notebooks/05n_NLP_on_HF_02.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "k4xT3AvDy7GF"
   },
   "source": [
    "# บทที่ 5n - การเทรนโมเดลบน Hugging Face พาร์ท 1.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "95vn8VX3y8eU"
   },
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/ai-builders/curriculum/blob/main/notebooks/05n_NLP_on_HF_02.ipynb)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PNaPLjHaNqYW",
    "outputId": "dd422387-aff0-4390-b36e-7a85dd03d322"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Installing collected packages: multidict, frozenlist, yarl, pyyaml, asynctest, async-timeout, aiosignal, tokenizers, sacremoses, huggingface-hub, fsspec, aiohttp, xxhash, transformers, sentencepiece, datasets\n",
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     ]
    }
   ],
   "source": [
    "!pip install datasets==1.17.0 transformers[sentencepiece]==4.34.0\n",
    "!pip install accelerate==0.23.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "w0mh3f-sN3av"
   },
   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "from transformers import AutoTokenizer, DataCollatorWithPadding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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     ]
    },
    "id": "ihCgoSbv0ABb",
    "outputId": "988f48b5-7755-495b-ae6a-138377b1adbd"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f46513a8bd9f4055acd2e7e5c5f10050",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/7.78k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7bdfa34f56b54b7baf87efe21f34e8bf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/4.47k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset glue/mrpc (download: 1.43 MiB, generated: 1.43 MiB, post-processed: Unknown size, total: 2.85 MiB) to /root/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4ee19202dd25474ebda499e83cdb6907",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c708da6f69dd4adebd9283f98e0992f6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading: 0.00B [00:00, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d5324f665faa48da94c8755573ac2613",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading: 0.00B [00:00, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "260f87379ec84067882dc4d59bb43d63",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading: 0.00B [00:00, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0e486607eb714f16ada56762bdc98bfd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "190b1d81a96e4afca5c0d0b1b7f7f938",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2c05175741e444fc91bf6731a3b199e1",
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      },
      "text/plain": [
       "0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset glue downloaded and prepared to /root/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad. Subsequent calls will reuse this data.\n"
     ]
    },
    {
     "data": {
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       "model_id": "d05171fe61864353a295a05d7dfc9051",
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     },
     "metadata": {},
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    },
    {
     "data": {
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       "model_id": "7d0a507b0b7d49dca3702e5d6d318a4c",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "a1f1c6c8c16347f4b32106120c440d2c",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "75d8d7a723144a26b6c1995f4aaca286",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/226k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5aac32e821d5408b9502dd844829ff36",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "raw_datasets = load_dataset(\"glue\", \"mrpc\") # mrpc =  Microsoft Research Paraphrase Corpus\n",
    "checkpoint = \"bert-base-uncased\"\n",
    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LPfyyn2Xva1T",
    "outputId": "3f18fd4f-b7bb-4fb5-d142-b7133ca43947"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
       "        num_rows: 3668\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
       "        num_rows: 408\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
       "        num_rows: 1725\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-OZZxhaxyetr",
    "outputId": "5b126425-fba0-4179-d618-6d16d38ee7ca"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'idx': 0,\n",
       " 'label': 1,\n",
       " 'sentence1': 'Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .',\n",
       " 'sentence2': 'Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_datasets['train'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rNo79FgrypJO",
    "outputId": "ab3d49cb-c07d-452a-e9cb-e49670e4bba4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'idx': 1,\n",
       " 'label': 0,\n",
       " 'sentence1': \"Yucaipa owned Dominick 's before selling the chain to Safeway in 1998 for $ 2.5 billion .\",\n",
       " 'sentence2': \"Yucaipa bought Dominick 's in 1995 for $ 693 million and sold it to Safeway for $ 1.8 billion in 1998 .\"}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_datasets['train'][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CzdifhAK6hVI",
