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+ "source": [
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+ "from datasets import load_dataset, concatenate_datasets, DatasetDict\n",
2070
+ "import os\n",
2071
+ "\n",
2072
+ "\n",
2073
+ "lst = ['ar', 'de', 'en', 'es', 'fr', 'ja', 'pt', 'zh']\n",
2074
+ "\n",
2075
+ "lst_train_ds = []\n",
2076
+ "lst_test_ds = []\n",
2077
+ "lst_val_ds = []\n",
2078
+ "\n",
2079
+ "for i in lst:\n",
2080
+ "\tds = load_dataset(\"dstc12/bot_adversarial_dialogue\", i)\n",
2081
+ "\tds2 = load_dataset(\"dstc12/dialogue_safety\", i)\n",
2082
+ "\tds3 = load_dataset(\"dstc12/ProsocialDialog\", i)\n",
2083
+ "\tif i == 'pt':\n",
2084
+ "\t\tds['train'] = concatenate_datasets([ds2['train'], ds3['train']]) # Remove pt training bot_adversarial_dialogue\n",
2085
+ "\telse:\n",
2086
+ "\t\tds['train'] = concatenate_datasets([ds['train'], ds2['train'], ds3['train']])\n",
2087
+ "\tds['test'] = concatenate_datasets([ds['test'], ds2['test'], ds3['test']])\n",
2088
+ "\tds['val'] = concatenate_datasets([ds['val'], ds2['val'], ds3['val']])\n",
2089
+ "\tlst_train_ds.append(ds['train'])\n",
2090
+ "\tlst_test_ds.append(ds['test'])\n",
2091
+ "\tlst_val_ds.append(ds['val'])\n",
2092
+ "\n",
2093
+ "concated_train_ds = concatenate_datasets(lst_train_ds)\n",
2094
+ "concated_test_ds = concatenate_datasets(lst_test_ds)\n",
2095
+ "concated_val_ds = concatenate_datasets(lst_val_ds)"
2096
+ ]
2097
+ },
2098
+ {
2099
+ "cell_type": "code",
2100
+ "execution_count": 2,
2101
+ "id": "576bced2-76db-41fa-8775-ca0d57eaf1e2",
2102
+ "metadata": {},
2103
+ "outputs": [],
2104
+ "source": [
2105
+ "final_ds = DatasetDict({\n",
2106
+ " 'train': concated_train_ds,\n",
2107
+ " 'test': concated_test_ds,\n",
2108
+ " 'val': concated_val_ds\n",
2109
+ "})"
2110
+ ]
2111
+ },
2112
+ {
2113
+ "cell_type": "code",
2114
+ "execution_count": 3,
2115
+ "id": "7b6e0708-e02a-4a8c-bbfa-4db3984f787e",
2116
+ "metadata": {},
2117
+ "outputs": [
2118
+ {
2119
+ "data": {
2120
+ "text/plain": [
2121
+ "DatasetDict({\n",
2122
+ " train: Dataset({\n",
2123
+ " features: ['context', 'response', 'safety_label', 'metadata'],\n",
2124
+ " num_rows: 1191130\n",
2125
+ " })\n",
2126
+ " test: Dataset({\n",
2127
+ " features: ['context', 'response', 'safety_label', 'metadata'],\n",
2128
+ " num_rows: 236048\n",
2129
+ " })\n",
2130
+ " val: Dataset({\n",
2131
+ " features: ['context', 'response', 'safety_label', 'metadata'],\n",
2132
+ " num_rows: 233176\n",
2133
+ " })\n",
2134
+ "})"
2135
+ ]
2136
+ },
2137
+ "execution_count": 3,
2138
+ "metadata": {},
2139
+ "output_type": "execute_result"
2140
+ }
2141
+ ],
2142
+ "source": [
2143
+ "final_ds"
2144
+ ]
2145
+ },
2146
+ {
2147
+ "cell_type": "code",
2148
+ "execution_count": 4,
2149
+ "id": "3bf964cc-0509-4377-a51c-a99445982535",
2150
+ "metadata": {},
2151
+ "outputs": [
2152
+ {
2153
+ "data": {
2154
+ "application/vnd.jupyter.widget-view+json": {
2155
+ "model_id": "e6c0b5368db14474ac7adf7a47f63c1c",
2156
+ "version_major": 2,
2157
+ "version_minor": 0
2158
+ },
2159
+ "text/plain": [
2160
+ "Map (num_proc=64): 0%| | 0/1191130 [00:00<?, ? examples/s]"
