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162 KiB
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162 KiB
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Instalacja pakietów"],"metadata":{"id":"ZXsOR6oJOJbd"}},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8l0hzptKNiZS","executionInfo":{"status":"ok","timestamp":1676652341429,"user_tz":-60,"elapsed":5296,"user":{"displayName":"Mikołaj Paterka","userId":"09090979117369578839"}},"outputId":"c8f3b15a-78e6-4b24-d7f8-7a76ef36cc76"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: transformers in /usr/local/lib/python3.8/dist-packages (4.26.1)\n","Requirement already satisfied: datasets in /usr/local/lib/python3.8/dist-packages (2.9.0)\n","Requirement already satisfied: torch in /usr/local/lib/python3.8/dist-packages (1.13.1+cu116)\n","Requirement already satisfied: sentencepiece in /usr/local/lib/python3.8/dist-packages (0.1.97)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (1.21.6)\n","Requirement already satisfied: huggingface-hub<1.0,>=0.11.0 in /usr/local/lib/python3.8/dist-packages (from transformers) (0.12.1)\n","Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.8/dist-packages (from transformers) (4.64.1)\n","Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from transformers) (2.25.1)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.8/dist-packages (from transformers) (6.0)\n","Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (2022.6.2)\n","Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.8/dist-packages (from transformers) (0.13.2)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from transformers) (3.9.0)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/dist-packages (from transformers) (23.0)\n","Requirement already satisfied: fsspec[http]>=2021.11.1 in /usr/local/lib/python3.8/dist-packages (from datasets) (2023.1.0)\n","Requirement already satisfied: pyarrow>=6.0.0 in /usr/local/lib/python3.8/dist-packages (from datasets) (9.0.0)\n","Requirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (from datasets) (1.3.5)\n","Requirement already satisfied: responses<0.19 in /usr/local/lib/python3.8/dist-packages (from datasets) (0.18.0)\n","Requirement already satisfied: xxhash in /usr/local/lib/python3.8/dist-packages (from datasets) (3.2.0)\n","Requirement already satisfied: dill<0.3.7 in /usr/local/lib/python3.8/dist-packages (from datasets) (0.3.6)\n","Requirement already satisfied: multiprocess in /usr/local/lib/python3.8/dist-packages (from datasets) (0.70.14)\n","Requirement already satisfied: aiohttp in /usr/local/lib/python3.8/dist-packages (from datasets) (3.8.3)\n","Requirement already satisfied: typing-extensions in /usr/local/lib/python3.8/dist-packages (from torch) (4.4.0)\n","Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (1.8.2)\n","Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (2.1.1)\n","Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (4.0.2)\n","Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (22.2.0)\n","Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (1.3.3)\n","Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (6.0.4)\n","Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (1.3.1)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (2.10)\n","Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (1.26.14)\n","Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (4.0.0)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (2022.12.7)\n","Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2.8.2)\n","Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2022.7.1)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n"]}],"source":["!pip install transformers datasets torch sentencepiece"]},{"cell_type":"markdown","source":["# Załadowanie datasetu"],"metadata":{"id":"dhN0rmb5Oi3d"}},{"cell_type":"code","source":["from datasets import load_dataset"],"metadata":{"id":"tnaDkwZ2Pbnn","executionInfo":{"status":"ok","timestamp":1676652341811,"user_tz":-60,"elapsed":389,"user":{"displayName":"Mikołaj Paterka","userId":"09090979117369578839"}}},"execution_count":3,"outputs":[]},{"cell_type":"code","source":["dataset = load_dataset(\"sms_spam\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":211,"referenced_widgets":["ba9d40cd07c34f4fb1f9fbd73092fbf6","9de313fab7ae49b080fb5192acb456c0","211c322124ff451c8d6cddbc842e650b","9e285551432c4b5daa6afa6049a0a88d","e355e0d5122a46d785cdc07117a66990","2db46abba29f4f9789de6fdbec1af7a0","9bff66fdf5c94944a63c5525685a71af","27f2295f6b0c4e2f97f511514901b60d","24dd9a57eb984bdaa61c77601a0f5052","752ab7a625c44966b4d0204355ed1d03","e575215f324e4f959cee7086a32c4039","7ae5fccd7dc442f0b6edf652210e8f45","a976451070bc43d3a5f2504e6b2441b6","842cb52b1c214db9a5704752497d5944","20dd392ebbbf4027827a722f1b5244de","b2f23e1760284b5f8b6428d499556825","dc6f0fbc82a84b6f9e01e085a66260b9","36540af07eb34debbc483e635a1aa978","67a513749d4d4e6f8fce80debaafe1a9","ab6920be0d57483db97d91d30b92bc0a","b3cd7dbebf6b488a8a4d7146e0dc89ec","620ed6142c3e4a96b49ce51dca45dbb4","c4ba45f6ee0e4edb9cb1ee26fadeb01a","f87e37e286a74e94adc773e74c2c09c9","c6ab86832ff94f818329bd34d6e6efe2","4c8fbd675bd24a279348316ad06b6676","abb70e15da2849edbc926078009f19de","e147057451f844de84f8c9eb7896550f","d9fd300cff5a4b5d97cddcb217677465","5e95b5a9e0724361a84b729b547ca3ad","148d65aedcb14ae59b2b79363ed919f7","37e3971106aa456981303debb9b4c225","0d66ab66ba6744f99ec4c63334eeb106","0ef3d6b2ea5542eea95c2a8058652cde","834d071dc2844826afe2cba68d7b7ee0","ddc9057d5fcb4f06ad92e89181850c2b","3d6f5d5597f3442ca8d50e82c37ea8f0","7c0f4fac68fc49f8b288d8348196ce73","7317d2612bf54f849071aa6c2846d2a0","a85008da935b414dba2c9cec5a81d998","7c15d362b51c4466a9d994208a1058b7","1e53fe4a87fc4ce3bdf2145fb1f1db7a","328ff44536394df59f5aad906b4ba472","0abafc781cd14f36af17144f84b4e0ee","b0e9810c15bf4877b12cbe5b2b391c11","b11ff7e8103d4f19808023ac61f1c495","a1e2e0b7b94b4c29b10e256d4665ac1c","f6308b3e42ee4759982e054f7f1fc13b","a525b423a38446e7acd4adf0806ce16b","d777deca350c43659ae1c9de6809f975","5b874a9c118b4aba94fb8df333829703","d907fcfb69d44b9585c46df7b97980cc","6b0be17848444747b51137f20854ad53","51f48056fa734f969b98d6779c75fda4","1a7a53fd227441e7bdcd31050b189a85","79b64a59aa904f46803c0013bfaa67c7","8c09a31ddcd94408baea9bb143b3608f","2af0dd8e534d4e25a89ac415d3f7a536","114db7aaead5484ba5379731f0f74de5","1f293d6ea070431e90f8ad1a79c42822","aed3d46ba465417cab5d75d09d395d54","5246a27a3ea94e9cb6d1baafe4a51cdd","2d5a8b6f1e70487781a88da6dc8b6aa5","90b9696a21794d99b9d4dcd34ec7866e","f1221b8b45f14027855dc31cbc932adc","1836db5096354e18810611ffd14c39b8"]},"id":"cCiAuRqrOkvV","executionInfo":{"status":"ok","timestamp":1676652344210,"user_tz":-60,"elapsed":2403,"user":{"displayName":"Mikołaj