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165 KiB
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1 line
165 KiB
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Instalacja pakietów"],"metadata":{"id":"ZXsOR6oJOJbd"}},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8l0hzptKNiZS","executionInfo":{"status":"ok","timestamp":1676655847146,"user_tz":-60,"elapsed":22906,"user":{"displayName":"Mike1 Studia1","userId":"14831644302217245682"}},"outputId":"8c0d45c5-f6e8-4db2-b8bb-24c258e6f19e"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting transformers\n"," Downloading transformers-4.26.1-py3-none-any.whl (6.3 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.3/6.3 MB\u001b[0m \u001b[31m42.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hCollecting datasets\n"," Downloading datasets-2.9.0-py3-none-any.whl (462 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m462.8/462.8 KB\u001b[0m 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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":1676655848707,"user_tz":-60,"elapsed":1224,"user":{"displayName":"Mike1 Studia1","userId":"14831644302217245682"}}},"execution_count":2,"outputs":[]},{"cell_type":"code","source":["dataset = load_dataset(\"sms_spam\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":232,"referenced_widgets":["25db59aa53d542bfb6d54da340006fcd","2eaefa22c2e14c40a2de9592676a8d82","1bf81e50052942208c5100bfdf0c985a","5422e114814145d1a85b423b93d4133c","0031c9a8c8724bce9aff6b3cafc94c2b","3d3b84701cbf468c96170f7563e42408","96bdc942fcca434798c89e4ec74b6dce","5cabe61f114a4c33ae677d9b5306c635","6b39afc3b8bc43768b376186aafe96de","7bd83463f6504145b0ca14ab78750d3a","61d94df2723e4e2d94cd74a1b884d409","d160c37c8eb241eda89f7713fd4d05c2","5fa8d14ffdbc45e6812b50e0d27e14fe","a54fa4aedddc46ba84224971eb1e5227","98c7b45870cc49b7bb3addd225e04301","6f3ada40efe54c13b2c14ad60a6b18e4","f82774bb70cc4f90b80d723f8aa9c7e4","b3f23480d62d4c50a8aaa03118f6b5ce","3a9cfb628ef04d8ca222584f40be05c4","9bdb6d0c6fa54454957211029a960159","67ab4e3216154b67bcdac51d4548ed75","524913d4b3ca4f729acfc975ea490b46","a543b6e11ab046748cdd6c946a1f9084","8c2521b501c64c90998bc05d1a76281c","bca85efcfcd546b9a43ce79a68b7f019","0f359da3c8b1402b8e391c2360531d19","1a6b59d6f8e54a30b69822e6b59fd99f","dccb6a9167aa4b60898047d46363d80e","545f9b3e11cf42818c25709159e7fb3e","6af88f49ff5346b9a0ad07faceede0bd","fc87784596044dd78d045989dbb53a91","331e1896aa424786a810a711143d478e","a39965de69714b26a78f8b976a33a42e","80a4187b45134a4ab30a7748e4f6ff28","326f7ae8ea614699a30da8dfe3f929be","c7574a234b8d492bba0fec988d8e8ae1","2282de3148204153873e6c678d3ab97d","9a9392cc5903455e9206d9c965f5005c","0c48c929e21f4858b9323b7f4ac220b2","17dd8779c5d642ceaa85e0d7c8524436","e5e5197e03c040a99a5d31c57f8a047c","a9aa4d15ad5143b1a5686972ef47186e","d23808437da04c37868bd7526e685a5c","6ac94e8230564af9b05ac561ab5d8e7b","4f03235725824725add5a46e03a5156e","af2ac44def004870a388284503a45b07","3a70f4da67754c5796c8ab5e8eaec187","1c0621a41647430d8dfda34f2f7d8e2f","d0d7c235a53e46e98f99229f26b665d0","accb4a02861c4ad3a7965ba707ab8a38","7d7810f28a2342848026eb36e967d8a5","f7f34fc698bc416383ae59828301469d","373bf37aada844a187f13af6d723333b","7f309cfb6bc54a5e8872524eb63354c2","c69df495e9324100b87aae91c9b34bf6","209b3c2d088b4512bf513e17e74e8a30","eef55327290e4e98b5ac4b2c3e8b4ad4","495b6416c57149bfa9d8418676c4541d","4c01dbed470a4fbc94aaedc3c679385f","dda3f23953f64ceead909262eb0f629d","6165a4a6a5454cd8b73c9702771dcae1","ff4d3372dcc14403aeb19ff960a518f5","dea72bac03c1492da956598f66465472","4b87d5b0da3c4a6484d1757a09de382b","fe6080acb5544dd8a3c512fbf38afd87","1b4bf9de6c59492091f84d80871545c5"]},"id":"cCiAuRqrOkvV","executionInfo":{"status":"ok","timestamp":1676655851010,"user_tz":-60,"elapsed":2305,"user":{"displayName":"Mike1 