notebooks/05n_NLP_on_HF_01.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_e85HFZEyl8W"
   },
   "source": [
    "# บทที่ 5n - การเทรนโมเดลบน Hugging Face พาร์ท 1.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dM7GQjaXyiRs"
   },
   "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_01.ipynb)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OpquWUtQI7Xg",
    "outputId": "7594b11f-9b8b-495a-f35a-4228c8b3f658"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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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": "5sG9zFnQI_yL"
   },
   "outputs": [],
   "source": [
    "import transformers\n",
    "from transformers import pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-VPhvTyavwIn"
   },
   "source": [
    "# Hugging Face ทำอะไรได้บ้าง?\n",
    "= งานส่วนใหญ่ของงานด้าน NLP ในปัจจุบันมักจะนิยมใช้งาน pre-trained language models ซึ่งก็จะใช้งาน Hugging Face เป็นหลัก ซึ่งในวันนี้จะมายกตัวอย่างงานที่ HF รองรับและสามารถทำได้!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "925WqySHJfIc"
   },
   "source": [
    "# การจำแนกข้อความ หรือ Sentiment analysis\n",
    "เป็นการแบ่งประเภทของข้อความโดยที่เราจะเน้นความสนใจไปที่ \"ความรู้สึก\" เช่น \n",
    "\"อาหารร้านนี้อร่อยมาก\" => ความรู้สึกเป็นบวก (positive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 180,
     "referenced_widgets": [
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    },
    "id": "A1Pa61SJJC7J",
    "outputId": "742ca138-e3bd-4ba6-db5f-fe8041c6f44a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No model was supplied, defaulted to distilbert-base-uncased-finetuned-sst-2-english (https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ddd2cd767346482da099bc1e7af380b6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/629 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bd7226c487e9424384dcf15861596e18",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f2861440efc5448792e61ba0cf8658c3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9fde5bb86ddc44768f53f64e33cd2f05",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[{'label': 'POSITIVE', 'score': 0.9598048329353333}]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = pipeline(\"sentiment-analysis\")\n",
    "classifier(\"I've been waiting for a HuggingFace course my whole life.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "N3YC8IgkJRGA",
    "outputId": "970ba911-d3ea-4388-ea90-a7b65e3732a8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'label': 'POSITIVE', 'score': 0.9598048329353333},\n",
       " {'label': 'NEGATIVE', 'score': 0.9994558691978455}]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier(\n",
    "    [\"I've been waiting for a HuggingFace course my whole life.\", \"I hate this so much!\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 227,
     "referenced_widgets": [
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    "id": "CBaFgE61gq-h",
    "outputId": "b462e86e-a482-486e-ba79-a52a04071539"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b5a8e90672484552aa2058a2997054ef",
       "version_major": 2,
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      "text/plain": [
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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     },
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    },
    {
     "data": {
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      "text/plain": [
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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     },
     "metadata": {},
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    },
    {
     "data": {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[{'label': 'pos', 'score': 0.9841656684875488}]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = pipeline(\"sentiment-analysis\",model=\"poom-sci/WangchanBERTa-finetuned-sentiment\")\n",
