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learningmachineaz
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cirtdan-azerbaijani-chatbot

Text2Text Generation
Transformers
Safetensors
Azerbaijani
mt5
chatbot
azerbaijani
question-answering
Inference Endpoints
Model card Files Community
renatk commited on
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Add example notebook

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  2. README.md +3 -0
Cirtdan.ipynb ADDED
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969
+ "_model_name": "DescriptionStyleModel",
970
+ "_view_count": null,
971
+ "_view_module": "@jupyter-widgets/base",
972
+ "_view_module_version": "1.2.0",
973
+ "_view_name": "StyleView",
974
+ "description_width": ""
975
+ }
976
+ }
977
+ }
978
+ }
979
+ },
980
+ "cells": [
981
+ {
982
+ "cell_type": "code",
983
+ "source": [
984
+ "!pip install huggingface_hub transformers transformers[sentencepiece]"
985
+ ],
986
+ "metadata": {
987
+ "colab": {
988
+ "base_uri": "https://localhost:8080/"
989
+ },
990
+ "id": "_rbUD_747eUJ",
991
+ "outputId": "17e5ae7b-4c79-439d-b651-86b56da00194"
992
+ },
993
+ "execution_count": 1,
994
+ "outputs": [
995
+ {
996
+ "output_type": "stream",
997
+ "name": "stdout",
998
+ "text": [
999
+ "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.10/dist-packages (0.19.3)\n",
1000
+ "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.35.2)\n",
1001
+ "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (3.13.1)\n",
1002
+ "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2023.6.0)\n",
1003
+ "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2.31.0)\n",
1004
+ "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.66.1)\n",
1005
+ "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (6.0.1)\n",
1006
+ "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.5.0)\n",
1007
+ "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (23.2)\n",
1008
+ "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.23.5)\n",
1009
+ "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.6.3)\n",
1010
+ "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.15.0)\n",
1011
+ "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.0)\n",
1012
+ "Requirement already satisfied: sentencepiece!=0.1.92,>=0.1.91 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.1.99)\n",
1013
+ "Requirement already satisfied: protobuf in /usr/local/lib/python3.10/dist-packages (from transformers) (3.20.3)\n",
1014
+ "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.3.2)\n",
1015
+ "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.4)\n",
1016
+ "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2.0.7)\n",
1017
+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2023.7.22)\n"
1018
+ ]
1019
+ }
1020
+ ]
1021
+ },
1022
+ {
1023
+ "cell_type": "code",
1024
+ "source": [
1025
+ "from huggingface_hub import notebook_login\n",
1026
+ "notebook_login()"
1027
+ ],
1028
+ "metadata": {
1029
+ "colab": {
1030
+ "base_uri": "https://localhost:8080/",
1031
+ "height": 145,
1032
+ "referenced_widgets": [
1033
+ "3de99e8d7a5d4cbfab93a6609fadb166",
1034
+ "ac054215310a44edb3f45bf8d257b830",
1035
+ "e74bfb50dff24f0d8790f213648f9f11",
1036
+ "1aa92ee0539d4187b93e375ec07078c7",
1037
+ "a746790e9a1144f5b7dd4f239a890a7f",
1038
+ "213610d83c704346bf9ff8d8558ab5c8",
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+ "3ef7ef8123884d58b5c222245d84b3de",
1040
+ "7326f47690464dbb8ff116d951b2c029",
1041
+ "d230346a0596443f8e2cd0beb90132e0",
