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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "K6KNj8R5pFOi",
"outputId": "73e388e8-294f-438d-ddc2-06ae7132580a"
},
"outputs": [],
"source": [
"!kaggle competitions download -c jigsaw-toxic-comment-classification-challenge\n",
"!unzip jigsaw-toxic-comment-classification-challenge"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-a6Sx13TqW2h",
"outputId": "eb6bb305-7b66-4f59-e1e3-24858c1309c4"
},
"outputs": [],
"source": [
"!unzip test.csv.zip \n",
"!unzip test_labels.csv.zip \n",
"!unzip train.csv.zip"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "Jt-aOqhVqavv"
},
"outputs": [],
"source": [
"import warnings\n",
"import pandas as pd\n",
"import torch\n",
"import numpy as np\n",
"from torch.utils.data import Dataset, DataLoader, RandomSampler, SequentialSampler\n",
"from sklearn.model_selection import train_test_split\n",
"from transformers import Trainer, TrainingArguments\n",
"from transformers import AutoTokenizer, AutoModelForSequenceClassification"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"warnings.filterwarnings('ignore')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mps:0\n"
]
}
],
"source": [
"# Use GPU\n",
"#device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
"device = \"mps:0\" if torch.backends.mps.is_available() else \"cpu\"\n",
"print(device)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "zMDF7x0H4VFW"
},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>comment_text</th>\n",
" <th>toxic</th>\n",
" <th>severe_toxic</th>\n",
" <th>obscene</th>\n",
" <th>threat</th>\n",
" <th>insult</th>\n",
" <th>identity_hate</th>\n",
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"text/plain": [
" id comment_text toxic \\\n",
"0 0000997932d777bf Explanation\\nWhy the edits made under my usern... 0 \n",
"\n",
" severe_toxic obscene threat insult identity_hate \n",
"0 0 0 0 0 0 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Load training text and label dataset\n",
"# Preprocess data\n",
"\n",
"#test_texts = pd.read_csv(\"test.csv\").values.tolist()\n",
"#test_labels = pd.read_csv('test_labels.csv').values.tolist()\n",
"\n",
"train = pd.read_csv('train.csv')\n",
"train.head(1)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"159571 (159571, 8)\n",
"id False\n",
"comment_text False\n",
"toxic False\n",
"severe_toxic False\n",
"obscene False\n",
"threat False\n",
"insult False\n",
"identity_hate False\n",
"dtype: bool\n",
"False\n"
]
}
],
"source": [
"# Any duplicates?\n",
"print(len(train['comment_text'].unique()), train.shape)\n",
"\n",
"# Any missing values?\n",
"print(train.isnull().any())\n",
"print(train.isnull().values.any())"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
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" .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>id</th>\n",
" <th>comment_text</th>\n",
" <th>toxic</th>\n",
" <th>severe_toxic</th>\n",
" <th>obscene</th>\n",
" <th>threat</th>\n",
" <th>insult</th>\n",
" <th>identity_hate</th>\n",
" <th>grouped_labels</th>\n",
" </tr>\n",
" </thead>\n",
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],
"text/plain": [
" id comment_text toxic \\\n",
"0 0000997932d777bf Explanation\\nWhy the edits made under my usern... 0 \n",
"\n",
" severe_toxic obscene threat insult identity_hate grouped_labels \n",
"0 0 0 0 0 0 [0, 0, 0, 0, 0, 0] "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Group labels to get right format for training\n",
"labels = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']\n",
"train['grouped_labels'] = train[labels].values.tolist()\n",
"train.head(1)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# Convert to list from dataframe\n",
"train_texts = train['comment_text'].values.tolist()\n",
"train_labels = train['grouped_labels'].values.tolist()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "vkxJ6NkFlc46",
"tags": []
},
"outputs": [],
"source": [
"# Use distilbert, a faster model of BERT which keeps 95% of the performance\n",
"model_name = \"bert-base-uncased\"\n",
"tokenizer = AutoTokenizer.from_pretrained(model_name)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 0, 1, 1, 0, 0] 11\n",
"[1, 1, 0, 1, 0, 0] 11\n",
"[1, 0, 0, 1, 0, 1] 7\n",
"[1, 1, 0, 0, 1, 1] 7\n",
"[1, 1, 1, 0, 0, 1] 6\n",
"[1, 1, 1, 1, 0, 0] 4\n",
"[0, 0, 0, 1, 1, 0] 3\n",
"[1, 0, 0, 1, 1, 1] 3\n",
"[1, 1, 0, 0, 0, 1] 3\n",
"[0, 0, 1, 0, 0, 1] 3\n",
"[0, 0, 1, 1, 0, 0] 2\n",
"[0, 0, 1, 1, 1, 0] 2\n",
"[1, 1, 0, 1, 1, 0] 1\n",
"[1, 1, 0, 1, 0, 1] 1\n",
"Name: grouped_labels, dtype: int64\n",
"df label indices with only one instance: [159029, 158498, 157010, 154553, 149180, 144159, 139501, 138026, 134459, 133505, 127410, 120395, 115766, 113304, 110056, 107881, 107096, 101089, 98699, 86746, 76454, 74607, 68264, 66350, 63687, 61934, 57594, 53408, 45101, 41461, 36141, 31191, 30566, 29445, 23374, 17187, 15977, 9487, 8979, 6316, 6063, 2374]\n"
]
}
],
"source": [
"# Also do preprocessing to see if there are any unique rows\n",
"# with that specfic combination of labels\n",
"# If that is the case, we want to include that row in the training data\n",
"\n",
"# Find unique label combinations\n",
"label_counts = train['grouped_labels'].astype(str).value_counts()\n",
"print(label_counts[-14:])\n",
"\n",
"# Take low frequency labels\n",
"low_freq = label_counts[label_counts<10].keys()\n",
"low_freq_inds = sorted(list(train[train['grouped_labels'].astype(str).isin(low_freq)].index), reverse=True)\n",