Paterka","userId":"09090979117369578839"}},"outputId":"e3b61204-b6b5-4d4e-f66f-48cde363888b"},"execution_count":4,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading builder script: 0%| | 0.00/3.21k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ba9d40cd07c34f4fb1f9fbd73092fbf6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading metadata: 0%| | 0.00/1.69k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7ae5fccd7dc442f0b6edf652210e8f45"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading readme: 0%| | 0.00/4.87k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c4ba45f6ee0e4edb9cb1ee26fadeb01a"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Downloading and preparing dataset sms_spam/plain_text to /root/.cache/huggingface/datasets/sms_spam/plain_text/1.0.0/53f051d3b5f62d99d61792c91acefe4f1577ad3e4c216fb0ad39e30b9f20019c...\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading data: 0%| | 0.00/203k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0ef3d6b2ea5542eea95c2a8058652cde"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split: 0%| | 0/5574 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b0e9810c15bf4877b12cbe5b2b391c11"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Dataset sms_spam downloaded and prepared to /root/.cache/huggingface/datasets/sms_spam/plain_text/1.0.0/53f051d3b5f62d99d61792c91acefe4f1577ad3e4c216fb0ad39e30b9f20019c. Subsequent calls will reuse this data.\n"]},{"output_type":"display_data","data":{"text/plain":[" 0%| | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"79b64a59aa904f46803c0013bfaa67c7"}},"metadata":{}}]},{"cell_type":"code","source":["dataset['train'][0]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JKFHPko3OnAV","executionInfo":{"status":"ok","timestamp":1676652344211,"user_tz":-60,"elapsed":14,"user":{"displayName":"Mikołaj Paterka","userId":"09090979117369578839"}},"outputId":"996c303d-4a72-4c59-8b4a-02bdd32396c9"},"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'sms': 'Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\\n',\n"," 'label': 0}"]},"metadata":{},"execution_count":5}]},{"cell_type":"markdown","source":["# Przygotowanie datasetu"],"metadata":{"id":"l140vJrgYxPr"}},{"cell_type":"code","source":["parsed_dataset = []\n","\n","for row in dataset['train']:\n"," text = row['sms'].replace(\"\\n\", \"\")\n"," new_row = {}\n"," new_row['sms'] = text\n"," if row['label'] == 0:\n"," new_row['label'] = \"False\"\n"," else:\n"," new_row['label'] = \"True\"\n"," parsed_dataset.append(new_row)\n","\n","parsed_dataset[0]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1boUF-YiY3_y","executionInfo":{"status":"ok","timestamp":1676652344211,"user_tz":-60,"elapsed":11,"user":{"displayName":"Mikołaj Paterka","userId":"09090979117369578839"}},"outputId":"ae00c79a-a17e-46e3-8cbd-2165042170d2"},"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'sms': 'Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...',\n"," 'label': 'False'}"]},"metadata":{},"execution_count":6}]},{"cell_type":"markdown","source":["# Tokenizer FLAN-T5"],"metadata":{"id":"O-J-jBDxPJcn"}},{"cell_type":"code","source":["from transformers import AutoTokenizer"],"metadata":{"id":"P23AYPX1PZ6g","executionInfo":{"status":"ok","timestamp":1676652347327,"user_tz":-60,"elapsed":3122,"user":{"displayName":"Mikołaj Paterka","userId":"09090979117369578839"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["tokenizer = AutoTokenizer.from_pretrained('google/flan-t5-base')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["130f4a8f140d43e9993f6f158d643f91","052168c2faa04049bec6605ec9fc654a","8baade75913f4bc28033f916db729edd","9032dbaf99f04ef8a410969b7345928f","d703073401d84414b90c3d71f9914e39","6178481579ef4b2086d730108c77132e","81761b679c9f462a907ee227ca4e0e3d","cad9c06510c44d76b8873ab1f1b9e0ce","da24d4f940734731980bbf4511b7f388","0137abcc3a8c4a7fbc77dfc578f8bd28","47340075461a44d981ecea3883273a3c","09f475e7464c4101a94dce60d034ac12","5244aabb420f4970954781124f07ee1c","c477c1291af84638a961dcf1d43a0eb5","9dd0b870a8da4380af8e15c6e4f02dae","d1c3f34e830841c39e7c86f754843903","e42f6346d52346779e2ec76061abf4db","6a53778d0eb041959e476ead69b761ad","4831844601e548849e3c3ae6c5c0f734","9ad9b94a8d5b41a99fae31fac4782dae","1a8502e4909b493e8a0015c9406250a2","faed3ee84d1649f6aee33545623c3414","bf8558287dc54fe19fd4eca5043dc182","bb3022078d654d90aad064587d5fcb81","79d8d0f473d54e518023d13b4896dcb0","1cdf0415e5234288a5ed6900a61bceb1","ef9cd89580534dea85fcd3ef8e129d37","0bd704ed93f249a882ea09a9b4760856","bb343e8421494468ba70a338e46ce97b","b5a1ca49058e4988a6a8f4f072bd8bb6","360ee70e58784c4ab7a08333afd4ff30","e51e0982800a4dc3bea1feeab7e3b5f1","72f4d1baccca4021b6392763c3f6068d","453344c1de7c4fef9ad94d31fd1c4972","afa1d46f350f439ea7371b29b11f12ee","29ce5457214f4b7cbac8a866ec805fa0","c9f71fbae55448d69d13216a4a88f040","fc0695782e4f4ddc8c3b9140bef9a482","0bf540f48dba48eaa2ef411032e8372c","bf7f88a03d1e4bb68ca7e3df611525ba","8d82e208f6b44e409050891ab76f1551","2ba133a583514a528fae49101dddd523","6e1f83ef1afc42e0a0d8562c1f871067","c0194dbf3d5842fa8c1437def7113be6"]},"id":"q5Jz0E_oPMBr","executionInfo":{"status":"ok","timestamp":1676652348258,"user_tz":-60,"elapsed":939,"user":{"displayName":"Mikołaj Paterka","userId":"09090979117369578839"}},"outputId":"a828b2ce-c419-49b0-dc1d-d6a3721021f4"},"execution_count":8,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading (…)okenizer_config.json: 0%| | 0.00/2.54k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"130f4a8f140d43e9993f6f158d643f91"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)\"spiece.model\";: 0%| | 0.00/792k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"09f475e7464c4101a94dce60d034ac12"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)/main/tokenizer.json: 0%| | 