sensai/examples/transformers.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import load_dataset, Features\n",
    "from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments\n",
    "import os\n",
    "from datasets import ClassLabel, Value\n",
    "\n",
    "# https://huggingface.co/docs/datasets/loading_datasets.html\n",
    "\n",
    "DATASET_DIR = os.environ.get(\"DATASET_DIR\", \"../input/sensai\")\n",
    "print(\"DATASET_DIR\", DATASET_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = load_dataset(\"holodata/sensai\", features=Features(\n",
    "                {\n",
    "                    \"body\": Value(\"string\"),\n",
    "                    \"toxic\": ClassLabel(num_classes=2, names=['0', '1'])\n",
    "                }\n",
    "            ))\n",
    "dataset = dataset['train']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = AutoModelForSequenceClassification.from_pretrained(\"bert-base-cased\", num_labels=2)\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "samples = dataset.shuffle().select(range(50000))\n",
    "\n",
    "def tokenize_function(examples):\n",
    "    return tokenizer(examples[\"body\"], padding=\"max_length\", truncation=True)\n",
    "\n",
    "tokenized_datasets = samples.map(tokenize_function, batched=True)\n",
    "tokenized_datasets.rename_column_(\"toxic\", \"label\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "splitset = tokenized_datasets.train_test_split(0.2)\n",
    "splitset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_args = TrainingArguments(\"test_trainer\")\n",
    "trainer = Trainer(\n",
    "    model=model, args=training_args, train_dataset=splitset['train'], eval_dataset=splitset['test']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer.train(resume_from_checkpoint=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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