Upload 6 files

config.json

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+{
+  "add_cross_attention": false,
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": null,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "eos_token_id": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "is_decoder": false,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2,
+    "LABEL_3": 3,
+    "LABEL_4": 4
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "tie_word_embeddings": true,
+  "transformers_version": "5.0.0",
+  "type_vocab_size": 2,
+  "use_cache": false,
+  "vocab_size": 30522
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c226235c9e11bb8b1d7b3d45843783e71c75dea19495ef08e0978bff7b831353
+size 437967852

test_inference.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "intro",
+   "metadata": {},
+   "source": [
+    "# Abuse Model Inference Test\n",
+    "\n",
+    "This notebook loads the saved production abuse model and its local artifacts from this folder, then runs inference on a calibrated set of example sentences. It performs inference only.\n",
+    "\n",
+    "Note: this checkpoint is a subtype classifier, not a generic toxicity detector. It predicts `Age`, `Ethnicity`, `Gender`, `Religion`, or `Not Cyberbullying`, and it responds most strongly when the text explicitly names the targeted group."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "load-notes",
+   "metadata": {},
+   "source": [
+    "## 1. Load the model artifacts\n",
+    "\n",
+    "The code below reads the local model config, tokenizer, weights, and label mapping directly from the current folder."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "load-code",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "37774b8a0e1f4d959c6e6a3293e0e766",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading weights:   0%|          | 0/201 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model directory: /Users/georgijkutivadze/projects/mago-text-scoring/app/app-models/prod-model-abuse\n",
+      "Device: cpu\n",
+      "Architecture: BertForSequenceClassification\n",
+      "Tokenizer: BertTokenizer\n",
+      "Max sequence length: 256\n",
+      "Artifacts: ['config.json', 'model-config.yaml', 'model.safetensors', 'test_inference.ipynb', 'tokenizer.json', 'tokenizer_config.json', 'training_args.bin']\n",
+      "Labels: ['Age', 'Ethnicity', 'Gender', 'Not Cyberbullying', 'Religion']\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pathlib import Path\n",
+    "import os\n",
+    "\n",
+    "import pandas as pd\n",
+    "import torch\n",
+    "import yaml\n",
+    "from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer\n",
+    "from transformers.utils import logging as hf_logging\n",
+    "\n",
+    "os.environ[\"HF_HUB_DISABLE_PROGRESS_BARS\"] = \"1\"\n",
+    "hf_logging.set_verbosity_error()\n",
+    "\n",
+    "MODEL_DIR = Path(\".\").resolve()\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "\n",
+    "with (MODEL_DIR / \"model-config.yaml\").open(\"r\", encoding=\"utf-8\") as handle:\n",
+    "    model_config = yaml.safe_load(handle)\n",
+    "\n",
+    "runtime_config = model_config.get(\"runtime\", {})\n",
+    "MAX_LEN = int(runtime_config.get(\"max_sequence_length\", 256))\n",
+    "PADDING = runtime_config.get(\"padding\", False)\n",
+    "\n",
+    "hf_config = AutoConfig.from_pretrained(MODEL_DIR, local_files_only=True)\n",
+    "tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR, local_files_only=True)\n",
+    "model = AutoModelForSequenceClassification.from_pretrained(\n",
+    "    MODEL_DIR,\n",
+    "    config=hf_config,\n",
+    "    local_files_only=True,\n",
+    ")\n",
+    "model.to(device)\n",
+    "model.eval()\n",
+    "\n",
+    "yaml_labels = model_config.get(\"labels\", {}).get(\"classes\", [])\n",
+    "label_names = [\n",
+    "    item.get(\"display_name\") or item.get(\"name\") or str(item.get(\"id\"))\n",
+    "    for item in sorted(yaml_labels, key=lambda item: item[\"id\"])\n",
+    "]\n",
+    "if not label_names:\n",
+    "    label_names = [hf_config.id2label[idx] for idx in sorted(hf_config.id2label)]\n",
+    "\n",
+    "artifact_files = sorted(path.name for path in MODEL_DIR.iterdir() if path.is_file())\n",
+    "\n",
+    "print(f\"Model directory: {MODEL_DIR}\")\n",
+    "print(f\"Device: {device}\")\n",
+    "print(f\"Architecture: {model.__class__.__name__}\")\n",