0.00/2.42M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"bf8558287dc54fe19fd4eca5043dc182"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)cial_tokens_map.json: 0%| | 0.00/2.20k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"453344c1de7c4fef9ad94d31fd1c4972"}},"metadata":{}}]},{"cell_type":"code","source":["sms = parsed_dataset[0]['sms']\n","print('Original: ', sms)\n","print('Tokenized: ', tokenizer.tokenize(sms))\n","print('Token IDs: ', tokenizer.convert_tokens_to_ids(tokenizer.tokenize(sms)))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dfxJQpoePsvI","executionInfo":{"status":"ok","timestamp":1676652348732,"user_tz":-60,"elapsed":477,"user":{"displayName":"Mikołaj Paterka","userId":"09090979117369578839"}},"outputId":"87fcb627-290c-4ebf-8712-53ff89d251ba"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["Original: Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\n","Tokenized: ['▁Go', '▁until', '▁jur', 'ong', '▁point', ',', '▁crazy', '.', '.', '▁Available', '▁only', '▁in', '▁bug', 'is', '▁', 'n', '▁great', '▁world', '▁la', '▁', 'e', '▁buffet', '...', '▁Cine', '▁there', '▁got', '▁', 'a', 'more', '▁wa', 't', '...']\n","Token IDs: [1263, 552, 10081, 2444, 500, 6, 6139, 5, 5, 8144, 163, 16, 8143, 159, 3, 29, 248, 296, 50, 3, 15, 15385, 233, 17270, 132, 530, 3, 9, 3706, 8036, 17, 233]\n"]}]},{"cell_type":"markdown","source":["# Few shot learning"],"metadata":{"id":"UpluhM8cU5Ir"}},{"cell_type":"code","source":["print(parsed_dataset[0]) #0\n","print(parsed_dataset[123]) #1\n","print(parsed_dataset[2000]) #0\n","print(parsed_dataset[3002]) #1"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7uNUkixPU85O","executionInfo":{"status":"ok","timestamp":1676652348733,"user_tz":-60,"elapsed":23,"user":{"displayName":"Mikołaj Paterka","userId":"09090979117369578839"}},"outputId":"b9db82da-7c31-4d59-b46f-8a49789e2d44"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["{'sms': 'Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...', 'label': 'False'}\n","{'sms': 'Todays Voda numbers ending 7548 are selected to receive a $350 award. If you have a match please call 08712300220 quoting claim code 4041 standard rates app', 'label': 'True'}\n","{'sms': \"LMAO where's your fish memory when I need it?\", 'label': 'False'}\n","{'sms': 'This message is free. Welcome to the new & improved Sex & Dogging club! To unsubscribe from this service reply STOP. msgs@150p 18+only', 'label': 'True'}\n"]}]},{"cell_type":"code","source":["non_spam_1 = \"Content of the text message: \" + parsed_dataset[0]['sms'] + \"\\nAnswer if this text message is spam or not: False\\n\\n\"\n","spam_1 = \"Content of the text message: \" + parsed_dataset[123]['sms'] + \"\\nAnswer if this text message is spam or not: True\\n\\n\"\n","non_spam_2 = \"Content of the text message: \" + parsed_dataset[2000]['sms'] + \"\\nAnswer if this text message is spam or not: False\\n\\n\"\n","spam_2 = \"Content of the text message: \" + parsed_dataset[3002]['sms'] + \"\\nAnswer if this text message is spam or not: True\\n\\n\"\n","\n","few_shot_prefix = non_spam_1 + spam_1 + non_spam_2 + spam_2 + \"Content of the text message: \"\n","print(few_shot_prefix)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lj0issBznZfK","executionInfo":{"status":"ok","timestamp":1676652348733,"user_tz":-60,"elapsed":16,"user":{"displayName":"Mikołaj Paterka","userId":"09090979117369578839"}},"outputId":"2012323d-eb35-4494-e8c6-6051963e9899"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["Content of the text message: Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\n","Answer if this text message is spam or not: False\n","\n","Content of the text message: Todays Voda numbers ending 7548 are selected to receive a $350 award. If you have a match please call 08712300220 quoting claim code 4041 standard rates app\n","Answer if this text message is spam or not: True\n","\n","Content of the text message: LMAO where's your fish memory when I need it?\n","Answer if this text message is spam or not: False\n","\n","Content of the text message: This message is free. Welcome to the new & improved Sex & Dogging club! To unsubscribe from this service reply STOP. msgs@150p 18+only\n","Answer if this text message is spam or not: True\n","\n","Content of the text message: \n"]}]},{"cell_type":"markdown","source":["# Load FLAN-T5 model"],"metadata":{"id":"okTx_ynMV0rH"}},{"cell_type":"code","source":["from transformers import AutoModelForSeq2SeqLM"],"metadata":{"id":"Eu-7Eed8WgN0","executionInfo":{"status":"ok","timestamp":1676652348734,"user_tz":-60,"elapsed":11,"user":{"displayName":"Mikołaj Paterka","userId":"09090979117369578839"}}},"execution_count":12,"outputs":[]},{"cell_type":"code","source":["model = AutoModelForSeq2SeqLM.from_pretrained('google/flan-t5-base')\n","\n","model.cuda()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["8b2028bc0dd34cd49ff063356924f2d1","b61f9f83affd4bffa20e35550e1dcf11","9402536c51f54eebb6f9d6ebff692ed3","27d09ed90c3048ef92deab33ed7b1b5c","fda0aada96374e159ddfad0831e795d5","81ea8a9d70a8416db4d60b4596e00f92","c2f2cf8567514612a788b4ade694daba","a771d23698b641e09bbfc51d004a7358","28ae83bc70ba41d7aad0f785d79e6600","032276c9596f4100965fb757c5741709","04be2018587746949e3def7c3a51d7a9","62e2e7c6fc834eca82be7f85c9fa9701","060b98b462b84394b4aa833f31abcc69","e572c1b7e70b4abc97e390ad78ac6f3f","3eb033bb16ce4534a7f7538c740c1d39","a38561dd1f81429d9292affd976070e0","2097b347701f4eda889728f50f62252f","3276c774bee745a791d14b3d7490cc8c","a47dba0fd1f04505927b837d252fe5fe","b4e93db0bd734f9ebccc281be866fede","6a8020dba5c94c69a4c39abc890b877e","20edf60841f54c5b9b5863a20a9ea0a5","6eb421544bdd4100b92e2dc12e783485","c3ee8e64d4024205896cb81a77d08733","29d772f88bc3404585377162d5762272","f7de2c8b9f1a4baf87a31cbadeb6424f","a8ea02156db1495999105c450d4172bf","d6136a686f1f4b7da80d48e12d0bc79c","a8e477092733491f9ece5528a272652d","a4f1cf289630449999dc9c279ad069aa","2aa24b5ff19844239e81903d07b732ab","ba343c49657048ad9cdd035090b9b590","691c2eaf31b84e9c89458b22160e4df5"]},"id":"JKv9O8kfV2zZ","executionInfo":{"status":"ok","timestamp":1676652375456,"user_tz":-60,"elapsed":26732,"user":{"displayName":"Mikołaj Paterka","userId":"09090979117369578839"}},"outputId":"7e410ecf-24f4-40fe-dd54-8c4ca1012f05"},"execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading (…)lve/main/config.json: 0%| | 0.00/1.40k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8b2028bc0dd34cd49ff063356924f2d1"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)\"pytorch_model.bin\";: 0%| | 0.00/990M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"62e2e7c6fc834eca82be7f85c9fa9701"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)neration_config.json: 0%| | 0.00/147 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6eb421544bdd4100b92e2dc12e783485"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["T5ForConditionalGeneration(\n"," (shared): Embedding(32128, 768)\n"," (encoder): T5Stack(\n"," (embed_tokens): Embedding(32128, 768)\n"," (block): ModuleList(\n"," (0): T5Block(\n"," (layer): ModuleList(\n"," (0): T5LayerSelfAttention(\n"," (SelfAttention): T5Attention(\n"," (q): Linear(in_features=768, out_features=768, bias=False)\n"," (k): Linear(in_features=768, out_features=768, bias=False)\n"," (v): Linear(in_features=768, out_features=768, bias=False)\n"," (o): Linear(in_features=768, out_features=768, bias=False)\n"," (relative_attention_bias): Embedding(32, 12)\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (1): T5LayerFF(\n"," (DenseReluDense): T5DenseGatedActDense(\n"," (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n"," (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n"," (wo): Linear(in_features=2048, out_features=768, bias=False)\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," (act): NewGELUActivation()\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," )\n"," )\n"," (1): T5Block(\n"," (layer): ModuleList(\n"," (0): T5LayerSelfAttention(\n"," (SelfAttention): T5Attention(\n"," (q): Linear(in_features=768, out_features=768, bias=False)\n"," (k): Linear(in_features=768, out_features=768, bias=False)\n"," (v): Linear(in_features=768, out_features=768, bias=False)\n"," (o): Linear(in_features=768, out_features=768, bias=False)\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (1): T5LayerFF(\n"," (DenseReluDense): T5DenseGatedActDense(\n"," (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n"," (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n"," (wo): Linear(in_features=2048, out_features=768, bias=False)\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," (act): NewGELUActivation()\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," )\n"," )\n"," (2): T5Block(\n"," (layer): ModuleList(\n"," (0): T5LayerSelfAttention(\n"," (SelfAttention): T5Attention(\n"," (q): Linear(in_features=768, out_features=768, bias=False)\n"," (k): Linear(in_features=768, out_features=768, bias=False)\n"," (v): Linear(in_features=768, out_features=768, bias=False)\n"," (o): Linear(in_features=768, out_features=768, bias=False)\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (1): T5LayerFF(\n"," (DenseReluDense): T5DenseGatedActDense(\n"," (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n"," (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n"," (wo): Linear(in_features=2048, out_features=768, bias=False)\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," (act): NewGELUActivation()\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," )\n"," )\n"," (3): T5Block(\n"," (layer): ModuleList(\n"," (0): T5LayerSelfAttention(\n"," (SelfAttention): T5Attention(\n"," (q): Linear(in_features=768, out_features=768, bias=False)\n"," (k): Linear(in_features=768, out_features=768, bias=False)\n"," (v): Linear(in_features=768, out_features=768, bias=False)\n"," (o): Linear(in_features=768, out_features=768, bias=False)\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (1): T5LayerFF(\n"," (DenseReluDense): T5DenseGatedActDense(\n"," (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n"," (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n"," (wo): Linear(in_features=2048, out_features=768, bias=False)\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," (act): NewGELUActivation()\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," )\n"," )\n"," (4): T5Block(\n"," (layer): ModuleList(\n"," (0): T5LayerSelfAttention(\n"," (SelfAttention): T5Attention(\n"," (q): Linear(in_features=768, out_features=768, bias=False)\n"," (k): Linear(in_features=768, out_features=768, bias=False)\n"," (v): Linear(in_features=768, out_features=768, bias=False)\n"," (o): Linear(in_features=768, out_features=768, bias=False)\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (1): T5LayerFF(\n"," (DenseReluDense): T5DenseGatedActDense(\n"," (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n"," (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n"," (wo): Linear(in_features=2048, out_features=768, bias=False)\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," (act): NewGELUActivation()\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," )\n"," )\n"," (5): T5Block(\n"," (layer): ModuleList(\n"," (0): T5LayerSelfAttention(\n"," (SelfAttention): T5Attention(\n"," (q): Linear(in_features=768, out_features=768, bias=False)\n"," (k): Linear(in_features=768, out_features=768, bias=False)\n"," (v): Linear(in_features=768, out_features=768, bias=False)\n"," (o): Linear(in_features=768, out_features=768, bias=False)\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (1): T5LayerFF(\n"," (DenseReluDense): T5DenseGatedActDense(\n"," (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n"," (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n"," (wo): Linear(in_features=2048, out_features=768, bias=False)\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," (act): NewGELUActivation()\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," )\n"," )\n"," (6): T5Block(\n"," (layer): ModuleList(\n"," (0): T5LayerSelfAttention(\n"," (SelfAttention): T5Attention(\n"," (q): Linear(in_features=768, out_features=768, bias=False)\n"," (k): Linear(in_features=768, out_features=768, bias=False)\n"," (v): Linear(in_features=768, out_features=768, bias=False)\n"," (o): Linear(in_features=768, out_features=768, bias=False)\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (1): T5LayerFF(\n"," (DenseReluDense): T5DenseGatedActDense(\n"," (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n"," (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n"