1042
+ "089fcdf6573f4c1a84ac45cc524714b4",
1043
+ "8e4205306ddd408284260ef13d23a11a",
1044
+ "fbb582816c6f4c6b80cea28c03986f54",
1045
+ "c5526296e5df4a638c9ea0407fb5fe1f",
1046
+ "1b66175f2ab64c1a962587f4ddcf32ab",
1047
+ "01a7e38799e349ef9d6e778cc9c20cca",
1048
+ "04ebfe640ac443c2909a01a746f4f50f",
1049
+ "206a605c0d8e48818a8bd641b30a41fa",
1050
+ "9b2f397e71214030a79736ae79692948",
1051
+ "e94d5db988a14d5b9435fc1c8d93ae77",
1052
+ "de9523dfa5134c1db057696555d45468",
1053
+ "920b8b5f45d04be3be5f85ceaccd4cf0",
1054
+ "ceb10fa240944d23b03856384a4685e7",
1055
+ "a25b243626574c7a87b09a60b0eb355f",
1056
+ "7da3a37544bc4adf82324109e6d9582e",
1057
+ "587e87d6a61944da941c7cd2368c701e",
1058
+ "6c24f9d01ee24347a5a1f6a8c1495bf1",
1059
+ "addbb553c7a04933874b33aadda425dc",
1060
+ "a2cab6a9a26945e78e30a6df23da8dc2",
1061
+ "f4d326dd84a1409c9b75071a70619098",
1062
+ "3240097357d24d54977bf06239006f44",
1063
+ "f913dbb00eed46b7a3e431d21ae36061",
1064
+ "57e9878a8e7d4c879a028c23884351a2"
1065
+ ]
1066
+ },
1067
+ "id": "kgICqyWk7yyS",
1068
+ "outputId": "ba2da87e-8a04-48b1-97c9-1da10a834653"
1069
+ },
1070
+ "execution_count": 2,
1071
+ "outputs": [
1072
+ {
1073
+ "output_type": "display_data",
1074
+ "data": {
1075
+ "text/plain": [
1076
+ "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
1077
+ ],
1078
+ "application/vnd.jupyter.widget-view+json": {
1079
+ "version_major": 2,
1080
+ "version_minor": 0,
1081
+ "model_id": "3de99e8d7a5d4cbfab93a6609fadb166"
1082
+ }
1083
+ },
1084
+ "metadata": {}
1085
+ }
1086
+ ]
1087
+ },
1088
+ {
1089
+ "cell_type": "code",
1090
+ "execution_count": 5,
1091
+ "metadata": {
1092
+ "colab": {
1093
+ "base_uri": "https://localhost:8080/"
1094
+ },
1095
+ "id": "gRNML7M21Nmn",
1096
+ "outputId": "0233d826-96a1-406f-b927-98c215e086f2"
1097
+ },
1098
+ "outputs": [
1099
+ {
1100
+ "output_type": "execute_result",
1101
+ "data": {
1102
+ "text/plain": [
1103
+ "MT5ForConditionalGeneration(\n",
1104
+ " (shared): Embedding(250112, 1024)\n",
1105
+ " (encoder): MT5Stack(\n",
1106
+ " (embed_tokens): Embedding(250112, 1024)\n",
1107
+ " (block): ModuleList(\n",
1108
+ " (0): MT5Block(\n",
1109
+ " (layer): ModuleList(\n",
1110
+ " (0): MT5LayerSelfAttention(\n",
1111
+ " (SelfAttention): MT5Attention(\n",
1112
+ " (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
1113
+ " (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
1114
+ " (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
1115
+ " (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
1116
+ " (relative_attention_bias): Embedding(32, 16)\n",
1117
+ " )\n",
1118
+ " (layer_norm): MT5LayerNorm()\n",
1119
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
1120
+ " )\n",
1121
+ " (1): MT5LayerFF(\n",
1122
+ " (DenseReluDense): MT5DenseGatedActDense(\n",
1123
+ " (wi_0): Linear(in_features=1024, out_features=2816, bias=False)\n",
1124
+ " (wi_1): Linear(in_features=1024, out_features=2816, bias=False)\n",
1125
+ " (wo): Linear(in_features=2816, out_features=1024, bias=False)\n",
1126
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
1127
+ " (act): NewGELUActivation()\n",
1128
+ " )\n",
1129
+ " (layer_norm): MT5LayerNorm()\n",
1130
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
1131
+ " )\n",
1132
+ " )\n",
1133
+ " )\n",
1134
+ " (1-23): 23 x MT5Block(\n",
1135
+ " (layer): ModuleList(\n",
1136
+ " (0): MT5LayerSelfAttention(\n",
1137
+ " (SelfAttention): MT5Attention(\n",
1138
+ " (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