Studia1","userId":"14831644302217245682"}},"outputId":"78480a8f-563d-42a5-c5dd-4bdfa1f8e78b"},"execution_count":3,"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":"25db59aa53d542bfb6d54da340006fcd"}},"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":"d160c37c8eb241eda89f7713fd4d05c2"}},"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":"a543b6e11ab046748cdd6c946a1f9084"}},"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":"80a4187b45134a4ab30a7748e4f6ff28"}},"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":"4f03235725824725add5a46e03a5156e"}},"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":"209b3c2d088b4512bf513e17e74e8a30"}},"metadata":{}}]},{"cell_type":"code","source":["dataset['train'][0]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JKFHPko3OnAV","executionInfo":{"status":"ok","timestamp":1676655851011,"user_tz":-60,"elapsed":7,"user":{"displayName":"Mike1 Studia1","userId":"14831644302217245682"}},"outputId":"f813fa0d-c053-4b6e-a5f6-f304b5447912"},"execution_count":4,"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":4}]},{"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":1676655851377,"user_tz":-60,"elapsed":371,"user":{"displayName":"Mike1 Studia1","userId":"14831644302217245682"}},"outputId":"1435212c-73cb-4660-d9ec-e06d7fe5e493"},"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"," 'label': 'False'}"]},"metadata":{},"execution_count":5}]},{"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":1676655853434,"user_tz":-60,"elapsed":2058,"user":{"displayName":"Mike1 Studia1","userId":"14831644302217245682"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["tokenizer = AutoTokenizer.from_pretrained('google/flan-t5-base')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["d5c9c4fcc72f46639246e0e733af5ff8","8b8719c742754a15baa3c14a9f2f1852","b309af1852fc4d8d8985f2690b768485","30237ebd73554b428d13a66f18d90069","e80a747c0d934144aa2b78cff101c87a","494c694bd92e49d3831d716cb90b4dd8","e03d7f43bdbc4deda2c35eaaece29fe2","56aa37b752a54936a09e2167f0d2c281","492a083bfe674cd496a91fc8c51a4646","58865aaf52a94747b20aac75ebb46426","6a609ebaee3b465fa35107769eb4cc9d","6a23888d5c8a4080b02e6b632f14c6ef","a3bc16f6a15e4fd4b377c59efb7c044c","5efe04cb883e45f0bbdfbbd3e395b15c","0dc054f35b374f878a24d535bdfad9fb","655003d887be4b0da175663424a146f1","054ba05fa0f349c69a8384b0978f298f","e56b4ae542e442c48a7363f923fe84e8","6cba84a4f97c49b0a070552fc5fbff76","5310dfef2ca940ce936ecd5076bfaf0d","959c58ebf0c341ecbcb6d6c652ff0113","084c1fb97ce1492da72682cfd07dc47d","7bd9d152451a4d5eb563b980c99fc211","c5e083e45aae422db9e3c2527e3823ce","a8d3b906a8c14700b59cb8a711660288","43f4a197420c46b1840dbfdf9c21266e","b0ff87a5842e473ea003412248fbadc7","cdc97857b95c4d889cc8b000daa87846","4bf9cf5009fb43a4bba04c5e42f25da8","a50180e15c284cea9950d107463eaaeb","d6e276e5fc60476783b10775a5e3ef7e","88061a9e33c64e1fa65cbdcd7f586564","2fc4ac2558194f869bf131192b053273","902a66286d5b4dffad200da7adf745c7","7f7349e82d58494d82c67bcea1a259fd","01c3ab1eda3a4b41a8108dae2d06bf95","cde3998b720b45f68d4c1733e93d6ea6","59900edf25e84470abcd1d3fc4cd0ef5","b87577f11a474ef39da02e0b8195524d","3df99a08280c4e16961a51acd5a2c701","3b91340d7d2e45b3bfba0c46b4bf476c","7083f9df7e92477cbf734faa7363c35a","ceddd92f40d44737acfced85e2118198","aa7a986d41594a8e8ffb08793ca56b0a"]},"id":"q5Jz0E_oPMBr","executionInfo":{"status":"ok","timestamp":1676655855532,"user_tz":-60,"elapsed":2100,"user":{"displayName":"Mike1 Studia1","userId":"14831644302217245682"}},"outputId":"d83aebe2-e6ae-463c-b189-140b429379bd"},"execution_count":7,"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":"d5c9c4fcc72f46639246e0e733af5ff8"}},"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":"6a23888d5c8a4080b02e6b632f14c6ef"}},"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":"7bd9d152451a4d5eb563b980c99fc211"}},"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":"902a66286d5b4dffad200da7adf745c7"}},"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":1676655855533,"user_tz":-60,"elapsed":13,"user":{"displayName":"Mike1 