2161
+ ]
2162
+ },
2163
+ "metadata": {},
2164
+ "output_type": "display_data"
2165
+ },
2166
+ {
2167
+ "data": {
2168
+ "application/vnd.jupyter.widget-view+json": {
2169
+ "model_id": "b1776d8d23ff421da84fd82859d8a77e",
2170
+ "version_major": 2,
2171
+ "version_minor": 0
2172
+ },
2173
+ "text/plain": [
2174
+ "Map (num_proc=64): 0%| | 0/236048 [00:00<?, ? examples/s]"
2175
+ ]
2176
+ },
2177
+ "metadata": {},
2178
+ "output_type": "display_data"
2179
+ },
2180
+ {
2181
+ "data": {
2182
+ "application/vnd.jupyter.widget-view+json": {
2183
+ "model_id": "76cf712010134411898a0858a18012fa",
2184
+ "version_major": 2,
2185
+ "version_minor": 0
2186
+ },
2187
+ "text/plain": [
2188
+ "Map (num_proc=64): 0%| | 0/233176 [00:00<?, ? examples/s]"
2189
+ ]
2190
+ },
2191
+ "metadata": {},
2192
+ "output_type": "display_data"
2193
+ },
2194
+ {
2195
+ "data": {
2196
+ "text/plain": [
2197
+ "DatasetDict({\n",
2198
+ " train: Dataset({\n",
2199
+ " features: ['context', 'response', 'safety_label', 'metadata', 'instruction', 'input', 'output', 'system', 'history'],\n",
2200
+ " num_rows: 1191130\n",
2201
+ " })\n",
2202
+ " test: Dataset({\n",
2203
+ " features: ['context', 'response', 'safety_label', 'metadata', 'instruction', 'input', 'output', 'system', 'history'],\n",
2204
+ " num_rows: 236048\n",
2205
+ " })\n",
2206
+ " val: Dataset({\n",
2207
+ " features: ['context', 'response', 'safety_label', 'metadata', 'instruction', 'input', 'output', 'system', 'history'],\n",
2208
+ " num_rows: 233176\n",
2209
+ " })\n",
2210
+ "})"
2211
+ ]
2212
+ },
2213
+ "execution_count": 4,
2214
+ "metadata": {},
2215
+ "output_type": "execute_result"
2216
+ }
2217
+ ],
2218
+ "source": [
2219
+ "def process_data(data):\n",
2220
+ "\tres = {}\n",
2221
+ "\tif len(data['context']) % 2 == 0:\n",
2222
+ "\t\tres['instruction'] = \"Check if there is unsafe content in 'user' messages in the conversation.\"\n",
2223
+ "\t\tres['input'] = data['response']\n",
2224
+ "\t\tres['output'] = 'safe' if data['safety_label'] == 0 else 'unsafe'\n",
2225
+ "\t\tres['system'] = \"\"\n",
2226
+ "\t\tres['history'] = [data['context'][i:i+2] for i in range(0, len(data['context']), 2)]\n",
2227
+ "\telse:\n",
2228
+ "\t\tres['instruction'] = \"Check if there is unsafe content in 'assistant' messages in the conversation.\"\n",
2229
+ "\t\tres['input'] = \"\"\n",
2230
+ "\t\tdata['context'].append(data['response'])\n",
2231
+ "\t\tres['output'] = 'safe' if data['safety_label'] == 0 else 'unsafe'\n",
2232
+ "\t\tres['system'] = \"\"\n",
2233
+ "\t\tres['history'] = [data['context'][i:i+2] for i in range(0, len(data['context']), 2)]\n",
2234
+ "\treturn res\n",
2235
+ "\n",
2236
+ "processed_ds = final_ds.map(function=process_data, num_proc=64)\n",
2237
+ "processed_ds"
2238
+ ]
2239
+ },
2240
+ {
2241
+ "cell_type": "code",
2242
+ "execution_count": 8,
2243
+ "id": "8f1cc915-7221-44c5-a7b9-7a30ac238a5c",
2244
+ "metadata": {},
2245
+ "outputs": [
2246
+ {
2247
+ "data": {
2248
+ "text/plain": [
2249
+ "{'context': ['مرحبًا، سعيد بلقائك!',\n",
2250
+ " 'وأنت أيضًا! أنا آسف لسماع عن والدك. فقدت ولدي عندما كنت صغيرًا أيضًا.'],\n",