    "classifier(\"อาหารร้านนี้อร่อยมาก\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yV9AV1qOJhy1"
   },
   "source": [
    "# การจำแนกข้อความ (Text classification) แบบที่มีคลาสเป็นของตัวเอง?\n",
    "โดยปกติแล้ว ถ้าเราจะเอาไปใช้บนโลกจริง มักจะมีคลาสคำตอบมากกว่าแค่ positive/negative ซึ่งเราสามารถเพิ่มคลาสเข้าไปเองได้ด้วย"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aiObDSHuJV2b",
    "outputId": "d91ecb62-0b1d-4dcb-eb3b-e435e5f6a3f0"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Failed to determine 'entailment' label id from the label2id mapping in the model config. Setting to -1. Define a descriptive label2id mapping in the model config to ensure correct outputs.\n",
      "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'labels': ['การเมือง', 'การศึกษา', 'ธุรกิจ'],\n",
       " 'scores': [0.3931845724582672, 0.30436158180236816, 0.3024538457393646],\n",
       " 'sequence': 'บทนี้ในรอบนี้จะมาเรียนเกี่ยวกับการใช้งาน Huggingface !!'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = pipeline(\"zero-shot-classification\",model=\"poom-sci/WangchanBERTa-finetuned-sentiment\")\n",
    "classifier(\n",
    "    \"บทนี้ในรอบนี้จะมาเรียนเกี่ยวกับการใช้งาน Huggingface !!\",\n",
    "    candidate_labels=[\"การศึกษา\", \"การเมือง\", \"ธุรกิจ\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iInqbCBIJuOk"
   },
   "source": [
    "# การสร้างข้อความ (Text generation)\n",
    "เป็นการต่อคำจากประโยคที่กำหนด โดยจะสามารถสร้างได้แค่คำ/ประโยคที่ตัวมันเคยเห็นเท่านั้น"
   ]
  },
  {
   "cell_type": "code",
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    },
    "id": "iH2BkQDuJaIr",
    "outputId": "19329b05-57fc-4ae2-f8c0-94d7906b438a"
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   "outputs": [
    {
     "data": {
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     },
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     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "Downloading:   0%|          | 0.00/3.21G [00:00<?, ?B/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "Downloading:   0%|          | 0.00/1.81M [00:00<?, ?B/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "Downloading:   0%|          | 0.00/1.15M [00:00<?, ?B/s]"
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     },
     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'generated_text': 'ในวันนี้เราจะมาเรียนเกี่ยวกับเรื่องของการ'}]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generator = pipeline(\"text-generation\",model='sberbank-ai/mGPT')\n",
    "generator(\"ในวันนี้เราจะมาเรียนเกี่ยวกับ\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f2zVFbR8Wi_3",
    "outputId": "63ff2b11-961c-4d82-9e9a-bf2f67aa0d29"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'generated_text': 'อากาศวันนี้\\nวันที่อากาศวันนี้\\nวันที่'}]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generator(\"อากาศวันนี้\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BRH51ppyJz_v"
   },
   "source": [
    "อีกทั้งยังสามารถกำหนดได้ว่าต้องการความยาวในการสร้างเท่าไหร่ หรือ จำนวนที่ต้องการสร้าง"
   ]
  },
  {
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    },
    "id": "ZyeWqg1hio24",
    "outputId": "fc860426-b0fe-42d7-e2ef-89ce73b9e2b5"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7f2628278232448fbad11d2652110c40",
       "version_major": 2,
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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     },
     "metadata": {},
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    },
    {
     "data": {
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    {
     "data": {
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     },
     "metadata": {},
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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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": "8cca79d2bdd148d5a5d83ca5240b163b",