    "outputId": "8ad1462e-139a-4d5f-804b-9f1203988812"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pos:0.6744820065430752 Neg:0.3255179934569248\n",
      "Pos:0.6838235294117647 Neg:0.3161764705882353\n",
      "Pos:0.664927536231884 Neg:0.33507246376811595\n"
     ]
    }
   ],
   "source": [
    "for k in raw_datasets.keys():\n",
    "  pos = raw_datasets[k]['label'].count(1)\n",
    "  neg = raw_datasets[k]['label'].count(0)\n",
    "  print(f\"Pos:{pos/raw_datasets[k].num_rows} Neg:{neg/raw_datasets[k].num_rows}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "v73B5HmT0LPC"
   },
   "source": [
    "# อะไรคือการตัดคำ?\n",
    "![causal_modeling.png](data:image/png;base64,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)\n",
    "\n",
    "จากตัวอย่าง: จะเห็นได้ว่าโมเดลของเรารับ input เป็นคำๆเพราะ (i) มีความตัวอักษรที่ยังพอดี หากเป็นประโยคก็จะยาวไป หากเป็นตัวอักษร โมเดลก็จะเรียนรู้ได้ไม่ดีพอ (ii) การตัดคำไม่ได้ยากขนาดนั้น และการรับทั้งประโยคเป็น input ยังต้องการทรัพยากรมาก \n",
    "\n",
    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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 163,
     "referenced_widgets": [
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    "id": "qxZKxNm9-A3M",
    "outputId": "625c15a1-4469-4026-df17-3ebafd6caba8"
   },
   "outputs": [
    {
     "data": {
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       "model_id": "10a2ee3fc48c48068f49eb0490945262",
       "version_major": 2,
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     },
     "metadata": {},
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    },
    {
     "data": {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9ed989b4307e4170bf51f1b3c5c812df",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'input_ids': [101, 7993, 170, 13809, 23763, 2443, 1110, 3014, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1]}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import BertTokenizer\n",
    "\n",
    "tokenizer = BertTokenizer.from_pretrained(\"bert-base-cased\")\n",
    "tokenizer(\"Using a Transformer network is simple\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "RrRB-dnv0Bpu"
   },
   "outputs": [],
   "source": [
    "def tokenize_function(example):\n",
    "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 113,
     "referenced_widgets": [
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      "edaba966ade54f7eb9d4979d757b67d1",
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    },
    "id": "Y_XUwXBe0DCD",
    "outputId": "e0a5811e-e22d-42d4-89a8-db9e2d198c2d"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ec7a5c1d888a4bce9436b9a3efc57971",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f5f20d32b0e047ca99cb24e1aac26e26",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eefac9bdfdd64b51b671f70994ffd4f7",
       "version_major": 2,
       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)\n",
    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1nmFf3ucyVMS"
   },
   "source": [
    "# กำหนด Training Arguments\n",
    "= ทาง Hugging Face มีค่า Default มาให้เราอยู่แล้ว ซึ่งสามารถปรับแต่งได้เยอะมาก!! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "sveng-qD0Eoi"
   },
   "outputs": [],
   "source": [
    "from transformers import TrainingArguments\n",
    "\n",
    "training_args = TrainingArguments(\"test-trainer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pbFQGVjl2RpP",
    "outputId": "030cf884-4ca0-4b73-fd06-9a7978a98945"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TrainingArguments(\n",
       "_n_gpu=1,\n",
       "adafactor=False,\n",
       "adam_beta1=0.9,\n",
       "adam_beta2=0.999,\n",
       "adam_epsilon=1e-08,\n",
       "bf16=False,\n",
       "bf16_full_eval=False,\n",
       "dataloader_drop_last=False,\n",
       "dataloader_num_workers=0,\n",
       "dataloader_pin_memory=True,\n",
       "ddp_bucket_cap_mb=None,\n",
       "ddp_find_unused_parameters=None,\n",
       "debug=[],\n",
       "deepspeed=None,\n",
       "disable_tqdm=False,\n",
       "do_eval=False,\n",
       "do_predict=False,\n",
       "do_train=False,\n",
       "eval_accumulation_steps=None,\n",
       "eval_steps=None,\n",
       "evaluation_strategy=IntervalStrategy.NO,\n",
       "fp16=False,\n",
       "fp16_backend=auto,\n",
       "fp16_full_eval=False,\n",
       "fp16_opt_level=O1,\n",
       "gradient_accumulation_steps=1,\n",
       "gradient_checkpointing=False,\n",
       "greater_is_better=None,\n",
       "group_by_length=False,\n",
       "half_precision_backend=auto,\n",
       "hub_model_id=None,\n",
       "hub_strategy=HubStrategy.EVERY_SAVE,\n",
       "hub_token=<HUB_TOKEN>,\n",
       "ignore_data_skip=False,\n",
       "label_names=None,\n",
       "label_smoothing_factor=0.0,\n",
       "learning_rate=5e-05,\n",
       "length_column_name=length,\n",
       "load_best_model_at_end=False,\n",
       "local_rank=-1,\n",
       "log_level=-1,\n",
       "log_level_replica=-1,\n",
       "log_on_each_node=True,\n",
