get_q.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "private_outputs": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qPTViDzFHuei"
      },
      "outputs": [],
      "source": [
        "!pip install bitsandbytes\n",
        "!pip install -q datasets loralib sentencepiece\n",
        "!pip install -q git+https://github.com/zphang/transformers@c3dc391\n",
        "!pip install -q git+https://github.com/huggingface/peft.git"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import PeftModel\n",
        "from transformers import LLaMATokenizer, LLaMAForCausalLM, GenerationConfig\n",
        "\n",
        "tokenizer = LLaMATokenizer.from_pretrained(\"decapoda-research/llama-7b-hf\")\n",
        "model = LLaMAForCausalLM.from_pretrained(\n",
        "    \"decapoda-research/llama-7b-hf\",\n",
        "    load_in_8bit=True,\n",
        "    device_map=\"auto\",\n",
        ")\n",
        "model = PeftModel.from_pretrained(model, \"tloen/alpaca-lora-7b\")"
      ],
      "metadata": {
        "id": "24lhDC65H9vG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_prompt(instruction, input=None):\n",
        "    if input:\n",
        "        return f\"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n",
        "\n",
        "### Instruction:\n",
        "{instruction}\n",
        "\n",
        "### Input:\n",
        "{input}\n",
        "\n",
        "### Response:\"\"\"\n",
        "    else:\n",
        "        return f\"\"\"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
        "\n",
        "### Instruction:\n",
        "{instruction}\n",
        "\n",
        "### Response:\"\"\""
      ],
      "metadata": {
        "id": "uebyUrCoIhRG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generation_config = GenerationConfig(\n",
        "    temperature=0.1,\n",
        "    top_p=0.75,\n",
        "    num_beams=4,\n",
        ")\n",
        "\n",
        "def evaluate(instruction, input=None):\n",
        "    prompt = generate_prompt(instruction, input)\n",
        "    inputs = tokenizer(prompt, return_tensors=\"pt\")\n",
        "    input_ids = inputs[\"input_ids\"].cuda()\n",
        "    generation_output = model.generate(\n",
        "        input_ids=input_ids,\n",
        "        generation_config=generation_config,\n",
        "        return_dict_in_generate=True,\n",
        "        output_scores=True,\n",
        "        max_new_tokens=256\n",
        "    )\n",
        "    for s in generation_output.sequences:\n",
        "        output = tokenizer.decode(s)\n",
        "        #print(\"Response:\", output.split(\"### Response:\")[1].strip())\n",
        "        return output.split(\"### Response:\")[1].strip()"
      ],
      "metadata": {
        "id": "IOgqBQEjIkeS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "des = \"Before he was Sherlock’s rival, Moriarty fought against the unfair class caste system in London by making sure corrupt nobility got their comeuppance. But even the best-intentioned plans can spin out of control—will Moriarty’s dream of a more just and equal world turn him into a hero…or a monster?\"\n",
        "allq= evaluate(f\"give me 5 natural language query which don't be too specific and not mention about manga name and character that will return a manga with the following description :\\n{des}\")\n",
        "#allq"
      ],
      "metadata": {
        "id": "YRSAwNMVPLqr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive',force_remount=True)"
      ],
      "metadata": {
        "id": "rnWb2-SPUqoU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/aib/df.csv\")"
      ],
      "metadata": {
        "id": "9RjIXBjBPyvS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[399, \"q\"]"
      ],
      "metadata": {
        "id": "UyFTQzIUQwoj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(5100, 5200):\n",
        "  des = df.loc[i, \"description\"]\n",
        "  name_s = df.loc[i, \"title\"]\n",
        "  if(\"(Light Novel)\" in name_s):\n",
        "    continue\n",
        "  if(\"(Novel)\" in name_s):\n",
        "     continue\n",
        "  if(\"(Webcomic)\" in name_s):\n",
        "     continue\n",
        "  if(\"(Doujinshi\" in name_s):\n",
        "      continue\n",
        "  allq= evaluate(f\"give me 1 natural language query which don't be too specific and not mention about manga name and character that will return a manga with the following description :\\n{des}\")\n",
        "  df.loc[i, \"q\"] = allq\n",
        "  print(i, name_s, \"done.\")"
      ],
      "metadata": {
        "id": "MOJQ1HXUUO1a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv(\"/content/drive/MyDrive/aib/df.csv\", encoding='utf-8', index=False)"
      ],
      "metadata": {
        "id": "SD5MhGmaibZJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "W8W1bEUWflj9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[610, \"q\"], df.loc[610, \"title\"]"
      ],
      "metadata": {
        "id": "zcXssi0afVaK"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}