+    "print(f\"Tokenizer: {tokenizer.__class__.__name__}\")\n",
+    "print(f\"Max sequence length: {MAX_LEN}\")\n",
+    "print(f\"Artifacts: {artifact_files}\")\n",
+    "print(f\"Labels: {label_names}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "helper-notes",
+   "metadata": {},
+   "source": [
+    "## 2. Define an inference helper\n",
+    "\n",
+    "This helper mirrors the production runtime settings, tokenizes input text, runs the model in evaluation mode, applies softmax, and returns the predicted label together with confidence, top-3 labels, and the full probability distribution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "helper-code",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@torch.inference_mode()\n",
+    "def predict_texts(texts):\n",
+    "    encoded = tokenizer(\n",
+    "        texts,\n",
+    "        return_tensors=\"pt\",\n",
+    "        truncation=bool(runtime_config.get(\"truncation\", True)),\n",
+    "        padding=\"max_length\" if PADDING else False,\n",
+    "        max_length=MAX_LEN,\n",
+    "    )\n",
+    "    encoded = {name: tensor.to(device) for name, tensor in encoded.items()}\n",
+    "    logits = model(**encoded).logits\n",
+    "    probabilities = torch.softmax(logits, dim=-1).cpu().tolist()\n",
+    "\n",
+    "    rows = []\n",
+    "    for text, input_ids, probs in zip(texts, encoded[\"input_ids\"].cpu().tolist(), probabilities):\n",
+    "        ranked_indices = sorted(range(len(probs)), key=probs.__getitem__, reverse=True)\n",
+    "        pred_idx = ranked_indices[0]\n",
+    "        rows.append(\n",
+    "            {\n",
+    "                \"text\": text,\n",
+    "                \"predicted_label\": label_names[pred_idx],\n",
+    "                \"confidence\": round(probs[pred_idx], 4),\n",
+    "                \"token_count\": sum(1 for token_id in input_ids if token_id != tokenizer.pad_token_id),\n",
+    "                \"top_3\": [\n",
+    "                    f\"{label_names[idx]} ({probs[idx]:.4f})\"\n",
+    "                    for idx in ranked_indices[:3]\n",
+    "                ],\n",
+    "                \"probabilities\": {\n",
+    "                    label: round(score, 4)\n",
+    "                    for label, score in zip(label_names, probs)\n",
+    "                },\n",
+    "            }\n",
+    "        )\n",
+    "\n",
+    "    return pd.DataFrame(rows)\n",
+    "\n",
+    "pd.set_option(\"display.max_colwidth\", 140)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "examples-notes",
+   "metadata": {},
+   "source": [
+    "## 3. Run inference on calibrated example sentences\n",
+    "\n",
+    "The first notebook version used generic insults and indirect phrasing, which made the checkpoint look inconsistent. The examples below are stronger subtype-targeted probes and should exercise all five labels much more reliably."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "examples-code",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>text</th>\n",
+       "      <th>predicted_label</th>\n",
+       "      <th>confidence</th>\n",
+       "      <th>token_count</th>\n",
+       "      <th>top_3</th>\n",
+       "      <th>probabilities</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>go do your homework you stupid child</td>\n",
+       "      <td>Age</td>\n",
+       "      <td>0.9971</td>\n",
+       "      <td>9</td>\n",
+       "      <td>[Age (0.9971), Not Cyberbullying (0.0014), Ethnicity (0.0009)]</td>\n",
+       "      <td>{'Age': 0.9971, 'Ethnicity': 0.0009, 'Gender': 0.0003, 'Not Cyberbullying': 0.0014, 'Religion': 0.0003}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>immigrants like you ruin everything in this country</td>\n",
+       "      <td>Ethnicity</td>\n",
+       "      <td>0.9979</td>\n",
+       "      <td>10</td>\n",
+       "      <td>[Ethnicity (0.9979), Not Cyberbullying (0.0013), Religion (0.0007)]</td>\n",
+       "      <td>{'Age': 0.0, 'Ethnicity': 0.9979, 'Gender': 0.0001, 'Not Cyberbullying': 0.0013, 'Religion': 0.0007}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>women are stupid and should stay quiet</td>\n",
+       "      <td>Gender</td>\n",
+       "      <td>0.9999</td>\n",
+       "      <td>9</td>\n",
+       "      <td>[Gender (0.9999), Not Cyberbullying (0.0001), Ethnicity (0.0000)]</td>\n",