1139
+ " (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
1140
+ " (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
1141
+ " (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
1142
+ " )\n",
1143
+ " (layer_norm): MT5LayerNorm()\n",
1144
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
1145
+ " )\n",
1146
+ " (1): MT5LayerFF(\n",
1147
+ " (DenseReluDense): MT5DenseGatedActDense(\n",
1148
+ " (wi_0): Linear(in_features=1024, out_features=2816, bias=False)\n",
1149
+ " (wi_1): Linear(in_features=1024, out_features=2816, bias=False)\n",
1150
+ " (wo): Linear(in_features=2816, out_features=1024, bias=False)\n",
1151
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
1152
+ " (act): NewGELUActivation()\n",
1153
+ " )\n",
1154
+ " (layer_norm): MT5LayerNorm()\n",
1155
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
1156
+ " )\n",
1157
+ " )\n",
1158
+ " )\n",
1159
+ " )\n",
1160
+ " (final_layer_norm): MT5LayerNorm()\n",
1161
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
1162
+ " )\n",
1163
+ " (decoder): MT5Stack(\n",
1164
+ " (embed_tokens): Embedding(250112, 1024)\n",
1165
+ " (block): ModuleList(\n",
1166
+ " (0): MT5Block(\n",
1167
+ " (layer): ModuleList(\n",
1168
+ " (0): MT5LayerSelfAttention(\n",
1169
+ " (SelfAttention): MT5Attention(\n",
1170
+ " (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
1171
+ " (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
1172
+ " (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
1173
+ " (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
1174
+ " (relative_attention_bias): Embedding(32, 16)\n",
1175
+ " )\n",
1176
+ " (layer_norm): MT5LayerNorm()\n",
1177
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
1178
+ " )\n",
1179
+ " (1): MT5LayerCrossAttention(\n",
1180
+ " (EncDecAttention): MT5Attention(\n",
1181
+ " (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
1182
+ " (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
1183
+ " (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
1184
+ " (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
1185
+ " )\n",
1186
+ " (layer_norm): MT5LayerNorm()\n",
1187
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
1188
+ " )\n",
1189
+ " (2): MT5LayerFF(\n",
1190
+ " (DenseReluDense): MT5DenseGatedActDense(\n",
1191
+ " (wi_0): Linear(in_features=1024, out_features=2816, bias=False)\n",
1192
+ " (wi_1): Linear(in_features=1024, out_features=2816, bias=False)\n",
1193
+ " (wo): Linear(in_features=2816, out_features=1024, bias=False)\n",
1194
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
1195
+ " (act): NewGELUActivation()\n",
1196
+ " )\n",
1197
+ " (layer_norm): MT5LayerNorm()\n",
1198
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
1199
+ " )\n",
1200
+ " )\n",
1201
+ " )\n",
1202
+ " (1-23): 23 x MT5Block(\n",
1203
+ " (layer): ModuleList(\n",
1204
+ " (0): MT5LayerSelfAttention(\n",
1205
+ " (SelfAttention): MT5Attention(\n",
1206
+ " (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
1207
+ " (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
1208
+ " (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
1209
+ " (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
1210
+ " )\n",
1211
+ " (layer_norm): MT5LayerNorm()\n",
1212
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
1213
+ " )\n",
1214
+ " (1): MT5LayerCrossAttention(\n",
1215
+ " (EncDecAttention): MT5Attention(\n",
1216
+ " (q): Linear(in_features=1024, out_features=1024, bias=False)\n",
1217