"print('df label indices with only one instance: ', low_freq_inds)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"low_freq_train_texts = [train_texts.pop(i) for i in low_freq_inds]\n",
"low_freq_train_labels = [train_labels.pop(i) for i in low_freq_inds]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# Add low freq values to training data\n",
"train_texts.extend(low_freq_train_texts)\n",
"train_labels.extend(low_freq_train_labels)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# Split datasets for training\n",
"train_texts, val_texts, train_labels, val_labels = train_test_split(train_texts, train_labels, test_size=.1)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# Shorten token to increase training speed, average is below this\n",
"max_length = 100\n",
"train_encodings = tokenizer(train_texts, truncation=True, padding=True, return_tensors=\"pt\", max_length=max_length).to(device)\n",
"val_encodings = tokenizer(val_texts, truncation=True, padding=True, return_tensors=\"pt\", max_length=max_length).to(device)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"class ToxicDataset(Dataset):\n",
" def __init__(self, encodings, labels):\n",
" self.encodings = encodings\n",
" self.labels = [[float(y) for y in x] for x in labels]\n",
"\n",
" def __getitem__(self, idx):\n",
" item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n",
" item['labels'] = torch.tensor(self.labels[idx])\n",
" return item\n",
"\n",
" def __len__(self):\n",
" return len(self.labels)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"train_dataset = ToxicDataset(train_encodings, train_labels)\n",
"val_dataset = ToxicDataset(val_encodings, val_labels)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias']\n",
"- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
"- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
"Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
}
],
"source": [
"model = AutoModelForSequenceClassification.from_pretrained(model_name,\n",
" num_labels=6,\n",
" ).to(device)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": true,
"id": "CI2B0V5D27gA",
"jupyter": {
"outputs_hidden": true
},
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n",
"PyTorch: setting up devices\n",
"***** Running training *****\n",
" Num examples = 127656\n",
" Num Epochs = 1\n",
" Instantaneous batch size per device = 16\n",
" Total train batch size (w. parallel, distributed & accumulation) = 16\n",
" Gradient Accumulation steps = 1\n",
" Total optimization steps = 7979\n",
" Number of trainable parameters = 109486854\n"
]
},
{
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" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Step</th>\n",
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"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[56], line 28\u001b[0m\n\u001b[1;32m 9\u001b[0m training_args \u001b[38;5;241m=\u001b[39m TrainingArgumentsWithMPSSupport(\n\u001b[1;32m 10\u001b[0m output_dir \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m./results\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 11\u001b[0m num_train_epochs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 18\u001b[0m logging_steps\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10\u001b[39m,\n\u001b[1;32m 19\u001b[0m )\n\u001b[1;32m 21\u001b[0m trainer \u001b[38;5;241m=\u001b[39m Trainer(\n\u001b[1;32m 22\u001b[0m model\u001b[38;5;241m=\u001b[39mmodel,\n\u001b[1;32m 23\u001b[0m args\u001b[38;5;241m=\u001b[39mtraining_args,\n\u001b[1;32m 24\u001b[0m train_dataset\u001b[38;5;241m=\u001b[39mtrain_dataset,\n\u001b[1;32m 25\u001b[0m eval_dataset\u001b[38;5;241m=\u001b[39mval_dataset,\n\u001b[1;32m 26\u001b[0m )\n\u001b[0;32m---> 28\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/transformers/trainer.py:1501\u001b[0m, in \u001b[0;36mTrainer.train\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel_wrapped \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel\n\u001b[1;32m 1498\u001b[0m inner_training_loop \u001b[38;5;241m=\u001b[39m find_executable_batch_size(\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_inner_training_loop, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_train_batch_size, args\u001b[38;5;241m.\u001b[39mauto_find_batch_size\n\u001b[1;32m 1500\u001b[0m )\n\u001b[0;32m-> 1501\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43minner_training_loop\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1502\u001b[0m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1503\u001b[0m \u001b[43m \u001b[49m\u001b[43mresume_from_checkpoint\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresume_from_checkpoint\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1504\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrial\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrial\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1505\u001b[0m \u001b[43m \u001b[49m\u001b[43mignore_keys_for_eval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_keys_for_eval\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1506\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/transformers/trainer.py:1749\u001b[0m, in \u001b[0;36mTrainer._inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m 1747\u001b[0m tr_loss_step \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining_step(model, inputs)\n\u001b[1;32m 1748\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1749\u001b[0m tr_loss_step \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtraining_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1751\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m 1752\u001b[0m args\u001b[38;5;241m.