       "logging_dir=test-trainer/runs/May01_02-25-20_6d8e8d049325,\n",
       "logging_first_step=False,\n",
       "logging_nan_inf_filter=True,\n",
       "logging_steps=500,\n",
       "logging_strategy=IntervalStrategy.STEPS,\n",
       "lr_scheduler_type=SchedulerType.LINEAR,\n",
       "max_grad_norm=1.0,\n",
       "max_steps=-1,\n",
       "metric_for_best_model=None,\n",
       "mp_parameters=,\n",
       "no_cuda=False,\n",
       "num_train_epochs=3.0,\n",
       "output_dir=test-trainer,\n",
       "overwrite_output_dir=False,\n",
       "past_index=-1,\n",
       "per_device_eval_batch_size=8,\n",
       "per_device_train_batch_size=8,\n",
       "prediction_loss_only=False,\n",
       "push_to_hub=False,\n",
       "push_to_hub_model_id=None,\n",
       "push_to_hub_organization=None,\n",
       "push_to_hub_token=<PUSH_TO_HUB_TOKEN>,\n",
       "remove_unused_columns=True,\n",
       "report_to=['tensorboard'],\n",
       "resume_from_checkpoint=None,\n",
       "run_name=test-trainer,\n",
       "save_on_each_node=False,\n",
       "save_steps=500,\n",
       "save_strategy=IntervalStrategy.STEPS,\n",
       "save_total_limit=None,\n",
       "seed=42,\n",
       "sharded_ddp=[],\n",
       "skip_memory_metrics=True,\n",
       "tf32=None,\n",
       "tpu_metrics_debug=False,\n",
       "tpu_num_cores=None,\n",
       "use_legacy_prediction_loop=False,\n",
       "warmup_ratio=0.0,\n",
       "warmup_steps=0,\n",
       "weight_decay=0.0,\n",
       "xpu_backend=None,\n",
       ")"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_args"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h_nFgwa0CN9f"
   },
   "source": [
    "# สร้างโมเดลเพื่อ Finetune !\n",
    "## AutoModelForSequeceClassification\n",
    "เป็นฟังก์ชั่นที่นำเอาโมเดลที่เราสนใจจะเอามาเทรน มาต่อกับ Linear Layer (เป็น layer สุดท้าย) โดยจะเรียกว่า \"หัว Classifier\" (Classifier Head) โดยเราจะใช้หัวนี้ในการทำนายประโยคนั้นเอง"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 157,
     "referenced_widgets": [
      "8fc1a5b9f75f41229818e5ddae2e20bb",
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      "7a9abd86fba4424aa2b901e472fe43d1",
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      "54448c8882654f518532a0b74837879d",
      "327fe939c3e3424492410e2e6a495ecf",
      "0c37be20e875468e89d8cf79e6628efa",
      "d1063c6454594c6d890e30999c7f3037"
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    },
    "id": "wPkWVYW_4OWB",
    "outputId": "6443118b-e652-458f-a839-8ae8b485e20d"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8fc1a5b9f75f41229818e5ddae2e20bb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/420M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.seq_relationship.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.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\n",
    "\n",
    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rL0YHQrPvSqn",
    "outputId": "a1423ebc-9829-44d4-a2da-b3d238a52d93"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BertForSequenceClassification(\n",
       "  (bert): BertModel(\n",
       "    (embeddings): BertEmbeddings(\n",
       "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
       "      (position_embeddings): Embedding(512, 768)\n",
       "      (token_type_embeddings): Embedding(2, 768)\n",
       "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "      (dropout): Dropout(p=0.1, inplace=False)\n",
       "    )\n",
       "    (encoder): BertEncoder(\n",
       "      (layer): ModuleList(\n",
       "        (0): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (1): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (2): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (3): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (4): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (5): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (6): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (7): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (8): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (9): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (10): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (11): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (pooler): BertPooler(\n",
       "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "      (activation): Tanh()\n",
       "    )\n",
       "  )\n",
       "  (dropout): Dropout(p=0.1, inplace=False)\n",
       "  (classifier): Linear(in_features=768, out_features=2, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3eyHMTc44P7F"
   },
   "outputs": [],
   "source": [
    "from transformers import Trainer\n",
    "\n",
    "trainer = Trainer(\n",
    "    model, # โมเดลที่ผ่านการเพิ่ม classifier head\n",
    "    training_args, # parameter จาก hugging face\n",
    "    train_dataset=tokenized_datasets[\"train\"],\n",
    "    eval_dataset=tokenized_datasets[\"validation\"],\n",
    "    data_collator=data_collator, # ตัวสร้าง padding\n",
    "    tokenizer=tokenizer,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 584
    },
    "id": "2pIES4RJ4Uo5",
