sentiment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f34cc5c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "imdb = load_dataset(\"imdb\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b764a148",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'text': 'I love sci-fi and am willing to put up with a lot. Sci-fi movies/TV are usually underfunded, under-appreciated and misunderstood. I tried to like this, I really did, but it is to good TV sci-fi as Babylon 5 is to Star Trek (the original). Silly prosthetics, cheap cardboard sets, stilted dialogues, CG that doesn\\'t match the background, and painfully one-dimensional characters cannot be overcome with a \\'sci-fi\\' setting. (I\\'m sure there are those of you out there who think Babylon 5 is good sci-fi TV. It\\'s not. It\\'s clichéd and uninspiring.) While US viewers might like emotion and character development, sci-fi is a genre that does not take itself seriously (cf. Star Trek). It may treat important issues, yet not as a serious philosophy. It\\'s really difficult to care about the characters here as they are not simply foolish, just missing a spark of life. Their actions and reactions are wooden and predictable, often painful to watch. The makers of Earth KNOW it\\'s rubbish as they have to always say \"Gene Roddenberry\\'s Earth...\" otherwise people would not continue watching. Roddenberry\\'s ashes must be turning in their orbit as this dull, cheap, poorly edited (watching it without advert breaks really brings this home) trudging Trabant of a show lumbers into space. Spoiler. So, kill off a main character. And then bring him back as another actor. Jeeez! Dallas all over again.',\n",
       " 'label': 0}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imdb[\"test\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a3bd80e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#load a DistilBERT tokenizer to preprocess the text field\n",
    "from transformers import AutoTokenizer\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9f237916",
   "metadata": {},
   "outputs": [],
   "source": [
    "#a preprocessing function to tokenize text and truncate sequences to be no longer than DistilBERT’s maximum input length\n",
    "def preprocess_function(examples):\n",
    "    return tokenizer(examples[\"text\"], truncation=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9830002e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Map: 100%|██████████████████████████████████████████████████████████████| 25000/25000 [00:06<00:00, 3772.48 examples/s]\n"
     ]
    }
   ],
   "source": [
    "#apply the preprocessing function over the entire dataset. You can speed up map by setting batched=True to process multiple elements of the dataset at once:\n",
    "tokenized_imdb = imdb.map(preprocess_function, batched=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "98ef2d38",
   "metadata": {},
   "outputs": [],
   "source": [
    "import evaluate\n",
    "\n",
    "accuracy = evaluate.load(\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "76b6bf56",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "def compute_metrics(eval_pred):\n",
    "    predictions, labels = eval_pred\n",
    "    predictions = np.argmax(predictions, axis=1)\n",
    "    return accuracy.compute(predictions=predictions, references=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d981ad36",
   "metadata": {},
   "outputs": [],
   "source": [
    "id2label = {0: \"NEGATIVE\", 1: \"POSITIVE\"}\n",
    "label2id = {\"NEGATIVE\": 0, \"POSITIVE\": 1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3b23fa8d",
   "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: ['pre_classifier.weight', 'classifier.bias', 'pre_classifier.bias', 'classifier.weight']\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, TrainingArguments, Trainer\n",
    "\n",
    "model = AutoModelForSequenceClassification.from_pretrained(\n",
    "    \"distilbert-base-uncased\", num_labels=2, id2label=id2label, label2id=label2id\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "253967c5",
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "Using the `Trainer` with `PyTorch` requires `accelerate>=0.20.1`: Please run `pip install transformers[torch]` or `pip install accelerate -U`",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[31], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m training_args \u001b[38;5;241m=\u001b[39m \u001b[43mTrainingArguments\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m      2\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mnewmodel\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m      3\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlearning_rate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2e-5\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m      4\u001b[0m \u001b[43m    \u001b[49m\u001b[43mper_device_train_batch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m16\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m      5\u001b[0m \u001b[43m    \u001b[49m\u001b[43mper_device_eval_batch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m16\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m      6\u001b[0m \u001b[43m    \u001b[49m\u001b[43mnum_train_epochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m      7\u001b[0m \u001b[43m    \u001b[49m\u001b[43mweight_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.01\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m      8\u001b[0m \u001b[43m    \u001b[49m\u001b[43mevaluation_strategy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mepoch\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m      9\u001b[0m \u001b[43m    \u001b[49m\u001b[43msave_strategy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mepoch\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m     10\u001b[0m \u001b[43m    \u001b[49m\u001b[43mload_best_model_at_end\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;32m     11\u001b[0m \u001b[43m)\u001b[49m\n\u001b[0;32m     13\u001b[0m trainer \u001b[38;5;241m=\u001b[39m Trainer(\n\u001b[0;32m     14\u001b[0m     model\u001b[38;5;241m=\u001b[39mmodel,\n\u001b[0;32m     15\u001b[0m     args\u001b[38;5;241m=\u001b[39mtraining_args,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     20\u001b[0m     compute_metrics\u001b[38;5;241m=\u001b[39mcompute_metrics,\n\u001b[0;32m     21\u001b[0m )\n\u001b[0;32m     23\u001b[0m trainer\u001b[38;5;241m.\u001b[39mtrain()\n",
      "File \u001b[1;32m<string>:121\u001b[0m, in \u001b[0;36m__init__\u001b[1;34m(self, output_dir, overwrite_output_dir, do_train, do_eval, do_predict, evaluation_strategy, prediction_loss_only, per_device_train_batch_size, per_device_eval_batch_size, per_gpu_train_batch_size, per_gpu_eval_batch_size, gradient_accumulation_steps, eval_accumulation_steps, eval_delay, learning_rate, weight_decay, adam_beta1, adam_beta2, adam_epsilon, max_grad_norm, num_train_epochs, max_steps, lr_scheduler_type, lr_scheduler_kwargs, warmup_ratio, warmup_steps, log_level, log_level_replica, log_on_each_node, logging_dir, logging_strategy, logging_first_step, logging_steps, logging_nan_inf_filter, save_strategy, save_steps, save_total_limit, save_safetensors, save_on_each_node, save_only_model, no_cuda, use_cpu, use_mps_device, seed, data_seed, jit_mode_eval, use_ipex, bf16, fp16, fp16_opt_level, half_precision_backend, bf16_full_eval, fp16_full_eval, tf32, local_rank, ddp_backend, tpu_num_cores, tpu_metrics_debug, debug, dataloader_drop_last, eval_steps, dataloader_num_workers, past_index, run_name, disable_tqdm, remove_unused_columns, label_names, load_best_model_at_end, metric_for_best_model, greater_is_better, ignore_data_skip, fsdp, fsdp_min_num_params, fsdp_config, fsdp_transformer_layer_cls_to_wrap, deepspeed, label_smoothing_factor, optim, optim_args, adafactor, group_by_length, length_column_name, report_to, ddp_find_unused_parameters, ddp_bucket_cap_mb, ddp_broadcast_buffers, dataloader_pin_memory, dataloader_persistent_workers, skip_memory_metrics, use_legacy_prediction_loop, push_to_hub, resume_from_checkpoint, hub_model_id, hub_strategy, hub_token, hub_private_repo, hub_always_push, gradient_checkpointing, gradient_checkpointing_kwargs, include_inputs_for_metrics, fp16_backend, push_to_hub_model_id, push_to_hub_organization, push_to_hub_token, mp_parameters, auto_find_batch_size, full_determinism, torchdynamo, ray_scope, ddp_timeout, torch_compile, torch_compile_backend, torch_compile_mode, dispatch_batches, split_batches, include_tokens_per_second, include_num_input_tokens_seen, neftune_noise_alpha)\u001b[0m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\transformers\\training_args.py:1493\u001b[0m, in \u001b[0;36mTrainingArguments.__post_init__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1487\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m version\u001b[38;5;241m.\u001b[39mparse(version\u001b[38;5;241m.\u001b[39mparse(torch\u001b[38;5;241m.\u001b[39m__version__)\u001b[38;5;241m.\u001b[39mbase_version) \u001b[38;5;241m==\u001b[39m version\u001b[38;5;241m.\u001b[39mparse(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m2.0.0\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfp16:\n\u001b[0;32m   1488\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m--optim adamw_torch_fused with --fp16 requires PyTorch>2.0\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   1490\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m   1491\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mframework \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1492\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m is_torch_available()\n\u001b[1;32m-> 1493\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m (\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[38;5;241m.\u001b[39mtype \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcuda\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   1494\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice\u001b[38;5;241m.\u001b[39mtype \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnpu\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   1495\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice\u001b[38;5;241m.