       "version_major": 2,
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      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[{'generated_text': 'ความรักให้เธอเพียงครั้งเดียว ทั้งหัวใจรักเธอได้เพียงแต่ภาพทรงจำ ฉันยังยิ้มให้เธออยู่ทุกวัน แต่วันนี้เธอเปลี่ยนไปแล้ว ไม่รู้ว่าเพราะวันนั้นหรือว่าเพราะวันนี้ฉันไม่มีเธอ เธอรู้ไหมฉันเหงา มีใคร'}]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import GPT2Model, GPT2TokenizerFast, AutoModelForCausalLM, AutoTokenizer\n",
    "\n",
    "model_name = \"tupleblog/generate-thai-lyrics\"\n",
    "model = AutoModelForCausalLM.from_pretrained(model_name)\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "model.config.pad_token_id = model.config.eos_token_id\n",
    "\n",
    "\n",
    "nlp = pipeline(\n",
    "    \"text-generation\",\n",
    "    model=model,\n",
    "    tokenizer=tokenizer\n",
    ")\n",
    "text = \"ความรัก\"\n",
    "nlp(text, max_length=100, top_k=40, temperature=0.8) # varying the temperature and top-k produce different output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CiQ67lAwJ_bF"
   },
   "source": [
    "# การเติมคำ (Mask filling)\n",
    "เป็นการเติมคำลงในช่องว่างที่เรากำหนดขึ้นมา"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 287,
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      "5934d635499d4302a0572dc5143dcf03",
      "5218f2241fa7467d945c982d0e8bcffa",
      "2d80e22ff45c4f48be40833fe0bf8be0",
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      "dcda72a8e2de42e9a62c29feae402106",
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    },
    "id": "Q3U85-PCJ8qo",
    "outputId": "77c3c0ca-36f2-4db0-e01e-6d8cffddb06f"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ecce4fb3394b4f71a354561da13fccb6",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "210a8b7534e8467fac8df8472efa3879",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1518a76b306c45acadabccc02afbb2ea",
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      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[{'score': 0.080071821808815,\n",
       "  'sequence': 'บทเรียนในวันนี้ เราจะเน้นการเขียนนิยาย กัน',\n",
       "  'token': 4012,\n",
       "  'token_str': 'นิยาย'},\n",
       " {'score': 0.034100402146577835,\n",
       "  'sequence': 'บทเรียนในวันนี้ เราจะเน้นการเขียนหนังสือ กัน',\n",
       "  'token': 287,\n",
       "  'token_str': 'หนังสือ'}]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import AutoTokenizer, AutoModelForMaskedLM\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"airesearch/wangchanberta-base-att-spm-uncased\")\n",
    "model = AutoModelForMaskedLM.from_pretrained(\"airesearch/wangchanberta-base-att-spm-uncased\")\n",
    "\n",
    "unmasker = pipeline(\"fill-mask\",model=model,tokenizer=tokenizer)\n",
    "unmasker(\"บทเรียนในวันนี้ เราจะเน้นการเขียน<mask>กัน\", top_k=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WVu0BWcYKDtT"
   },
   "source": [
    "# การจำแนกชื่อเฉพาะ (Name entity recognition)\n",
    "เป็น Task ที่ระบุว่าคำไหนบ้างในประโยคที่เป็นคำเฉพาะ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 485,
     "referenced_widgets": [
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    },
    "id": "PBKXACy6KBjE",
    "outputId": "b1f6ff88-f1dd-42f4-8b1c-494ceec94737"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No model was supplied, defaulted to dbmdz/bert-large-cased-finetuned-conll03-english (https://huggingface.co/dbmdz/bert-large-cased-finetuned-conll03-english)\n"
     ]
    },
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       "model_id": "ea52d771728848e58cf141bee266617f",
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       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "ce9294a7910d4e18b68ef0f57bccf036",