    "outputId": "06a564c1-d260-490b-a105-54bf1855402f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The following columns in the training set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: sentence2, idx, sentence1.\n",
      "***** Running training *****\n",
      "  Num examples = 3668\n",
      "  Num Epochs = 3\n",
      "  Instantaneous batch size per device = 8\n",
      "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
      "  Gradient Accumulation steps = 1\n",
      "  Total optimization steps = 1377\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='1377' max='1377' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [1377/1377 06:48, Epoch 3/3]\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",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>0.642700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1000</td>\n",
       "      <td>0.615100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving model checkpoint to test-trainer/checkpoint-500\n",
      "Configuration saved in test-trainer/checkpoint-500/config.json\n",
      "Model weights saved in test-trainer/checkpoint-500/pytorch_model.bin\n",
      "tokenizer config file saved in test-trainer/checkpoint-500/tokenizer_config.json\n",
      "Special tokens file saved in test-trainer/checkpoint-500/special_tokens_map.json\n",
      "Saving model checkpoint to test-trainer/checkpoint-1000\n",
      "Configuration saved in test-trainer/checkpoint-1000/config.json\n",
      "Model weights saved in test-trainer/checkpoint-1000/pytorch_model.bin\n",
      "tokenizer config file saved in test-trainer/checkpoint-1000/tokenizer_config.json\n",
      "Special tokens file saved in test-trainer/checkpoint-1000/special_tokens_map.json\n",
      "\n",
      "\n",
      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=1377, training_loss=0.6010539955909302, metrics={'train_runtime': 409.3483, 'train_samples_per_second': 26.882, 'train_steps_per_second': 3.364, 'total_flos': 420167799858720.0, 'train_loss': 0.6010539955909302, 'epoch': 3.0})"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bKuxvocqCgj_"
   },
   "source": [
    "# ลองให้ทำนายของบน Validation Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 125
    },
    "id": "xPOfoSg_4Va1",
    "outputId": "91d4729c-423c-4e56-c168-e1fbc6a54c35"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The following columns in the test set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: sentence2, idx, sentence1.\n",
      "***** Running Prediction *****\n",
      "  Num examples = 408\n",
      "  Batch size = 8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='51' max='51' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [51/51 00:04]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(408, 2) (408,)\n"
     ]
    }
   ],
   "source": [
    "predictions = trainer.predict(tokenized_datasets[\"validation\"])\n",
    "print(predictions.predictions.shape, predictions.label_ids.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LutCDI4ifB8m",
    "outputId": "14ed3272-e316-4644-9daa-2902c25382a3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 0, 1, 1])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.label_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VtnSi5Al_kGT",
    "outputId": "028dfab1-058d-430d-e93e-ea17b9d0ba94"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.8269311   1.624018  ]\n",
      " [-0.20901814  0.18285246]\n",
      " [-0.6377912   1.3980953 ]\n",
      " [-0.737342    1.3662398 ]\n",
      " [-0.39176607  0.59216356]] [1 1 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "preds = np.argmax(predictions.predictions, axis=-1)\n",
    "print(predictions.predictions[:5],preds[:5]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CtySCALQ1YKA"
   },
   "source": [
    "# วัดความแม่นยำบน Dev set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67,
     "referenced_widgets": [
      "4a0b0d2fc70d433590f3d633e9c806e9",
      "218cd233abc84973838ad062deb48270",
      "3822987e15c1444c92749eda31a4b220",
      "4b6d6f877e474734bf2f3ea78c1b7732",
      "3529abaa7d9b479d87f19997d8de547a",
      "56022b4493994645afc34be031979592",
      "bcb8fdafd88e4a6b966fbad6ab665c40",
      "e152631d3e1c4cfea29aa0e417b94b5c",
      "a3f9ca14733e49c88d34b7b4aafd8695",
      "104c76fff9c640fcb21b9bbb329afa74",
      "44328d0eb2d64e80b6ad05aa3567c42b"
     ]
    },
    "id": "N9ZANgJU4Xob",
    "outputId": "47502a1a-baaa-45fd-c3d6-8237947475a6"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4a0b0d2fc70d433590f3d633e9c806e9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/1.86k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.7426470588235294, 'f1': 0.8351648351648352}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datasets import load_metric\n",
    "\n",
    "metric = load_metric(\"glue\", \"mrpc\")\n",
    "metric.compute(predictions=preds, references=predictions.label_ids)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kCzlXTpVw3sE"
   },
   "source": [
    "# วัดความแม่นยำบน Test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YLyCMM5gfgok",