," (wo): Linear(in_features=2048, out_features=768, bias=False)\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," (act): NewGELUActivation()\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," )\n"," )\n"," (7): T5Block(\n"," (layer): ModuleList(\n"," (0): T5LayerSelfAttention(\n"," (SelfAttention): T5Attention(\n"," (q): Linear(in_features=768, out_features=768, bias=False)\n"," (k): Linear(in_features=768, out_features=768, bias=False)\n"," (v): Linear(in_features=768, out_features=768, bias=False)\n"," (o): Linear(in_features=768, out_features=768, bias=False)\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (1): T5LayerFF(\n"," (DenseReluDense): T5DenseGatedActDense(\n"," (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n"," (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n"," (wo): Linear(in_features=2048, out_features=768, bias=False)\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," (act): NewGELUActivation()\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," )\n"," )\n"," (8): T5Block(\n"," (layer): ModuleList(\n"," (0): T5LayerSelfAttention(\n"," (SelfAttention): T5Attention(\n"," (q): Linear(in_features=768, out_features=768, bias=False)\n"," (k): Linear(in_features=768, out_features=768, bias=False)\n"," (v): Linear(in_features=768, out_features=768, bias=False)\n"," (o): Linear(in_features=768, out_features=768, bias=False)\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (1): T5LayerFF(\n"," (DenseReluDense): T5DenseGatedActDense(\n"," (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n"," (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n"," (wo): Linear(in_features=2048, out_features=768, bias=False)\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," (act): NewGELUActivation()\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," )\n"," )\n"," (9): T5Block(\n"," (layer): ModuleList(\n"," (0): T5LayerSelfAttention(\n"," (SelfAttention): T5Attention(\n"," (q): Linear(in_features=768, out_features=768, bias=False)\n"," (k): Linear(in_features=768, out_features=768, bias=False)\n"," (v): Linear(in_features=768, out_features=768, bias=False)\n"," (o): Linear(in_features=768, out_features=768, bias=False)\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (1): T5LayerFF(\n"," (DenseReluDense): T5DenseGatedActDense(\n"," (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n"," (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n"," (wo): Linear(in_features=2048, out_features=768, bias=False)\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," (act): NewGELUActivation()\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," )\n"," )\n"," (10): T5Block(\n"," (layer): ModuleList(\n"," (0): T5LayerSelfAttention(\n"," (SelfAttention): T5Attention(\n"," (q): Linear(in_features=768, out_features=768, bias=False)\n"," (k): Linear(in_features=768, out_features=768, bias=False)\n"," (v): Linear(in_features=768, out_features=768, bias=False)\n"," (o): Linear(in_features=768, out_features=768, bias=False)\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (1): T5LayerFF(\n"," (DenseReluDense): T5DenseGatedActDense(\n"," (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n"," (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n"," (wo): Linear(in_features=2048, out_features=768, bias=False)\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," (act): NewGELUActivation()\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," )\n"," )\n"," (11): T5Block(\n"," (layer): ModuleList(\n"," (0): T5LayerSelfAttention(\n"," (SelfAttention): T5Attention(\n"," (q): Linear(in_features=768, out_features=768, bias=False)\n"," (k): Linear(in_features=768, out_features=768, bias=False)\n"," (v): Linear(in_features=768, out_features=768, bias=False)\n"," (o): Linear(in_features=768, out_features=768, bias=False)\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (1): T5LayerFF(\n"," (DenseReluDense): T5DenseGatedActDense(\n"," (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n"," (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n"," (wo): Linear(in_features=2048, out_features=768, bias=False)\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," (act): NewGELUActivation()\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," )\n"," )\n"," )\n"," (final_layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (decoder): T5Stack(\n"," (embed_tokens): Embedding(32128, 768)\n"," (block): ModuleList(\n"," (0): T5Block(\n"," (layer): ModuleList(\n"," (0): T5LayerSelfAttention(\n"," (SelfAttention): T5Attention(\n"," (q): Linear(in_features=768, out_features=768, bias=False)\n"," (k): Linear(in_features=768, out_features=768, bias=False)\n"," (v): Linear(in_features=768, out_features=768, bias=False)\n"," (o): Linear(in_features=768, out_features=768, bias=False)\n"," (relative_attention_bias): Embedding(32, 12)\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (1): T5LayerCrossAttention(\n"," (EncDecAttention): T5Attention(\n"," (q): Linear(in_features=768, out_features=768, bias=False)\n"," (k): Linear(in_features=768, out_features=768, bias=False)\n"," (v): Linear(in_features=768, out_features=768, bias=False)\n"," (o): Linear(in_features=768, out_features=768, bias=False)\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (2): T5LayerFF(\n"," (DenseReluDense): T5DenseGatedActDense(\n"," (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n"," (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n"," (wo): Linear(in_features=2048, out_features=768, bias=False)\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," (act): NewGELUActivation()\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," )\n"," )\n"," (1): T5Block(\n"," (layer): ModuleList(\n"," (0): T5LayerSelfAttention(\n"," (SelfAttention): T5Attention(\n"," (q): Linear(in_features=768, out_features=768, bias=False)\n"," (k): Linear(in_features=768, out_features=768, bias=False)\n"," (v): Linear(in_features=768, out_features=768, bias=False)\n"," (o): Linear(in_features=768, out_features=768, bias=False)\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (1): T5LayerCrossAttention(\n"," (EncDecAttention): T5Attention(\n"," (q): Linear(in_features=768, out_features=768, bias=False)\n"," (k): Linear(in_features=768