Fewshot_OPT_PROMPTING.ipynb

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        "### Few-shot Prompting for Compositionality+Reasoning with Large Language Models \n",
        "\n",
        "\n",
        "**[ Code from ]** [Eunchan Lee of UNIST-LIL-LAB](https://github.com/purang2)\n",
        "\n",
        "\n",
        "Option\n",
        "```(python)\n",
        "- LLM : \"Meta AI/opt-6.7b\"\n",
        "- Number of data: 2개\n",
        "\n",
        "- Data [0]\n",
        "    - Q: '[A man on a bike] [vs] [a bike on a man]. Please Explain what is more natural.', \n",
        "    - A: '[N]: man, bike [V]: on [SEP] In nature, man can ride bike but bike cannot ride man. [Answer] [A man on a bike] is more natural.'\n",
        "- Data [1]\n",
        "    - Q: '[The grass is eating the horse] [vs] [The horse is eating the grass.] Please Explain what is more natural.'\n",
        "    - A: '[N]: grass, horse [V]: eat [SEP] In nature, horse can eat the grass because horses are animal but grass cannot eat horse because grass is a plant. [Answer] [The horse is eating the grass] is more natural.'\n",
        "```"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "install\n",
        "- torch\n",
        "- pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "True\n"
          ]
        }
      ],
      "source": [
        "# GPU 사용 가능 -> True, GPU 사용 불가 -> False\n",
        "import os\n",
        "import torch\n",
        "print(torch.cuda.is_available())\n",
        "os.environ[\"CUDA_VISIBLE_DEVICES\"]= \"0\""
      ]
    },
    {
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      "cell_type": "markdown",
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        "id": "KfKGHuv7LfF5"
      },
      "source": [
        "Put datapair"
      ]
    },
    {
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        "id": "0Ki7stDebE_D",
        "outputId": "2c41e7b1-310d-4011-b9c9-6eedd6c39c26"
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          "text": [
            "['[A man on a bike] [vs] [a bike on a man]. Please Explain what is more natural.', '[The grass is eating the horse] [vs] [The horse is eating the grass.] Please Explain what is more natural.']\n",
            "['[N]: man, bike [V]: on [SEP] In nature, man can ride bike but bike cannot ride man. [Answer] [A man on a bike] is more natural.', '[N]: grass, horse [V]: eat [SEP] In nature, horse can eat the grass because horses are animal but grass cannot eat horse because grass is a plant. [Answer] [The horse is eating the grass] is more natural.']\n"
          ]
        }
      ],
      "source": [
        "few_src = [] \n",
        "few_tgt = []\n",
        "\n",
        "\n",
        "def put_datapair(src, tgt):\n",
        "  few_src.append(src)\n",
        "  few_tgt.append(tgt)\n",
        "\n",
        "\n",
        "#put_datapair(\"What's difference between \\\" a man on a bike \\\" vs \\\"a bike on a man\\\" ?\",\"In \\\"a man on a bike,\\\" the preposition \\\"on\\\" suggests that the man is riding or sitting on the bike. In this sentence, the bike is the means of transportation, and the man is the one using it. In contrast, \\\"a bike on a man\\\" reverses the relationship between the nouns, implying that the bike is positioned on top of the man.\")\n",
        "#put_datapair(\"What's difference between \\\" a brown cat is looking at a gray dog and sitting in a white bathtub\\\" vs \\\"a gray cat is looking at a white dog and sitting in a brown bathtub\\\"?\", \"\\\"A brown cat is looking at a gray dog and sitting in a white bathtub\\\": This sentence describes a scene where a brown-colored cat is looking at a gray-colored dog while sitting in a white-colored bathtub. In contrast \\\"A gray cat is looking at a white dog and sitting in a brown bathtub\\\": This sentence describes where a gray-colored cat is looking at a white-colored dog while sitting in a brown-colored bathtub.\")\n",
        "#put_datapair(\"What's difference between \\\"a red paved road behind the white house\\\" vs \\\"a red house behind the white paved road\\\"?\", \"\\\"A red paved road behind the white house\\\": This sentence describes a scenario where there is a road with red paving located behind a white house. The main focus of the description is the position of the red paved road in relation to the white house. \\\"A red house behind the white paved road\\\": This sentence describes a different scenario where there is a red house located behind a road with white paving. In this case, the main focus of the description is the position of the red house in relation to the white paved road.\")\n",
        "\n",
        "put_datapair(\"[A man on a bike] [vs] [a bike on a man]. Please Explain what is more natural\", \"[N]: man, bike [V]: on [SEP] In nature, man can ride bike but bike cannot ride man. [Answer] [A man on a bike] is more natural.\")\n",
        "put_datapair(\"[The grass is eating the horse] [vs] [The horse is eating the grass.] Please Explain what is more natural\", \"[N]: grass, horse [V]: eat [SEP] In nature, horse can eat the grass because horses are animal but grass cannot eat horse because grass is a plant. [Answer] [The horse is eating the grass] is more natural.\")\n",
        "\n",
        "\n",
        "print(few_src)\n",
        "print(few_tgt)\n"
      ]
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      "source": [