    "outputId": "4a6ee950-9a82-412f-c096-d8b89451753d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 1, 1, 1])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 125
    },
    "id": "lHD3xocCw6NS",
    "outputId": "6876b5a8-e9a6-4997-a23c-9d4632a031f6"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The following columns in the test set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: sentence2, idx, sentence1.\n",
      "***** Running Prediction *****\n",
      "  Num examples = 1725\n",
      "  Batch size = 8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='267' max='51' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [51/51 02:17]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.7188405797101449, 'f1': 0.8125241592578275}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = trainer.predict(tokenized_datasets[\"test\"])\n",
    "preds = np.argmax(predictions.predictions, axis=-1)\n",
    "metric.compute(predictions=preds, references=predictions.label_ids)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XYAjiJuJCl4g"
   },
   "source": [
    "# แล้วจะรู้ได้ไงว่าโมเดลเราดีแล้ว?\n",
    "= ก็วัด dev set ตอนเทรนเลยสิ!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2E5CI8rd4ay-"
   },
   "outputs": [],
   "source": [
    "def compute_metrics(eval_preds):\n",
    "    metric = load_metric(\"glue\", \"mrpc\")\n",
    "    logits, labels = eval_preds\n",
    "    predictions = np.argmax(logits, axis=-1)\n",
    "    return metric.compute(predictions=predictions, references=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AIylhrC94dot",
    "outputId": "3eae99ae-7677-457f-c612-a3c2f5e47bd3"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PyTorch: setting up devices\n",
      "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",
      "loading configuration file https://huggingface.co/bert-base-uncased/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/3c61d016573b14f7f008c02c4e51a366c67ab274726fe2910691e2a761acf43e.37395cee442ab11005bcd270f3c34464dc1704b715b5d7d52b1a461abe3b9e4e\n",
      "Model config BertConfig {\n",
      "  \"_name_or_path\": \"bert-base-uncased\",\n",
      "  \"architectures\": [\n",
      "    \"BertForMaskedLM\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"gradient_checkpointing\": false,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 768,\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 3072,\n",
      "  \"layer_norm_eps\": 1e-12,\n",
      "  \"max_position_embeddings\": 512,\n",
      "  \"model_type\": \"bert\",\n",
      "  \"num_attention_heads\": 12,\n",
      "  \"num_hidden_layers\": 12,\n",
      "  \"pad_token_id\": 0,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"transformers_version\": \"4.15.0\",\n",
      "  \"type_vocab_size\": 2,\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 30522\n",
      "}\n",
      "\n",
      "loading weights file https://huggingface.co/bert-base-uncased/resolve/main/pytorch_model.bin from cache at /root/.cache/huggingface/transformers/a8041bf617d7f94ea26d15e218abd04afc2004805632abc0ed2066aa16d50d04.faf6ea826ae9c5867d12b22257f9877e6b8367890837bd60f7c54a29633f7f2f\n",
      "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.seq_relationship.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.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": [
    "training_args = TrainingArguments(\"test-trainer\", evaluation_strategy=\"epoch\")\n",
    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)\n",
    "\n",
    "trainer = Trainer(\n",
    "    model,\n",
    "    training_args,\n",
    "    train_dataset=tokenized_datasets[\"train\"],\n",
    "    eval_dataset=tokenized_datasets[\"validation\"],\n",
    "    data_collator=data_collator,\n",
    "    tokenizer=tokenizer,\n",
    "    compute_metrics=compute_metrics,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 829
    },
    "id": "-XLSLtR34euO",
    "outputId": "9aefba8b-3239-4ddf-ad1c-47c6b1fe9743"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The following columns in the training set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: sentence2, idx, sentence1.\n",
      "***** Running training *****\n",
      "  Num examples = 3668\n",
      "  Num Epochs = 3\n",
      "  Instantaneous batch size per device = 8\n",
      "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
      "  Gradient Accumulation steps = 1\n",
      "  Total optimization steps = 1377\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='1377' max='1377' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [1377/1377 07:00, Epoch 3/3]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.553349</td>\n",
       "      <td>0.715686</td>\n",
       "      <td>0.814696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.606500</td>\n",
       "      <td>0.761304</td>\n",
       "      <td>0.730392</td>\n",
       "      <td>0.827586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.446600</td>\n",
       "      <td>0.911134</td>\n",
       "      <td>0.740196</td>\n",