Studia1","userId":"14831644302217245682"}},"outputId":"c3542a4d-1bbf-4579-e6c3-9641b9802201"},"execution_count":8,"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":1676655855534,"user_tz":-60,"elapsed":11,"user":{"displayName":"Mike1 Studia1","userId":"14831644302217245682"}},"outputId":"bfd36343-8751-4525-d745-c4612ecae5bc"},"execution_count":9,"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 = \"The text message contains the following content: \" + parsed_dataset[0]['sms'] + \"\\nIs it true that the content of a text message is spam: False\\n\\n\"\n","spam_1 = \"The text message contains the following content: \" + parsed_dataset[123]['sms'] + \"\\nIs it true that the content of a text message is spam: True\\n\\n\"\n","non_spam_2 = \"The text message contains the following content: \" + parsed_dataset[2000]['sms'] + \"\\nIs it true that the content of a text message is spam: False\\n\\n\"\n","spam_2 = \"The text message contains the following content: \" + parsed_dataset[3002]['sms'] + \"\\nIs it true that the content of a text message is spam: True\\n\\n\"\n","\n","few_shot_prefix = non_spam_1 + spam_1 + non_spam_2 + spam_2 + \"The text message contains the following content: \"\n","print(few_shot_prefix)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lj0issBznZfK","executionInfo":{"status":"ok","timestamp":1676655855534,"user_tz":-60,"elapsed":8,"user":{"displayName":"Mike1 Studia1","userId":"14831644302217245682"}},"outputId":"b808dc44-b404-4c61-a4ca-c225961b22ac"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["The text message contains the following content: Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\n","Is it true that the content of a text message is spam: False\n","\n","The text message contains the following content: 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","Is it true that the content of a text message is spam: True\n","\n","The text message contains the following content: LMAO where's your fish memory when I need it?\n","Is it true that the content of a text message is spam: False\n","\n","The text message contains the following content: This message is free. Welcome to the new & improved Sex & Dogging club! To unsubscribe from this service reply STOP. msgs@150p 18+only\n","Is it true that the content of a text message is spam: True\n","\n","The text message contains the following content: \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":1676655855535,"user_tz":-60,"elapsed":6,"user":{"displayName":"Mike1 Studia1","userId":"14831644302217245682"}}},"execution_count":11,"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":["9a6fea9adcaf4099ab6c0860aca91af1","c98679bf3f404645ba349c9040bcaf3d","59449a1ef9b840b581757da9342f64c4","a7a1bc5e99834919a68f30b1fff34ba7","0c7e07a6e45b42e486c50df3665e3000","eea6649f7b4647b8b30a2e9dcb2cc0b8","ed3f60219b4148e7b45bdf31f11f4617","89157fabea4c4a0ea563d9e60f7cfa54","b08aaa725b7445a28894a8f5f82c599b","01a7431d2ed440c2875caa50288d36fd","f062075516064894ad208522903485ab","022661137b474760b175a0b0ddeb844c","814cd3e8fc0441d8857d15976e7b0d0d","90e31b2327a34aecb4e483b3a724a721","db9b101ac0e34e659d4cb59a0721fb7d","f0ec79de51c547c789fb9cbd6ede9bc0","88d8c48bf9ef47c49cbabb12bec227f7","8fe33fb112614f1181fb9a9a5f92cf50","60a0e9d6ddd24a6a81742db75543d52c","ed1fc00a86bd4eb0b19b4df04fc01c05","0f935ce070ec40e983d97b0b5245e128","8f0ac859443a4098ab96f37bacb232b1","817d648b4b2746068c566e92cd07601d","1bd7cb6802654dc9a8545b44b1cd74dc","8d6609cdcb554d59b7e2a74fbd8272b3","8e6a5f8ef175462a9d4570631ab46232","ba87e1b9ed8149f3b0ddfdc63ac1dd83","9ba43ff9c97f458d9eea292e0b04bb72","cc48f3926f63490ea63cc74e4df6815c","612651def7564e56949cde5f4a340f2c","783b209f9a58455288153f267aae517b","3c90af09603e4f9b8d1d9730f8d3660c","0d98d0018cfb4cfab57b9ef7ff5ac394"]},"id":"JKv9O8kfV2zZ","executionInfo":{"status":"ok","timestamp":1676655876634,"user_tz":-60,"elapsed":21105,"user":{"displayName":"Mike1 