, out_features=768, bias=False)\n"," (v): Linear(in_features=768, out_features=768, bias=False)\n"," (o): Linear(in_features=768, out_features=768, bias=False)\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (2): T5LayerFF(\n"," (DenseReluDense): T5DenseGatedActDense(\n"," (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n"," (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n"," (wo): Linear(in_features=2048, out_features=768, bias=False)\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," (act): NewGELUActivation()\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," )\n"," )\n"," (2): T5Block(\n"," (layer): ModuleList(\n"," (0): T5LayerSelfAttention(\n"," (SelfAttention): T5Attention(\n"," (q): Linear(in_features=768, out_features=768, bias=False)\n"," (k): Linear(in_features=768, out_features=768, bias=False)\n"," (v): Linear(in_features=768, out_features=768, bias=False)\n"," (o): Linear(in_features=768, out_features=768, bias=False)\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (1): T5LayerCrossAttention(\n"," (EncDecAttention): T5Attention(\n"," (q): Linear(in_features=768, out_features=768, bias=False)\n"," (k): Linear(in_features=768, out_features=768, bias=False)\n"," (v): Linear(in_features=768, out_features=768, bias=False)\n"," (o): Linear(in_features=768, out_features=768, bias=False)\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (2): T5LayerFF(\n"," (DenseReluDense): T5DenseGatedActDense(\n"," (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n"," (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n"," (wo): Linear(in_features=2048, out_features=768, bias=False)\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," (act): NewGELUActivation()\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," )\n"," )\n"," (3): T5Block(\n"," (layer): ModuleList(\n"," (0): T5LayerSelfAttention(\n"," (SelfAttention): T5Attention(\n"," (q): Linear(in_features=768, out_features=768, bias=False)\n"," (k): Linear(in_features=768, out_features=768, bias=False)\n"," (v): Linear(in_features=768, out_features=768, bias=False)\n"," (o): Linear(in_features=768, out_features=768, bias=False)\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (1): T5LayerCrossAttention(\n"," (EncDecAttention): T5Attention(\n"," (q): Linear(in_features=768, out_features=768, bias=False)\n"," (k): Linear(in_features=768, out_features=768, bias=False)\n"," (v): Linear(in_features=768, out_features=768, bias=False)\n"," (o): Linear(in_features=768, out_features=768, bias=False)\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (2): T5LayerFF(\n"," (DenseReluDense): T5DenseGatedActDense(\n"," (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n"," (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n"," (wo): Linear(in_features=2048, out_features=768, bias=False)\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," (act): NewGELUActivation()\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," )\n"," )\n"," (4): T5Block(\n"," (layer): ModuleList(\n"," (0): T5LayerSelfAttention(\n"," (SelfAttention): T5Attention(\n"," (q): Linear(in_features=768, out_features=768, bias=False)\n"," (k): Linear(in_features=768, out_features=768, bias=False)\n"," (v): Linear(in_features=768, out_features=768, bias=False)\n"," (o): Linear(in_features=768, out_features=768, bias=False)\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (1): T5LayerCrossAttention(\n"," (EncDecAttention): T5Attention(\n"," (q): Linear(in_features=768, out_features=768, bias=False)\n"," (k): Linear(in_features=768, out_features=768, bias=False)\n"," (v): Linear(in_features=768, out_features=768, bias=False)\n"," (o): Linear(in_features=768, out_features=768, bias=False)\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (2): T5LayerFF(\n"," (DenseReluDense): T5DenseGatedActDense(\n"," (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n"," (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n"," (wo): Linear(in_features=2048, out_features=768, bias=False)\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," (act): NewGELUActivation()\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," )\n"," )\n"," (5): T5Block(\n"," (layer): ModuleList(\n"," (0): T5LayerSelfAttention(\n"," (SelfAttention): T5Attention(\n"," (q): Linear(in_features=768, out_features=768, bias=False)\n"," (k): Linear(in_features=768, out_features=768, bias=False)\n"," (v): Linear(in_features=768, out_features=768, bias=False)\n"," (o): Linear(in_features=768, out_features=768, bias=False)\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (1): T5LayerCrossAttention(\n"," (EncDecAttention): T5Attention(\n"," (q): Linear(in_features=768, out_features=768, bias=False)\n"," (k): Linear(in_features=768, out_features=768, bias=False)\n"," (v): Linear(in_features=768, out_features=768, bias=False)\n"," (o): Linear(in_features=768, out_features=768, bias=False)\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (2): T5LayerFF(\n"," (DenseReluDense): T5DenseGatedActDense(\n"," (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n"," (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n"," (wo): Linear(in_features=2048, out_features=768, bias=False)\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," (act): NewGELUActivation()\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," )\n"," )\n"," (6): T5Block(\n"," (layer): ModuleList(\n"," (0): T5LayerSelfAttention(\n"," (SelfAttention): T5Attention(\n"," (q): Linear(in_features=768, out_features=768, bias=False)\n"," (k): Linear(in_features=768, out_features=768, bias=False)\n"," (v): Linear(in_features=768, out_features=768, bias=False)\n"," (o): Linear(in_features=768, out_features=768, bias=False)\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (1): T5LayerCrossAttention(\n"," (EncDecAttention): T5Attention(\n"," (q): Linear(in_features=768, out_features=768, bias=False)\n"," (k): Linear(in_features=768, out_features=768, bias=False)\n"," (v): Linear(in_features=768, out_features=768, bias=False)\n"," (o): Linear(in_features=768, out_features=768, bias=False)\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (2): T5LayerFF(\n"," (DenseReluDense): T5DenseGatedActDense(\n"," (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