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       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/transformers/pipelines/token_classification.py:136: UserWarning: `grouped_entities` is deprecated and will be removed in version v5.0.0, defaulted to `aggregation_strategy=\"AggregationStrategy.SIMPLE\"` instead.\n",
      "  f'`grouped_entities` is deprecated and will be removed in version v5.0.0, defaulted to `aggregation_strategy=\"{aggregation_strategy}\"` instead.'\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'end': 18,\n",
       "  'entity_group': 'PER',\n",
       "  'score': 0.9981694,\n",
       "  'start': 11,\n",
       "  'word': 'Sylvain'},\n",
       " {'end': 45,\n",
       "  'entity_group': 'ORG',\n",
       "  'score': 0.9796019,\n",
       "  'start': 33,\n",
       "  'word': 'Hugging Face'},\n",
       " {'end': 57,\n",
       "  'entity_group': 'LOC',\n",
       "  'score': 0.9932106,\n",
       "  'start': 49,\n",
       "  'word': 'Brooklyn'}]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ner = pipeline(\"ner\", grouped_entities=True)\n",
    "ner(\"My name is Sylvain and I work at Hugging Face in Brooklyn.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p3eRDoAsKHzM"
   },
   "source": [
    "# ระบบถาม-ตอบ (Question answering)\n",
    "ให้ผู้ใช้งานตั้งคำถามขึ้นมา และต้องป้อนข้อความที่เกี่ยวข้องที่ใช้ในการตอบคำถามนั้นๆ ตัวโมเดลจะหาคำตอบจากข้อความเพื่อตอบคำถาม"
   ]
  },
  {
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    {
     "data": {
      "text/plain": [
       "{'answer': ' เป็นโรงเรียนชายล้วน ระดับชั้นมัธยมศึกษาขนาดใหญ่พิเศษ',\n",
       " 'end': 134,\n",
       " 'score': 0.4492504894733429,\n",
       " 'start': 81}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "question_answerer = pipeline(\"question-answering\",model=\"airesearch/wangchanberta-base-wiki-20210520-spm-finetune-qa\")\n",
    "question_answerer(\n",
    "    question=\"สวนกุหลาบเป็นโรงเรียนอะไร?\",\n",
    "    context=\"โรงเรียนสวนกุหลาบวิทยาลัย (Suankularb Wittayalai School) (อักษรย่อ : ส.ก. / S.K.) เป็นโรงเรียนชายล้วน ระดับชั้นมัธยมศึกษาขนาดใหญ่พิเศษ สังกัดสำนักงานเขตพื้นที่การศึกษามัธยมศึกษาเขต 1 สำนักงานคณะกรรมการการศึกษาขั้นพื้นฐาน (ชื่อเดิม: กรมสามัญศึกษา) กระทรวงศึกษาธิการ ก่อตั้งโดย พระบาทสมเด็จพระจุลจอมเกล้าเจ้าอยู่หัว ได้รับการสถาปนาขึ้นในวันที่ 8 มีนาคม พ.ศ. 2424 (ขณะนั้นนับวันที่ 1 เมษายน เป็นวันขึ้นปีใหม่ เมื่อนับอย่างสากลถือเป็น พ.ศ. 2425) โดยเป็นโรงเรียนรัฐบาลแห่งแรกของประเทศไทย\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_R6i6y_fKMGq"
   },
   "source": [
    "# การสรุป (Summarization)\n",
    "เป็นการนำข้อความหรือประโยคมาย่อให้ได้แค่ข้อความสั้นๆ หรือใจความสำคัญนั้นเอง"
   ]
  },
  {
   "cell_type": "code",
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    "id": "0MqQypNCKKC9",
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    {
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    {
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    {
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    {
     "data": {
      "text/plain": [
       "[{'summary_text': 'พระกาโตะมีความดังเมื่อเกิดเป็นข่าวดัง'}]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summarizer = pipeline(\"summarization\",model=\"thanathorn/mt5-cpe-kmutt-thai-sentence-sum\")\n",
    "summarizer(\n",
    "    \"\"\"\n",
    "    นับเป็น “เน็ตไอดอล” อีกคนหนึ่งก็ว่าได้สำหรับ “พระกาโตะ” พระนักเทศน์ชื่อดัง ที่มีลีลาลูกเล่นตามแบบฉบับหนุ่มปักษ์ใต้ ที่เรียกเสียงฮาได้ตลอดให้ไปเทศน์ที่ไหน บวกกับการเทศน์ที่ไม่เหมือนใคร แถมมีคำคมสอนใจถูกใจวัยโจ๋\n",
    "    แต่ล่าสุดเกิดเป็นข่าวดัง เมื่อมีการปล่อยคลิปเสียงคล้าย พระกาโตะ พร้อมกับข้อความไลน์ตกลงกันระหว่างหญิงสาวรายหนึ่ง กับเสียงผู้ชายคนหนึ่ง ที่ระบุว่า เป็นพระนักเทศน์ภาคใต้ มีชื่อเสียงในโลกออนไลน์ ซึ่งเนื้อหาในคลิปพูดถึง หลวงพี่แอบมีเพศสัมพันธ์กับหญิงสาวในรถยนต์ อ้างว่าไม่บาป จนโลกโซเชียลพูดว่าอาจจะเป็น “หลวงพี่กาโตะ” โดยล่าสุดพระกาโตะ ออกมาชี้แจงแล้วว่าไม่ใช่เสียงของตน\n",