       "      <td>0.819113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The following columns in the evaluation set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: sentence2, idx, sentence1.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 408\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to test-trainer/checkpoint-500\n",
      "Configuration saved in test-trainer/checkpoint-500/config.json\n",
      "Model weights saved in test-trainer/checkpoint-500/pytorch_model.bin\n",
      "tokenizer config file saved in test-trainer/checkpoint-500/tokenizer_config.json\n",
      "Special tokens file saved in test-trainer/checkpoint-500/special_tokens_map.json\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: sentence2, idx, sentence1.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 408\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to test-trainer/checkpoint-1000\n",
      "Configuration saved in test-trainer/checkpoint-1000/config.json\n",
      "Model weights saved in test-trainer/checkpoint-1000/pytorch_model.bin\n",
      "tokenizer config file saved in test-trainer/checkpoint-1000/tokenizer_config.json\n",
      "Special tokens file saved in test-trainer/checkpoint-1000/special_tokens_map.json\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: sentence2, idx, sentence1.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 408\n",
      "  Batch size = 8\n",
      "\n",
      "\n",
      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=1377, training_loss=0.46636780605163797, metrics={'train_runtime': 421.3708, 'train_samples_per_second': 26.115, 'train_steps_per_second': 3.268, 'total_flos': 420167799858720.0, 'train_loss': 0.46636780605163797, 'epoch': 3.0})"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Nj3coayGxYeF"
   },
   "source": [
    "# วัดผลบน Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 125
    },
    "id": "9WMe6LFaxYIv",
    "outputId": "92c1d77c-51ca-4536-e6dc-a97a3dbff910"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The following columns in the test set  don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: sentence2, idx, sentence1.\n",
      "***** Running Prediction *****\n",
      "  Num examples = 1725\n",
      "  Batch size = 8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='216' max='216' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [216/216 00:17]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.7402898550724638, 'f1': 0.8114478114478115}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = trainer.predict(tokenized_datasets[\"test\"])\n",
    "preds = np.argmax(predictions.predictions, axis=-1)\n",
    "metric.compute(predictions=preds, references=predictions.label_ids)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pMh1lVwq9L6K"
   },
   "source": [
    "# แล้วถ้าถนัดเขียน Pytorch แบบปกติ? (hand-craft loop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "b_pz3NycxkXN",
    "outputId": "fada3d4c-d9df-40c2-88bc-1115f436d29a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
       "        num_rows: 3668\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
       "        num_rows: 408\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'],\n",
       "        num_rows: 1725\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenized_datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d96_-Mj14gw3",
    "outputId": "90f239fc-8bda-4131-b3b3-9960c46b3d69"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['attention_mask', 'input_ids', 'labels', 'token_type_ids']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenized_datasets = tokenized_datasets.remove_columns([\"sentence1\", \"sentence2\", \"idx\"])\n",
    "tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n",
    "tokenized_datasets.set_format(\"torch\")\n",
    "tokenized_datasets[\"train\"].column_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "VRMdF11o9RO8"
   },
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "\n",
    "train_dataloader = DataLoader(\n",
    "    tokenized_datasets[\"train\"], shuffle=True, batch_size=8, collate_fn=data_collator\n",
    ")\n",
    "eval_dataloader = DataLoader(\n",
    "    tokenized_datasets[\"validation\"], batch_size=8, collate_fn=data_collator\n",
    ")\n",
    "test_dataloader = DataLoader(\n",
    "    tokenized_datasets[\"test\"], batch_size=8, collate_fn=data_collator\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cf4QeKom9U63",
    "outputId": "e0301a72-bc6b-41c3-aa24-43de9892b188"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'attention_mask': torch.Size([8, 70]),\n",
       " 'input_ids': torch.Size([8, 70]),\n",
       " 'labels': torch.Size([8]),\n",
       " 'token_type_ids': torch.Size([8, 70])}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for batch in train_dataloader:\n",
    "    break\n",
    "{k: v.shape for k, v in batch.items()} # ตรงนี้ต้องให้มั่นใจว่าออกมาแบบนี้!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xzDn7C8N9WmI",
