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transformers[sentencepiece]" + ] + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import torch\n", + "\n", + "from datasets import load_dataset\n", + "from transformers import (\n", + " AutoTokenizer,\n", + " AutoModelForSequenceClassification,\n", + " TrainingArguments,\n", + " Trainer\n", + ")\n", + "\n", + "import evaluate" + ], + "metadata": { + "id": "9h94V9WxUTED" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "raw_dataset = load_dataset(\"imdb\")\n", + "\n", + "checkpoint = \"distilbert-base-uncased\"\n", + "\n", + "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n", + "\n", + "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels = 2)\n", + "\n", + "def tokenize_function(example):\n", + " return tokenizer(\n", + " example[\"text\"],\n", + " truncation = True,\n", + " padding = \"max_length\",\n", + " max_length = 256\n", + " )\n", + "\n", + "tokenized_dataset = 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