Training_Notebook.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "abb1a27d",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_name = \"rotten_tomatoes\"\n",
    "tokenizer_name = \"distilbert-base-uncased\"\n",
    "model_name = \"distilbert-base-uncased\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "72bfcb34",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.weight', 'pre_classifier.weight', 'classifier.bias', 'pre_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": [
    "from transformers import AutoModelForSequenceClassification\n",
    "model = AutoModelForSequenceClassification.from_pretrained(model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "00c4c362",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import TrainingArguments\n",
    "training_args = TrainingArguments(\n",
    "    output_dir=\"path/to/save/folder/\",\n",
    "    learning_rate=2e-5,\n",
    "    per_device_train_batch_size=8,\n",
    "    per_device_eval_batch_size=8,\n",
    "    num_train_epochs=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ac896671",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer\n",
    "tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1e3d3896",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "dataset = load_dataset(dataset_name)  # doctest: +IGNORE_RESULT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a3f2e005",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tokenize_dataset(dataset):\n",
    "    return tokenizer(dataset[\"text\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4490b9a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.map(tokenize_dataset, batched=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "43b6fb9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import DataCollatorWithPadding\n",
    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0baadb94",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import Trainer\n",
    "from transformers import TrainerCallback\n",
    "import time\n",
    "\n",
    "class MyCallback(TrainerCallback):\n",
    "    def on_epoch_begin(self, args, state, control, **kwargs):\n",
    "        self.start_time = time.time()\n",
    "        self.forward_pass_time = 0\n",
    "\n",
    "        self.batch_start_time = 0\n",
    "        self.batch_end_time = 0\n",
    "        self.batch_time = 0\n",
    "\n",
    "    def on_train_batch_begin(self, batch, logs=None):\n",
    "        self.batch_start_time = time.time()\n",
    "\n",
    "    def on_train_batch_end(self, batch, logs=None):\n",
    "        self.batch_end_time = time.time()\n",
    "        self.batch_time = self.batch_end_time - self.batch_start_time\n",
    "        self.forward_pass_time += self.batch_time\n",
    "\n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        end_time = time.time()\n",
    "        epoch_time = end_time - self.start_time\n",
    "        fwd_pass_percent = round((self.forward_pass_time/epoch_time)*100,2)\n",
    "        wgt_pass_time = epoch_time - self.forward_pass_time\n",
    "        wgt_and_bkp_percent = round((wgt_pass_time/epoch_time)*100,2)\n",
    "        print(f\"\\n  - Total:\\t {round(epoch_time,10)}s\")\n",
    "        print(f\"  - Forward:\\t {round(self.forward_pass_time,10)}s ({fwd_pass_percent}%)\")\n",
    "        print(f\"  - Wgt&BkP:\\t {round(wgt_pass_time,10)}s ({wgt_and_bkp_percent}%)\")\n",
    "        \n",
    "        with tf.profiler.experimental.Profile(\"logs/profile/\"):\n",
    "            with tf.profiler.experimental.Trace(\"Backpropagation\", step_num=epoch):\n",
    "                model.evaluate(x_test, y_test, verbose=2)\n",
    "\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=dataset[\"train\"],\n",
    "    eval_dataset=dataset[\"test\"],\n",
    "    tokenizer=tokenizer,\n",
    "    data_collator=data_collator,\n",
    "    callbacks=[MyCallback()]\n",
    ")  # doctest: +SKIP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6424d70",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You're using a DistilBertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
     ]
    },
    {
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       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='181' max='2134' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [ 181/2134 02:39 < 28:55, 1.13 it/s, Epoch 0.17/2]\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table><p>"
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   "source": [
    "trainer.train()\n",
    "#For tasks - like translation or summarization - that use a sequence-to-sequence model, use the Seq2SeqTrainer \n",
    "#and Seq2SeqTrainingArguments classes instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24080828",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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