    "outputId": "31dd69a2-82bb-4d1c-cc2c-b55d30385138"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading configuration file https://huggingface.co/bert-base-uncased/resolve/main/config.json from cache at /root/.cache/huggingface/transformers/3c61d016573b14f7f008c02c4e51a366c67ab274726fe2910691e2a761acf43e.37395cee442ab11005bcd270f3c34464dc1704b715b5d7d52b1a461abe3b9e4e\n",
      "Model config BertConfig {\n",
      "  \"_name_or_path\": \"bert-base-uncased\",\n",
      "  \"architectures\": [\n",
      "    \"BertForMaskedLM\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"gradient_checkpointing\": false,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 768,\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 3072,\n",
      "  \"layer_norm_eps\": 1e-12,\n",
      "  \"max_position_embeddings\": 512,\n",
      "  \"model_type\": \"bert\",\n",
      "  \"num_attention_heads\": 12,\n",
      "  \"num_hidden_layers\": 12,\n",
      "  \"pad_token_id\": 0,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"transformers_version\": \"4.15.0\",\n",
      "  \"type_vocab_size\": 2,\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 30522\n",
      "}\n",
      "\n",
      "loading weights file https://huggingface.co/bert-base-uncased/resolve/main/pytorch_model.bin from cache at /root/.cache/huggingface/transformers/a8041bf617d7f94ea26d15e218abd04afc2004805632abc0ed2066aa16d50d04.faf6ea826ae9c5867d12b22257f9877e6b8367890837bd60f7c54a29633f7f2f\n",
      "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.seq_relationship.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.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": [
    "model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2) #สร้างโมเดลเหมือนเดิม"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZiHM2K-vycda",
    "outputId": "f416a6b1-c7d7-41d6-b28d-93f17f396c71"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BertForSequenceClassification(\n",
       "  (bert): BertModel(\n",
       "    (embeddings): BertEmbeddings(\n",
       "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
       "      (position_embeddings): Embedding(512, 768)\n",
       "      (token_type_embeddings): Embedding(2, 768)\n",
       "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "      (dropout): Dropout(p=0.1, inplace=False)\n",
       "    )\n",
       "    (encoder): BertEncoder(\n",
       "      (layer): ModuleList(\n",
       "        (0): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (1): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (2): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (3): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (4): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (5): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (6): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (7): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (8): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (9): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (10): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (11): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (pooler): BertPooler(\n",
       "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "      (activation): Tanh()\n",
       "    )\n",
       "  )\n",
       "  (dropout): Dropout(p=0.1, inplace=False)\n",
       "  (classifier): Linear(in_features=768, out_features=2, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XrKus5Wy9el-",
    "outputId": "80d86d7a-c779-481f-ed45-c5d379f99d9b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.5808, grad_fn=<NllLossBackward0>) torch.Size([8, 2])\n"
     ]
    }
   ],
   "source": [
    "outputs = model(**batch)\n",
    "print(outputs.loss, outputs.logits.shape) # ทดสอบก่อนว่า training data ของเรามันถูกไหม ถ้าถูกมันควรจะมี loss ออกมา"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cQS19WFcy_w7",
    "outputId": "ae26a28e-bf42-44bf-950d-8f408353c39f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.6011, 0.4220],\n",
       "        [0.5971, 0.4249],\n",
       "        [0.6049, 0.4225],\n",
       "        [0.6133, 0.4402],\n",
       "        [0.6084, 0.4223],\n",
       "        [0.5957, 0.4190],\n",
       "        [0.5940, 0.4214],\n",
       "        [0.6037, 0.4143]], grad_fn=<AddmmBackward0>)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outputs.logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-FsGhykC9e8K"
   },
   "outputs": [],
   "source": [
    "from transformers import AdamW\n",
    "\n",
    "optimizer = AdamW(model.parameters(), lr=5e-5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eJiG7Q0n9g7l",
    "outputId": "d7c16ce4-36c6-4245-b682-519cf74f4bfe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1377\n"
     ]
    }
   ],
   "source": [
    "from transformers import get_scheduler\n",
    "\n",
    "num_epochs = 3\n",
    "num_training_steps = num_epochs * len(train_dataloader)\n",
    "lr_scheduler = get_scheduler(\n",
    "    \"linear\",\n",
    "    optimizer=optimizer,\n",
    "    num_warmup_steps=0,\n",
    "    num_training_steps=num_training_steps,\n",
    ")\n",
    "print(num_training_steps) # Learning Rate Schedules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "S6yfQ1Tw9jma",
