.957342</td>\n",
+              "      <td>0.813906</td>\n",
+              "      <td>0.879816</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>2</td>\n",
+              "      <td>0.291849</td>\n",
+              "      <td>0.327647</td>\n",
+              "      <td>0.963289</td>\n",
+              "      <td>0.807717</td>\n",
+              "      <td>0.878670</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>3</td>\n",
+              "      <td>0.294966</td>\n",
+              "      <td>0.337061</td>\n",
+              "      <td>0.966046</td>\n",
+              "      <td>0.798774</td>\n",
+              "      <td>0.874483</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>4</td>\n",
+              "      <td>0.294204</td>\n",
+              "      <td>0.336354</td>\n",
+              "      <td>0.965874</td>\n",
+              "      <td>0.799486</td>\n",
+              "      <td>0.874839</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table><p>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Writing model shards:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "e4b80c2927a2436cbac0301a4b1b542a"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Writing model shards:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "fcb5de4827f64b5fa0298924db1fb608"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Writing model shards:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "1a482ecb20a843f7a1e6e93967c5ef4a"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Writing model shards:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "3e70f81cccba419e9a6ed31ca8224883"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "There were missing keys in the checkpoint model loaded: ['deberta.embeddings.LayerNorm.weight', 'deberta.embeddings.LayerNorm.bias', 'deberta.encoder.layer.0.attention.output.LayerNorm.weight', 'deberta.encoder.layer.0.attention.output.LayerNorm.bias', 'deberta.encoder.layer.0.output.LayerNorm.weight', 'deberta.encoder.layer.0.output.LayerNorm.bias', 'deberta.encoder.layer.1.attention.output.LayerNorm.weight', 'deberta.encoder.layer.1.attention.output.LayerNorm.bias', 'deberta.encoder.layer.1.output.LayerNorm.weight', 'deberta.encoder.layer.1.output.LayerNorm.bias', 'deberta.encoder.layer.2.attention.output.LayerNorm.weight', 'deberta.encoder.layer.2.attention.output.LayerNorm.bias', 'deberta.encoder.layer.2.output.LayerNorm.weight', 'deberta.encoder.layer.2.output.LayerNorm.bias', 'deberta.encoder.layer.3.attention.output.LayerNorm.weight', 'deberta.encoder.layer.3.attention.output.LayerNorm.bias', 'deberta.encoder.layer.3.output.LayerNorm.weight', 'deberta.encoder.layer.3.output.LayerNorm.bias', 'deberta.encoder.layer.4.attention.output.LayerNorm.weight', 'deberta.encoder.layer.4.attention.output.LayerNorm.bias', 'deberta.encoder.layer.4.output.LayerNorm.weight', 'deberta.encoder.layer.4.output.LayerNorm.bias', 'deberta.encoder.layer.5.attention.output.LayerNorm.weight', 'deberta.encoder.layer.5.attention.output.LayerNorm.bias', 'deberta.encoder.layer.5.output.LayerNorm.weight', 'deberta.encoder.layer.5.output.LayerNorm.bias', 'deberta.encoder.layer.6.attention.output.LayerNorm.weight', 'deberta.encoder.layer.6.attention.output.LayerNorm.bias', 'deberta.encoder.layer.6.output.LayerNorm.weight', 'deberta.encoder.layer.6.output.LayerNorm.bias', 'deberta.encoder.layer.7.attention.output.LayerNorm.weight', 'deberta.encoder.layer.7.attention.output.LayerNorm.bias', 'deberta.encoder.layer.7.output.LayerNorm.weight', 'deberta.encoder.layer.7.output.LayerNorm.bias', 'deberta.encoder.layer.8.attention.output.LayerNorm.weight', 'deberta.encoder.layer.8.attention.output.LayerNorm.bias', 'deberta.encoder.layer.8.output.LayerNorm.weight', 'deberta.encoder.layer.8.output.LayerNorm.bias', 'deberta.encoder.layer.9.attention.output.LayerNorm.weight', 'deberta.encoder.layer.9.attention.output.LayerNorm.bias', 'deberta.encoder.layer.9.output.LayerNorm.weight', 'deberta.encoder.layer.9.output.LayerNorm.bias', 'deberta.encoder.layer.10.attention.output.LayerNorm.weight', 'deberta.encoder.layer.10.attention.output.LayerNorm.bias', 'deberta.encoder.layer.10.output.LayerNorm.weight', 'deberta.encoder.layer.10.output.LayerNorm.bias', 'deberta.encoder.layer.11.attention.output.LayerNorm.weight', 'deberta.encoder.layer.11.attention.output.LayerNorm.bias', 'deberta.encoder.layer.11.output.LayerNorm.weight', 'deberta.encoder.layer.11.output.LayerNorm.bias', 'deberta.encoder.LayerNorm.weight', 'deberta.encoder.LayerNorm.bias'].\n",
