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https://huggingface.co/transformers/custom_datasets.html","metadata":{"execution":{"iopub.execute_input":"2021-07-31T16:34:08.909724Z","iopub.status.busy":"2021-07-31T16:34:08.908565Z","iopub.status.idle":"2021-07-31T16:34:08.911888Z","shell.execute_reply":"2021-07-31T16:34:08.911155Z"},"papermill":{"duration":0.021984,"end_time":"2021-07-31T16:34:08.912098","exception":false,"start_time":"2021-07-31T16:34:08.890114","status":"completed"},"tags":[]},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"!curl https://raw.githubusercontent.com/pytorch/xla/master/contrib/scripts/env-setup.py -o pytorch-xla-env-setup.py\n!python pytorch-xla-env-setup.py --version 1.7 --apt-packages libomp5 libopenblas-dev\n!pip install numpy","metadata":{"_kg_hide-input":true,"papermill":{"duration":88.593334,"end_time":"2021-07-31T16:35:37.516601","exception":false,"start_time":"2021-07-31T16:34:08.923267","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-07-23T21:23:22.547462Z","iopub.execute_input":"2024-07-23T21:23:22.548339Z","iopub.status.idle":"2024-07-23T21:23:38.211374Z","shell.execute_reply.started":"2024-07-23T21:23:22.548289Z","shell.execute_reply":"2024-07-23T21:23:38.210231Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n                                 Dload  Upload   Total   Spent    Left  Speed\n100    14  100    14    0     0     84      0 --:--:-- --:--:-- --:--:--    84\n  File \"/kaggle/working/pytorch-xla-env-setup.py\", line 1\n    404: Not Found\n    ^^^\nSyntaxError: illegal target for annotation\nRequirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (1.26.4)\n","output_type":"stream"}]},{"cell_type":"code","source":"!wget https://s3-ap-southeast-1.amazonaws.com/he-public-data/dataset52a7b21.zip\n!unzip dataset52a7b21.zip\n!rm dataset/.~lock.train.csv#\n!rm dataset52a7b21.zip","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","papermill":{"duration":109.148203,"end_time":"2021-07-31T16:37:26.705140","exception":false,"start_time":"2021-07-31T16:35:37.556937","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-07-23T21:23:42.084454Z","iopub.execute_input":"2024-07-23T21:23:42.085029Z","iopub.status.idle":"2024-07-23T21:25:02.933614Z","shell.execute_reply.started":"2024-07-23T21:23:42.084981Z","shell.execute_reply":"2024-07-23T21:25:02.932481Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"--2024-07-23 21:23:42--  https://s3-ap-southeast-1.amazonaws.com/he-public-data/dataset52a7b21.zip\nResolving s3-ap-southeast-1.amazonaws.com (s3-ap-southeast-1.amazonaws.com)... 52.219.124.202, 52.219.40.206, 52.219.41.34, ...\nConnecting to s3-ap-southeast-1.amazonaws.com (s3-ap-southeast-1.amazonaws.com)|52.219.124.202|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 1061576029 (1012M) [binary/octet-stream]\nSaving to: 'dataset52a7b21.zip'\n\ndataset52a7b21.zip  100%[===================>]   1012M  19.9MB/s    in 51s     \n\n2024-07-23 21:24:34 (20.0 MB/s) - 'dataset52a7b21.zip' saved [1061576029/1061576029]\n\nArchive:  dataset52a7b21.zip\n   creating: dataset/\n  inflating: dataset/train.csv       \n  inflating: dataset/sample_submission.csv  \n  inflating: dataset/test.csv        \n  inflating: dataset/.~lock.train.csv#  \n","output_type":"stream"}]},{"cell_type":"code","source":"import csv\nimport pickle\nimport pandas as pd\nimport numpy as np\ntrain = pd.read_csv(\"dataset/train.csv\", escapechar = \"\\\\\", quoting = csv.QUOTE_NONE)","metadata":{"papermill":{"duration":44.323005,"end_time":"2021-07-31T16:38:11.181120","exception":false,"start_time":"2021-07-31T16:37:26.858115","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-07-23T21:25:50.333269Z","iopub.execute_input":"2024-07-23T21:25:50.333970Z","iopub.status.idle":"2024-07-23T21:26:31.304771Z","shell.execute_reply.started":"2024-07-23T21:25:50.333930Z","shell.execute_reply":"2024-07-23T21:26:31.303953Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"class_counts = train.BROWSE_NODE_ID.value_counts()\ndrop_indices = class_counts[class_counts<50].index\ntrain = train[~train.BROWSE_NODE_ID.isin(drop_indices)]\ntrain.head()","metadata":{"papermill":{"duration":0.849696,"end_time":"2021-07-31T16:38:12.176562","exception":false,"start_time":"2021-07-31T16:38:11.326866","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-07-23T21:26:34.772891Z","iopub.execute_input":"2024-07-23T21:26:34.773767Z","iopub.status.idle":"2024-07-23T21:26:35.363088Z","shell.execute_reply.started":"2024-07-23T21:26:34.773726Z","shell.execute_reply":"2024-07-23T21:26:35.362159Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"                                               TITLE  \\\n0         Pete The Cat Bedtime Blues Doll, 14.5 Inch   \n1  The New Yorker NYHM014 Refrigerator Magnet, 2 ...   \n3   Amway Nutrilite Kids Chewable Iron Tablets (100)   \n4  Teacher Planner Company A4 6 Lesson Academic T...   \n5  Men'S Full Sleeve Raglan T-Shirts Denim T-Shir...   \n\n                                         DESCRIPTION  \\\n0  Pete the Cat is the coolest, most popular cat ...   \n1  The New Yorker Handsome Cello Wrapped Hard Mag...   \n3                                                NaN   \n4                                                NaN   \n5  Men'S Full Sleeve Raglan T-Shirts Denim T-Shir...   \n\n                                       BULLET_POINTS              BRAND  \\\n0  [Pete the Cat Bedtime Blues plush doll,Based o...        MerryMakers   \n1  [Cat In A Tea Cup by New Yorker cover artist G...     The New Yorker   \n3  [Nutrilite Kids,Chewable Iron Tablets,Quantity...              Amway   \n4                                                NaN                NaN   \n5  [Color: Blue,Sleeve: Full Sleeve,Material: Cot...  Bhavya Enterprise   \n\n   BROWSE_NODE_ID  \n0               0  \n1               1  \n3               3  \n4               4  \n5               5  ","text/html":"<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>TITLE</th>\n      <th>DESCRIPTION</th>\n      <th>BULLET_POINTS</th>\n      <th>BRAND</th>\n      <th>BROWSE_NODE_ID</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Pete The Cat Bedtime Blues Doll, 14.5 Inch</td>\n      <td>Pete the Cat is the coolest, most popular cat ...</td>\n      <td>[Pete the Cat Bedtime Blues plush doll,Based o...</td>\n      <td>MerryMakers</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>The New Yorker NYHM014 Refrigerator Magnet, 2 ...</td>\n      <td>The New Yorker Handsome Cello Wrapped Hard Mag...</td>\n      <td>[Cat In A Tea Cup by New Yorker cover artist G...</td>\n      <td>The New Yorker</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Amway Nutrilite Kids Chewable Iron Tablets (100)</td>\n      <td>NaN</td>\n      <td>[Nutrilite Kids,Chewable Iron Tablets,Quantity...</td>\n      <td>Amway</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Teacher Planner Company A4 6 Lesson Academic T...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Men'S Full Sleeve Raglan T-Shirts Denim T-Shir...</td>\n      <td>Men'S Full Sleeve Raglan T-Shirts Denim T-Shir...</td>\n      <td>[Color: Blue,Sleeve: Full Sleeve,Material: Cot...</td>\n      <td>Bhavya Enterprise</td>\n      <td>5</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\ntrain_split, val_split = train_test_split(train, test_size=.05)\nfrom transformers import AlbertTokenizerFast\ntokenizer = AlbertTokenizerFast.from_pretrained('albert-base-v2')","metadata":{"papermill":{"duration":7.193163,"end_time":"2021-07-31T16:38:19.515529","exception":false,"start_time":"2021-07-31T16:38:12.322366","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-07-23T21:26:46.264623Z","iopub.execute_input":"2024-07-23T21:26:46.265086Z","iopub.status.idle":"2024-07-23T21:26:47.928361Z","shell.execute_reply.started":"2024-07-23T21:26:46.265060Z","shell.execute_reply":"2024-07-23T21:26:47.927564Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"label_map = {}\nfor idx, value in enumerate(train.BROWSE_NODE_ID.unique()):\n    label_map[value] = idx\n\nwith open('lable_map.pickle', 'wb') as handle:\n    pickle.dump(label_map, handle, protocol=pickle.HIGHEST_PROTOCOL)","metadata":{"papermill":{"duration":0.193721,"end_time":"2021-07-31T16:38:19.855085","exception":false,"start_time":"2021-07-31T16:38:19.661364","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-07-23T21:26:52.317607Z","iopub.execute_input":"2024-07-23T21:26:52.317993Z","iopub.status.idle":"2024-07-23T21:26:52.354542Z","shell.execute_reply.started":"2024-07-23T21:26:52.317964Z","shell.execute_reply":"2024-07-23T21:26:52.353618Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"import