\u001b[39mlogging_nan_inf_filter\n\u001b[1;32m 1753\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_torch_tpu_available()\n\u001b[1;32m 1754\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m (torch\u001b[38;5;241m.\u001b[39misnan(tr_loss_step) \u001b[38;5;129;01mor\u001b[39;00m torch\u001b[38;5;241m.\u001b[39misinf(tr_loss_step))\n\u001b[1;32m 1755\u001b[0m ):\n\u001b[1;32m 1756\u001b[0m \u001b[38;5;66;03m# if loss is nan or inf simply add the average of previous logged losses\u001b[39;00m\n\u001b[1;32m 1757\u001b[0m tr_loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m tr_loss \u001b[38;5;241m/\u001b[39m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mglobal_step \u001b[38;5;241m-\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_globalstep_last_logged)\n",
"File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/transformers/trainer.py:2526\u001b[0m, in \u001b[0;36mTrainer.training_step\u001b[0;34m(self, model, inputs)\u001b[0m\n\u001b[1;32m 2524\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdeepspeed\u001b[38;5;241m.\u001b[39mbackward(loss)\n\u001b[1;32m 2525\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2526\u001b[0m \u001b[43mloss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2528\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loss\u001b[38;5;241m.\u001b[39mdetach()\n",
"File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/torch/_tensor.py:488\u001b[0m, in \u001b[0;36mTensor.backward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m 478\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 479\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[1;32m 480\u001b[0m Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[1;32m 481\u001b[0m (\u001b[38;5;28mself\u001b[39m,),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 486\u001b[0m inputs\u001b[38;5;241m=\u001b[39minputs,\n\u001b[1;32m 487\u001b[0m )\n\u001b[0;32m--> 488\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 489\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\n\u001b[1;32m 490\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/anaconda3/lib/python3.10/site-packages/torch/autograd/__init__.py:204\u001b[0m, in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m 199\u001b[0m retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n\u001b[1;32m 201\u001b[0m \u001b[38;5;66;03m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[1;32m 202\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[1;32m 203\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[0;32m--> 204\u001b[0m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[1;32m 205\u001b[0m \u001b[43m \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 206\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_unreachable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"class TrainingArgumentsWithMPSSupport(TrainingArguments):\n",
" @property\n",
" def device(self) -> torch.device:\n",
" if torch.backends.mps.is_available():\n",
" return torch.device(\"mps\")\n",
" else:\n",
" return torch.device(\"cpu\")\n",
"\n",
"training_args = TrainingArgumentsWithMPSSupport(\n",
" output_dir = './results',\n",
" num_train_epochs=1,\n",
" per_device_train_batch_size=16,\n",
" per_device_eval_batch_size=16,\n",
" warmup_steps=500,\n",
" learning_rate=5e-5,\n",
" weight_decay=0.01,\n",
" logging_dir='./logs',\n",
" logging_steps=10,\n",
")\n",
"\n",
"trainer = Trainer(\n",
" model=model,\n",
" args=training_args,\n",
" train_dataset=train_dataset,\n",
" eval_dataset=val_dataset,\n",
")\n",
"\n",
"trainer.train()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Saving model checkpoint to ./model_checkpoint/done\n",
"Configuration saved in ./model_checkpoint/done/config.json\n",
"Model weights saved in ./model_checkpoint/done/pytorch_model.bin\n"
]
}
],
"source": [
"trainer.save_model('./model_checkpoint/done')"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from transformers import BertTokenizer, BertForSequenceClassification\n",
"#saved = DistilBertModel.from_pretrained('./model_checkpoint/trained', num_labels=6, problem_type=\"multi_label_classification\")\n",
"saved = BertForSequenceClassification.from_pretrained('./model_checkpoint/fine_tuned')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'trainer' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[19], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241m.\u001b[39mevaluate()\n",
"\u001b[0;31mNameError\u001b[0m: name 'trainer' is not defined"
]
}
],
"source": [
"trainer.evaluate()"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[[0.4601849317550659,\n",
" 0.0626736581325531,\n",
" 0.1962047964334488,\n",
" 0.0715285912156105,\n",
" 0.1363525241613388,\n",
" 0.0730554461479187]]"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"text = \"fun\"\n",
"encoded_input = tokenizer(text, return_tensors=\"pt\")\n",
"outputs = saved(**encoded_input)\n",
"predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)\n",
"predictions = predictions.cpu().detach().numpy()\n",
"predictions.tolist()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"res = [1 if x >= 0.5 else 0 for x in predictions[0]]"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[1, 0, 0, 0, 0, 0]"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|