+ " (k): Linear(in_features=1024, out_features=1024, bias=False)\n",
1218
+ " (v): Linear(in_features=1024, out_features=1024, bias=False)\n",
1219
+ " (o): Linear(in_features=1024, out_features=1024, bias=False)\n",
1220
+ " )\n",
1221
+ " (layer_norm): MT5LayerNorm()\n",
1222
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
1223
+ " )\n",
1224
+ " (2): MT5LayerFF(\n",
1225
+ " (DenseReluDense): MT5DenseGatedActDense(\n",
1226
+ " (wi_0): Linear(in_features=1024, out_features=2816, bias=False)\n",
1227
+ " (wi_1): Linear(in_features=1024, out_features=2816, bias=False)\n",
1228
+ " (wo): Linear(in_features=2816, out_features=1024, bias=False)\n",
1229
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
1230
+ " (act): NewGELUActivation()\n",
1231
+ " )\n",
1232
+ " (layer_norm): MT5LayerNorm()\n",
1233
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
1234
+ " )\n",
1235
+ " )\n",
1236
+ " )\n",
1237
+ " )\n",
1238
+ " (final_layer_norm): MT5LayerNorm()\n",
1239
+ " (dropout): Dropout(p=0.1, inplace=False)\n",
1240
+ " )\n",
1241
+ " (lm_head): Linear(in_features=1024, out_features=250112, bias=False)\n",
1242
+ ")"
1243
+ ]
1244
+ },
1245
+ "metadata": {},
1246
+ "execution_count": 5
1247
+ }
1248
+ ],
1249
+ "source": [
1250
+ "import torch\n",
1251
+ "from transformers import AutoTokenizer, MT5ForConditionalGeneration\n",
1252
+ "\n",
1253
+ "model_name = 'learningmachineaz/cirtdan-azerbaijani-chatbot'\n",
1254
+ "\n",
1255
+ "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
1256
+ "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
1257
+ "model = MT5ForConditionalGeneration.from_pretrained(model_name).to(device)\n",
1258
+ "model.eval()"
1259
+ ]
1260
+ },
1261
+ {
1262
+ "cell_type": "code",
1263
+ "source": [
1264
+ "prompt = \"<SC6>Sən bir oğlansan, müxtəlif məsələlər üzrə məsləhətçi. Çox ağıllısan. Həmsöhbətinə kömək etmək istəyirəm. Dialoqu davam et:\\nHəmsöhbət: salam! necəsən?\\nSən: <extra_id_0>\"\n",
1265
+ "\n",
1266
+ "input_ids = tokenizer.encode(prompt, return_tensors='pt').to(device)\n",
1267
+ "\n",
1268
+ "out_ids = model.generate(\n",
1269
+ " input_ids=input_ids,\n",
1270
+ " do_sample=True,\n",
1271
+ " temperature=0.9,\n",
1272
+ " max_new_tokens=128,\n",
1273
+ " top_p=0.85,\n",
1274
+ " top_k=2,\n",
1275
+ " repetition_penalty=1.2\n",
1276
+ ")\n",
1277
+ "\n",
1278
+ "output = tokenizer.decode(out_ids[0][1:])\n",
1279
+ "\n",
1280
+ "if '</s>' in output:\n",
1281
+ " output = output[:output.find('</s>')].strip()\n",
1282
+ "\n",
1283
+ "output = output.replace('<extra_id_0>', '').strip()\n",
1284
+ "output = output.split('Həmsöhbət')[0].strip()\n",
1285
+ "print(output)"
1286
+ ],
1287
+ "metadata": {
1288
+ "colab": {
1289
+ "base_uri": "https://localhost:8080/"
1290
+ },
1291
+ "id": "IVwZ1iRS1TSc",
1292
+ "outputId": "52878039-1719-4284-ec3b-f79df04629ea"
1293
+ },
1294
+ "execution_count": 7,
1295
+ "outputs": [
1296
+ {
1297
+ "output_type": "stream",
1298
+ "name": "stdout",
1299
+ "text": [
1300
+ "Salam.\n"
1301
+ ]
1302
+ }
1303
+ ]
1304
+ }
1305
+ ]
1306
+ }
README.md CHANGED
@@ -68,6 +68,9 @@ Cırtdan: Bəli, yağış yağdı.
68
 
69
  ## INFERENCE EXAMPLE:
70
 
 
 
 
71
  ```python
72
  import torch
73
  from transformers import AutoTokenizer, MT5ForConditionalGeneration
 
68
 
69
  ## INFERENCE EXAMPLE:
70
 
71
+ You need to install sentencepiece.
72
+ > pip install transformers[sentencepiece]
73
+
74
  ```python
75
  import torch
76
  from transformers import AutoTokenizer, MT5ForConditionalGeneration