        "### Prompt\n",
        "\n",
        "***Reference: [A Recipe for Arbitrary Text Style Transfer with Large Language Models, (Reif et al. (Google Research), ACL 2022)](https://aclanthology.org/2022.acl-short.94/)***"
      ]
    },
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            "Q: [A man on a bike] [vs] [a bike on a man]. Please Explain what is more natural.. \n",
            "A: [N]: man, bike [V]: on [SEP] In nature, man can ride bike but bike cannot ride man. [Answer] [A man on a bike] is more natural.\n",
            "\n",
            "Q: [The grass is eating the horse] [vs] [The horse is eating the grass.] Please Explain what is more natural.. \n",
            "A: [N]: grass, horse [V]: eat [SEP] In nature, horse can eat the grass because horses are animal but grass cannot eat horse because grass is a plant. [Answer] [The horse is eating the grass] is more natural.\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "in_context_text = ''\n",
        "\n",
        "for i in range(len(few_src)):\n",
        "  #in_context_text += f'Here is some text: {few_src[i]}. Here is a rewrite of the text, which is more simple: {few_tgt[i]}\\n'\n",
        "  in_context_text += f'Q: {few_src[i]}. \\nA: {few_tgt[i]}\\n\\n'\n",
        "  \n",
        "print(in_context_text)"
      ]
    },
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          "text": [
            "Loading checkpoint shards: 100%|██████████| 2/2 [00:14<00:00,  7.12s/it]\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "from transformers import AutoModelForCausalLM, AutoModelForSeq2SeqLM\n",
        "from transformers import pipeline\n",
        "\n",
        "model_zoo = [\"bigscience/bloom-7b1\",\"bigscience/test-bloomd-6b3\",\"bigscience/T0pp\",\"lmsys/vicuna-13b-delta-v1.1\",\"decapoda-research/llama-7b-hf\",\"facebook/opt-6.7b\"]\n",
        "\n",
        "model_name = model_zoo[5]\n",
        "\n",
        "\n",
        "if model_name.find('t5') > -1 or model_name.lower().find('t0') > -1: #model = t5, t0 [encoder-decoder]\n",
        "  tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "  model = AutoModelForSeq2SeqLM.from_pretrained(model_name)\n",
        "  nlg_pipeline = pipeline('text2text-generation',model=model, tokenizer=tokenizer)\n",
        "\n",
        "else: #model = gpt,etc [decoder-only]\n",
        "  tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "  model = AutoModelForCausalLM.from_pretrained(model_name)\n",
        "  #nlg_pipeline = pipeline(model=model, tokenizer=tokenizer)"
      ]
    },
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      "attachments": {},
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      "metadata": {
        "id": "1mfaqHtbKWYD"
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      "source": [
        "model inference"
      ]
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          "text": [
            "OUTPUT\n",
            "\n",
            "  \n",
            "A: [N]: boy, coffee [V]: drink [SEP] In nature, boy can drink coffee because boy is a human being but coffee cannot drink boy because coffee is a drink. [Answer] [The boy is drinking the coffee] is more natural.</s>\n"
          ]
        }
      ],
      "source": [
        "\n",
        "test_input_text = \"[The coffee is drinking a boy] [vs] [A boy is drinkng the coffee.] Please Explain what is more natural\"\n",
        "#test_input_text = \"Italy's Capital is Roma\"\n",
        "test_output_length = 100 #token length\n",
        "\n",
        "\n",
        "if model_name.find('t5') > -1 or model_name.lower().find('t0') > -1: #model = t5, t0 [encoder-decoder]\n",
        "  def generate_text(pipe, text, num_return_sequences=5, max_length=512):\n",
        "    text = f\"{text}\"\n",
        "    out = pipe(text, num_return_sequences=num_return_sequences, max_length=max_length, num_beams=5, no_repeat_ngram_size=2,)\n",
        "    return [x['generated_text'] for x in out]\n",
        "\n",
        "  #target_text = 'kjkjkjkj'\n",
        "  #src_text = in_context_text + f\"Here is some text: {test_input_text}. Here is a rewrite of the text, which is more simple: \"\n",
        "  src_text = in_context_text + f\"Q: {test_input_text} \\nA: \"\n",
        "  #src_text = f\"Q: {test_input_text}. \\nA: \"\n",
        "  \n",
        "\n",
        "  print(\"Input text:\", src_text)\n",
        "  test_output_text = generate_text(nlg_pipeline, src_text, num_return_sequences=1, max_length=test_output_length)\n",
        "\n",
        "\n",
        "  #you can cook this output anything you want!  \n",
        "  print(test_output_text)\n",
        "\n",
        "\n",
        "\n",
        "else:  #model = GPT, ETC\n",
        "  src_text = in_context_text + f\"Q: {test_input_text} \\nA:\"\n",
        "  tokens = tokenizer.encode(src_text, return_tensors='pt')\n",
        "  gen_tokens = model.generate(tokens, do_sample=True, temperature=0.8, max_length=len(tokens[0])+test_output_length)\n",
        "  generated = tokenizer.batch_decode(gen_tokens)[0]\n",
        "  \n",
        "  test_output_text = generated[len(src_text):]\n",
        "  #print(generated)\n",
        "\n",
        "  #you can cook this output anything you want!\n",
        "  print(\"OUTPUT\\n\\n\",test_output_text)\n"
      ]
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