+            "There were unexpected keys in the checkpoint model loaded: ['deberta.embeddings.LayerNorm.beta', 'deberta.embeddings.LayerNorm.gamma', 'deberta.encoder.layer.0.attention.output.LayerNorm.beta', 'deberta.encoder.layer.0.attention.output.LayerNorm.gamma', 'deberta.encoder.layer.0.output.LayerNorm.beta', 'deberta.encoder.layer.0.output.LayerNorm.gamma', 'deberta.encoder.layer.1.attention.output.LayerNorm.beta', 'deberta.encoder.layer.1.attention.output.LayerNorm.gamma', 'deberta.encoder.layer.1.output.LayerNorm.beta', 'deberta.encoder.layer.1.output.LayerNorm.gamma', 'deberta.encoder.layer.2.attention.output.LayerNorm.beta', 'deberta.encoder.layer.2.attention.output.LayerNorm.gamma', 'deberta.encoder.layer.2.output.LayerNorm.beta', 'deberta.encoder.layer.2.output.LayerNorm.gamma', 'deberta.encoder.layer.3.attention.output.LayerNorm.beta', 'deberta.encoder.layer.3.attention.output.LayerNorm.gamma', 'deberta.encoder.layer.3.output.LayerNorm.beta', 'deberta.encoder.layer.3.output.LayerNorm.gamma', 'deberta.encoder.layer.4.attention.output.LayerNorm.beta', 'deberta.encoder.layer.4.attention.output.LayerNorm.gamma', 'deberta.encoder.layer.4.output.LayerNorm.beta', 'deberta.encoder.layer.4.output.LayerNorm.gamma', 'deberta.encoder.layer.5.attention.output.LayerNorm.beta', 'deberta.encoder.layer.5.attention.output.LayerNorm.gamma', 'deberta.encoder.layer.5.output.LayerNorm.beta', 'deberta.encoder.layer.5.output.LayerNorm.gamma', 'deberta.encoder.layer.6.attention.output.LayerNorm.beta', 'deberta.encoder.layer.6.attention.output.LayerNorm.gamma', 'deberta.encoder.layer.6.output.LayerNorm.beta', 'deberta.encoder.layer.6.output.LayerNorm.gamma', 'deberta.encoder.layer.7.attention.output.LayerNorm.beta', 'deberta.encoder.layer.7.attention.output.LayerNorm.gamma', 'deberta.encoder.layer.7.output.LayerNorm.beta', 'deberta.encoder.layer.7.output.LayerNorm.gamma', 'deberta.encoder.layer.8.attention.output.LayerNorm.beta', 'deberta.encoder.layer.8.attention.output.LayerNorm.gamma', 'deberta.encoder.layer.8.output.LayerNorm.beta', 'deberta.encoder.layer.8.output.LayerNorm.gamma', 'deberta.encoder.layer.9.attention.output.LayerNorm.beta', 'deberta.encoder.layer.9.attention.output.LayerNorm.gamma', 'deberta.encoder.layer.9.output.LayerNorm.beta', 'deberta.encoder.layer.9.output.LayerNorm.gamma', 'deberta.encoder.layer.10.attention.output.LayerNorm.beta', 'deberta.encoder.layer.10.attention.output.LayerNorm.gamma', 'deberta.encoder.layer.10.output.LayerNorm.beta', 'deberta.encoder.layer.10.output.LayerNorm.gamma', 'deberta.encoder.layer.11.attention.output.LayerNorm.beta', 'deberta.encoder.layer.11.attention.output.LayerNorm.gamma', 'deberta.encoder.layer.11.output.LayerNorm.beta', 'deberta.encoder.layer.11.output.LayerNorm.gamma', 'deberta.encoder.LayerNorm.beta', 'deberta.encoder.LayerNorm.gamma'].\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "------------------------------------------------------------\n",
+            "Training complete.\n"
+          ]
+        }
+      ],
+      "source": [
+        "# STEP 9: Train\n",
+        "# Expected time: ~2 hours on A100\n",
+        "# Saves checkpoint to Drive after every epoch — safe to disconnect and resume\n",
+        "from transformers import Trainer\n",
+        "\n",
+        "trainer = Trainer(\n",
+        "    model=model,\n",
+        "    args=training_args,\n",
+        "    train_dataset=train_ds,\n",
+        "    eval_dataset=val_ds,\n",
+        "    compute_metrics=compute_metrics,\n",
+        ")\n",
+        "\n",
+        "print(\"Starting DeBERTa training...\")\n",
+        "print(\"-\" * 60)\n",
+        "trainer.train(resume_from_checkpoint=RESUME_FROM)\n",
+        "print(\"-\" * 60)\n",
+        "print(\"Training complete.\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "id": "b30dc3e8",
+      "metadata": {
+        "id": "b30dc3e8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 240,
+          "referenced_widgets": [
+            "854e3b61f62447539eeeaaef0327ef41",
+            "c5b63bebedec4faa896732dc7fcd57d3",
+            "af4bd081095f44159d9e306214517d96",
+            "cd06ae910190422db918464fea8170d9",
+            "f8d5a66689dd45d585b4a4ae860f700d",
+            "646f3df7ad3c4dd19015dd200da0e119",
+            "f6fcb0fe393f418fa12906f9a46fc4ff",
+            "27044acba275480881ee9eb5dc574b88",