+       "      <td>{'Age': 0.0, 'Ethnicity': 0.0, 'Gender': 0.9999, 'Not Cyberbullying': 0.0001, 'Religion': 0.0}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>muslims like you are the problem with this country</td>\n",
+       "      <td>Religion</td>\n",
+       "      <td>1.0000</td>\n",
+       "      <td>11</td>\n",
+       "      <td>[Religion (1.0000), Not Cyberbullying (0.0000), Age (0.0000)]</td>\n",
+       "      <td>{'Age': 0.0, 'Ethnicity': 0.0, 'Gender': 0.0, 'Not Cyberbullying': 0.0, 'Religion': 1.0}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>the project update was sent to the team this morning</td>\n",
+       "      <td>Not Cyberbullying</td>\n",
+       "      <td>0.9999</td>\n",
+       "      <td>12</td>\n",
+       "      <td>[Not Cyberbullying (0.9999), Gender (0.0000), Religion (0.0000)]</td>\n",
+       "      <td>{'Age': 0.0, 'Ethnicity': 0.0, 'Gender': 0.0, 'Not Cyberbullying': 0.9999, 'Religion': 0.0}</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                   text    predicted_label  \\\n",
+       "0                  go do your homework you stupid child                Age   \n",
+       "1   immigrants like you ruin everything in this country          Ethnicity   \n",
+       "2                women are stupid and should stay quiet             Gender   \n",
+       "3    muslims like you are the problem with this country           Religion   \n",
+       "4  the project update was sent to the team this morning  Not Cyberbullying   \n",
+       "\n",
+       "   confidence  token_count  \\\n",
+       "0      0.9971            9   \n",
+       "1      0.9979           10   \n",
+       "2      0.9999            9   \n",
+       "3      1.0000           11   \n",
+       "4      0.9999           12   \n",
+       "\n",
+       "                                                                 top_3  \\\n",
+       "0       [Age (0.9971), Not Cyberbullying (0.0014), Ethnicity (0.0009)]   \n",
+       "1  [Ethnicity (0.9979), Not Cyberbullying (0.0013), Religion (0.0007)]   \n",
+       "2    [Gender (0.9999), Not Cyberbullying (0.0001), Ethnicity (0.0000)]   \n",
+       "3        [Religion (1.0000), Not Cyberbullying (0.0000), Age (0.0000)]   \n",
+       "4     [Not Cyberbullying (0.9999), Gender (0.0000), Religion (0.0000)]   \n",
+       "\n",
+       "                                                                                             probabilities  \n",
+       "0  {'Age': 0.9971, 'Ethnicity': 0.0009, 'Gender': 0.0003, 'Not Cyberbullying': 0.0014, 'Religion': 0.0003}  \n",
+       "1     {'Age': 0.0, 'Ethnicity': 0.9979, 'Gender': 0.0001, 'Not Cyberbullying': 0.0013, 'Religion': 0.0007}  \n",
+       "2           {'Age': 0.0, 'Ethnicity': 0.0, 'Gender': 0.9999, 'Not Cyberbullying': 0.0001, 'Religion': 0.0}  \n",
+       "3                 {'Age': 0.0, 'Ethnicity': 0.0, 'Gender': 0.0, 'Not Cyberbullying': 0.0, 'Religion': 1.0}  \n",
+       "4              {'Age': 0.0, 'Ethnicity': 0.0, 'Gender': 0.0, 'Not Cyberbullying': 0.9999, 'Religion': 0.0}  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "example_sentences = [\n",
+    "    \"go do your homework you stupid child\",\n",
+    "    \"immigrants like you ruin everything in this country\",\n",
+    "    \"women are stupid and should stay quiet\",\n",
+    "    \"muslims like you are the problem with this country\",\n",
+    "    \"the project update was sent to the team this morning\",\n",
+    "]\n",
+    "\n",
+    "results = predict_texts(example_sentences)\n",
+    "results\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "diagnostic-code",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Diagnostic: sample bank produced 5 distinct labels across 5 examples.\n"
+     ]
+    }
+   ],
+   "source": [
+    "distinct_labels = results[\"predicted_label\"].nunique()\n",
+    "if distinct_labels == 1:\n",
+    "    print(\n",
+    "        \"Warning: all sample predictions collapsed to one label. \"\n",
+    "        \"That usually means the sample bank is too weak for this checkpoint or the export should be re-verified.\"\n",
+    "    )\n",
+    "else:\n",
+    "    print(f\"Diagnostic: sample bank produced {distinct_labels} distinct labels across {len(results)} examples.\")\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "is_local": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dc0513f2640d90a5a8eeb288dbfef0a4bc3ddb6aa614bbd1d3090e5d0875c194
+size 5201