torch\n\nclass Dataset(torch.utils.data.Dataset):\n    def __init__(self, df, tokenizer, is_train=True, label_map={}, max_length=128):\n        self.df = df\n        self.title = df.TITLE.values\n        self.desc = df.DESCRIPTION.values\n        self.bullets = df.BULLET_POINTS.apply(lambda x: x[1:-1] if len(x)>0 and x[0]=='[' else x).values\n        self.tokenizer = tokenizer\n        if is_train:\n            self.labels = df.BROWSE_NODE_ID.apply(lambda x: label_map[x]).values\n            self.label_map = label_map\n        self.is_train = is_train\n        self.max_length = max_length\n \n    def __getitem__(self, idx):\n        req_string = self.title[idx] + ' ~ '\n        if torch.rand(1)>0.5:\n            req_string += self.desc[idx]\n        req_string += ' ~ '\n        if torch.rand(1)>0.5:\n            req_string += self.bullets[idx]\n        tokenized_data = tokenizer.tokenize(req_string)\n        to_append = [\"[CLS]\"] + tokenized_data[:self.max_length - 2] + [\"[SEP]\"]\n        input_ids = tokenizer.convert_tokens_to_ids(to_append)\n        input_mask = [1] * len(input_ids)\n        padding = [0] * (self.max_length - len(input_ids))\n        input_ids += padding\n        input_mask += padding\n        item = {\n            \"input_ids\": torch.tensor(input_ids, dtype=torch.long),\n            \"attention_mask\": torch.tensor(input_mask, dtype=torch.long)\n        }\n        if self.is_train:\n            item['labels'] = torch.tensor(self.labels[idx], dtype=torch.long)\n        return item\n \n    def __len__(self):\n        return len(self.df)\n\ntrain_dataset = Dataset(train_split.fillna(\"\"), tokenizer, is_train=True, label_map=label_map)\nval_dataset = Dataset(val_split.fillna(\"\"), tokenizer, is_train=True, label_map=label_map)","metadata":{"papermill":{"duration":9.886137,"end_time":"2021-07-31T16:38:29.887214","exception":false,"start_time":"2021-07-31T16:38:20.001077","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-07-23T21:28:22.073098Z","iopub.execute_input":"2024-07-23T21:28:22.073381Z","iopub.status.idle":"2024-07-23T21:28:31.121423Z","shell.execute_reply.started":"2024-07-23T21:28:22.073356Z","shell.execute_reply":"2024-07-23T21:28:31.120613Z"},"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"code","source":"from transformers import AlbertForSequenceClassification, Trainer, TrainingArguments\n\ntraining_args = TrainingArguments(\n    output_dir='./results',          # output directory\n    num_train_epochs=2,              # total number of training epochs\n    per_device_train_batch_size=32, # batch size per device during training\n    per_device_eval_batch_size=32,  # batch size for evaluation\n                # number of warmup steps for learning rate scheduler\n    weight_decay=0.01,               # strength of weight decay\n    logging_dir='./logs',            # directory for storing logs\n   \n    dataloader_num_workers=4,\n    report_to=\"tensorboard\",\n    label_smoothing_factor=0.1,\n  \n    evaluation_strategy=\"steps\",\n    eval_steps=100,                # Evaluation and Save happens every 500 steps\n    save_strategy=\"steps\",\n    \n            # Only last 5 models are saved. Older ones are deleted.\n    load_best_model_at_end=True,     # best model is always saved\n    prediction_loss_only=True,\n)\n\n\nmodel = AlbertForSequenceClassification.from_pretrained('albert-base-v2')\nmodel.classifier = torch.nn.Linear(768, len(label_map))\nmodel.num_labels = len(label_map)","metadata":{"papermill":{"duration":15.319753,"end_time":"2021-07-31T16:38:45.354223","exception":false,"start_time":"2021-07-31T16:38:30.034470","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-07-23T21:31:14.356179Z","iopub.execute_input":"2024-07-23T21:31:14.356452Z","iopub.status.idle":"2024-07-23T21:31:14.596594Z","shell.execute_reply.started":"2024-07-23T21:31:14.356428Z","shell.execute_reply":"2024-07-23T21:31:14.595886Z"},"trusted":true},"execution_count":30,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/transformers/training_args.py:1494: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n  warnings.warn(\nSome weights of AlbertForSequenceClassification were not initialized