Databases combination + finetuning DistilGPT2 .ipynb

{"cells":[{"cell_type":"markdown","source":["# Imports and installations"],"metadata":{"id":"bILFcZUoB_gD"}},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_JHcifutBKNP","executionInfo":{"status":"ok","timestamp":1661648247678,"user_tz":-120,"elapsed":14544,"user":{"displayName":"Sergio Muñoz Gonzalez","userId":"13292202149038080080"}},"outputId":"b6ec899d-fde4-40cd-bfa3-bed8b976198a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"UH5u5bKuJd_8","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1661649488039,"user_tz":-120,"elapsed":17587,"user":{"displayName":"Sergio Muñoz Gonzalez","userId":"13292202149038080080"}},"outputId":"8815b8aa-39cd-4fcb-f613-abced2d398f8"},"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-4.21.2-py3-none-any.whl (4.7 MB)\n","\u001b[K     |████████████████████████████████| 4.7 MB 5.2 MB/s \n","\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2022.6.2)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.21.6)\n","Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.12.0)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (6.0)\n","Requirement already satisfied: packaging>=20.0 in 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transformers-4.21.2\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting datasets\n","  Downloading datasets-2.4.0-py3-none-any.whl (365 kB)\n","\u001b[K     |████████████████████████████████| 365 kB 5.1 MB/s \n","\u001b[?25hCollecting multiprocess\n","  Downloading multiprocess-0.70.13-py37-none-any.whl (115 kB)\n","\u001b[K     |████████████████████████████████| 115 kB 62.1 MB/s \n","\u001b[?25hRequirement already satisfied: huggingface-hub<1.0.0,>=0.1.0 in /usr/local/lib/python3.7/dist-packages (from datasets) (0.9.1)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from datasets) (1.21.6)\n","Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.7/dist-packages (from datasets) (4.64.0)\n","Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.7/dist-packages (from datasets) (2.23.0)\n","Requirement already satisfied: dill<0.3.6 in 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(1.15.0)\n","Installing collected packages: urllib3, xxhash, responses, multiprocess, datasets\n","  Attempting uninstall: urllib3\n","    Found existing installation: urllib3 1.24.3\n","    Uninstalling urllib3-1.24.3:\n","      Successfully uninstalled urllib3-1.24.3\n","Successfully installed datasets-2.4.0 multiprocess-0.70.13 responses-0.18.0 urllib3-1.25.11 xxhash-3.0.0\n"]},{"output_type":"display_data","data":{"application/vnd.colab-display-data+json":{"pip_warning":{"packages":["urllib3"]}}},"metadata":{}}],"source":["!pip install transformers\n","!pip install datasets"]},{"cell_type":"code","source":["import pandas as pd\n","from sklearn.model_selection import train_test_split\n","from datasets import concatenate_datasets, load_dataset, Dataset\n","from transformers import (AutoModelWithLMHead,\n","                          AutoTokenizer,\n","                          TFAutoModelWithLMHead,\n","                          TFAutoModelForCausalLM,\n","                          AutoModelForCausalLM,\n","                          AutoConfig,\n","                          Trainer,\n","                          TextDataset,\n","                          DataCollatorForLanguageModeling,\n","                          TrainingArguments,\n","                          TrainerCallback,\n","                          pipeline\n",")\n","\n","import os\n","from tqdm import tqdm"],"metadata":{"id":"6ecN2igja19i"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# CREATING AND PROCESSING DATASET PAWS-x"],"metadata":{"id":"iF3qPBtfa3gT"}},{"cell_type":"markdown","source":["## Loading datasets"],"metadata":{"id":"Rz8E7p402fJ0"}},{"cell_type":"code","source":["dataset_en = load_dataset(\"paws-x\",'en')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":201,"referenced_widgets":["d1afe7fcd3294fc198f8334f637b2a78","f478eb9c599b4eeeb3856a8c8d47a15b","d36a7f07e5dc45c0bd88bc6a9b3b160f","52e5b2d5e90f42479f9f20dbf69e5d6f","dc9c1d41beed4476a0141aa6a383dcf1","d5c7af8fe441437690d320408d56694f","40823156b5914224bebf64f99a835a69","27f167751f2748059bf7b2076b9b06fe","de2baf0cae754874a23df4b9da3d3fe3","d8489b8c6ab54d6bb3c3c815cd031350","b3beb1aa146c47edb3582bb459cc8762","5f372930204b4ca28d5fc12611c25c71","9d0d02f944b8453ca3aaefdaa33f65df","31afb62fe9a54e79be56a62fa94aa3e6","318597ab84f14884bd591d422d4bd9d8","d6e9e9a6a00842d9a7e7d9eb61bc57e0","260724caceb24908993f4b64e00da272","8ff6fa8cfd734fbbb7322619fb3a2f38","957d87fa54964ec0a9f1a1b1a3dee1fa","c1e79bb09d5d44c0a33fc49179574158","fc31a73970b949588b4eebd747a559a1","46914f1d04db47cc905a718e02eca760","394f80ab70db40d18b0dd06289cb9eb2","e5cfb03045cd4cb6a07d045006a07201","ac672809af6f404b9d6411b95f5f87d6","c94c436afbb44f18b8b902e762299c7d","00904117bb4049a7af1934e6f4b02790","8c23670949584cbda5deb1395b03ba8f","f031768b5c5143bfaf6d427e09c080c2","854f2830e8d14ce69f524ba4fd811717","453bac31bc234952b775a1849c3342e5","825c58107d47401dadebf4bc19d72d73","09a3a463abeb487faf52c933962c2ab7","6b60230908974216abd631344af65780","7ee2fbebaf3741808beac63e67696a83","50ac1ccdf8044fc39c68672c1e6df51c","9deef375637342ffa436cdf3173797e0","a1bb161c4e434536b5534fcae68a7a47","788d23dd087f46049afeec47cf63c451","fb801ec4d3134903835e72d66e0bca1e","ccf164fccb8149f4af319f7bac8da0d2","bde2bd54ca8346b3a0fb220a28917afa","530c90d9357f4cbc84aeadf0f4aa075e","b6b8714e9c474f2abfec3dacf7f81941","ead72879674641588ad05d8ec8740c4f","e4661f9c41f14f47b45d2140bd78e23b","193ed5f551a543c6af6db406e6c25336","52773e0b7f66446e9817b506daf006eb","449aaddee4b546869fd8b060eeb2b5e1","f1616287501e4495b9adfe715c6c95b7","0dcdf4f752ae4b84a455096d7d86243f","3410f7a5ef8740a0904870f15f75185b","1b2406f69d254aac9ff4beec7757e724","5a538478dce44d2a93e5c581076d5639","89c5a8fc28cc4ee2a753b73ee803c878","8dfd27daf2384a52b8bb5f8e4738805f","3d3f7977027d47d6815fcd64d8506241","a710750b88c34869aaa2d02bc8d67295","65987bda05794c95aadb44683d88d361","2eac8c24b8d1453b8a31a10daf0d69be","56ffacb2fd6b491a81c14ed6a37da7f7","2dacd98fc192442d9473e902a8d14222","8ce2bfbfefda4b31acd060edf425586a","b4379559bf9f417aa105089c531902ee","889595b4055e4aeb83dc133fafca4779","9d3fa326b2ca466d92826ddd611b84da","9950174cc42847b2a5992868f636cff2","0bb13dc8c95a46bc86be2977817ef2c5","460bbe1523164775b545659e1ae43ff4","44d14a4e5d7e4547b849b250e3aa4268","cd026d6ad1594dfda232abdb78a91a8f","6a764dc9398042d0be405c394d06988e","3af906c666144bd88cb8b02490064315","f0041a2e405244be837d41d27c04a48b","252a36f5239a44d8b6987739efc58b55","abc7590273e74b7782e05430c75d0f56","dc96fe88e7b647a79c3078aa6e366b3c"]},"id":"I8SgGSEJ2i1t","executionInfo":{"status":"ok","timestamp":1650910222825,"user_tz":-120,"elapsed":8347,"user":{"displayName":"Sergio Muñoz Gonzalez","userId":"13292202149038080080"}},"outputId":"53ecd836-3cf3-46e6-bced-e4e3b0d70114"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading builder script:   0%|          | 0.00/2.50k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d1afe7fcd3294fc198f8334f637b2a78"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading metadata:   0%|          | 0.00/1.65k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5f372930204b4ca28d5fc12611c25c71"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Downloading and preparing dataset pawsx/en (download: 28.88 MiB, generated: 12.59 MiB, post-processed: Unknown size, total: 41.47 MiB) to /root/.cache/huggingface/datasets/pawsx/en/1.1.0/37673404a6de6d0fa2574661e77940d10d3be3bf51bb4f08c8fa079fd56b9755...\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 0.00/30.3M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"394f80ab70db40d18b0dd06289cb9eb2"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split:   0%|          | 0/49401 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6b60230908974216abd631344af65780"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating test split:   0%|          | 0/2000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ead72879674641588ad05d8ec8740c4f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating validation split:   0%|          | 0/2000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8dfd27daf2384a52b8bb5f8e4738805f"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Dataset pawsx downloaded and prepared to /root/.cache/huggingface/datasets/pawsx/en/1.1.0/37673404a6de6d0fa2574661e77940d10d3be3bf51bb4f08c8fa079fd56b9755. Subsequent calls will reuse this data.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/3 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9950174cc42847b2a5992868f636cff2"}},"metadata":{}}]},{"cell_type":"code","source":["dataset_es = load_dataset(\"paws-x\",'es')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":105,"referenced_widgets":["c8e917d110db490b9bd8dc8c9411e58e","b6f4048269e14c699adfb1c278b92b9a","791b959290ce4342b058c629d9dc2c68","cfd8e2dbe37a4a1abb299aa2de681506","6a0e0e8314b84c60b22169a3e8fe7cff","159b59e983ab4147ab359e6045061194","bd2b5e32339243d7a820ec5b64a9b796","6b44f7feab164c25a6ae13d5d073772a","a49f8d84547f4067a64bffed4becc59a","7bb775c527cc4d72820fff857a945839","102e8332f8f74dc49c2418fcca73ae0d","9c4c7082daa84ffcb8dcf102b82284e6","aa6f7746048a4252afd0c222eef9eb08","6374b8352b5c4c5a95504e06cd931fe8","df859f2a129c40658bdce367f3dc9fa2","d2bc6be2666141bfa02130e8a2308811","7c1d7e8930ee4a5b8912efba6565b250","9335332d9b794fbdb5af7d7785034168","61282a593b9242708d3a41ed2680f489","7ebf1fdfdfb54359ab11c2e811b1661f","83e5750e85da496caee60e801105a347","467f084678cb47c2b0ce12230b165907","a3eb5ba5a2aa4a49af675fe90dcbcc21","82a806880faf40ca9aa7266196d98fcb","46d2c5a422b449d0999b7f413010ac79","d2871e3d2cbd470ca9faff218fc471f9","bee1e586e7b04e9e83602bd52bbb4c44","38ae7fa5e3f24f1487aa4a9b8eb5aece","c6536cba85564bc18a863eb94f07d111","aaa776b81cd6481291d17e34d149f570","8a84b12c3df8433198df9d0718c0df12","3417de8143aa4954ac9ed54594837f5d","7bd285ad9ab849949d9d382d3d8b7b19","7776650718de4d8f83f4e82bc32d366c","47bec87e3a124a4988d49ea3e1f81e1d","d524de7fd3cf4544be6006f13de3e1a9","242a03d832f94fbb9dae045a728cac4c","96d87a92fbb4478283e41106ceb10c87","0ba63e92246b46c484ce93bad1e5ecfa","b2a1f45cd1a24359beb47706a794c3be","89d149e452d84ea0baccd4cbd502365e","40599e93fe634d04a08dd26400ebbb10","a7485b9841094571b79b05f3f2ff9cdb","f246022b7dd6425196aee5f94c5a2564"]},"id":"da7xr1xjdQlC","executionInfo":{"status":"ok","timestamp":1650910228988,"user_tz":-120,"elapsed":6165,"user":{"displayName":"Sergio Muñoz Gonzalez","userId":"13292202149038080080"}},"outputId":"0e167dbc-4498-4abd-fc7d-39f1d67faf34"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading and preparing dataset pawsx/es (download: 28.88 MiB, generated: 13.20 MiB, post-processed: Unknown size, total: 42.08 MiB) to /root/.cache/huggingface/datasets/pawsx/es/1.1.0/37673404a6de6d0fa2574661e77940d10d3be3bf51bb4f08c8fa079fd56b9755...\n"]},{"output_type":"display_data","data":{"text/plain":["Generating train split:   0%|          | 0/49401 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c8e917d110db490b9bd8dc8c9411e58e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating test split:   0%|          | 0/2000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9c4c7082daa84ffcb8dcf102b82284e6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating validation split:   0%|          | 0/2000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a3eb5ba5a2aa4a49af675fe90dcbcc21"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Dataset pawsx downloaded and prepared to /root/.cache/huggingface/datasets/pawsx/es/1.1.0/37673404a6de6d0fa2574661e77940d10d3be3bf51bb4f08c8fa079fd56b9755. Subsequent calls will reuse this data.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/3 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7776650718de4d8f83f4e82bc32d366c"}},"metadata":{}}]},{"cell_type":"code","source":["dataset_es"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xeepvTqMybqR","executionInfo":{"status":"ok","timestamp":1650910228989,"user_tz":-120,"elapsed":14,"user":{"displayName":"Sergio Muñoz Gonzalez","userId":"13292202149038080080"}},"outputId":"ef509e5b-c0dc-4a0e-ed59-b7541437d749"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['id', 'sentence1', 'sentence2', 'label'],\n","        num_rows: 49401\n","    })\n","    test: Dataset({\n","        features: ['id', 'sentence1', 'sentence2', 'label'],\n","        num_rows: 2000\n","    })\n","    validation: Dataset({\n","        features: ['id', 'sentence1', 'sentence2', 'label'],\n","        num_rows: 2000\n","    })\n","})"]},"metadata":{},"execution_count":5}]},{"cell_type":"code","source":["dataset_en"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"doKVCLTpg8vh","executionInfo":{"status":"ok","timestamp":1650910228989,"user_tz":-120,"elapsed":7,"user":{"displayName":"Sergio Muñoz Gonzalez","userId":"13292202149038080080"}},"outputId":"4432276e-f828-48a5-e06b-d90ec514bcc7"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['id', 'sentence1', 'sentence2', 'label'],\n","        num_rows: 49401\n","    })\n","    test: Dataset({\n","        features: ['id', 'sentence1', 'sentence2', 'label'],\n","        num_rows: 2000\n","    })\n","    validation: Dataset({\n","        features: ['id', 'sentence1', 'sentence2', 'label'],\n","        num_rows: 2000\n","    })\n","})"]},"metadata":{},"execution_count":6}]},{"cell_type":"markdown","source":["## Concatenate default datasets"],"metadata":{"id":"WOygI4dzA5-Z"}},{"cell_type":"code","source":["dataset_es = concatenate_datasets([dataset_es['train'], dataset_es['train'], dataset_es['validation']])\n","dataset_en = concatenate_datasets([dataset_en['train'], dataset_en['train'], dataset_en['validation']])"],"metadata":{"id":"N67krQVZgVbQ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["dataset_en"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HPOcivFrg5Qx","executionInfo":{"status":"ok","timestamp":1650910228990,"user_tz":-120,"elapsed":5,"user":{"displayName":"Sergio Muñoz Gonzalez","userId":"13292202149038080080"}},"outputId":"7ffcf08a-4ed2-40e2-bfb7-91f1fe782dbc"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Dataset({\n","    features: ['id', 'sentence1', 'sentence2', 'label'],\n","    num_rows: 100802\n","})"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","source":["lan_es_column = ['es'] * len(dataset_es)\n","lan_en_column = ['en'] * len(dataset_en)\n","dataset_es = dataset_es.add_column(\"lan\", lan_es_column )\n","dataset_en = dataset_en.add_column(\"lan\", lan_en_column)"],"metadata":{"id":"v4F755_-g2mY"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["dataset_comb = concatenate_datasets([dataset_es, dataset_en])"],"metadata":{"id":"0iZ72_ImgriI"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["dataset_comb"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"f27UuwejhJrV","executionInfo":{"status":"ok","timestamp":1650910229508,"user_tz":-120,"elapsed":3,"user":{"displayName":"Sergio Muñoz Gonzalez","userId":"13292202149038080080"}},"outputId":"8d6e27df-77f0-4131-a8ed-d086c681055b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Dataset({\n","    features: ['id', 'sentence1', 'sentence2', 'label', 'lan'],\n","    num_rows: 201604\n","})"]},"metadata":{},"execution_count":11}]},{"cell_type":"markdown","source":["## Filtering"],"metadata":{"id":"CX9jscQz-V3f"}},{"cell_type":"code","source":["dataset_comb = dataset_comb.filter(lambda example: example['label'] == 1)\n","print(dataset_comb)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":121,"referenced_widgets":["77e6b467dc2341979ecb9217d727266c","ed45a4d235f24831aac425b2599dc33a","5c08db9476494a65b9b680b510fd3cf3","23d2a35065674fedb1f28706ad91ddd3","d1a1413e6e5b44859b33a582f3a1eaab","20d56a4297274b8981783889d24a4c34","d8d1d59eed96485e9730161e50c05dad","105be6b714194e928dbee89306b43b25","f66ee576a6454ca7a1e5cafe7fc57aa0","e463eccd6fb14ab79476f046ac16a99e","7a5fac8c76e34ebe8b9e52df6b809cb9"]},"id":"awCHd6fh-Sg5","executionInfo":{"status":"ok","timestamp":1650910232460,"user_tz":-120,"elapsed":2955,"user":{"displayName":"Sergio Muñoz Gonzalez","userId":"13292202149038080080"}},"outputId":"f0793765-9a79-4130-b63a-ca3ccb04ae2e"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/202 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"77e6b467dc2341979ecb9217d727266c"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Dataset({\n","    features: ['id', 'sentence1', 'sentence2', 'label', 'lan'],\n","    num_rows: 89026\n","})\n"]}]},{"cell_type":"markdown","source":["## Shuffling"],"metadata":{"id":"DUoMm9SviRRF"}},{"cell_type":"code","source":["PAWS_X =  dataset_comb.shuffle(seed=42)"],"metadata":{"id":"ycGH7R8oiOk1"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## To dataframe"],"metadata":{"id":"esBxHwyI8P_3"}},{"cell_type":"code","source":["PAWS_X = pd.DataFrame(PAWS_X, index = PAWS_X['id'] )\n"],"metadata":{"id":"PoKerh9Bjjk9"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["PAWS_X = PAWS_X.drop('id', 1)"],"metadata":{"id":"2wuJzJmz04FH","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1650910256610,"user_tz":-120,"elapsed":17,"user":{"displayName":"Sergio Muñoz Gonzalez","userId":"13292202149038080080"}},"outputId":"f5e87581-84f0-48e4-ebcc-f75614b38dc7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n","  \"\"\"Entry point for launching an IPython kernel.\n"]}]},{"cell_type":"code","source":["PAWS_X"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"IMF9x9bJq8tW","executionInfo":{"status":"ok","timestamp":1650910256611,"user_tz":-120,"elapsed":15,"user":{"displayName":"Sergio Muñoz Gonzalez","userId":"13292202149038080080"}},"outputId":"ba30eacc-e6ee-478d-e0c2-6aa0742c6a49"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                               sentence1  \\\n","19990  La esposa de Quinatzin era una princesa de Hue...   \n","10801  The academy consists of east hall , central wi...   \n","20285  Critical Millennium is a 2010 graphic novel pu...   \n","39049  Stenolechia zelosaris is a moth of the family ...   \n","28878  Gary Lucy was paired with Vanilla Ice in serie...   \n","...                                                  ...   \n","29788  El 30 de agosto de 1853, la hija de Stephen Ch...   \n","8953   Other types of course offered by the Departmen...   \n","12708  Algunos tienen efectos negativos, algunos posi...   \n","605    Con el debilitamiento del dólar canadiense, la...   \n","20544  He also appeared in 53 games for the Greensbor...   \n","\n","                                               sentence2  label lan  \n","19990  La esposa de Quinatzin era una princesa de Hue...      1  es  \n","10801  The academy consists of eastern hall , central...      1  en  \n","20285  Critical Millennium is a novel published by Ar...      1  en  \n","39049  Stenolechia zelosaris is a moth from the famil...      1  en  \n","28878  Gary Lucy was paired with Vanilla Ice in serie...      1  en  \n","...                                                  ...    ...  ..  \n","29788  El 30 de agosto de 1853, la hija de Stephen Ch...      1  es  \n","8953   Other types of courses offered by the departme...      1  en  \n","12708  Algunos tienen efectos negativos y algunos pos...      1  es  \n","605    En noviembre y diciembre de 2015, con el debil...      1  es  \n","20544  He also appeared in 53 games for the Piedmont ...      1  en  \n","\n","[89026 rows x 4 columns]"],"text/html":["\n","  <div id=\"df-3fdeadeb-e4d5-4b49-88ef-19ca9fbbf707\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>sentence1</th>\n","      <th>sentence2</th>\n","      <th>label</th>\n","      <th>lan</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>19990</th>\n","      <td>La esposa de Quinatzin era una princesa de Hue...</td>\n","      <td>La esposa de Quinatzin era una princesa de Hue...</td>\n","      <td>1</td>\n","      <td>es</td>\n","    </tr>\n","    <tr>\n","      <th>10801</th>\n","      <td>The academy consists of east hall , central wi...</td>\n","      <td>The academy consists of eastern hall , central...</td>\n","      <td>1</td>\n","      <td>en</td>\n","    </tr>\n","    <tr>\n","      <th>20285</th>\n","      <td>Critical Millennium is a 2010 graphic novel pu...</td>\n","      <td>Critical Millennium is a novel published by Ar...</td>\n","      <td>1</td>\n","      <td>en</td>\n","    </tr>\n","    <tr>\n","      <th>39049</th>\n","      <td>Stenolechia zelosaris is a moth of the family ...</td>\n","      <td>Stenolechia zelosaris is a moth from the famil...</td>\n","      <td>1</td>\n","      <td>en</td>\n","    </tr>\n","    <tr>\n","      <th>28878</th>\n","      <td>Gary Lucy was paired with Vanilla Ice in serie...</td>\n","      <td>Gary Lucy was paired with Vanilla Ice in serie...</td>\n","      <td>1</td>\n","      <td>en</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>29788</th>\n","      <td>El 30 de agosto de 1853, la hija de Stephen Ch...</td>\n","      <td>El 30 de agosto de 1853, la hija de Stephen Ch...</td>\n","      <td>1</td>\n","      <td>es</td>\n","    </tr>\n","    <tr>\n","      <th>8953</th>\n","      <td>Other types of course offered by the Departmen...</td>\n","      <td>Other types of courses offered by the departme...</td>\n","      <td>1</td>\n","      <td>en</td>\n","    </tr>\n","    <tr>\n","      <th>12708</th>\n","      <td>Algunos tienen efectos negativos, algunos posi...</td>\n","      <td>Algunos tienen efectos negativos y algunos pos...</td>\n","      <td>1</td>\n","      <td>es</td>\n","    </tr>\n","    <tr>\n","      <th>605</th>\n","      <td>Con el debilitamiento del dólar canadiense, la...</td>\n","      <td>En noviembre y diciembre de 2015, con el debil...</td>\n","      <td>1</td>\n","      <td>es</td>\n","    </tr>\n","    <tr>\n","      <th>20544</th>\n","      <td>He also appeared in 53 games for the Greensbor...</td>\n","      <td>He also appeared in 53 games for the Piedmont ...</td>\n","      <td>1</td>\n","      <td>en</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>89026 rows × 4 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3fdeadeb-e4d5-4b49-88ef-19ca9fbbf707')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 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Gonzalez","userId":"13292202149038080080"}},"outputId":"f5f89f8b-079c-4063-e506-668bec493fb2"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n","  \"\"\"Entry point for launching an IPython kernel.\n"]}]},{"cell_type":"code","source":["PAWS_X = PAWS_X.reset_index(drop=True)\n"],"metadata":{"id":"Mijo3JX1ClpM"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["PAWS_X"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"u0TdAGN5rnn6","executionInfo":{"status":"ok","timestamp":1650910256613,"user_tz":-120,"elapsed":13,"user":{"displayName":"Sergio Muñoz Gonzalez","userId":"13292202149038080080"}},"outputId":"91440f2c-80b5-4f4e-e697-e7f4a1d96bf8"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                               sentence1  \\\n","0      La esposa de Quinatzin era una princesa de Hue...   \n","1      The academy consists of east hall , central wi...   \n","2      Critical Millennium is a 2010 graphic novel pu...   \n","3      Stenolechia zelosaris is a moth of the family ...   \n","4      Gary Lucy was paired with Vanilla Ice in serie...   \n","...                                                  ...   \n","89021  El 30 de agosto de 1853, la hija de Stephen Ch...   \n","89022  Other types of course offered by the Departmen...   \n","89023  Algunos tienen efectos negativos, algunos posi...   \n","89024  Con el debilitamiento del dólar canadiense, la...   \n","89025  He also appeared in 53 games for the Greensbor...   \n","\n","                                               sentence2 lan  \n","0      La esposa de Quinatzin era una princesa de Hue...  es  \n","1      The academy consists of eastern hall , central...  en  \n","2      Critical Millennium is a novel published by Ar...  en  \n","3      Stenolechia zelosaris is a moth from the famil...  en  \n","4      Gary Lucy was paired with Vanilla Ice in serie...  en  \n","...                                                  ...  ..  \n","89021  El 30 de agosto de 1853, la hija de Stephen Ch...  es  \n","89022  Other types of courses offered by the departme...  en  \n","89023  Algunos tienen efectos negativos y algunos pos...  es  \n","89024  En noviembre y diciembre de 2015, con el debil...  es  \n","89025  He also appeared in 53 games for the Piedmont ...  en  \n","\n","[89026 rows x 3 columns]"],"text/html":["\n","  <div id=\"df-fc120fa6-4a23-44c8-b0b8-1096eb6ed800\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","      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Millennium is a novel published by Ar...</td>\n","      <td>en</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Stenolechia zelosaris is a moth of the family ...</td>\n","      <td>Stenolechia zelosaris is a moth from the famil...</td>\n","      <td>en</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Gary Lucy was paired with Vanilla Ice in serie...</td>\n","      <td>Gary Lucy was paired with Vanilla Ice in serie...</td>\n","      <td>en</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>89021</th>\n","      <td>El 30 de agosto de 1853, la hija de Stephen Ch...</td>\n","      <td>El 30 de agosto de 1853, la hija de Stephen Ch...</td>\n","      <td>es</td>\n","    </tr>\n","    <tr>\n","      <th>89022</th>\n","      <td>Other types of course offered by the Departmen...</td>\n","      <td>Other types of courses offered by the departme...</td>\n","      <td>en</td>\n","    </tr>\n","    <tr>\n","      <th>89023</th>\n","      <td>Algunos tienen efectos negativos, algunos posi...</td>\n","      <td>Algunos tienen efectos negativos y algunos pos...</td>\n","      <td>es</td>\n","    </tr>\n","    <tr>\n","      <th>89024</th>\n","      <td>Con el debilitamiento del dólar canadiense, la...</td>\n","      <td>En noviembre y diciembre de 2015, con el debil...</td>\n","      <td>es</td>\n","    </tr>\n","    <tr>\n","      <th>89025</th>\n","      <td>He also appeared in 53 games for the Greensbor...</td>\n","      <td>He also appeared in 53 games for the Piedmont ...</td>\n","      <td>en</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>89026 rows × 3 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fc120fa6-4a23-44c8-b0b8-1096eb6ed800')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              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CREATING AND PROCESSING DATASET TAPACO"],"metadata":{"id":"u_oOknesBwz_"}},{"cell_type":"markdown","source":["## Loading datasets"],"metadata":{"id":"RcM1cWvJlUi3"}},{"cell_type":"code","source":["tapaco_es = load_dataset(\"tapaco\", \"es\")\n","tapaco_en = load_dataset(\"tapaco\", \"en\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":117,"referenced_widgets":["a65e1fadbcb9481b9cc7862a71331439","f4f3647fee4b480f9b5ed26388acf0d9","ea6c7997b8bf4736933d467f9b95caca","f55491e174174989aff8cbb2ce6596bd","c6977b76f05248c0bf8510b132891123","b53bf8be121141149c0be5fdb2daad6e","8230a7da7aa74ff5846cc622bd29c90e","a4802f019d0845409a27c49626cd3dbf","3777e85c7c0448709a2adfe1c404e33b","4621c728c62d4a0aa89dda7595b1d3c1","a3908f9117ad49c88b7b0eef63c49e02","d6a3e08031374bf7a6efd52ae6d898cf","63da28b822a0449ab1a2161f68af88e8","2aca90f46dd14fa7b389f9990306d057","4acec65c8d854b9484c22e1656b9c10e","4b9624db42e648e49f5edcf71adff802","98eb7cb71dc94528ab8f902922b5d7ce","692d99fbd4194fb6883f1271eec2e242","37059278d7e340e8b7b2c683fb7949ce","0125e4c5ec8d4113a930936aed46b8fb","1d7ec51eec81447686cb3bc4ef65ef44","e6fabd681a494e8d9fc5a624d29794dd"]},"id":"oVBfYU5sAcA1","executionInfo":{"status":"ok","timestamp":1650629163340,"user_tz":-120,"elapsed":1140,"user":{"displayName":"Sergio Muñoz Gonzalez","userId":"13292202149038080080"}},"outputId":"83efa6a9-a222-4c3d-864d-a3ac66c93203"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Reusing dataset tapaco (/root/.cache/huggingface/datasets/tapaco/es/1.0.0/71d200534b520a174927a8f0479c06220a0a6fb5201a84ebfce19006c6354698)\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":"a65e1fadbcb9481b9cc7862a71331439"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Reusing dataset tapaco (/root/.cache/huggingface/datasets/tapaco/en/1.0.0/71d200534b520a174927a8f0479c06220a0a6fb5201a84ebfce19006c6354698)\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":"d6a3e08031374bf7a6efd52ae6d898cf"}},"metadata":{}}]},{"cell_type":"code","source":["tapaco_es['train']"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XlOvPs9qcx4C","executionInfo":{"status":"ok","timestamp":1650629168141,"user_tz":-120,"elapsed":192,"user":{"displayName":"Sergio Muñoz Gonzalez","userId":"13292202149038080080"}},"outputId":"1b9348c5-447a-4fc2-8219-dcb98ff50ba2"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Dataset({\n","    features: ['paraphrase_set_id', 'sentence_id', 'paraphrase', 'lists', 'tags', 'language'],\n","    num_rows: 85064\n","})"]},"metadata":{},"execution_count":81}]},{"cell_type":"code","source":["tapaco_en['train']"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Vc8R66-hmalv","executionInfo":{"status":"ok","timestamp":1650629169427,"user_tz":-120,"elapsed":193,"user":{"displayName":"Sergio Muñoz Gonzalez","userId":"13292202149038080080"}},"outputId":"6078d30a-75de-401e-ff84-7325c44f5419"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Dataset({\n","    features: ['paraphrase_set_id', 'sentence_id', 'paraphrase', 'lists', 'tags', 'language'],\n","    num_rows: 158053\n","})"]},"metadata":{},"execution_count":82}]},{"cell_type":"markdown","source":["## Processsing datasets"],"metadata":{"id":"uai991QqlYAS"}},{"cell_type":"code","source":["\n","\n","#Loading dataset\n","dataset1 = load_dataset('tapaco', 'en')\n","dataset2 = load_dataset('tapaco', 'es')\n","def process_tapaco_dataset(dataset, out_file):\n","    tapaco = []\n","    # The dataset has only train split.