    "    “หลวงพี่กาโตะ” หรือ พระพงศกร ปภัสสโร รักษาการเจ้าอาวาสวัดเพ็ญญาติ อ.ฉวาง จ.นครศรีธรรมราช ก่อนบวชมีชื่อว่า พงศกร จันทร์แก้ว ชื่อเล่นว่า แรมโบ้ อายุ 23 ปี เริ่มบวชเมื่อปี 2560 โดยสัญญากับแม่ที่ป่วยด้วยโรคมะเร็ง ซึ่งก่อนหน้านี้ หลวงพี่กาโตะ ได้โควตาเรียนต่อมหาวิทยาลัย คณะนิเทศศาสตร์ มหาวิทยาลัยราชภัฏสุราษฎร์ธานี แต่แม่ป่วยจึงตัดสินใจไม่เรียนต่อ ออกมาดูแลแม่แทน\n",
    "\"\"\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sPn3O6s0liRv"
   },
   "source": [
    "# การสร้าง Vector Representation "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iR1QfS5lmC8x"
   },
   "source": [
    "แปลงจากประโยค (input) ให้กลายเป็น sentence vector โดยใช้ Library อื่นร่วมกับ Huggingface"
   ]
  },
  {
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    "id": "haqpMstrlQof",
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   "outputs": [
    {
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    {
     "data": {
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    {
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     "metadata": {},
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   ],
   "source": [
    "from sentence_transformers import SentenceTransformer, util\n",
    "model = SentenceTransformer('mrp/simcse-model-wangchanberta')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MSXAp9XYY7mf",
    "outputId": "1721685f-60c1-424c-cb78-ec66baf324db"
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   "outputs": [
    {
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      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.encode('วันนี้อากาศดีมาก')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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    },
    "id": "HvbRxFVSnwFR",
    "outputId": "49dcc1f6-7f66-42e3-c1ea-78abaffb5fac"
   },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "อาหารญี่ปุ่น-ราเมน \t\t เมนูโปรดของฉันคือผัดไทย \t\t Score: 0.5647\n",
      "อาหารญี่ปุ่น-ราเมน \t\t วันนี้จะไปกินกิมจิกับเพื่อนๆ \t\t Score: 0.6585\n",
      "อาหารญี่ปุ่น-ราเมน \t\t อยากกินข้าวหมูทอดทงคัตสึ \t\t Score: 0.6587\n",
      "อาหารไทย-ต้มยำกุ้ง \t\t เมนูโปรดของฉันคือผัดไทย \t\t Score: 0.6971\n",
      "อาหารไทย-ต้มยำกุ้ง \t\t วันนี้จะไปกินกิมจิกับเพื่อนๆ \t\t Score: 0.5462\n",
      "อาหารไทย-ต้มยำกุ้ง \t\t อยากกินข้าวหมูทอดทงคัตสึ \t\t Score: 0.4839\n",
      "อาหารเกาหลี-ต๊อกโบกี \t\t เมนูโปรดของฉันคือผัดไทย \t\t Score: 0.5731\n",
      "อาหารเกาหลี-ต๊อกโบกี \t\t วันนี้จะไปกินกิมจิกับเพื่อนๆ \t\t Score: 0.5532\n",
      "อาหารเกาหลี-ต๊อกโบกี \t\t อยากกินข้าวหมูทอดทงคัตสึ \t\t Score: 0.6118\n"
     ]
    }
   ],
   "source": [
    "# Two lists of sentences\n",
    "sentences1 = ['อาหารญี่ปุ่น-ราเมน',\n",
    "             'อาหารไทย-ต้มยำกุ้ง',\n",
    "             'อาหารเกาหลี-ต๊อกโบกี']\n",
    "\n",
    "sentences2 = ['เมนูโปรดของฉันคือผัดไทย',\n",
    "              'วันนี้จะไปกินกิมจิกับเพื่อนๆ',\n",
    "              'อยากกินข้าวหมูทอดทงคัตสึ']\n",
    "\n",
    "#Compute embedding for both lists\n",
    "embeddings1 = model.encode(sentences1, convert_to_tensor=True)\n",
    "embeddings2 = model.encode(sentences2, convert_to_tensor=True)\n",
    "\n",
    "#Compute cosine-similarits\n",
    "cosine_scores = util.cos_sim(embeddings1, embeddings2)\n",
    "\n",
    "#Output the pairs with their score\n",
    "for i in range(len(sentences1)):\n",
    "  for j in range(len(sentences2)):\n",
    "    print(\"{} \\t\\t {} \\t\\t Score: {:.4f}\".format(sentences1[i], sentences2[j], cosine_scores[i][j]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z_CTrQBaKUap"
   },
   "source": [
    "# ข้อดีของการใช้ Hugging Face?\n",
    "*   มีโมเดลแจกฟรีเยอะมาก หลายโมเดลพร้อมใช้งาน \n",
    "*   สามารถนำโมเดลที่แจกฟรีไปเทรนต่อได้\n",
    "*   ทุกอย่างฟรีหมด!"
   ]
  },
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