from the model checkpoint at albert-base-v2 and are newly initialized: ['classifier.bias', 'classifier.weight']\nYou should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","output_type":"stream"}]},{"cell_type":"code","source":"trainer = Trainer(\n    model=model,                         # the instantiated 🤗 Transformers model to be trained\n    args=training_args,                  # training arguments, defined above\n    train_dataset=train_dataset,         # training dataset\n    eval_dataset=val_dataset             # evaluation dataset\n)\n\ntrainer.train()","metadata":{"papermill":{"duration":14219.590342,"end_time":"2021-07-31T20:35:45.092589","exception":false,"start_time":"2021-07-31T16:38:45.502247","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-07-23T21:31:26.420088Z","iopub.execute_input":"2024-07-23T21:31:26.420463Z"},"trusted":true},"execution_count":null,"outputs":[{"name":"stderr","text":"Token indices sequence length is longer than the specified maximum sequence length for this model (641 > 512). Running this sequence through the model will result in indexing errors\nToken indices sequence length is longer than the specified maximum sequence length for this model (631 > 512). Running this sequence through the model will result in indexing errors\nToken indices sequence length is longer than the specified maximum sequence length for this model (634 > 512). Running this sequence through the model will result in indexing errors\nToken indices sequence length is longer than the specified maximum sequence length for this model (758 > 512). Running this sequence through the model will result in indexing errors\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"\n    <div>\n      \n      <progress value='201' max='167032' style='width:300px; height:20px; vertical-align: middle;'></progress>\n      [   201/167032 10:41 < 149:28:36, 0.31 it/s, Epoch 0.00/2]\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      <th>Validation Loss</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>100</td>\n      <td>No log</td>\n      <td>6.835758</td>\n    </tr>\n  </tbody>\n</table><p>\n    <div>\n      \n      <progress value='3146' max='4396' style='width:300px; height:20px; vertical-align: middle;'></progress>\n      [3146/4396 06:43 < 02:40, 7.79 it/s]\n    </div>\n    "},"metadata":{}},{"name":"stderr","text":"Token indices sequence length is longer than the specified maximum sequence length for this model (594 > 512). Running this sequence through the model will result in indexing errors\nToken indices sequence length is longer than the specified maximum sequence length for this model (933 > 512). Running this sequence through the model will result in indexing errors\nToken indices sequence length is longer than the specified maximum sequence length for this model (1151 > 512). Running this sequence through the model will result in indexing errors\nToken indices sequence length is longer than the specified maximum sequence length for this model (600 > 512). Running this sequence through the model will result in indexing errors\nToken indices sequence length is longer than the specified maximum sequence length for this model (792 > 512). Running this sequence through the model will result in indexing errors\nToken indices sequence length is longer than the specified maximum sequence length for this model (775 > 512). Running this sequence through the model will result in indexing errors\nToken indices sequence length is longer than the specified maximum sequence length for this model (568 > 512). Running this sequence through the model will result in indexing errors\nToken indices sequence length is longer than the specified maximum sequence length for this model (740 > 512). Running this sequence through the model will result in indexing errors\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{"papermill":{"duration":0.172515,"end_time":"2021-07-31T20:35:45.435357","exception":false,"start_time":"2021-07-31T20:35:45.262842","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"papermill":{"duration":0.152244,"end_time":"2021-07-31T20:35:45.741461","exception":false,"start_time":"2021-07-31T20:35:45.589217","status":"completed"},"tags":[]},"execution_count":null,"outputs":[]}]}