\n","    for data in tqdm(dataset[\"train\"]):\n","        keys = data.keys()\n","        tapaco.append([data[key] for key in keys])\n","    tapaco_df = pd.DataFrame(\n","        data=tapaco,\n","        columns=[\n","            \"paraphrase_set_id\",\n","            \"sentence_id \t\",\n","            \"paraphrase\",\n","            \"lists\", \n","            \"tags\", \n","            \"language\",\n","        ],\n","    )\n","    tapaco_df.to_csv(out_file, sep=\"\\t\", index=None)\n","    return tapaco_df\n","\n","tapaco_df_en = process_tapaco_dataset(dataset1,r\"/content/drive/MyDrive/working directory/tfm/Distilled GPT2 finetuning/datasets/tapaco/tapaco_en.csv\")\n","tapaco_df_es = process_tapaco_dataset(dataset2,r\"/content/drive/MyDrive/working directory/tfm/Distilled GPT2 finetuning/datasets/tapaco/tapaco_es.csv\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rG9faaVPC2Qq","executionInfo":{"status":"ok","timestamp":1650629270151,"user_tz":-120,"elapsed":28679,"user":{"displayName":"Sergio Muñoz Gonzalez","userId":"13292202149038080080"}},"outputId":"d9ddcbdc-609a-4ca1-f4c2-7ae2a9fabbee"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["100%|██████████| 158053/158053 [00:27<00:00, 5833.37it/s]\n","100%|██████████| 85064/85064 [00:18<00:00, 4523.44it/s]\n"]}]},{"cell_type":"code","source":["def generate_tapaco_paraphrase_dataset(dataset):\n","    dataset_df = dataset[[\"paraphrase\", \"paraphrase_set_id\"]]\n","    non_single_labels = (\n","        dataset_df[\"paraphrase_set_id\"]\n","        .value_counts()[dataset_df[\"paraphrase_set_id\"].value_counts() > 1]\n","        .index.tolist()\n","    )\n","    tapaco_df_sorted = dataset_df.loc[\n","        dataset_df[\"paraphrase_set_id\"].isin(non_single_labels)\n","    ]\n","    tapaco_paraphrases_dataset = []\n","\n","    for paraphrase_set_id in tqdm(tapaco_df_sorted[\"paraphrase_set_id\"].unique()):\n","        id_wise_paraphrases = tapaco_df_sorted[\n","            tapaco_df_sorted[\"paraphrase_set_id\"] == paraphrase_set_id\n","        ]\n","        len_id_wise_paraphrases = (\n","            id_wise_paraphrases.shape[0]\n","            if id_wise_paraphrases.shape[0] % 2 == 0\n","            else id_wise_paraphrases.shape[0] - 1\n","        )\n","        for ix in range(0, len_id_wise_paraphrases, 2):\n","            current_phrase = id_wise_paraphrases.iloc[ix][0]\n","            for count_ix in range(ix + 1, ix + 2):\n","                next_phrase = id_wise_paraphrases.iloc[ix + 1][0]\n","                tapaco_paraphrases_dataset.append([current_phrase, next_phrase])\n","    tapaco_paraphrases_dataset_df = pd.DataFrame(\n","        tapaco_paraphrases_dataset, columns=[\"Text\", \"Paraphrase\"]\n","    )\n"," \n","    return tapaco_paraphrases_dataset_df\n","    \n","tapaco_paraphrases_dataset_df_es = generate_tapaco_paraphrase_dataset(tapaco_df_es)\n","tapaco_paraphrases_dataset_df_en = generate_tapaco_paraphrase_dataset(tapaco_df_en)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ScoIL5rrcpny","executionInfo":{"status":"ok","timestamp":1650630577047,"user_tz":-120,"elapsed":1306899,"user":{"displayName":"Sergio Muñoz Gonzalez","userId":"13292202149038080080"}},"outputId":"743bf1ec-e05f-4690-92ca-c9cb1acad0b6"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["100%|██████████| 32691/32691 [04:23<00:00, 124.02it/s]\n","100%|██████████| 62044/62044 [17:22<00:00, 59.50it/s]\n"]}]},{"cell_type":"code","source":["tapaco_paraphrases_dataset_df_es['lan'] = ['es'] * len(tapaco_paraphrases_dataset_df_es)\n","tapaco_paraphrases_dataset_df_en['lan'] = ['en'] * len(tapaco_paraphrases_dataset_df_en)"],"metadata":{"id":"_zxZgRpVo4Cz"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Concatenat en es datasets"],"metadata":{"id":"EnPpDSkJvlfS"}},{"cell_type":"code","source":["tapaco_x = pd.concat([tapaco_paraphrases_dataset_df_en, tapaco_paraphrases_dataset_df_es], ignore_index=True, sort=False)"],"metadata":{"id":"0tiZmsHOuvqo"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Shuffling"],"metadata":{"id":"FTPxoB-wlgjx"}},{"cell_type":"code","source":["tapaco_x = tapaco_x.sample(frac=1)"],"metadata":{"id":"Ls_zNkeMwU17"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tapaco_x = tapaco_x.reset_index(drop=True)"],"metadata":{"id":"3VkJipjEwZhx"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tapaco_x.to_csv(\"/content/drive/MyDrive/working directory/tfm/Distilled GPT2 finetuning/datasets/tapaco/tapaco_x.csv\", index=None)"],"metadata":{"id":"BfycJQ4ZwrtB"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# CREATING AND PROCESSING COMBINED DATASET(PAWSX + Tapaco)"],"metadata":{"id":"kg1Ae_tjxNMx"}},{"cell_type":"markdown","source":["## Load datasets"],"metadata":{"id":"J4fJ-xRZ2yPr"}},{"cell_type":"code","source":["  tapaco_x = pd.read_csv(\"/content/drive/MyDrive/working directory/tfm/Distilled GPT2 finetuning/datasets/tapaco/tapaco_x.csv\")\n","  PAWS_X = pd.read_csv('/content/drive/MyDrive/working directory/tfm/Distilled GPT2 finetuning/datasets/PAWSX/PAWS_X.csv')"],"metadata":{"id":"CvxwWfOdxe36"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["PAWS_X = PAWS_X.rename(columns={'sentence1': 'Text', 'sentence2': 'Paraphrase'})"],"metadata":{"id":"Y3Sqxa_j1yI7"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Concatenate"],"metadata":{"id":"IJqFCMQ-2vQZ"}},{"cell_type":"code","source":["dataset_comb = pd.concat([tapaco_x, PAWS_X], ignore_index=True, sort=False)"],"metadata":{"id":"ddwO5vLuzdzR"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["dataset_comb"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"Sb6Qg_lf8dIb","executionInfo":{"status":"ok","timestamp":1650910684794,"user_tz":-120,"elapsed":190,"user":{"displayName":"Sergio Muñoz Gonzalez","userId":"13292202149038080080"}},"outputId":"98bca5d4-4f88-4f8f-a127-00914ab911e5"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                                     Text  \\\n","0                       ¡Dese prisa que perdemos el tren!   \n","1                                  I squeezed Tom's hand.   \n","2                         Tom didn't know who to believe.   \n","3                             Quemaron un poco de carbón.   \n","4                                I don't think about you.   \n","...                                                   ...   \n","201527  El 30 de agosto de 1853, la hija de Stephen Ch...   \n","201528  Other types of course offered by the Departmen...   \n","201529  Algunos tienen efectos negativos, algunos posi...   \n","201530  Con el debilitamiento del dólar canadiense, la...   \n","201531  He also appeared in 53 games for the Greensbor...   \n","\n","                                               Paraphrase lan  \n","0              ¡Apúrate o se nos hace tarde para el tren!  es  \n","1                                     I shook Tom's hand.  en  \n","2                  Tom didn't know who he should believe.  en  \n","3                            Ellos quemaron algún carbón.  es  \n","4                               I am not thinking of you.  en  \n","...                                                   ...  ..  \n","201527  El 30 de agosto de 1853, la hija de Stephen Ch...  es  \n","201528  Other types of courses offered by the departme...  en  \n","201529  Algunos tienen efectos negativos y algunos pos...  es  \n","201530  En noviembre y diciembre de 2015, con el debil...  es  \n","201531  He also appeared in 53 games for the Piedmont ...  en  \n","\n","[201532 rows x 3 columns]"],"text/html":["\n","  <div id=\"df-563c2b4a-5928-4b98-a643-ec3214820c24\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Text</th>\n","      <th>Paraphrase</th>\n","      <th>lan</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>¡Dese prisa que perdemos el tren!</td>\n","      <td>¡Apúrate o se nos hace tarde para el tren!</td>\n","      <td>es</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>I squeezed Tom's hand.</td>\n","      <td>I shook Tom's hand.</td>\n","      <td>en</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Tom didn't know who to believe.</td>\n","      <td>Tom didn't know who he should believe.</td>\n","      <td>en</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Quemaron un poco de carbón.</td>\n","      <td>Ellos quemaron algún carbón.</td>\n","      <td>es</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>I don't think about you.</td>\n","      <td>I am not thinking of you.</td>\n","      <td>en</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>201527</th>\n","      <td>El 30 de agosto de 1853, la hija de Stephen Ch...</td>\n","      <td>El 30 de agosto de 1853, la hija de Stephen Ch...</td>\n","      <td>es</td>\n","    </tr>\n","    <tr>\n","      <th>201528</th>\n","      <td>Other types of course offered by the Departmen...</td>\n","      <td>Other types of courses offered by the departme...</td>\n","      <td>en</td>\n","    </tr>\n","    <tr>\n","      <th>201529</th>\n","      <td>Algunos tienen efectos negativos, algunos posi...</td>\n","      <td>Algunos tienen efectos negativos y algunos pos...</td>\n","      <td>es</td>\n","    </tr>\n","    <tr>\n","      <th>201530</th>\n","      <td>Con el debilitamiento del dólar canadiense, la...</td>\n","      <td>En noviembre y diciembre de 2015, con el debil...</td>\n","      <td>es</td>\n","    </tr>\n","    <tr>\n","      <th>201531</th>\n","      <td>He also appeared in 53 games for the Greensbor...</td>\n","      <td>He also appeared in 53 games for the Piedmont ...</td>\n","      <td>en</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>201532 rows × 3 columns</p>\n","</div>\n","      <button 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Bridge is a historic bridge in the city...   \n","201530  Sin embargo, Michael Jackson, Prince y Madonna...   \n","201531  Björn Borg derrotó a Guillermo Vilas, 6 - 1, 6...   \n","\n","                                               Paraphrase lan  \n","0                              El gato se comió al ratón.  es  \n","1       Dirigida por Paul Krasny, escrita por Nancy St...  es  \n","2                                 Where is the cafeteria?  en  \n","3                                  Everybody was stunned.  en  \n","4       Navegó por Sydney y llegó el 14 de septiembre ...  es  \n","...                                                   ...  ..  \n","201527                                 ¿Me reconoces aún?  es  \n","201528                     Are you making her a sandwich?  en  \n","201529  Ross Bridge is an historic bridge in the town ...  en  \n","201530  Sin embargo, Michael Jackson, Prince y Madonna...  es  \n","201531  Björn Borg derrotó a Guillermo Vilas, 6 - 1, 6...  es  \n","\n","[201532 rows x 3 columns]"],"text/html":["\n","  <div id=\"df-99e0fd3c-0693-4ab0-a281-2a33594eb3ff\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Text</th>\n","      <th>Paraphrase</th>\n","      <th>lan</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>El gato se come al pequeño ratón.</td>\n","      <td>El gato se comió al ratón.</td>\n","      <td>es</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Fue escrito por Nancy Steen y Neil Thompson, d...</td>\n","      <td>Dirigida por Paul Krasny, escrita por Nancy St...</td>\n","      <td>es</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Where is the coffee shop?</td>\n","      <td>Where is the cafeteria?</td>\n","      <td>en</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Everybody was startled.</td>\n","      <td>Everybody was stunned.</td>\n","      <td>en</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Navegó por Sydney y llegó a Río de Janeiro el ...</td>\n","      <td>Navegó por Sydney y llegó el 14 de septiembre ...</td>\n","      <td>es</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>201527</th>\n","      <td>¿Recuerdas quién soy yo?</td>\n","      <td>¿Me reconoces aún?</td>\n","      <td>es</td>\n","    </tr>\n","    <tr>\n","      <th>201528</th>\n","      <td>Are you making him a sandwich?</td>\n","      <td>Are you making her a sandwich?</td>\n","      <td>en</td>\n","    </tr>\n","    <tr>\n","      <th>201529</th>\n","      <td>Ross - Bridge is a historic bridge in the city...</td>\n","      <td>Ross Bridge is an historic bridge in the town ...</td>\n","      <td>en</td>\n","    </tr>\n","    <tr>\n","      <th>201530</th>\n","      <td>Sin embargo, Michael Jackson, Prince y Madonna...</td>\n","      <td>Sin embargo, Michael Jackson, Prince y Madonna...</td>\n","      <td>es</td>\n","    </tr>\n","    <tr>\n","      <th>201531</th>\n","      <td>Björn Borg derrotó a Guillermo Vilas, 6 - 1, 6...</td>\n","      <td>Björn Borg derrotó a Guillermo Vilas, 6 - 1, 6...</td>\n","      <td>es</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>201532 rows × 3 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-99e0fd3c-0693-4ab0-a281-2a33594eb3ff')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-99e0fd3c-0693-4ab0-a281-2a33594eb3ff button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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  \n","3                                 Everybody was startled.   \n","5                                Did Tom have to do that?   \n","6                                Tom wouldn't talk to me.   \n","7                                    Time is running out.   \n","...                                                   ...   \n","201519                          She cottoned to him soon.   \n","201521                  He tends to be late for meetings.   \n","201525  Only those who believe in the future believe i...   \n","201528                     Are you making him a sandwich?   \n","201529  Ross - Bridge is a historic bridge in the city...   \n","\n","                                               Paraphrase lan  \n","2                                 Where is the cafeteria?  en  \n","3                                  Everybody was stunned.  en  \n","5                              Was Tom forced to do that?  en  \n","6                                Tom doesn't speak to me.  en  \n","7                                              Time flew.  en  \n","...                                                   ...  ..  \n","201519                  She began to like him right away.  en  \n","201521                      He's always late to meetings.  en  \n","201525  Only in trusting the future is it possible to ...  en  \n","201528                     Are you making her a sandwich?  en  \n","201529  Ross Bridge is an historic bridge in the town ...  en  \n","\n","[118039 rows x 3 columns]"],"text/html":["\n","  <div id=\"df-0ac0835d-9584-4a13-af1b-94350263ec21\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Text</th>\n","      <th>Paraphrase</th>\n","      <th>lan</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2</th>\n","      <td>Where is the coffee shop?</td>\n","      <td>Where is the cafeteria?</td>\n","      <td>en</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Everybody was startled.</td>\n","      <td>Everybody was stunned.</td>\n","      <td>en</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>Did Tom have to do that?</td>\n","      <td>Was Tom forced to do that?</td>\n","      <td>en</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Tom wouldn't talk to me.</td>\n","      <td>Tom doesn't speak to me.</td>\n","      <td>en</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Time is running out.</td>\n","      <td>Time flew.</td>\n","      <td>en</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>201519</th>\n","      <td>She cottoned to him soon.</td>\n","      <td>She began to like him right away.</td>\n","      <td>en</td>\n","    </tr>\n","    <tr>\n","      <th>201521</th>\n","      <td>He tends to be late for meetings.</td>\n","      <td>He's always late to meetings.</td>\n","      <td>en</td>\n","    </tr>\n","    <tr>\n","      <th>201525</th>\n","      <td>Only those who believe in the future believe i...</td>\n","      <td>Only in trusting the future is it possible to ...</td>\n","      <td>en</td>\n","    </tr>\n","    <tr>\n","      <th>201528</th>\n","      <td>Are you making him a sandwich?</td>\n","      <td>Are you making her a sandwich?</td>\n","      <td>en</td>\n","    </tr>\n","    <tr>\n","      <th>201529</th>\n","      <td>Ross - Bridge is a historic bridge in the city...</td>\n","      <td>Ross Bridge is an historic bridge in the town ...</td>\n","      <td>en</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>118039 rows × 3 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0ac0835d-9584-4a13-af1b-94350263ec21')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-0ac0835d-9584-4a13-af1b-94350263ec21 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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  \n","3                                 Everybody was startled.   \n","5                                Did Tom have to do that?   \n","6                                Tom wouldn't talk to me.   \n","7                                    Time is running out.   \n","...                                                   ...   \n","201519                          She cottoned to him soon.   \n","201521                  He tends to be late for meetings.   \n","201525  Only those who believe in the future believe i...   \n","201528                     Are you making him a sandwich?   \n","201529  Ross - Bridge is a historic bridge in the city...   \n","\n","                                               Paraphrase lan  \\\n","2                                 Where is the cafeteria?  en   \n","3                                  Everybody was stunned.  en   \n","5                              Was Tom forced to do that?  en   \n","6                                Tom doesn't speak to me.  en   \n","7                                              Time flew.  en   \n","...                                                   ...  ..   \n","201519                  She began to like him right away.  en   \n","201521                      He's always late to meetings.  en   \n","201525  Only in trusting the future is it possible to ...  en   \n","201528                     Are you making her a sandwich?  en   \n","201529  Ross Bridge is an historic bridge in the town ...  en   \n","\n","                                                 combined  \n","2       <s> Where is the coffee shop? </s> === <p> Whe...  \n","3       <s> Everybody was startled. </s> === <p> Every...  \n","5       <s> Did Tom have to do that? </s> === <p> Was ...  \n","6       <s> Tom wouldn't talk to me. </s> === <p> Tom ...  \n","7       <s> Time is running out. </s> === <p> Time fle...  \n","...                                                   ...  \n","201519  <s> She cottoned to him soon. </s> === <p> She...  \n","201521  <s> He tends to be late for meetings. </s> ===...  \n","201525  <s> Only those who believe in the future belie...  \n","201528  <s> Are you making him a sandwich? </s> === <p...  \n","201529  <s> Ross - Bridge is a historic bridge in the ...  \n","\n","[118039 rows x 4 columns]"],"text/html":["\n","  <div id=\"df-5543b2b6-3c72-46a0-95a9-cf71ed8e7206\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Text</th>\n","      <th>Paraphrase</th>\n","      <th>lan</th>\n","      <th>combined</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2</th>\n","      <td>Where is the coffee shop?</td>\n","      <td>Where is the cafeteria?</td>\n","      <td>en</td>\n","      <td>&lt;s&gt; Where is the coffee shop? &lt;/s&gt; === &lt;p&gt; Whe...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Everybody was startled.</td>\n","      <td>Everybody was stunned.</td>\n","      <td>en</td>\n","      <td>&lt;s&gt; Everybody was startled. &lt;/s&gt; === &lt;p&gt; Every...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>Did Tom have to do that?</td>\n","      <td>Was Tom forced to do that?</td>\n","      <td>en</td>\n","      <td>&lt;s&gt; Did Tom have to do that? &lt;/s&gt; === &lt;p&gt; Was ...</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Tom wouldn't talk to me.</td>\n","      <td>Tom doesn't speak to me.</td>\n","      <td>en</td>\n","      <td>&lt;s&gt; Tom wouldn't talk to me. &lt;/s&gt; === &lt;p&gt; Tom ...</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Time is running out.</td>\n","      <td>Time flew.</td>\n","      <td>en</td>\n","      <td>&lt;s&gt; Time is running out. &lt;/s&gt; === &lt;p&gt; Time fle...</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>201519</th>\n","      <td>She cottoned to him soon.</td>\n","      <td>She began to like him right away.</td>\n","      <td>en</td>\n","      <td>&lt;s&gt; She cottoned to him soon. &lt;/s&gt; === &lt;p&gt; She...</td>\n","    </tr>\n","    <tr>\n","      <th>201521</th>\n","      <td>He tends to be late for meetings.</td>\n","      <td>He's always late to meetings.</td>\n","      <td>en</td>\n","      <td>&lt;s&gt; He tends to be late for meetings. &lt;/s&gt; ===...</td>\n","    </tr>\n","    <tr>\n","      <th>201525</th>\n","      <td>Only those who believe in the future believe i...</td>\n","      <td>Only in trusting the future is it possible to ...</td>\n","      <td>en</td>\n","      <td>&lt;s&gt; Only those who believe in the future belie...</td>\n","    </tr>\n","    <tr>\n","      <th>201528</th>\n","      <td>Are you making him a sandwich?</td>\n","      <td>Are you making her a sandwich?</td>\n","      <td>en</td>\n","      <td>&lt;s&gt; Are you making him a sandwich? &lt;/s&gt; === &lt;p...</td>\n","    </tr>\n","    <tr>\n","      <th>201529</th>\n","      <td>Ross - Bridge is a historic bridge in the city...</td>\n","      <td>Ross Bridge is an historic bridge in the town ...</td>\n","      <td>en</td>\n","      <td>&lt;s&gt; Ross - Bridge is a historic bridge in the ...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>118039 rows × 4 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5543b2b6-3c72-46a0-95a9-cf71ed8e7206')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-5543b2b6-3c72-46a0-95a9-cf71ed8e7206 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":10}]},{"cell_type":"code","source":["dataset_comb_tr, dataset_comb_test = train_test_split(dataset_comb, test_size=0.2)\n","dataset_comb_test, dataset_comb_val = train_test_split(dataset_comb_test, test_size=0.5)"],"metadata":{"id":"xhMflH3h-3g9"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["dataset_comb_tr = dataset_comb_tr.reset_index(drop=True)\n","dataset_comb_test = dataset_comb_test.reset_index(drop=True)\n","dataset_comb_val = dataset_comb_test.reset_index(drop=True)"],"metadata":{"id":"tlKxdeBRA0Go"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(len(dataset_comb_tr), len(dataset_comb_test), len(dataset_comb_val))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8eRnY7qGAbn8","executionInfo":{"status":"ok","timestamp":1661648266865,"user_tz":-120,"elapsed":3,"user":{"displayName":"Sergio Muñoz Gonzalez","userId":"13292202149038080080"}},"outputId":"c15cd9c7-b1e4-4030-e42f-9b0fa848c652"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["94431 11804 11804\n"]}]},{"cell_type":"code","source":["dataset_comb_tr[['combined','lan']]"],"metadata":{"id":"b9CR3FFw-uuH"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["dataset_comb.to_csv('/content/drive/MyDrive/working_directory/tfm/Distilled_GPT2_finetuning/datasets/Combined(PAWSX + Tapaco)/distilgpt2_input/dataset_comb_input.csv', index = False)\n","dataset_comb_tr.combined.to_csv('/content/drive/MyDrive/working_directory/tfm/Distilled_GPT2_finetuning/datasets/Combined(PAWSX + Tapaco)/distilgpt2_input/dataset_comb_tr.csv', index = False)\n","dataset_comb_test.combined.to_csv('/content/drive/MyDrive/working_directory/tfm/Distilled_GPT2_finetuning/datasets/Combined(PAWSX + Tapaco)/distilgpt2_input/dataset_comb_test.csv', index = False)\n","dataset_comb_val.combined.to_csv('/content/drive/MyDrive/working_directory/tfm/Distilled_GPT2_finetuning/datasets/Combined(PAWSX + Tapaco)/distilgpt2_input/dataset_comb_val.csv', index = False)"],"metadata":{"id":"CnLU6hALQ_e6"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["dataset_comb_tr = dataset_comb[['combined']]\n","dataset_comb_test = dataset_comb[['combined']]\n","dataset_comb_val = dataset_comb[['combined']]"],"metadata":{"id":"yuOjlVMH9532"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["dataset_comb_tr"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"hqe9RUvOBIk3","executionInfo":{"status":"ok","timestamp":1661649352244,"user_tz":-120,"elapsed":266,"user":{"displayName":"Sergio Muñoz Gonzalez","userId":"13292202149038080080"}},"outputId":"89579696-d0f3-4aeb-e49e-6f230ed5ee66"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                                 combined\n","2       <s> Where is the coffee shop? </s> === <p> Whe...\n","3       <s> Everybody was startled. </s> === <p> Every...\n","5       <s> Did Tom have to do that? </s> === <p> Was ...\n","6       <s> Tom wouldn't talk to me. </s> === <p> Tom ...\n","7       <s> Time is running out. </s> === <p> Time fle...\n","...                                                   ...\n","201519  <s> She cottoned to him soon. </s> === <p> She...\n","201521  <s> He tends to be late for meetings. </s> ===...\n","201525  <s> Only those who believe in the future belie...\n","201528  <s> Are you making him a sandwich? </s> === <p...\n","201529  <s> Ross - Bridge is a historic bridge in the ...\n","\n","[118039 rows x 1 columns]"],"text/html":["\n","  <div id=\"df-7c19492a-df99-439b-a3a1-f12d3c84a667\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>combined</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2</th>\n","      <td>&lt;s&gt; Where is the coffee shop? &lt;/s&gt; === &lt;p&gt; Whe...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>&lt;s&gt; Everybody was startled. &lt;/s&gt; === &lt;p&gt; Every...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>&lt;s&gt; Did Tom have to do that? &lt;/s&gt; === &lt;p&gt; Was ...</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>&lt;s&gt; Tom wouldn't talk to me. &lt;/s&gt; === &lt;p&gt; Tom ...</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>&lt;s&gt; Time is running out. &lt;/s&gt; === &lt;p&gt; Time fle...</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>201519</th>\n","      <td>&lt;s&gt; She cottoned to him soon. &lt;/s&gt; === &lt;p&gt; She...</td>\n","    </tr>\n","    <tr>\n","      <th>201521</th>\n","      <td>&lt;s&gt; He tends to be late for meetings. &lt;/s&gt; ===...</td>\n","    </tr>\n","    <tr>\n","      <th>201525</th>\n","      <td>&lt;s&gt; Only those who believe in the future belie...</td>\n","    </tr>\n","    <tr>\n","      <th>201528</th>\n","      <td>&lt;s&gt; Are you making him a sandwich? &lt;/s&gt; === &lt;p...</td>\n","    </tr>\n","    <tr>\n","      <th>201529</th>\n","      <td>&lt;s&gt; Ross - Bridge is a historic bridge in the ...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>118039 rows × 1 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7c19492a-df99-439b-a3a1-f12d3c84a667')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-7c19492a-df99-439b-a3a1-f12d3c84a667 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-7c19492a-df99-439b-a3a1-f12d3c84a667');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? 