Studia1","userId":"14831644302217245682"}},"outputId":"785dda59-be66-4f68-88e2-0ff978a4d7e7"},"execution_count":12,"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":"9a6fea9adcaf4099ab6c0860aca91af1"}},"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":"022661137b474760b175a0b0ddeb844c"}},"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":"817d648b4b2746068c566e92cd07601d"}},"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":12}]},{"cell_type":"markdown","source":["# Helper functions"],"metadata":{"id":"F_SDAwxoawDy"}},{"cell_type":"code","source":["import torch"],"metadata":{"id":"rdWMg_KJZEZH","executionInfo":{"status":"ok","timestamp":1676655876634,"user_tz":-60,"elapsed":16,"user":{"displayName":"Mike1 Studia1","userId":"14831644302217245682"}}},"execution_count":13,"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":1676655876634,"user_tz":-60,"elapsed":15,"user":{"displayName":"Mike1 Studia1","userId":"14831644302217245682"}}},"execution_count":14,"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":1676655876635,"user_tz":-60,"elapsed":15,"user":{"displayName":"Mike1 Studia1","userId":"14831644302217245682"}},"outputId":"c3e396e1-5ba3-42bc-9fb8-d22bb6052bdc"},"execution_count":15,"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'] + \"\\nIs it true that the content of a text message is spam: \"\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=250)\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":1676656668059,"user_tz":-60,"elapsed":484351,"user":{"displayName":"Mike1 Studia1","userId":"14831644302217245682"}},"outputId":"fe21ecec-0bee-4730-9b15-0a86aa695dec"},"execution_count":17,"outputs":[{"output_type":"stream","name":"stdout","text":["0.2551203736974488\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":1676657297829,"user_tz":-60,"elapsed":315,"user":{"displayName":"Mike1 Studia1","userId":"14831644302217245682"}},"outputId":"ea897a66-802e-4bdc-93b5-f9f81f2129dc"},"execution_count":18,"outputs":[{"output_type":"stream","name":"stdout","text":["Sample prediction: No, 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":1676657301430,"user_tz":-60,"elapsed":1271,"user":{"displayName":"Mike1 Studia1","userId":"14831644302217245682"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"79633d5e-37ca-4374-874b-f4ba75f238b0"},"execution_count":19,"outputs":[{"output_type":"stream","name":"stdout","text":["Calculating Matthews Corr. Coef. for each batch...\n","Total MCC: -0.020\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_3_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","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1676657344651,"user_tz":-60,"elapsed":38045,"user":{"displayName":"Mike1 Studia1","userId":"14831644302217245682"}},"outputId":"39895ea5-7683-4df5-fd99-5a67ce5cc2e8"},"execution_count":20,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/gdrive/\n","Saving model to /content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/Ver_3_FLAN-T5_Model\n"]},{"output_type":"execute_result","data":{"text/plain":["('/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/Ver_3_FLAN-T5_Model/tokenizer_config.json',\n"," '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/Ver_3_FLAN-T5_Model/special_tokens_map.json',\n"," '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/Ver_3_FLAN-T5_Model/spiece.model',\n"," '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/Ver_3_FLAN-T5_Model/added_tokens.json',\n"," '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/Ver_3_FLAN-T5_Model/tokenizer.json')"]},"metadata":{},"execution_count":20}]},{"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-"}}]} |