n"," (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n"," (wo): Linear(in_features=2048, out_features=768, bias=False)\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," (act): NewGELUActivation()\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," )\n"," )\n"," (7): T5Block(\n"," (layer): ModuleList(\n"," (0): T5LayerSelfAttention(\n"," (SelfAttention): T5Attention(\n"," (q): Linear(in_features=768, out_features=768, bias=False)\n"," (k): Linear(in_features=768, out_features=768, bias=False)\n"," (v): Linear(in_features=768, out_features=768, bias=False)\n"," (o): Linear(in_features=768, out_features=768, bias=False)\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (1): T5LayerCrossAttention(\n"," (EncDecAttention): T5Attention(\n"," (q): Linear(in_features=768, out_features=768, bias=False)\n"," (k): Linear(in_features=768, out_features=768, bias=False)\n"," (v): Linear(in_features=768, out_features=768, bias=False)\n"," (o): Linear(in_features=768, out_features=768, bias=False)\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (2): T5LayerFF(\n"," (DenseReluDense): T5DenseGatedActDense(\n"," (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n"," (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n"," (wo): Linear(in_features=2048, out_features=768, bias=False)\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," (act): NewGELUActivation()\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," )\n"," )\n"," (8): T5Block(\n"," (layer): ModuleList(\n"," (0): T5LayerSelfAttention(\n"," (SelfAttention): T5Attention(\n"," (q): Linear(in_features=768, out_features=768, bias=False)\n"," (k): Linear(in_features=768, out_features=768, bias=False)\n"," (v): Linear(in_features=768, out_features=768, bias=False)\n"," (o): Linear(in_features=768, out_features=768, bias=False)\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (1): T5LayerCrossAttention(\n"," (EncDecAttention): T5Attention(\n"," (q): Linear(in_features=768, out_features=768, bias=False)\n"," (k): Linear(in_features=768, out_features=768, bias=False)\n"," (v): Linear(in_features=768, out_features=768, bias=False)\n"," (o): Linear(in_features=768, out_features=768, bias=False)\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (2): T5LayerFF(\n"," (DenseReluDense): T5DenseGatedActDense(\n"," (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n"," (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n"," (wo): Linear(in_features=2048, out_features=768, bias=False)\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," (act): NewGELUActivation()\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," )\n"," )\n"," (9): T5Block(\n"," (layer): ModuleList(\n"," (0): T5LayerSelfAttention(\n"," (SelfAttention): T5Attention(\n"," (q): Linear(in_features=768, out_features=768, bias=False)\n"," (k): Linear(in_features=768, out_features=768, bias=False)\n"," (v): Linear(in_features=768, out_features=768, bias=False)\n"," (o): Linear(in_features=768, out_features=768, bias=False)\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (1): T5LayerCrossAttention(\n"," (EncDecAttention): T5Attention(\n"," (q): Linear(in_features=768, out_features=768, bias=False)\n"," (k): Linear(in_features=768, out_features=768, bias=False)\n"," (v): Linear(in_features=768, out_features=768, bias=False)\n"," (o): Linear(in_features=768, out_features=768, bias=False)\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (2): T5LayerFF(\n"," (DenseReluDense): T5DenseGatedActDense(\n"," (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n"," (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n"," (wo): Linear(in_features=2048, out_features=768, bias=False)\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," (act): NewGELUActivation()\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," )\n"," )\n"," (10): T5Block(\n"," (layer): ModuleList(\n"," (0): T5LayerSelfAttention(\n"," (SelfAttention): T5Attention(\n"," (q): Linear(in_features=768, out_features=768, bias=False)\n"," (k): Linear(in_features=768, out_features=768, bias=False)\n"," (v): Linear(in_features=768, out_features=768, bias=False)\n"," (o): Linear(in_features=768, out_features=768, bias=False)\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (1): T5LayerCrossAttention(\n"," (EncDecAttention): T5Attention(\n"," (q): Linear(in_features=768, out_features=768, bias=False)\n"," (k): Linear(in_features=768, out_features=768, bias=False)\n"," (v): Linear(in_features=768, out_features=768, bias=False)\n"," (o): Linear(in_features=768, out_features=768, bias=False)\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (2): T5LayerFF(\n"," (DenseReluDense): T5DenseGatedActDense(\n"," (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n"," (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n"," (wo): Linear(in_features=2048, out_features=768, bias=False)\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," (act): NewGELUActivation()\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," )\n"," )\n"," (11): T5Block(\n"," (layer): ModuleList(\n"," (0): T5LayerSelfAttention(\n"," (SelfAttention): T5Attention(\n"," (q): Linear(in_features=768, out_features=768, bias=False)\n"," (k): Linear(in_features=768, out_features=768, bias=False)\n"," (v): Linear(in_features=768, out_features=768, bias=False)\n"," (o): Linear(in_features=768, out_features=768, bias=False)\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (1): T5LayerCrossAttention(\n"," (EncDecAttention): T5Attention(\n"," (q): Linear(in_features=768, out_features=768, bias=False)\n"," (k): Linear(in_features=768, out_features=768, bias=False)\n"," (v): Linear(in_features=768, out_features=768, bias=False)\n"," (o): Linear(in_features=768, out_features=768, bias=False)\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (2): T5LayerFF(\n"," (DenseReluDense): T5DenseGatedActDense(\n"," (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n"," (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n"," (wo): Linear(in_features=2048, out_features=768, bias=False)\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," (act): NewGELUActivation()\n"," )\n"," (layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," )\n"," )\n"," )\n"," (final_layer_norm): T5LayerNorm()\n"," (dropout): Dropout(p=0.1, inplace=False)\n"," )\n"," (lm_head): Linear(in_features=768, out_features=32128, bias=False)\n",")"]},"metadata":{},"execution_count":13}]},{"cell_type":"markdown","source":["# Helper