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Tapaco)/distilgpt2_input/dataset_comb_tr.txt', sep='\\t', index = False)\n","dataset_comb_test.combined.to_csv('/content/drive/MyDrive/working_directory/tfm/Distilled_GPT2_finetuning/datasets/Combined(PAWSX + Tapaco)/distilgpt2_input/dataset_comb_test.txt', sep='\\t', index = False)\n","dataset_comb_val.combined.to_csv('/content/drive/MyDrive/working_directory/tfm/Distilled_GPT2_finetuning/datasets/Combined(PAWSX + Tapaco)/distilgpt2_input/dataset_comb_val.txt', sep='\\t', index = False)"],"metadata":{"id":"tpj5YYMZ9LsO"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## TRAINING DISTILLED GPT2"],"metadata":{"id":"TEzrN3haFLv3"}},{"cell_type":"markdown","source":["## With hugging facetrainer API"],"metadata":{"id":"1C4PbjTuX7zM"}},{"cell_type":"code","source":["!pip install wandb\n"],"metadata":{"id":"_agRqaTyad38","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1661649515461,"user_tz":-120,"elapsed":13938,"user":{"displayName":"Sergio Muñoz Gonzalez","userId":"13292202149038080080"}},"outputId":"39cbdaf8-37b0-4011-ee0e-757bcf965f59"},"execution_count":null,"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 wandb\n","  Downloading wandb-0.13.2-py2.py3-none-any.whl (1.8 MB)\n","\u001b[K     |████████████████████████████████| 1.8 MB 1.3 MB/s \n","\u001b[?25hCollecting docker-pycreds>=0.4.0\n","  Downloading docker_pycreds-0.4.0-py2.py3-none-any.whl (9.0 kB)\n","Collecting shortuuid>=0.5.0\n","  Downloading shortuuid-1.0.9-py3-none-any.whl (9.4 kB)\n","Requirement already satisfied: promise<3,>=2.0 in /usr/local/lib/python3.7/dist-packages (from wandb) (2.3)\n","Requirement already satisfied: 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sentry_sdk-1.9.0-py2.py3-none-any.whl (156 kB)\n","\u001b[K     |████████████████████████████████| 156 kB 31.9 MB/s \n","\u001b[?25hBuilding wheels for collected packages: pathtools\n","  Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for pathtools: filename=pathtools-0.1.2-py3-none-any.whl size=8806 sha256=0d7dffca22c6d148535d04c3d75d9dd9f4f31045947ea45117e647851275ba86\n","  Stored in directory: /root/.cache/pip/wheels/3e/31/09/fa59cef12cdcfecc627b3d24273699f390e71828921b2cbba2\n","Successfully built pathtools\n","Installing collected packages: smmap, gitdb, shortuuid, setproctitle, sentry-sdk, pathtools, GitPython, docker-pycreds, wandb\n","Successfully installed GitPython-3.1.27 docker-pycreds-0.4.0 gitdb-4.0.9 pathtools-0.1.2 sentry-sdk-1.9.0 setproctitle-1.3.2 shortuuid-1.0.9 smmap-5.0.0 wandb-0.13.2\n"]}]},{"cell_type":"code","source":["import wandb\n","from wandb.keras import WandbCallback\n","wandb.login()\n"],"metadata":{"id":"yY4-UXYKdiqd","colab":{"base_uri":"https://localhost:8080/","height":124},"executionInfo":{"status":"ok","timestamp":1661649632111,"user_tz":-120,"elapsed":116655,"user":{"displayName":"Sergio Muñoz Gonzalez","userId":"13292202149038080080"}},"outputId":"db1a7778-e7fd-41f8-a4b3-eca9d814f097"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["\n","        window._wandbApiKey = new Promise((resolve, reject) => {\n","            function loadScript(url) {\n","            return new Promise(function(resolve, reject) {\n","                let newScript = document.createElement(\"script\");\n","                newScript.onerror = reject;\n","                newScript.onload = resolve;\n","                document.body.appendChild(newScript);\n","                newScript.src = url;\n","            });\n","            }\n","            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n","            const iframe = document.createElement('iframe')\n","            iframe.style.cssText = \"width:0;height:0;border:none\"\n","            document.body.appendChild(iframe)\n","            const handshake = new Postmate({\n","                container: iframe,\n","                url: 'https://wandb.ai/authorize'\n","            });\n","            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n","            handshake.then(function(child) {\n","                child.on('authorize', data => {\n","                    clearTimeout(timeout)\n","                    resolve(data)\n","                });\n","            });\n","            })\n","        });\n","    "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n","\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n","wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit: "]},{"name":"stdout","output_type":"stream","text":["··········\n"]},{"output_type":"stream","name":"stderr","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"]},{"output_type":"execute_result","data":{"text/plain":["True"]},"metadata":{},"execution_count":33}]},{"cell_type":"code","source":["%env WANDB_PROJECT=distillgpt2TrainerAPI"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"j-coJreEd02e","executionInfo":{"status":"ok","timestamp":1661649632112,"user_tz":-120,"elapsed":9,"user":{"displayName":"Sergio Muñoz Gonzalez","userId":"13292202149038080080"}},"outputId":"bfe797f1-da5d-488e-d649-1c4f955e85e0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["env: WANDB_PROJECT=distillgpt2TrainerAPI\n"]}]},{"cell_type":"code","source":["tokenizer = AutoTokenizer.from_pretrained(\"distilgpt2\")\n","\n","model = AutoModelForCausalLM.from_pretrained(\"distilgpt2\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":177,"referenced_widgets":["94ccaa83dd044c3fbc78e1784410c5f7","1f88faf9b7d845d89418d9c19ea70646","b7ddf56a8fb14489a0ecbcbd5e47a136","c33589caa50246908c834dd98f6d2396","983eb03139b34aa68c4a74352d869790","970de49bdd0f4554b515e90ac6b97026","f81cbf54b90645bdbc99f0fc48de0178","bce48503a8f84f0ca9e4cdd0998d40b5","91958b843b964b4f929edf4be63f15e2","3da542025586427e9d762d94c428ccc6","b4c856c460074267b3cdddb0c9229a9b","b635b38a772c4390af3c41a79db48554","1e4dd28a24b94c7bb46e34f1fd49527c","4aa50782a8b0458490891a9e9af0c0c5","500824cb39c1400697d32812af1606e8","c98f13c0adc943ce8c0fb28d7812cbd3","617edb29268e4e93ae5dc6b642555094","93411b1c41474509ae9939c010569ad7","703981b3cbb143a89ee023c5019796af","e68872f90981421f8e15b76a19ce3168","7cb7c4df8ae249d288c9ed5f7d88689b","61f617ec0e3c4b3980326f4a4a5ac388","0b3e7756f7ff4b96b839192207556ac6","dd1cdb45c8fa4cb69cf027eef042017d","f09003814810423ead352b45c5c591fc","95cec5153faa4bc99748d8ece4630010","1006b04eac814078b54e484b0925c06b","941de532122c4ef7a4b979fe6dee7ef8","ecd60b0307a44f8fba8a737bc801cec3","af3db011a375483282224b72b63c9eae","b5f2c294bca74e1685fa4235ac2e557d","e03986e3d4cd4152b0648647c04e3d07","953ad17971d143eb8864b47d196d04c9","80895927ae344218ad1571124a8c141b","5b2103437f5543ce99400acfaca6e17f","e2ae218addfe46a2b4d3235a050a49df","7092fcd4a09743778e483c0b916ec7d6","5c488d48dd1045dabc2db83756ef6e1e","4888795d109a49aaaa1de945e81c1119","3f6098045a1a4870b2d8ccffdda7875a","0581435cbe954131a7f76939d3b4bbfc","e422969d3cc842808db390c337332445","5defff626a014b3c8dded631f6bbe233","eb2eea6495bf497d97aae6cf200ffa4c","1af8e45d0bbf46f894beab6e962386b1","63227b041ffa4327a389330bc961780b","9ec4403698be441ca23eda162fa7ac24","b9dc76a652bd4f01a9b69e175276d0ad","69b7d07a87ea4db1b19431772e9dee5a","aa8f2fa1af55428ba42697f525988ea3","f1db43da99b648829f437267f029bd85","7f3fecf3384d4becb06f83811734a60a","5d8250a07c7346fa86bce5b8f9f0ebf9","662c46466c0443a4bdd0af0b78455c83","4386177a20e547ba985abab3c4641654"]},"id":"ZpX_6LvHR7Ho","executionInfo":{"status":"ok","timestamp":1661649652758,"user_tz":-120,"elapsed":20652,"user":{"displayName":"Sergio Muñoz Gonzalez","userId":"13292202149038080080"}},"outputId":"71d29f5a-fa19-4c87-bb7d-d36214fb9f50"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading config.json:   0%|          | 0.00/762 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"94ccaa83dd044c3fbc78e1784410c5f7"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading vocab.json:   0%|          | 0.00/0.99M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b635b38a772c4390af3c41a79db48554"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading merges.txt:   0%|          | 0.00/446k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0b3e7756f7ff4b96b839192207556ac6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading tokenizer.json:   0%|          | 0.00/1.29M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"80895927ae344218ad1571124a8c141b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading pytorch_model.bin:   0%|          | 0.00/336M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1af8e45d0bbf46f894beab6e962386b1"}},"metadata":{}}]},{"cell_type":"code","source":["text_path = '/content/drive/MyDrive/working_directory/tfm/Distilled_GPT2_finetuning/datasets/Combined(PAWSX + Tapaco)/distilgpt2_input/'"],"metadata":{"id":"e826FtomSZJg"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["data_collator = DataCollatorForLanguageModeling (tokenizer=tokenizer, mlm=False)"],"metadata":{"id":"oorZp9TOSCye"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_dataset = TextDataset(\n","tokenizer=tokenizer,\n","file_path=text_path+'dataset_comb_tr.txt',\n","block_size=512)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nyBpIuA4SQzq","executionInfo":{"status":"ok","timestamp":1661649723551,"user_tz":-120,"elapsed":23099,"user":{"displayName":"Sergio Muñoz Gonzalez","userId":"13292202149038080080"}},"outputId":"e82027db-564c-4cfb-9295-94bc7ba47510"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Token indices sequence length is longer than the specified maximum sequence length for this model (5002966 > 1024). Running this sequence through the model will result in indexing errors\n"]}]},{"cell_type":"code","source":["test_dataset = TextDataset(\n","tokenizer=tokenizer,\n","file_path=text_path+'dataset_comb_test.txt',\n","block_size=512)"],"metadata":{"id":"FYr8kpfcSQjU"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["eval_dataset = TextDataset(\n","tokenizer=tokenizer,\n","file_path=text_path+'dataset_comb_val.txt',\n","block_size=512)"],"metadata":{"id":"Jwvc9nOySPxr"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#Run 16 optimizing parameters\n","epochs = 5\n","batch_size = 8\n","\n","training_args =TrainingArguments (\n","      output_dir='/content/drive/MyDrive/working_directory/tfm/Distilled GPT2 finetuning/output_model',\n","      num_train_epochs=epochs,\n","      per_device_train_batch_size=batch_size,\n","      warmup_steps=500,\n","      save_steps=2000,\n","      logging_steps=10,\n","      learning_rate=1e-5,\n","      \n",")"],"metadata":{"id":"QOOQJX9bTLuD"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from datasets import load_metric\n","metric = load_metric('accuracy')\n","\n","def compute_metrics(eval_pred):\n","    predictions, labels = eval_pred\n","    predictions = np.argmax(predictions, axis=1)\n","    return metric.compute(predictions=predictions, references=labels)"],"metadata":{"id":"HTgP3BmipFAP","colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["cefa6695e0fd4bf6a3e2330d3077d9a7","7540aefb09c740b5a81b0830b92ee673","1b3be7fe09204255b654281b39254db4","ca409d03be7941c5ba937b225238aa36","96e8399b4d2349c2968fd3e722cd477c","0040fd6cb536486a808203f06f14aaeb","164b56b02fbd445391a6ca5eca290125","90acba0b0337490fa710a86600aed79b","2b2f4e8578784867acba8938841f1c55","80a85fbb53844df594610f55e7120f81","32021121725546598a4ac979825cea8a"]},"executionInfo":{"status":"ok","timestamp":1661649828386,"user_tz":-120,"elapsed":608,"user":{"displayName":"Sergio Muñoz Gonzalez","userId":"13292202149038080080"}},"outputId":"6f441ca3-47cb-45ee-f844-736fca97d9fb"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading builder script:   0%|          | 0.00/1.65k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cefa6695e0fd4bf6a3e2330d3077d9a7"}},"metadata":{}}]},{"cell_type":"code","source":["\n","trainer = Trainer(  #AdamW / weigth decay\n","model=model,\n","args=training_args,\n","data_collator=data_collator,\n","train_dataset=train_dataset,\n","eval_dataset=eval_dataset\n",")"],"metadata":{"id":"u9JU-ENwV8dX"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["trainer.train()"],"metadata":{"id":"65sKtFiRbCoV","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1661652326335,"user_tz":-120,"elapsed":2477839,"user":{"displayName":"Sergio Muñoz Gonzalez","userId":"13292202149038080080"}},"outputId":"4e72bae7-857f-46f8-ed53-2d55206ae9cd"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n","  FutureWarning,\n","***** Running training *****\n","  Num examples = 9771\n","  Num Epochs = 5\n","  Instantaneous batch size per device = 8\n","  Total train batch size (w. parallel, distributed & accumulation) = 8\n","  Gradient Accumulation steps = 1\n","  Total optimization steps = 6110\n","Automatic Weights & Biases logging enabled, to disable set os.environ[\"WANDB_DISABLED\"] = \"true\"\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Changes to your `wandb` environment variables will be ignored because your `wandb` session has already started. For more information on how to modify your settings with `wandb.init()` arguments, please refer to <a href=\"https://wandb.me/wandb-init\" target=\"_blank\">the W&B docs</a>."]