\u001b[39mtype \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxpu\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   1496\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m (get_xla_device_type(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGPU\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   1497\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfp16 \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfp16_full_eval)\n\u001b[0;32m   1498\u001b[0m ):\n\u001b[0;32m   1499\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m   1500\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFP16 Mixed precision training with AMP or APEX (`--fp16`) and FP16 half precision evaluation\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1501\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m (`--fp16_full_eval`) can only be used on CUDA or NPU devices or certain XPU devices (with IPEX).\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1502\u001b[0m     )\n\u001b[0;32m   1504\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m   1505\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mframework \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1506\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m is_torch_available()\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1513\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbf16 \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbf16_full_eval)\n\u001b[0;32m   1514\u001b[0m ):\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\transformers\\training_args.py:1941\u001b[0m, in \u001b[0;36mTrainingArguments.device\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1937\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1938\u001b[0m \u001b[38;5;124;03mThe device used by this process.\u001b[39;00m\n\u001b[0;32m   1939\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1940\u001b[0m requires_backends(\u001b[38;5;28mself\u001b[39m, [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtorch\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m-> 1941\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_setup_devices\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\transformers\\utils\\generic.py:54\u001b[0m, in \u001b[0;36mcached_property.__get__\u001b[1;34m(self, obj, objtype)\u001b[0m\n\u001b[0;32m     52\u001b[0m cached \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(obj, attr, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m     53\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cached \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m---> 54\u001b[0m     cached \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     55\u001b[0m     \u001b[38;5;28msetattr\u001b[39m(obj, attr, cached)\n\u001b[0;32m     56\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cached\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\transformers\\training_args.py:1841\u001b[0m, in \u001b[0;36mTrainingArguments._setup_devices\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1839\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_sagemaker_mp_enabled():\n\u001b[0;32m   1840\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_accelerate_available(min_version\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.20.1\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m-> 1841\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(\n\u001b[0;32m   1842\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUsing the `Trainer` with `PyTorch` requires `accelerate>=0.20.1`: Please run `pip install transformers[torch]` or `pip install accelerate -U`\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1843\u001b[0m         )\n\u001b[0;32m   1844\u001b[0m     AcceleratorState\u001b[38;5;241m.\u001b[39m_reset_state(reset_partial_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m   1845\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdistributed_state \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "\u001b[1;31mImportError\u001b[0m: Using the `Trainer` with `PyTorch` requires `accelerate>=0.20.1`: Please run `pip install transformers[torch]` or `pip install accelerate -U`"
     ]
    }
   ],
   "source": [
    "training_args = TrainingArguments(\n",
    "    output_dir=\"newmodel\",\n",
    "    learning_rate=2e-5,\n",
    "    per_device_train_batch_size=16,\n",
    "    per_device_eval_batch_size=16,\n",
    "    num_train_epochs=2,\n",
    "    weight_decay=0.01,\n",
    "    evaluation_strategy=\"epoch\",\n",
    "    save_strategy=\"epoch\",\n",
    "    load_best_model_at_end=True,\n",
    ")\n",
    "\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=tokenized_imdb[\"train\"],\n",
    "    eval_dataset=tokenized_imdb[\"test\"],\n",
    "    tokenizer=tokenizer,\n",
    "    data_collator=data_collator,\n",
    "    compute_metrics=compute_metrics,\n",
    ")\n",
    "\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9646c20a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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