functions"],"metadata":{"id":"F_SDAwxoawDy"}},{"cell_type":"code","source":["import torch"],"metadata":{"id":"rdWMg_KJZEZH","executionInfo":{"status":"ok","timestamp":1676652375456,"user_tz":-60,"elapsed":8,"user":{"displayName":"Mikołaj Paterka","userId":"09090979117369578839"}}},"execution_count":14,"outputs":[]},{"cell_type":"code","source":["def calculate_accuracy(preds, target):\n"," results_ok = 0.0\n"," results_false = 0.0\n","\n"," for idx, pred in enumerate(preds):\n"," if pred == target[idx]:\n"," results_ok += 1.0\n"," else:\n"," results_false += 1.0\n","\n"," return results_ok / (results_ok + results_false)"],"metadata":{"id":"FzUi8908ax61","executionInfo":{"status":"ok","timestamp":1676652375457,"user_tz":-60,"elapsed":7,"user":{"displayName":"Mikołaj Paterka","userId":"09090979117369578839"}}},"execution_count":15,"outputs":[]},{"cell_type":"code","source":["if torch.cuda.is_available(): \n"," device = torch.device(\"cuda\")\n","\n"," print('There are %d GPU(s) available.' % torch.cuda.device_count())\n"," print('We will use the GPU:', torch.cuda.get_device_name(0))\n","\n","else:\n"," print('No GPU available, using the CPU instead.')\n"," device = torch.device(\"cpu\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"86i7iRmtW-6L","executionInfo":{"status":"ok","timestamp":1676652375457,"user_tz":-60,"elapsed":6,"user":{"displayName":"Mikołaj Paterka","userId":"09090979117369578839"}},"outputId":"f2f5e476-8fed-4d32-e80a-72f33b07f8af"},"execution_count":16,"outputs":[{"output_type":"stream","name":"stdout","text":["There are 1 GPU(s) available.\n","We will use the GPU: Tesla T4\n"]}]},{"cell_type":"markdown","source":["# Predykcja"],"metadata":{"id":"H_YI3bS3VHQE"}},{"cell_type":"code","source":["parsed_dataset = parsed_dataset[1:123] + parsed_dataset[124:2000] + parsed_dataset[2001:3002] + parsed_dataset[3003:]\n","predictions = []\n","expected = []\n","\n","for row in parsed_dataset:\n"," input_text = few_shot_prefix + row['sms'] + \"\\nAnswer if this text message is spam or not: \"\n"," input_ids = tokenizer(input_text, return_tensors=\"pt\").input_ids.to(device)\n","\n"," generated_ids = model.generate(input_ids, do_sample=True, temperature=0.9, max_length=400)\n"," generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)\n"," \n"," predictions.append(generated_text)\n"," expected.append(row['label'])\n","\n","acc = calculate_accuracy(predictions, expected)\n","print(acc)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vunLGuBGVGmh","executionInfo":{"status":"ok","timestamp":1676652961996,"user_tz":-60,"elapsed":432382,"user":{"displayName":"Mikołaj Paterka","userId":"09090979117369578839"}},"outputId":"7421dcc1-592b-484c-f8ad-b3ea88805c3d"},"execution_count":18,"outputs":[{"output_type":"stream","name":"stdout","text":["0.503593244699964\n"]}]},{"cell_type":"code","source":["print(\"Sample prediction: {}, expected: {}\".format(predictions[101], expected[101]))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"20WbYZmLaDl7","executionInfo":{"status":"ok","timestamp":1676652961997,"user_tz":-60,"elapsed":18,"user":{"displayName":"Mikołaj Paterka","userId":"09090979117369578839"}},"outputId":"9a0f5f23-78ef-48b3-f6cc-4c64aad653ed"},"execution_count":19,"outputs":[{"output_type":"stream","name":"stdout","text":["Sample prediction: False, expected: False\n"]}]},{"cell_type":"markdown","source":["# MCC Score"],"metadata":{"id":"dLYc9WXz_B1o"}},{"cell_type":"code","source":["from sklearn.metrics import matthews_corrcoef\n","\n","print('Calculating Matthews Corr. Coef. for each batch...')\n","matthews = matthews_corrcoef(expected, predictions) \n","print('Total MCC: %.3f' % matthews)"],"metadata":{"id":"hPEPpXXX_DXR","executionInfo":{"status":"ok","timestamp":1676652962709,"user_tz":-60,"elapsed":722,"user":{"displayName":"Mikołaj Paterka","userId":"09090979117369578839"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"e5035c3e-32eb-438b-aa77-3c7d6db51b53"},"execution_count":20,"outputs":[{"output_type":"stream","name":"stdout","text":["Calculating Matthews Corr. Coef. for each batch...\n","Total MCC: 0.010\n"]}]},{"cell_type":"markdown","source":["# Save model"],"metadata":{"id":"GPhCp068_Iwq"}},{"cell_type":"code","source":["from google.colab import drive\n","\n","drive.mount('/content/gdrive/', force_remount=True)\n","\n","output_dir = '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/Ver_1_FLAN-T5_Model'\n","print(\"Saving model to %s\" % output_dir)\n","\n","model_to_save = model.module if hasattr(model, 'module') else model\n","model_to_save.save_pretrained(output_dir)\n","tokenizer.save_pretrained(output_dir)"],"metadata":{"id":"avafCMoS_KDF","executionInfo":{"status":"ok","timestamp":1676653169797,"user_tz":-60,"elapsed":207094,"user":{"displayName":"Mikołaj Paterka","userId":"09090979117369578839"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"f8143c19-99cd-4e13-d40c-918f5b2c8941"},"execution_count":21,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/gdrive/\n","Saving model to /content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/Ver_1_FLAN-T5_Model\n"]},{"output_type":"execute_result","data":{"text/plain":["('/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/Ver_1_FLAN-T5_Model/tokenizer_config.json',\n"," '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/Ver_1_FLAN-T5_Model/special_tokens_map.json',\n"," '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/Ver_1_FLAN-T5_Model/spiece.model',\n"," '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/Ver_1_FLAN-T5_Model/added_tokens.json',\n"," '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/Ver_1_FLAN-T5_Model/tokenizer.json')"]},"metadata":{},"execution_count":21}]},{"cell_type":"markdown","source":["# Bibliografia\n","- https://huggingface.co/docs/transformers/main/en/model_doc/flan-t5\n","- https://mccormickml.com/2019/07/22/BERT-fine-tuning/#a1-saving--loading-fine-tuned-model\n","- https://huggingface.co/docs/transformers/model_doc/t5#training"],"metadata":{"id":"wHzm2_nDA6i-"}}]} |