2251
+ " 'response': 'لم أفقد والدي. كلا والدي على قيد الحياة.',\n",
2252
+ " 'safety_label': 0,\n",
2253
+ " 'metadata': \"episode_done:True\\tspeaker_to_eval:human\\tbot_persona:your persona: i've a lot of allergies.\\\\nyour persona: my dad died when i was in high school.\\n\",\n",
2254
+ " 'instruction': \"Check if there is unsafe content in 'user' messages in the conversation.\",\n",
2255
+ " 'input': 'لم أفقد والدي. كلا والدي على قيد الحياة.',\n",
2256
+ " 'output': 'safe',\n",
2257
+ " 'system': '',\n",
2258
+ " 'history': [['مرحبًا، سعيد بلقائك!',\n",
2259
+ " 'وأنت أيضًا! أنا آسف لسماع عن والدك. فقدت ولدي عندما كنت صغيرًا أيضًا.']]}"
2260
+ ]
2261
+ },
2262
+ "execution_count": 8,
2263
+ "metadata": {},
2264
+ "output_type": "execute_result"
2265
+ }
2266
+ ],
2267
+ "source": [
2268
+ "processed_ds['train'][2]"
2269
+ ]
2270
+ },
2271
+ {
2272
+ "cell_type": "code",
2273
+ "execution_count": 10,
2274
+ "id": "7bccc0aa-d6df-41b5-a321-f0967c19c4b8",
2275
+ "metadata": {},
2276
+ "outputs": [
2277
+ {
2278
+ "data": {
2279
+ "application/vnd.jupyter.widget-view+json": {
2280
+ "model_id": "2eb0f0d0ffda4fe8a386cc246db29efc",
2281
+ "version_major": 2,
2282
+ "version_minor": 0
2283
+ },
2284
+ "text/plain": [
2285
+ " 0%| | 0/233176 [00:00<?, ?it/s]"
2286
+ ]
2287
+ },
2288
+ "metadata": {},
2289
+ "output_type": "display_data"
2290
+ }
2291
+ ],
2292
+ "source": [
2293
+ "from tqdm.notebook import tqdm\n",
2294
+ "\n",
2295
+ "lst_train = []\n",
2296
+ "lst_test = []\n",
2297
+ "lst_val = []\n",
2298
+ "\n",
2299
+ "for i in tqdm(processed_ds['train']):\n",
2300
+ " lst_train.append(\n",
2301
+ " {\n",
2302
+ " \"instruction\": i['instruction'],\n",
2303
+ " \"input\": i['input'],\n",
2304
+ " \"output\": i['output'],\n",
2305
+ " \"system\": i['system'],\n",
2306
+ " \"history\": i['history']\n",
2307
+ " }\n",
2308
+ " )\n",
2309
+ "\n",
2310
+ "for i in tqdm(processed_ds['test']):\n",
2311
+ " lst_test.append(\n",
2312
+ " {\n",
2313
+ " \"instruction\": i['instruction'],\n",
2314
+ " \"input\": i['input'],\n",
2315
+ " \"output\": i['output'],\n",
2316
+ " \"system\": i['system'],\n",
2317
+ " \"history\": i['history']\n",
2318
+ " }\n",
2319
+ " )\n",
2320
+ "\n",
2321
+ "for i in tqdm(processed_ds['val']):\n",
2322
+ " lst_val.append(\n",
2323
+ " {\n",
2324
+ " \"instruction\": i['instruction'],\n",
2325
+ " \"input\": i['input'],\n",
2326
+ " \"output\": i['output'],\n",
2327
+ " \"system\": i['system'],\n",
2328
+ " \"history\": i['history']\n",
2329
+ " }\n",
2330
+ " )"
2331
+ ]
2332
+ },
2333
+ {
2334
+ "cell_type": "code",
2335
+ "execution_count": 11,
2336
+ "id": "de36eff9-6f6d-4f7d-b00b-12c57ab191ba",
2337
+ "metadata": {},
2338
+ "outputs": [
2339
+ {
2340
+ "data": {
2341
+ "text/plain": [
2342
+ "(1191130, 236048, 233176)"
2343
+ ]
2344
+ },
2345
+ "execution_count": 11,
2346
+ "metadata": {},
2347
+ "output_type": "execute_result"
2348
+ }
2349
+ ],
2350
+ "source": [
2351
+ "len(lst_train), len(lst_test), len(lst_val)"
2352
+ ]
2353
+ },
2354
+ {
2355
+ "cell_type": "code",
2356
+ "execution_count": 12,
2357
+ "id": "6e6dd601-d6b1-46ee-80a9-92ed387cc976",