+            "c62ef080f6214b919db856c23674196c",
+            "b3be825128a041e69344611c3600af6e",
+            "7db91b5b17d4495eacd18a6b45378a2a"
+          ]
+        },
+        "outputId": "0bc39624-dc38-403a-fb01-a05b4ce6016c"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Writing model shards:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "854e3b61f62447539eeeaaef0327ef41"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Model saved to: /content/drive/MyDrive/Mapped_Data/safety_guardrail\n",
+            "\n",
+            "Contents:\n",
+            "  checkpoint-11604/\n",
+            "  checkpoint-17406/\n",
+            "  checkpoint-23208/\n",
+            "  config.json                                   0.0 MB\n",
+            "  model.safetensors                             368.9 MB\n",
+            "  tokenizer.json                                8.3 MB\n",
+            "  tokenizer_config.json                         0.0 MB\n",
+            "  training_args.bin                             0.0 MB\n"
+          ]
+        }
+      ],
+      "source": [
+        "# STEP 10: Save final model\n",
+        "import os\n",
+        "trainer.save_model(SAVE_DIR)\n",
+        "tokenizer.save_pretrained(SAVE_DIR)\n",
+        "\n",
+        "print(f\"Model saved to: {SAVE_DIR}\")\n",
+        "print(\"\\nContents:\")\n",
+        "for f in sorted(os.listdir(SAVE_DIR)):\n",
+        "    fpath = os.path.join(SAVE_DIR, f)\n",
+        "    size  = os.path.getsize(fpath)/1e6 if os.path.isfile(fpath) else 0\n",
+        "    print(f\"  {f:<45} {size:.1f} MB\" if size > 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": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "    <div>\n",
+              "      \n",
+              "      <progress value='363' max='363' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+              "      [363/363 00:28]\n",
+              "    </div>\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": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "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",
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+            "9a8f1049e2cf432dbc9813624881d4ec"
+          ]
+        }
+      },
+      "id": "hyiXb58FO4mG",
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Loading weights:   0%|          | 0/202 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "b91341c94bb84d6ca74049cffa760907"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "SANITY CHECK — real guardrail inference:\n",
+            "  ✅ pred=crisis (0.906) | I want to kill myself and I have a plan.\n",
+            "  ✅ pred=crisis (0.891) | I think everyone would be better off without me.\n",
+            "  ✅ pred=non-crisis (0.099) | I'm stressed about my thesis deadline.\n",
+            "  ✅ pred=non-crisis (0.209) | this exam is literally killing me lol\n",
+            "\n",
+            "✅ Checkpoint verified — safe to close Colab.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "76b787a4",
+      "metadata": {
+        "id": "76b787a4"
+      },
+      "source": [
+        "## ✅ Training Complete — Next Steps\n",
+        "\n",
+        "### 1. Download checkpoint from Drive\n",
+        "Navigate to `G:/My Drive/empathrag/safety_guardrail/` and copy the folder to:\n",
+        "```\n",
+        "models/safety_guardrail/\n",
+        "```\n",
+        "in your local repo.\n",
+        "\n",
+        "### 2. Verify it loads locally\n",
+        "```python\n",
+        "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
+        "import torch\n",
+        "\n",
+        "tok   = AutoTokenizer.from_pretrained(\"models/safety_guardrail\")\n",
+        "model = AutoModelForSequenceClassification.from_pretrained(\"models/safety_guardrail\").eval()\n",
+        "\n",
+        "HYPOTHESIS = \"This person is expressing suicidal ideation or intent to self-harm.\"\n",
+        "test_text  = \"I think everyone would be better off without me.\"\n",
+        "\n",
+        "enc  = tok(test_text, HYPOTHESIS, return_tensors=\"pt\", truncation=True, max_length=256)\n",
+        "with torch.no_grad():\n",
+        "    prob_crisis = torch.softmax(model(**enc).logits, dim=-1)[0, 0].item()\n",
+        "\n",
+        "print(f\"Crisis probability: {prob_crisis:.3f}  (fires if > 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"
+      ]
+    }
+  ],
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