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mlxrandom\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Tracking run with wandb version 0.13.2"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Run data is saved locally in <code>/content/wandb/run-20220828_012408-y84ey7hy</code>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Syncing run <strong><a href=\"https://wandb.ai/lxrandom/distillgpt2TrainerAPI/runs/y84ey7hy\" target=\"_blank\">/content/drive/MyDrive/working directory/tfm/Distilled GPT2 finetuning/output_model</a></strong> to <a href=\"https://wandb.ai/lxrandom/distillgpt2TrainerAPI\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='6110' max='6110' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [6110/6110 41:11, Epoch 5/5]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Step</th>\n","      <th>Training Loss</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>10</td>\n","      <td>2.557400</td>\n","    </tr>\n","    <tr>\n","      <td>20</td>\n","      <td>2.457100</td>\n","    </tr>\n","    <tr>\n","      <td>30</td>\n","      <td>2.452100</td>\n","    </tr>\n","    <tr>\n","      <td>40</td>\n","      <td>2.423000</td>\n","    </tr>\n","    <tr>\n","      <td>50</td>\n","      <td>2.368500</td>\n","    </tr>\n","    <tr>\n","      <td>60</td>\n","      <td>2.290000</td>\n","    </tr>\n","    <tr>\n","      <td>70</td>\n","      <td>2.275000</td>\n","    </tr>\n","    <tr>\n","      <td>80</td>\n","      <td>2.176100</td>\n","    </tr>\n","    <tr>\n","      <td>90</td>\n","      <td>2.205200</td>\n","    </tr>\n","    <tr>\n","      <td>100</td>\n","      <td>2.185300</td>\n","    </tr>\n","    <tr>\n","      <td>110</td>\n","      <td>2.143600</td>\n","    </tr>\n","    <tr>\n","      <td>120</td>\n","      <td>2.109400</td>\n","    </tr>\n","    <tr>\n","      <td>130</td>\n","      <td>2.082600</td>\n","    </tr>\n","    <tr>\n","      <td>140</td>\n","      <td>2.043700</td>\n","    </tr>\n","    <tr>\n","      <td>150</td>\n","      <td>2.056400</td>\n","    </tr>\n","    <tr>\n","      <td>160</td>\n","      <td>2.028400</td>\n","    </tr>\n","    <tr>\n","      <td>170</td>\n","      <td>2.018900</td>\n","    </tr>\n","    <tr>\n","      <td>180</td>\n","      <td>2.007400</td>\n","    </tr>\n","    <tr>\n","      <td>190</td>\n","      <td>1.931400</td>\n","    </tr>\n","    <tr>\n","      <td>200</td>\n","      <td>1.942600</td>\n","    </tr>\n","    <tr>\n","      <td>210</td>\n","      <td>1.978600</td>\n","    </tr>\n","    <tr>\n","      <td>220</td>\n","      <td>1.940000</td>\n","    </tr>\n","    <tr>\n","      <td>230</td>\n","      <td>1.947500</td>\n","    </tr>\n","    <tr>\n","      <td>240</td>\n","      <td>1.927700</td>\n","    </tr>\n","    <tr>\n","      <td>250</td>\n","      <td>1.891500</td>\n","    </tr>\n","    <tr>\n","      <td>260</td>\n","      <td>1.904300</td>\n","    </tr>\n","    <tr>\n","      <td>270</td>\n","      <td>1.901800</td>\n","    </tr>\n","    <tr>\n","      <td>280</td>\n","      <td>1.894200</td>\n","    </tr>\n","    <tr>\n","      <td>290</td>\n","      <td>1.892800</td>\n","    </tr>\n","    <tr>\n","      <td>300</td>\n","      <td>1.876300</td>\n","    </tr>\n","    <tr>\n","      <td>310</td>\n","      <td>1.872600</td>\n","    </tr>\n","    <tr>\n","      <td>320</td>\n","      <td>1.909400</td>\n","    </tr>\n","    <tr>\n","      <td>330</td>\n","      <td>1.873400</td>\n","    </tr>\n","    <tr>\n","      <td>340</td>\n","      <td>1.874400</td>\n","    </tr>\n","    <tr>\n","      <td>350</td>\n","      <td>1.820700</td>\n","    </tr>\n","    <tr>\n","      <td>360</td>\n","      <td>1.861000</td>\n","    </tr>\n","    <tr>\n","      <td>370</td>\n","      <td>1.846800</td>\n","    </tr>\n","    <tr>\n","      <td>380</td>\n","      <td>1.876400</td>\n","    </tr>\n","    <tr>\n","      <td>390</td>\n","      <td>1.834200</td>\n","    </tr>\n","    <tr>\n","      <td>400</td>\n","      <td>1.828200</td>\n","    </tr>\n","    <tr>\n","      <td>410</td>\n","      <td>1.812800</td>\n","    </tr>\n","    <tr>\n","      <td>420</td>\n","      <td>1.832200</td>\n","    </tr>\n","    <tr>\n","      <td>430</td>\n","      <td>1.796500</td>\n","    </tr>\n","    <tr>\n","      <td>440</td>\n","      <td>1.855800</td>\n","    </tr>\n","    <tr>\n","      <td>450</td>\n","      <td>1.781300</td>\n","    </tr>\n","    <tr>\n","      <td>460</td>\n","      <td>1.808700</td>\n","    </tr>\n","    <tr>\n","      <td>470</td>\n","      <td>1.843500</td>\n","    </tr>\n","    <tr>\n","      <td>480</td>\n","      <td>1.821000</td>\n","    </tr>\n","    <tr>\n","      <td>490</td>\n","      <td>1.788900</td>\n","    </tr>\n","    <tr>\n","      <td>500</td>\n","      <td>1.808100</td>\n","    </tr>\n","    <tr>\n","      <td>510</td>\n","      <td>1.808000</td>\n","    </tr>\n","    <tr>\n","      <td>520</td>\n","      <td>1.818600</td>\n","    </tr>\n","    <tr>\n","      <td>530</td>\n","      <td>1.792900</td>\n","    </tr>\n","    <tr>\n","      <td>540</td>\n","      <td>1.825600</td>\n","    </tr>\n","    <tr>\n","      <td>550</td>\n","      <td>1.793200</td>\n","    </tr>\n","    <tr>\n","      <td>560</td>\n","      <td>1.810000</td>\n","    </tr>\n","    <tr>\n","      <td>570</td>\n","      <td>1.786900</td>\n","    </tr>\n","    <tr>\n","      <td>580</td>\n","      <td>1.765300</td>\n","    </tr>\n","    <tr>\n","      <td>590</td>\n","      <td>1.784000</td>\n","    </tr>\n","    <tr>\n","      <td>600</td>\n","      <td>1.794100</td>\n","    </tr>\n","    <tr>\n","      <td>610</td>\n","      <td>1.784200</td>\n","    </tr>\n","    <tr>\n","      <td>620</td>\n","      <td>1.758300</td>\n","    </tr>\n","    <tr>\n","      <td>630</td>\n","      <td>1.750700</td>\n","    </tr>\n","    <tr>\n","      <td>640</td>\n","      <td>1.754400</td>\n","    </tr>\n","    <tr>\n","      <td>650</td>\n","      <td>1.753300</td>\n","    </tr>\n","    <tr>\n","      <td>660</td>\n","      <td>1.767900</td>\n","    </tr>\n","    <tr>\n","      <td>670</td>\n","      <td>1.768700</td>\n","    </tr>\n","    <tr>\n","      <td>680</td>\n","      <td>1.768800</td>\n","    </tr>\n","    <tr>\n","      <td>690</td>\n","      <td>1.740500</td>\n","    </tr>\n","    <tr>\n","      <td>700</td>\n","      <td>1.766200</td>\n","    </tr>\n","    <tr>\n","      <td>710</td>\n","      <td>1.742900</td>\n","    </tr>\n","    <tr>\n","      <td>720</td>\n","      <td>1.731600</td>\n","    </tr>\n","    <tr>\n","      <td>730</td>\n","      <td>1.737700</td>\n","    </tr>\n","    <tr>\n","      <td>740</td>\n","      <td>1.740000</td>\n","    </tr>\n","    <tr>\n","      <td>750</td>\n","      <td>1.741400</td>\n","    </tr>\n","    <tr>\n","      <td>760</td>\n","      <td>1.752400</td>\n","    </tr>\n","    <tr>\n","      <td>770</td>\n","      <td>1.737700</td>\n","    </tr>\n","    <tr>\n","      <td>780</td>\n","      <td>1.715000</td>\n","    </tr>\n","    <tr>\n","      <td>790</td>\n","      <td>1.714100</td>\n","    </tr>\n","    <tr>\n","      <td>800</td>\n","      <td>1.716200</td>\n","    </tr>\n","    <tr>\n","      <td>810</td>\n","      <td>1.739600</td>\n","    </tr>\n","    <tr>\n","      <td>820</td>\n","      <td>1.710500</td>\n","    </tr>\n","    <tr>\n","      <td>830</td>\n","      <td>1.686500</td>\n","    </tr>\n","    <tr>\n","      <td>840</td>\n","      <td>1.708900</td>\n","    </tr>\n","    <tr>\n","      <td>850</td>\n","      <td>1.710400</td>\n","    </tr>\n","    <tr>\n","      <td>860</td>\n","      <td>1.740100</td>\n","    </tr>\n","    <tr>\n","      <td>870</td>\n","      <td>1.743300</td>\n","    </tr>\n","    <tr>\n","      <td>880</td>\n","      <td>1.706900</td>\n","    </tr>\n","    <tr>\n","      <td>890</td>\n","      <td>1.683900</td>\n","    </tr>\n","    <tr>\n","      <td>900</td>\n","      <td>1.719300</td>\n","    </tr>\n","    <tr>\n","      <td>910</td>\n","      <td>1.718900</td>\n","    </tr>\n","    <tr>\n","      <td>920</td>\n","      <td>1.704300</td>\n","    </tr>\n","    <tr>\n","      <td>930</td>\n","      <td>1.706700</td>\n","    </tr>\n","    <tr>\n","      <td>940</td>\n","      <td>1.723000</td>\n","    </tr>\n","    <tr>\n","      <td>950</td>\n","      <td>1.712900</td>\n","    </tr>\n","    <tr>\n","      <td>960</td>\n","      <td>1.684500</td>\n","    </tr>\n","    <tr>\n","      <td>970</td>\n","      <td>1.690400</td>\n","    </tr>\n","    <tr>\n","      <td>980</td>\n","      <td>1.714300</td>\n","    </tr>\n","    <tr>\n","      <td>990</td>\n","      <td>1.745400</td>\n","    </tr>\n","    <tr>\n","      <td>1000</td>\n","      <td>1.699000</td>\n","    </tr>\n","    <tr>\n","      <td>1010</td>\n","      <td>1.689700</td>\n","    </tr>\n","    <tr>\n","      <td>1020</td>\n","      <td>1.730200</td>\n","    </tr>\n","    <tr>\n","      <td>1030</td>\n","      <td>1.683200</td>\n","    </tr>\n","    <tr>\n","      <td>1040</td>\n","      <td>1.688600</td>\n","    </tr>\n","    <tr>\n","      <td>1050</td>\n","      <td>1.727800</td>\n","    </tr>\n","    <tr>\n","      <td>1060</td>\n","      <td>1.666100</td>\n","    </tr>\n","    <tr>\n","      <td>1070</td>\n","      <td>1.680300</td>\n","    </tr>\n","    <tr>\n","      <td>1080</td>\n","      <td>1.703300</td>\n","    </tr>\n","    <tr>\n","      <td>1090</td>\n","      <td>1.706800</td>\n","    </tr>\n","    <tr>\n","      <td>1100</td>\n","      <td>1.650200</td>\n","    </tr>\n","    <tr>\n","      <td>1110</td>\n","      <td>1.668000</td>\n","    </tr>\n","    <tr>\n","      <td>1120</td>\n","      <td>1.687700</td>\n","    </tr>\n","    <tr>\n","      <td>1130</td>\n","      <td>1.666500</td>\n","    </tr>\n","    <tr>\n","      <td>1140</td>\n","      <td>1.658700</td>\n","    </tr>\n","    <tr>\n","      <td>1150</td>\n","      <td>1.669400</td>\n","    </tr>\n","    <tr>\n","      <td>1160</td>\n","      <td>1.672600</td>\n","    </tr>\n","    <tr>\n","      <td>1170</td>\n","      <td>1.651700</td>\n","    </tr>\n","    <tr>\n","      <td>1180</td>\n","      <td>1.642600</td>\n","    </tr>\n","    <tr>\n","      <td>1190</td>\n","      <td>1.664800</td>\n","    </tr>\n","    <tr>\n","      <td>1200</td>\n","      <td>1.668000</td>\n","    </tr>\n","    <tr>\n","      <td>1210</td>\n","      <td>1.630000</td>\n","    </tr>\n","    <tr>\n","      <td>1220</td>\n","      <td>1.650700</td>\n","    </tr>\n","    <tr>\n","      <td>1230</td>\n","      <td>1.632400</td>\n","    </tr>\n","    <tr>\n","      <td>1240</td>\n","      <td>1.612000</td>\n","    </tr>\n","    <tr>\n","      <td>1250</td>\n","      <td>1.596300</td>\n","    </tr>\n","    <tr>\n","      <td>1260</td>\n","      <td>1.623000</td>\n","    </tr>\n","    <tr>\n","      <td>1270</td>\n","      <td>1.629000</td>\n","    </tr>\n","    <tr>\n","      <td>1280</td>\n","      <td>1.637300</td>\n","    </tr>\n","    <tr>\n","      <td>1290</td>\n","      <td>1.608600</td>\n","    </tr>\n","    <tr>\n","      <td>1300</td>\n","      <td>1.615100</td>\n","    </tr>\n","    <tr>\n","      <td>1310</td>\n","      <td>1.621700</td>\n","    </tr>\n","    <tr>\n","      <td>1320</td>\n","      <td>1.613800</td>\n","    </tr>\n","    <tr>\n","      <td>1330</td>\n","      <td>1.621500</td>\n","    </tr>\n","    <tr>\n","      <td>1340</td>\n","      <td>1.615600</td>\n","    </tr>\n","    <tr>\n","      <td>1350</td>\n","      <td>1.656100</td>\n","    </tr>\n","    <tr>\n","      <td>1360</td>\n","      <td>1.630000</td>\n","    </tr>\n","    <tr>\n","      <td>1370</td>\n","      <td>1.624700</td>\n","    </tr>\n","    <tr>\n","      <td>1380</td>\n","      <td>1.587000</td>\n","    </tr>\n","    <tr>\n","      <td>1390</td>\n","      <td>1.636600</td>\n","    </tr>\n","    <tr>\n","      <td>1400</td>\n","      <td>1.583200</td>\n","    </tr>\n","    <tr>\n","      <td>1410</td>\n","      <td>1.607200</td>\n","    </tr>\n","    <tr>\n","      <td>1420</td>\n","      <td>1.595200</td>\n","    </tr>\n","    <tr>\n","      <td>1430</td>\n","      <td>1.619300</td>\n","    </tr>\n","    <tr>\n","      <td>1440</td>\n","      <td>1.605300</td>\n","    </tr>\n","    <tr>\n","      <td>1450</td>\n","      <td>1.599400</td>\n","    </tr>\n","    <tr>\n","      <td>1460</td>\n","      <td>1.652900</td>\n","    </tr>\n","    <tr>\n","      <td>1470</td>\n","      <td>1.603600</td>\n","    </tr>\n","    <tr>\n","      <td>1480</td>\n","      <td>1.613800</td>\n","    </tr>\n","    <tr>\n","      <td>1490</td>\n","      <td>1.588000</td>\n","    </tr>\n","    <tr>\n","      <td>1500</td>\n","      <td>1.583500</td>\n","    </tr>\n","    <tr>\n","      <td>1510</td>\n","      <td>1.602200</td>\n","    </tr>\n","    <tr>\n","      <td>1520</td>\n","      <td>1.593000</td>\n","    </tr>\n","    <tr>\n","      <td>1530</td>\n","      <td>1.594600</td>\n","    </tr>\n","    <tr>\n","      <td>1540</td>\n","      <td>1.611000</td>\n","    </tr>\n","    <tr>\n","      <td>1550</td>\n","      <td>1.579300</td>\n","    </tr>\n","    <tr>\n","      <td>1560</td>\n","      <td>1.599900</td>\n","    </tr>\n","    <tr>\n","      <td>1570</td>\n","      <td>1.589200</td>\n","    </tr>\n","    <tr>\n","      <td>1580</td>\n","      <td>1.602100</td>\n","    </tr>\n","    <tr>\n","      <td>1590</td>\n","      <td>1.593400</td>\n","    </tr>\n","    <tr>\n","      <td>1600</td>\n","      <td>1.587700</td>\n","    </tr>\n","    <tr>\n","      <td>1610</td>\n","      <td>1.606000</td>\n","    </tr>\n","    <tr>\n","      <td>1620</td>\n","      <td>1.599000</td>\n","    </tr>\n","    <tr>\n","      <td>1630</td>\n","      <td>1.575800</td>\n","    </tr>\n","    <tr>\n","      <td>1640</td>\n","      <td>1.601500</td>\n","    </tr>\n","    <tr>\n","      <td>1650</td>\n","      <td>1.600300</td>\n","    </tr>\n","    <tr>\n","      <td>1660</td>\n","      <td>1.610200</td>\n","    </tr>\n","    <tr>\n","      <td>1670</td>\n","      <td>1.606100</td>\n","    </tr>\n","    <tr>\n","      <td>1680</td>\n","      <td>1.568000</td>\n","    </tr>\n","    <tr>\n","      <td>1690</td>\n","      <td>1.576500</td>\n","    </tr>\n","    <tr>\n","      <td>1700</td>\n","      <td>1.557400</td>\n","    </tr>\n","    <tr>\n","      <td>1710</td>\n","      <td>1.591400</td>\n","    </tr>\n","    <tr>\n","      <td>1720</td>\n","      <td>1.588500</td>\n","    </tr>\n","    <tr>\n","      <td>1730</td>\n","      <td>1.589600</td>\n","    </tr>\n","    <tr>\n","      <td>1740</td>\n","      <td>1.605900</td>\n","    </tr>\n","    <tr>\n","      <td>1750</td>\n","      <td>1.572300</td>\n","    </tr>\n","    <tr>\n","      <td>1760</td>\n","      <td>1.564400</td>\n","    </tr>\n","    <tr>\n","      <td>1770</td>\n","      <td>1.590700</td>\n","    </tr>\n","    <tr>\n","      <td>1780</td>\n","      <td>1.562600</td>\n","    </tr>\n","    <tr>\n","      <td>1790</td>\n","      <td>1.570800</td>\n","    </tr>\n","    <tr>\n","      <td>1800</td>\n","      <td>1.579500</td>\n","    </tr>\n","    <tr>\n","      <td>1810</td>\n","      <td>1.526000</td>\n","    </tr>\n","    <tr>\n","      <td>1820</td>\n","      <td>1.608900</td>\n","    </tr>\n","    <tr>\n","      <td>1830</td>\n","      <td>1.587600</td>\n","    </tr>\n","    <tr>\n","      <td>1840</td>\n","      <td>1.574700</td>\n","    </tr>\n","    <tr>\n","      <td>1850</td>\n","      <td>1.558800</td>\n","    </tr>\n","    <tr>\n","      <td>1860</td>\n","      <td>1.544700</td>\n","    </tr>\n","    <tr>\n","      <td>1870</td>\n","      <td>1.560500</td>\n","    </tr>\n","    <tr>\n","      <td>1880</td>\n","      <td>1.586800</td>\n","    </tr>\n","    <tr>\n","      <td>1890</td>\n","      <td>1.563100</td>\n","    </tr>\n","    <tr>\n","      <td>1900</td>\n","      <td>1.545100</td>\n","    </tr>\n","    <tr>\n","      <td>1910</td>\n","      <td>1.595800</td>\n","    </tr>\n","    <tr>\n","      <td>1920</td>\n","      <td>1.561500</td>\n","    </tr>\n","    <tr>\n","      <td>1930</td>\n","      <td>1.527300</td>\n","    </tr>\n","    <tr>\n","      <td>1940</td>\n","      <td>1.563400</td>\n","    </tr>\n","    <tr>\n","      <td>1950</td>\n","      <td>1.552300</td>\n","    </tr>\n","    <tr>\n","      <td>1960</td>\n","      <td>1.559900</td>\n","    </tr>\n","    <tr>\n","      <td>1970</td>\n","      <td>1.530200</td>\n","    </tr>\n","    <tr>\n","      <td>1980</td>\n","      <td>1.575600</td>\n","    </tr>\n","    <tr>\n","      <td>1990</td>\n","      <td>1.548800</td>\n","    </tr>\n","    <tr>\n","      <td>2000</td>\n","      <td>1.540100</td>\n","    </tr>\n","    <tr>\n","      <td>2010</td>\n","      <td>1.521100</td>\n","    </tr>\n","    <tr>\n","      <td>2020</td>\n","      <td>1.575300</td>\n","    </tr>\n","    <tr>\n","      <td>2030</td>\n","      <td>1.539300</td>\n","    </tr>\n","    <tr>\n","      <td>2040</td>\n","      <td>1.546800</td>\n","    </tr>\n","    <tr>\n","      <td>2050</td>\n","      <td>1.568000</td>\n","    </tr>\n","    <tr>\n","      <td>2060</td>\n","      <td>1.514600</td>\n","    </tr>\n","    <tr>\n","      <td>2070</td>\n","      <td>1.577800</td>\n","    </tr>\n","    <tr>\n","      <td>2080</td>\n","      <td>1.563300</td>\n","    </tr>\n","    <tr>\n","      <td>2090</td>\n","      <td>1.583600</td>\n","    </tr>\n","    <tr>\n","      <td>2100</td>\n","      <td>1.550000</td>\n","    </tr>\n","    <tr>\n","      <td>2110</td>\n","      <td>1.545700</td>\n","    </tr>\n","    <tr>\n","      <td>2120</td>\n","      <td>1.551400</td>\n","    </tr>\n","    <tr>\n","      <td>2130</td>\n","      <td>1.551900</td>\n","    </tr>\n","    <tr>\n","      <td>2140</td>\n","      <td>1.541300</td>\n","    </tr>\n","    <tr>\n","      <td>2150</td>\n","      <td>1.557800</td>\n","    </tr>\n","    <tr>\n","      <td>2160</td>\n","      <td>1.554000</td>\n","    </tr>\n","    <tr>\n","      <td>2170</td>\n","      <td>1.531600</td>\n","    </tr>\n","    <tr>\n","      <td>2180</td>\n","      <td>1.549500</td>\n","    </tr>\n","    <tr>\n","      <td>2190</td>\n","      <td>1.576000</td>\n","    </tr>\n","    <tr>\n","      <td>2200</td>\n","      <td>1.570000</td>\n","    </tr>\n","    <tr>\n","      <td>2210</td>\n","      <td>1.547400</td>\n","    </tr>\n","    <tr>\n","      <td>2220</td>\n","      <td>1.541200</td>\n","    </tr>\n","    <tr>\n","      <td>2230</td>\n","      <td>1.571700</td>\n","    </tr>\n","    <tr>\n","      <td>2240</td>\n","      <td>1.545900</td>\n","    </tr>\n","    <tr>\n","      <td>2250</td>\n","      <td>1.544800</td>\n","    </tr>\n","    <tr>\n","      <td>2260</td>\n","      <td>1.537700</td>\n","    </tr>\n","    <tr>\n","      <td>2270</td>\n","      <td>1.523900</td>\n","    </tr>\n","    <tr>\n","      <td>2280</td>\n","      <td>1.528800</td>\n","    </tr>\n","    <tr>\n","      <td>2290</td>\n","      <td>1.560800</td>\n","    </tr>\n","    <tr>\n","      <td>2300</td>\n","      <td>1.545800</td>\n","    </tr>\n","    <tr>\n","      <td>2310</td>\n","      <td>1.551400</td>\n","    </tr>\n","    <tr>\n","      <td>2320</td>\n","      <td>1.531700</td>\n","    </tr>\n","    <tr>\n","      <td>2330</td>\n","      <td>1.558500</td>\n","    </tr>\n","    <tr>\n","      <td>2340</td>\n","      <td>1.507500</td>\n","    </tr>\n","    <tr>\n","      <td>2350</td>\n","      <td>1.561500</td>\n","    </tr>\n","    <tr>\n","      <td>2360</td>\n","      <td>1.528000</td>\n","    </tr>\n","    <tr>\n","      <td>2370</td>\n","      <td>1.527900</td>\n","    </tr>\n","    <tr>\n","      <td>2380</td>\n","      <td>1.544100</td>\n","    </tr>\n","    <tr>\n","      <td>2390</td>\n","      <td>1.552700</td>\n","    </tr>\n","    <tr>\n","      <td>2400</td>\n","      <td>1.539300</td>\n","    </tr>\n","    <tr>\n","      <td>2410</td>\n","      <td>1.516200</td>\n","    </tr>\n","    <tr>\n","      <td>2420</td>\n","      <td>1.545800</td>\n","    </tr>\n","    <tr>\n","      <td>2430</td>\n","      <td>1.538400</td>\n","    </tr>\n","    <tr>\n","      <td>2440</td>\n","      <td>1.541700</td>\n","    </tr>\n","    <tr>\n","      <td>2450</td>\n","      <td>1.544700</td>\n","    </tr>\n","    <tr>\n","      <td>2460</td>\n","      <td>1.542000</td>\n","    </tr>\n","    <tr>\n","      <td>2470</td>\n","      <td>1.503200</td>\n","    </tr>\n","    <tr>\n","      <td>2480</td>\n","      <td>1.476500</td>\n","    </tr>\n","    <tr>\n","      <td>2490</td>\n","      <td>1.508100</td>\n","    </tr>\n","    <tr>\n","      <td>2500</td>\n","      <td>1.499500</td>\n","    </tr>\n","    <tr>\n","      <td>2510</td>\n","      <td>1.464700</td>\n","    </tr>\n","    <tr>\n","      <td>2520</td>\n","      <td>1.486800</td>\n","    </tr>\n","    <tr>\n","      <td>2530</td>\n","      <td>1.502500</td>\n","    </tr>\n","    <tr>\n","      <td>2540</td>\n","      <td>1.509400</td>\n","    </tr>\n","    <tr>\n","      <td>2550</td>\n","      <td>1.491200</td>\n","    </tr>\n","    <tr>\n","      <td>2560</td>\n","      <td>1.464300</td>\n","    </tr>\n","    <tr>\n","      <td>2570</td>\n","      <td>1.508400</td>\n","    </tr>\n","    <tr>\n","      <td>2580</td>\n","      <td>1.473900</td>\n","    </tr>\n","    <tr>\n","      <td>2590</td>\n","      <td>1.521400</td>\n","    </tr>\n","    <tr>\n","      <td>2600</td>\n","      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</tr>\n","    <tr>\n","      <td>4760</td>\n","      <td>1.374100</td>\n","    </tr>\n","    <tr>\n","      <td>4770</td>\n","      <td>1.379400</td>\n","    </tr>\n","    <tr>\n","      <td>4780</td>\n","      <td>1.387800</td>\n","    </tr>\n","    <tr>\n","      <td>4790</td>\n","      <td>1.406000</td>\n","    </tr>\n","    <tr>\n","      <td>4800</td>\n","      <td>1.409400</td>\n","    </tr>\n","    <tr>\n","      <td>4810</td>\n","      <td>1.421200</td>\n","    </tr>\n","    <tr>\n","      <td>4820</td>\n","      <td>1.378600</td>\n","    </tr>\n","    <tr>\n","      <td>4830</td>\n","      <td>1.411800</td>\n","    </tr>\n","    <tr>\n","      <td>4840</td>\n","      <td>1.404200</td>\n","    </tr>\n","    <tr>\n","      <td>4850</td>\n","      <td>1.383400</td>\n","    </tr>\n","    <tr>\n","      <td>4860</td>\n","      <td>1.401200</td>\n","    </tr>\n","    <tr>\n","      <td>4870</td>\n","      <td>1.424000</td>\n","    </tr>\n","    <tr>\n","      <td>4880</td>\n","      <td>1.362600</td>\n","    </tr>\n","    <tr>\n","      <td>4890</td>\n","      <td>1.409800</td>\n","    </tr>\n","    <tr>\n","      <td>4900</td>\n","      <td>1.397900</td>\n","    </tr>\n","    <tr>\n","      <td>4910</td>\n","      <td>1.383200</td>\n","    </tr>\n","    <tr>\n","      <td>4920</td>\n","      <td>1.365900</td>\n","    </tr>\n","    <tr>\n","      <td>4930</td>\n","      <td>1.371800</td>\n","    </tr>\n","    <tr>\n","      <td>4940</td>\n","      <td>1.391400</td>\n","    </tr>\n","    <tr>\n","      <td>4950</td>\n","      <td>1.393700</td>\n","    </tr>\n","    <tr>\n","      <td>4960</td>\n","      <td>1.392400</td>\n","    </tr>\n","    <tr>\n","      <td>4970</td>\n","      <td>1.388600</td>\n","    </tr>\n","    <tr>\n","      <td>4980</td>\n","      <td>1.379100</td>\n","    </tr>\n","    <tr>\n","      <td>4990</td>\n","      <td>1.347000</td>\n","    </tr>\n","    <tr>\n","      <td>5000</td>\n","      <td>1.379300</td>\n","    </tr>\n","    <tr>\n","      <td>5010</td>\n","      <td>1.368000</td>\n","    </tr>\n","    <tr>\n","      <td>5020</td>\n","      <td>1.363600</td>\n","    </tr>\n","    <tr>\n","      <td>5030</td>\n","      <td>1.402200</td>\n","    </tr>\n","    <tr>\n","      <td>5040</td>\n","      <td>1.374600</td>\n","    </tr>\n","    <tr>\n","      <td>5050</td>\n","      <td>1.372600</td>\n","    </tr>\n","    <tr>\n","      <td>5060</td>\n","      <td>1.363100</td>\n","    </tr>\n","    <tr>\n","      <td>5070</td>\n","      <td>1.369900</td>\n","    </tr>\n","    <tr>\n","      <td>5080</td>\n","      <td>1.365200</td>\n","    </tr>\n","    <tr>\n","      <td>5090</td>\n","      <td>1.352300</td>\n","    </tr>\n","    <tr>\n","      <td>5100</td>\n","      <td>1.370400</td>\n","    </tr>\n","    <tr>\n","      <td>5110</td>\n","      <td>1.379700</td>\n","    </tr>\n","    <tr>\n","      <td>5120</td>\n","      <td>1.371700</td>\n","    </tr>\n","    <tr>\n","      <td>5130</td>\n","      <td>1.387500</td>\n","    </tr>\n","    <tr>\n","      <td>5140</td>\n","      <td>1.372200</td>\n","    </tr>\n","    <tr>\n","      <td>5150</td>\n","      <td>1.361700</td>\n","    </tr>\n","    <tr>\n","      <td>5160</td>\n","      <td>1.357900</td>\n","    </tr>\n","    <tr>\n","      <td>5170</td>\n","      <td>1.373400</td>\n","    </tr>\n","    <tr>\n","      <td>5180</td>\n","      <td>1.364300</td>\n","    </tr>\n","    <tr>\n","      <td>5190</td>\n","      <td>1.375000</td>\n","    </tr>\n","    <tr>\n","      <td>5200</td>\n","      <td>1.361000</td>\n","    </tr>\n","    <tr>\n","      <td>5210</td>\n","      <td>1.368400</td>\n","    </tr>\n","    <tr>\n","      <td>5220</td>\n","      <td>1.374100</td>\n","    </tr>\n","    <tr>\n","      <td>5230</td>\n","      <td>1.374800</td>\n","    </tr>\n","    <tr>\n","      <td>5240</td>\n","      <td>1.376300</td>\n","    </tr>\n","    <tr>\n","      <td>5250</td>\n","      <td>1.390300</td>\n","    </tr>\n","    <tr>\n","      <td>5260</td>\n","      <td>1.409500</td>\n","    </tr>\n","    <tr>\n","      <td>5270</td>\n","      <td>1.382800</td>\n","    </tr>\n","    <tr>\n","      <td>5280</td>\n","      <td>1.378500</td>\n","    </tr>\n","    <tr>\n","      <td>5290</td>\n","      <td>1.368600</td>\n","    </tr>\n","    <tr>\n","      <td>5300</td>\n","      <td>1.365900</td>\n","    </tr>\n","    <tr>\n","      <td>5310</td>\n","      <td>1.366800</td>\n","    </tr>\n","    <tr>\n","      <td>5320</td>\n","      <td>1.395400</td>\n","    </tr>\n","    <tr>\n","      <td>5330</td>\n","      <td>1.336800</td>\n","    </tr>\n","    <tr>\n","      <td>5340</td>\n","      <td>1.367900</td>\n","    </tr>\n","    <tr>\n","      <td>5350</td>\n","      <td>1.376500</td>\n","    </tr>\n","    <tr>\n","      <td>5360</td>\n","      <td>1.403600</td>\n","    </tr>\n","    <tr>\n","      <td>5370</td>\n","      <td>1.378900</td>\n","    </tr>\n","    <tr>\n","      <td>5380</td>\n","      <td>1.361100</td>\n","    </tr>\n","    <tr>\n","      <td>5390</td>\n","      <td>1.355600</td>\n","    </tr>\n","    <tr>\n","      <td>5400</td>\n","      <td>1.364600</td>\n","    </tr>\n","    <tr>\n","      <td>5410</td>\n","      <td>1.367500</td>\n","    </tr>\n","    <tr>\n","      <td>5420</td>\n","      <td>1.367900</td>\n","    </tr>\n","    <tr>\n","      <td>5430</td>\n","      <td>1.370100</td>\n","    </tr>\n","    <tr>\n","      <td>5440</td>\n","      <td>1.371400</td>\n","    </tr>\n","    <tr>\n","      <td>5450</td>\n","      <td>1.383500</td>\n","    </tr>\n","    <tr>\n","      <td>5460</td>\n","      <td>1.370200</td>\n","    </tr>\n","    <tr>\n","      <td>5470</td>\n","      <td>1.392300</td>\n","    </tr>\n","    <tr>\n","      <td>5480</td>\n","      <td>1.393200</td>\n","    </tr>\n","    <tr>\n","      <td>5490</td>\n","      <td>1.338300</td>\n","    </tr>\n","    <tr>\n","      <td>5500</td>\n","      <td>1.378600</td>\n","    </tr>\n","    <tr>\n","      <td>5510</td>\n","      <td>1.359900</td>\n","    </tr>\n","    <tr>\n","      <td>5520</td>\n","      <td>1.371000</td>\n","    </tr>\n","    <tr>\n","      <td>5530</td>\n","      <td>1.363200</td>\n","    </tr>\n","    <tr>\n","      <td>5540</td>\n","      <td>1.384200</td>\n","    </tr>\n","    <tr>\n","      <td>5550</td>\n","      <td>1.371200</td>\n","    </tr>\n","    <tr>\n","      <td>5560</td>\n","      <td>1.337700</td>\n","    </tr>\n","    <tr>\n","      <td>5570</td>\n","      <td>1.392600</td>\n","    </tr>\n","    <tr>\n","      <td>5580</td>\n","      <td>1.347600</td>\n","    </tr>\n","    <tr>\n","      <td>5590</td>\n","      <td>1.398200</td>\n","    </tr>\n","    <tr>\n","      <td>5600</td>\n","      <td>1.346300</td>\n","    </tr>\n","    <tr>\n","      <td>5610</td>\n","      <td>1.384000</td>\n","    </tr>\n","    <tr>\n","      <td>5620</td>\n","      <td>1.392700</td>\n","    </tr>\n","    <tr>\n","      <td>5630</td>\n","      <td>1.362600</td>\n","    </tr>\n","    <tr>\n","      <td>5640</td>\n","      <td>1.374600</td>\n","    </tr>\n","    <tr>\n","      <td>5650</td>\n","      <td>1.355500</td>\n","    </tr>\n","    <tr>\n","      <td>5660</td>\n","      <td>1.358900</td>\n","    </tr>\n","    <tr>\n","      <td>5670</td>\n","      <td>1.357100</td>\n","    </tr>\n","    <tr>\n","      <td>5680</td>\n","      <td>1.369000</td>\n","    </tr>\n","    <tr>\n","      <td>5690</td>\n","      <td>1.362500</td>\n","    </tr>\n","    <tr>\n","      <td>5700</td>\n","      <td>1.358200</td>\n","    </tr>\n","    <tr>\n","      <td>5710</td>\n","      <td>1.382000</td>\n","    </tr>\n","    <tr>\n","      <td>5720</td>\n","      <td>1.348200</td>\n","    </tr>\n","    <tr>\n","      <td>5730</td>\n","      <td>1.360600</td>\n","    </tr>\n","    <tr>\n","      <td>5740</td>\n","      <td>1.354600</td>\n","    </tr>\n","    <tr>\n","      <td>5750</td>\n","      <td>1.382100</td>\n","    </tr>\n","    <tr>\n","      <td>5760</td>\n","      <td>1.345700</td>\n","    </tr>\n","    <tr>\n","      <td>5770</td>\n","      <td>1.395300</td>\n","    </tr>\n","    <tr>\n","      <td>5780</td>\n","      <td>1.375100</td>\n","    </tr>\n","    <tr>\n","      <td>5790</td>\n","      <td>1.372300</td>\n","    </tr>\n","    <tr>\n","      <td>5800</td>\n","      <td>1.371900</td>\n","    </tr>\n","    <tr>\n","      <td>5810</td>\n","      <td>1.359800</td>\n","    </tr>\n","    <tr>\n","      <td>5820</td>\n","      <td>1.370900</td>\n","    </tr>\n","    <tr>\n","      <td>5830</td>\n","      <td>1.370600</td>\n","    </tr>\n","    <tr>\n","      <td>5840</td>\n","      <td>1.368300</td>\n","    </tr>\n","    <tr>\n","      <td>5850</td>\n","      <td>1.341600</td>\n","    </tr>\n","    <tr>\n","      <td>5860</td>\n","      <td>1.376800</td>\n","    </tr>\n","    <tr>\n","      <td>5870</td>\n","      <td>1.365400</td>\n","    </tr>\n","    <tr>\n","      <td>5880</td>\n","      <td>1.373200</td>\n","    </tr>\n","    <tr>\n","      <td>5890</td>\n","      <td>1.389400</td>\n","    </tr>\n","    <tr>\n","      <td>5900</td>\n","      <td>1.395600</td>\n","    </tr>\n","    <tr>\n","      <td>5910</td>\n","      <td>1.380600</td>\n","    </tr>\n","    <tr>\n","      <td>5920</td>\n","      <td>1.353500</td>\n","    </tr>\n","    <tr>\n","      <td>5930</td>\n","      <td>1.371400</td>\n","    </tr>\n","    <tr>\n","      <td>5940</td>\n","      <td>1.379700</td>\n","    </tr>\n","    <tr>\n","      <td>5950</td>\n","      <td>1.370600</td>\n","    </tr>\n","    <tr>\n","      <td>5960</td>\n","      <td>1.397400</td>\n","    </tr>\n","    <tr>\n","      <td>5970</td>\n","      <td>1.380700</td>\n","    </tr>\n","    <tr>\n","      <td>5980</td>\n","      <td>1.357400</td>\n","    </tr>\n","    <tr>\n","      <td>5990</td>\n","      <td>1.369200</td>\n","    </tr>\n","    <tr>\n","      <td>6000</td>\n","      <td>1.387100</td>\n","    </tr>\n","    <tr>\n","      <td>6010</td>\n","      <td>1.365600</td>\n","    </tr>\n","    <tr>\n","      <td>6020</td>\n","      <td>1.361200</td>\n","    </tr>\n","    <tr>\n","      <td>6030</td>\n","      <td>1.361000</td>\n","    </tr>\n","    <tr>\n","      <td>6040</td>\n","      <td>1.358100</td>\n","    </tr>\n","    <tr>\n","      <td>6050</td>\n","      <td>1.356100</td>\n","    </tr>\n","    <tr>\n","      <td>6060</td>\n","      <td>1.360700</td>\n","    </tr>\n","    <tr>\n","      <td>6070</td>\n","      <td>1.347900</td>\n","    </tr>\n","    <tr>\n","      <td>6080</td>\n","      <td>1.349300</td>\n","    </tr>\n","    <tr>\n","      <td>6090</td>\n","      <td>1.386800</td>\n","    </tr>\n","    <tr>\n","      <td>6100</td>\n","      <td>1.348900</td>\n","    </tr>\n","    <tr>\n","      <td>6110</td>\n","      <td>1.370300</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Saving model checkpoint to /content/drive/MyDrive/working directory/tfm/Distilled GPT2 finetuning/output_model/checkpoint-2000\n","Configuration saved in /content/drive/MyDrive/working directory/tfm/Distilled GPT2 finetuning/output_model/checkpoint-2000/config.json\n","Model weights saved in /content/drive/MyDrive/working directory/tfm/Distilled GPT2 finetuning/output_model/checkpoint-2000/pytorch_model.bin\n","Saving model checkpoint to /content/drive/MyDrive/working directory/tfm/Distilled GPT2 finetuning/output_model/checkpoint-4000\n","Configuration saved in /content/drive/MyDrive/working directory/tfm/Distilled GPT2 finetuning/output_model/checkpoint-4000/config.json\n","Model weights saved in /content/drive/MyDrive/working directory/tfm/Distilled GPT2 finetuning/output_model/checkpoint-4000/pytorch_model.bin\n","Saving model checkpoint to /content/drive/MyDrive/working directory/tfm/Distilled GPT2 finetuning/output_model/checkpoint-6000\n","Configuration saved in /content/drive/MyDrive/working directory/tfm/Distilled GPT2 finetuning/output_model/checkpoint-6000/config.json\n","Model weights saved in /content/drive/MyDrive/working directory/tfm/Distilled GPT2 finetuning/output_model/checkpoint-6000/pytorch_model.bin\n","\n","\n","Training completed. Do not forget to share your model on huggingface.co/models =)\n","\n","\n"]},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=6110, training_loss=1.5307064488749653, metrics={'train_runtime': 2477.6395, 'train_samples_per_second': 19.718, 'train_steps_per_second': 2.466, 'total_flos': 6382826375086080.0, 'train_loss': 1.5307064488749653, 'epoch': 5.0})"]},"metadata":{},"execution_count":46}]},{"cell_type":"code","source":["trainer.save_model()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MeMTIZ2hGsaM","executionInfo":{"status":"ok","timestamp":1661652327667,"user_tz":-120,"elapsed":1102,"user":{"displayName":"Sergio Muñoz Gonzalez","userId":"13292202149038080080"}},"outputId":"37345b38-9ae0-4559-fb4b-da692cf09b6e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Saving model checkpoint to /content/drive/MyDrive/working directory/tfm/Distilled GPT2 finetuning/output_model\n","Configuration saved in /content/drive/MyDrive/working directory/tfm/Distilled GPT2 finetuning/output_model/config.json\n","Model weights saved in /content/drive/MyDrive/working directory/tfm/Distilled GPT2 finetuning/output_model/pytorch_model.bin\n"]}]},{"cell_type":"code","source":["model = AutoModelWithLMHead.from_pretrained('/content/drive/MyDrive/working_directory/tfm/Distilled_GPT2_finetuning/DistilGPT2_output_model')"],"metadata":{"id":"jCeHhrS7m6Do"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tokenizer = AutoTokenizer.from_pretrained(\"distilgpt2\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["67a7e227e76a4829b3ec4f18c79faf98","86dfb9fe6cfa4b5b8714afeec3bb636d","5fdfe24f87bd42d1aeae35c3c8b6dee7","a2da7fd73ab94570910c257a1def1697","6beedbb55f5347958ce3e772eb7e8499","001e0aa2f1ed4846994a46ac86a86d6f","592c706388d345be80981d16dcd2e34c","ee736a05e18746a3a69f07349010039c","c98504298d30471e8cbbfbad9f77741b","731881d867a04a3fa072f00bc37d3133","007951469acd4fa4ba810cf8c120756b","b3deed9d19b64313bdee0a8c0025170d","d4c1b0c87fa04840ade8f9cb268dab9c","65fe9d55df594f5e9b620ea786af83cd","7287bbdcf3534233bf9d498429d3e86c","9b8b4334b10543308c728c3b4d6a45a8","51558992c92e43ac89601b418afceaa9","38581fdb2bee4e90b076b3c24e328f61","b3ed9684924e471bb81c19d0f9d3e938","15fe64deba8f4236b9313ca60b12037d","3c2f7ad1459a419a82a2776108ce1b34","e7c500ea4f97415e91dac6d2cf728af1","5266fe9aa4964079879f47b83a946078","2445b64a53844b73b3b655e266669f26","e7c88baadb9246ef8e7dfd1c6f371e61","0221905e1b7a44b5beb3fc5a62685703","a9afbd245c3a46d7a6d49cad16aabf1d","2fc651743bb94c2e9c26439722c51b85","313cee4e8ff144f7ab3027403c3e1de9","d77dc5bce149442a9b4daf858ecf5ab8","cbab193303474019bec40989a95712fb","5d3121ecde8f4ec6a369f8bc2451ebed","afc655b797da4c57b6d9c6d9a104553a","cb973d6d1a3c483c9044bcffffc89842","5f05c2bb1b1a4f7b8488f2e08e463037","2cdc1c9ea2b040f09925410a08f80401","5633e13c0e2148f38f8062760a851daa","cd65241a51fd4f76b268b572085a27b6","a4abf198e9cf4ef99bee5361fefb9365","aea22adc47164c9587f27a9fef27e562","e9399526facd4819b19dd24677894969","94d84a8c73f84125b401ac4ed8f3e820","9f9deda0ec924d9a9ab2a2535ba68ff3","6bf6daa53597432ea02c600401dcd5a0"]},"id":"Eq6Mjmo7nVKP","executionInfo":{"status":"ok","timestamp":1651675249031,"user_tz":-120,"elapsed":5322,"user":{"displayName":"Sergio Muñoz Gonzalez","userId":"13292202149038080080"}},"outputId":"050db67b-5931-4cdd-d5b8-b2d278062adb"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/762 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"67a7e227e76a4829b3ec4f18c79faf98"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/0.99M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b3deed9d19b64313bdee0a8c0025170d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/446k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5266fe9aa4964079879f47b83a946078"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/1.29M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cb973d6d1a3c483c9044bcffffc89842"}},"metadata":{}}]},{"cell_type":"code","source":["generator = pipeline('text-generation', model=model, tokenizer=tokenizer)"],"metadata":{"id":"THF0i9Q8naue"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def clean_paraphrase(input_sentence):\n","  p = generator('<s> '+input_sentence+' </s> === <p> ')\n","  return p[0]['generated_text'].split(' </s> === <p> ')[1].split(' </p>')[0]"],"metadata":{"id":"IWEQ5_td_fZV"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Testing training With tensorflow workflow"],"metadata":{"id":"rZpqPYWOD70E"}},{"cell_type":"code","source":["%env WANDB_PROJECT=distillgpt2Tensorflow"],"metadata":{"id":"o4ldd4vued9j","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1651241945983,"user_tz":-120,"elapsed":244,"user":{"displayName":"Sergio Muñoz Gonzalez","userId":"13292202149038080080"}},"outputId":"81ea8e52-d840-43d8-9874-bc8684780a4f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["env: WANDB_PROJECT=distillgpt2Tensorflow\n"]}]},{"cell_type":"code","source":["  dataset_comb_tr = pd.read_csv('/content/drive/MyDrive/working directory/tfm/Distilled GPT2 finetuning/datasets/Combined(PAWSX + Tapaco)/dataset_comb_tr.csv')\n","  dataset_comb_test = pd.read_csv('/content/drive/MyDrive/working directory/tfm/Distilled GPT2 finetuning/datasets/Combined(PAWSX + Tapaco)/dataset_comb_test.csv')\n","  dataset_comb_val = pd.read_csv('/content/drive/MyDrive/working directory/tfm/Distilled GPT2 finetuning/datasets/Combined(PAWSX + Tapaco)/dataset_comb_val.csv')"],"metadata":{"id":"A5VLIpDaD-8N"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["  dataset_comb_tr[dataset_comb_tr['combined'].map(type) != str]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49},"id":"YNAj2zHEGLCS","executionInfo":{"status":"ok","timestamp":1651241950818,"user_tz":-120,"elapsed":245,"user":{"displayName":"Sergio Muñoz Gonzalez","userId":"13292202149038080080"}},"outputId":"3da70124-b8c6-400b-a0ee-7107c7165a59"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Empty DataFrame\n","Columns: [combined, lan]\n","Index: []"],"text/html":["\n","  <div id=\"df-d1d39257-92b8-4a05-b48b-872a44d56dc2\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>combined</th>\n","      <th>lan</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d1d39257-92b8-4a05-b48b-872a44d56dc2')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 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= tokenizer.eos_token"],"metadata":{"id":"bRs89f6pWuRW"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["dataset_comb_tr = Dataset.from_pandas(dataset_comb_tr)\n","dataset_comb_test = Dataset.from_pandas(dataset_comb_test)\n","dataset_comb_val = Dataset.from_pandas(dataset_comb_val)"],"metadata":{"id":"QFnlQU06IOaA"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["dataset_comb_test"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AOLxlvfqhK98","executionInfo":{"status":"ok","timestamp":1651235744440,"user_tz":-120,"elapsed":236,"user":{"displayName":"Sergio Muñoz Gonzalez","userId":"13292202149038080080"}},"outputId":"ea3d5ba1-0a85-4172-9221-63780397a7b8"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Dataset({\n","    features: ['combined', 'lan'],\n","    num_rows: 20153\n","})"]},"metadata":{},"execution_count":111}]},{"cell_type":"code","source":["def tokenize_function(example):\n","    return tokenizer(example['combined'], truncation=True, max_length=512) # same than block size"],"metadata":{"id":"eE4ixBVPIw3Q"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tokenized_train_dataset =   dataset_comb_tr.map(tokenize_function, batched=True)\n","tokenized_test_dataset =   dataset_comb_test.map(tokenize_function, batched=True)\n","tokenized_val_dataset =  dataset_comb_val.map(tokenize_function, batched=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":113,"referenced_widgets":["260cdc968bde4013990a581fd58b9c05","7324d98c009f47039211a757a98c0192","ec78f7ff3191422e863e7265f7820688","3c3cabc8801f4d4c8a44d3eaf25aaf79","a8fb1317e14b4c0981fc2e57b69f40bf","79a4954072904fa6848fe52d6a4ff208","a982a157a940481b99b38eb7f25e5514","638b71dbbd694caebb121da0ae565a99","31d9ce5b1d54432eac5b8d8c4bd3167d","5dd10c9c80404ef2aa36cd49ac6c5b02","2f0fad1d0ac74d3a879eddd95e4e55e9","7e4351f9f248461490c370745acfeb4c","b738d0f9fb0f4c99a47c7c6f320afa72","581dfbebff5c4ad5af91debb267a996c","69e60aff4e4e4a219ec9a7a9258c896b","f8c26a6eadbd4160a4f5945eef51ec02","18bdca4edec2437ebe2c356cc88fbc51","540b8a74de614258b50c850468abf36a","c86e791e708642ce8bb2e7b27cde14f6","8565ca29f62449afbade871c123b3fcc","549da5b1b2b74bbdb2acc0d750320709","49c04db29d8b4691a00c3cce4c2675dd","c97ed421e9e849fda3a974b979bf8510","011ad7d6af5a45a793840cc5babe5c38","28f59d583dbc443c9b4fe66ef2956335","45236dbc894d4574add2c8d84181b3a4","372a5c13393e4eb4a5c7ae12aaa8b388","e1bbee25c50044c3afa37a9d3d0b916a","c54513fd5268482dbccc25816133d4a5","6c9ef390485149c99a8e16b6dbc7636b","a6e155af2d9844fba39a88fd84ee188f","a4767b9ce5a44db98f2e1f3a423288e2","18a550ede6434ae9a9c45ab70026c49b"]},"id":"hnC__tspHPLx","executionInfo":{"status":"ok","timestamp":1651230363937,"user_tz":-120,"elapsed":20305,"user":{"displayName":"Sergio Muñoz Gonzalez","userId":"13292202149038080080"}},"outputId":"8873a954-fb47-4135-f770-c1a8452b0199"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/162 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"260cdc968bde4013990a581fd58b9c05"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/21 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7e4351f9f248461490c370745acfeb4c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/21 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c97ed421e9e849fda3a974b979bf8510"}},"metadata":{}}]},{"cell_type":"code","source":["tokenized_train_dataset"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Z-PmXr-NQUHu","executionInfo":{"status":"ok","timestamp":1651237496552,"user_tz":-120,"elapsed":208,"user":{"displayName":"Sergio Muñoz Gonzalez","userId":"13292202149038080080"}},"outputId":"a9ea9256-cee1-4908-eead-3da77e54b5e0"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Dataset({\n","    features: ['combined', 'lan', 'input_ids', 'attention_mask'],\n","    num_rows: 161225\n","})"]},"metadata":{},"execution_count":163}]},{"cell_type":"code","source":["data_collator =  DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False,  return_tensors=\"tf\")"],"metadata":{"id":"c-Ljger4-IAi"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_ds = tokenized_train_dataset.to_tf_dataset(\n","    columns=['input_ids', 'attention_mask','labels'],\n","    shuffle=True,\n","    collate_fn=data_collator,\n","    batch_size=8,\n","\tdrop_remainder=True,\n",")\n","eval_ds = tokenized_train_dataset.to_tf_dataset(\n","    columns=['input_ids', 'attention_mask','labels'],\n","    shuffle=True,\n","    collate_fn=data_collator,\n","    batch_size=8,\n","\tdrop_remainder=True,\n",")\n","test_ds = tokenized_train_dataset.to_tf_dataset(\n","    columns=['input_ids', 'attention_mask','labels'],\n","    shuffle=True,\n","    collate_fn=data_collator,\n","    batch_size=8,\n","\tdrop_remainder=True,\n",")"],"metadata":{"id":"VYDatRHAC6p_"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["trainlist=list(train_ds.as_numpy_iterator())"],"metadata":{"id":"0riy8lCB8bUD"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["input_ids,attencion_mask = trainlist[0]"],"metadata":{"id":"48_5RazlX3qS"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["model(*trainlist[0][0])"],"metadata":{"id":"wmgU25YYVbwP"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import os\n","import psutil\n","import tensorflow as tf\n","from itertools import chain\n","from datasets import load_dataset\n","from tensorflow.keras.optimizers import Adam\n","from tensorflow.keras.losses import SparseCategoricalCrossentropy\n","from tensorflow.keras.optimizers.schedules import PolynomialDecay\n","from transformers import AutoTokenizer, TFAutoModelForCausalLM, DataCollatorWithPadding\n","\n","num_train_steps = len(tf_ds) * NUM_EPOCHS\n","lr_scheduler = PolynomialDecay(\n","    initial_learning_rate=5e-5,\n","    end_learning_rate=0.0,\n","    decay_steps=num_train_steps)"],"metadata":{"id":"SkCn62TtWj7l"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["opt = 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