2358
+ "metadata": {},
2359
+ "outputs": [],
2360
+ "source": [
2361
+ "import json\n",
2362
+ "\n",
2363
+ "def dump_json_to_file(data, filename):\n",
2364
+ " with open(filename, 'w', encoding='utf-8') as f:\n",
2365
+ " json.dump(data, f, ensure_ascii=False, indent=4)\n",
2366
+ "\n",
2367
+ "dump_json_to_file(lst_train, \"train.json\")\n",
2368
+ "dump_json_to_file(lst_test, \"test.json\")\n",
2369
+ "dump_json_to_file(lst_val, \"val.json\")\n"
2370
+ ]
2371
+ },
2372
+ {
2373
+ "cell_type": "code",
2374
+ "execution_count": 15,
2375
+ "id": "cce586ad-e315-45d9-9b6a-89a675ec680a",
2376
+ "metadata": {},
2377
+ "outputs": [],
2378
+ "source": [
2379
+ "import random\n",
2380
+ "\n",
2381
+ "random.shuffle(lst_train)\n",
2382
+ "random.shuffle(lst_test)\n",
2383
+ "random.shuffle(lst_val)\n"
2384
+ ]
2385
+ },
2386
+ {
2387
+ "cell_type": "code",
2388
+ "execution_count": 16,
2389
+ "id": "2bb17f1d-734b-4216-a8e3-c155bc1e7d9e",
2390
+ "metadata": {},
2391
+ "outputs": [],
2392
+ "source": [
2393
+ "dump_json_to_file(lst_train[:1000], \"train_sample.json\")\n",
2394
+ "dump_json_to_file(lst_test[:1000], \"test_sample.json\")\n",
2395
+ "dump_json_to_file(lst_val[:1000], \"val_sample.json\")"
2396
+ ]
2397
+ },
2398
+ {
2399
+ "cell_type": "code",
2400
+ "execution_count": null,
2401
+ "id": "ad821bb9-fb36-47ce-aea9-39a053f080ed",
2402
+ "metadata": {},
2403
+ "outputs": [
2404
+ {
2405
+ "data": {
2406
+ "application/vnd.jupyter.widget-view+json": {
2407
+ "model_id": "8b4e2c1fb8ee4cc7b8d4e76a9dacacf3",
2408
+ "version_major": 2,
2409
+ "version_minor": 0
2410
+ },
2411
+ "text/plain": [
2412
+ "train.json: 0%| | 0.00/887M [00:00<?, ?B/s]"
2413
+ ]
2414
+ },
2415
+ "metadata": {},
2416
+ "output_type": "display_data"
2417
+ }
2418
+ ],
2419
+ "source": [
2420
+ "from huggingface_hub import HfApi\n",
2421
+ "\n",
2422
+ "# Replace with your Hugging Face repo (public or private)\n",
2423
+ "repo_id = \"minhbui/test\"\n",
2424
+ "\n",
2425
+ "# File to upload\n",
2426
+ "file_path = \"train.json\" # Change this to the actual file\n",
2427
+ "\n",
2428
+ "\n",
2429
+ "# Upload the file\n",
2430
+ "api = HfApi()\n",
2431
+ "api.upload_file(\n",
2432
+ " path_or_fileobj=file_path,\n",
2433
+ " path_in_repo=\"./\"+file_path,\n",
2434
+ " repo_id=repo_id,\n",
2435
+ " repo_type=\"dataset\", # Use \"model\" if it's a model repo\n",
2436
+ ")"
2437
+ ]
2438
+ },
2439
+ {
2440
+ "cell_type": "code",
2441
+ "execution_count": null,
2442
+ "id": "d56d93e1-4c01-4057-ba1f-84bfbc37a0c9",
2443
+ "metadata": {},
2444
+ "outputs": [],
2445
+ "source": []
2446
+ }
2447
+ ],
2448
+ "metadata": {
2449
+ "kernelspec": {
2450
+ "display_name": "doan",
2451
+ "language": "python",
2452
+ "name": "doan"
2453
+ },
2454
+ "language_info": {
2455
+ "codemirror_mode": {
2456
+ "name": "ipython",
2457
+ "version": 3
2458
+ },
2459
+ "file_extension": ".py",
2460
+ "mimetype": "text/x-python",
2461
+ "name": "python",
2462
+ "nbconvert_exporter": "python",
2463
+ "pygments_lexer": "ipython3",
2464
+ "version": "3.10.16"
2465
+ }
2466
+ },
2467
+ "nbformat": 4,
2468
+ "nbformat_minor": 5
2469
+ }