    "outputId": "ec17800f-0037-43fb-bca0-7b8cefa7e1f2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\") # ตรงนี้สำหรับคนใช้ GPU/CPU ในการเทรน\n",
    "model.to(device)\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "31ac84e36f804173879541c8d49cc417",
      "dd1bbb66a2a14672a74e28f5bbee323c",
      "7031a96857ca436fa7e3048acf8c2531",
      "7cf132088b854166932cdab9d6869ef3",
      "d57746acb4544ae594f437003ff677db",
      "a77631e3663d435cb081591fd18857ec",
      "35b5d406bd3141e49cf7a9b413aca805",
      "ebd192cc33db4c79a520dd1a5451eafe",
      "e5b9b07dd0de4ca0b50a884b6e99f672",
      "335f499329ae4fb4a23ed1b8ab48aa5b",
      "908e825c12a34db09728e76182b40365"
     ]
    },
    "id": "_vBztqIn9oK5",
    "outputId": "02ff33cf-00d8-4442-ad8b-c7b58ce06f7c"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "31ac84e36f804173879541c8d49cc417",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1377 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tqdm.auto import tqdm\n",
    "\n",
    "progress_bar = tqdm(range(num_training_steps))\n",
    "\n",
    "model.train()\n",
    "for epoch in range(num_epochs):\n",
    "    for batch in train_dataloader:\n",
    "        batch = {k: v.to(device) for k, v in batch.items()}\n",
    "        outputs = model(**batch)\n",
    "        loss = outputs.loss\n",
    "        loss.backward()\n",
    "\n",
    "        optimizer.step()\n",
    "        lr_scheduler.step()\n",
    "        optimizer.zero_grad()\n",
    "        progress_bar.update(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FL4rul8LzYHo",
    "outputId": "1def88e0-dba7-4783-fdff-e00bf82b51ab"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BertForSequenceClassification(\n",
       "  (bert): BertModel(\n",
       "    (embeddings): BertEmbeddings(\n",
       "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
       "      (position_embeddings): Embedding(512, 768)\n",
       "      (token_type_embeddings): Embedding(2, 768)\n",
       "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "      (dropout): Dropout(p=0.1, inplace=False)\n",
       "    )\n",
       "    (encoder): BertEncoder(\n",
       "      (layer): ModuleList(\n",
       "        (0): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (1): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (2): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (3): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (4): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (5): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (6): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (7): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (8): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (9): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (10): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (11): BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (pooler): BertPooler(\n",
       "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "      (activation): Tanh()\n",
       "    )\n",
       "  )\n",
       "  (dropout): Dropout(p=0.1, inplace=False)\n",
       "  (classifier): Linear(in_features=768, out_features=2, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datasets import load_metric\n",
    "\n",
    "metric = load_metric(\"glue\", \"mrpc\")\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wFSTvuazzyX6"
   },
   "source": [
    "# วัดผลบน Eval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Rorbcrdn9rDP",
    "outputId": "31ad1247-4297-45e3-b5d4-22922a9d3b17"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.6838235294117647, 'f1': 0.8122270742358079}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for batch in eval_dataloader:\n",
    "    batch = {k: v.to(device) for k, v in batch.items()}\n",
    "    with torch.no_grad():\n",
    "        outputs = model(**batch)\n",
    "\n",
    "    logits = outputs.logits\n",
    "    predictions = torch.argmax(logits, dim=-1)\n",
    "    metric.add_batch(predictions=predictions, references=batch[\"labels\"])\n",
    "\n",
    "metric.compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7GBby-jnzz-i"
   },
   "source": [
    "# วัดผลบน Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RNZ6Bx6Xzxyf",
    "outputId": "9b519b91-dc95-4639-c675-3ad5f8536e6f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.664927536231884, 'f1': 0.7987465181058496}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for batch in test_dataloader:\n",
    "    batch = {k: v.to(device) for k, v in batch.items()}\n",
    "    with torch.no_grad():\n",
    "        outputs = model(**batch)\n",
    "\n",
    "    logits = outputs.logits\n",
    "    predictions = torch.argmax(logits, dim=-1)\n",
    "    metric.add_batch(predictions=predictions, references=batch[\"labels\"])\n",
    "\n",
    "metric.compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jw1JojDsnant"
   },
   "source": [
    "# แล้วถ้าจะใส่ Test set ลงใน Hand-craft loop จะต้องทำยังไง?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "4cxooB15z41d"
   },
   "outputs": [],
   "source": []
  }
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