├── Fewshot_GPT&T5_StyleTransfer.ipynb ├── Fewshot_LLM_Prompting.ipynb ├── Fewshot_OPT_PROMPTING.ipynb ├── LICENSE ├── PPT-Sildes ├── LabSeminar_NLGwithPrompting.pdf ├── LabSeminar_NLGwithPrompting.pptx └── readme.md ├── README.md ├── Textchat2Prompt ├── NMT_KO │ ├── NMT_KO.ipynb │ └── readme.md ├── TextChat2Prompt_inference.ipynb ├── only_paper_pdf.pdf ├── paper_pdf.pdf └── readme.md └── notebook ├── KoGPT_Infer.ipynb ├── KoGPT_KorQuAD.ipynb ├── KoGPT_NSMC_Selection2_prompt1.ipynb ├── KoGPT_TextChat2Prompt.ipynb ├── KoGPT_nsmc1.ipynb ├── KoGPT_nsmc1_result.ipynb ├── KoGPT_nsmc_PROMPT2.ipynb ├── KoGPT_nsmc_prompt#3.ipynb ├── KoGPT_nsmc_prompt2_수정better.ipynb ├── KoGPT_nsmc_selection3.ipynb └── readme.md /Fewshot_GPT&T5_StyleTransfer.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "machine_shape": "hm", 8 | "authorship_tag": 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| "metadata": { 1738 | "id": "view-in-github", 1739 | "colab_type": "text" 1740 | }, 1741 | "source": [ 1742 | "\"Open" 1743 | ] 1744 | }, 1745 | { 1746 | "cell_type": "markdown", 1747 | "source": [ 1748 | "### Manual(수동) Prompt Engineering for Dialog2Prompt with Large Language Model \n", 1749 | "\n", 1750 | "## **[ Produced by ]**\n", 1751 | "\n", 1752 | "[Eunchan Lee of KNU-BrainAI](https://github.com/purang2)\n", 1753 | "\n", 1754 | "\n" 1755 | ], 1756 | "metadata": { 1757 | "id": "nr5CTloRas9w" 1758 | } 1759 | }, 1760 | { 1761 | "cell_type": "code", 1762 | "execution_count": 1, 1763 | "metadata": { 1764 | "id": "LX_l7Im6Wc59" 1765 | }, 1766 | "outputs": [], 1767 | "source": [ 1768 | "import gc\n", 1769 | "import torch \n", 1770 | " \n", 1771 | "gc.collect()\n", 1772 | "torch.cuda.empty_cache()" 1773 | ] 1774 | }, 1775 | { 1776 | "cell_type": "code", 1777 | "source": [ 1778 | "!pip install transformers \n", 1779 | "!pip install accelerate" 1780 | ], 1781 | "metadata": { 1782 | "colab": { 1783 | "base_uri": "https://localhost:8080/" 1784 | }, 1785 | "id": "rDURuRwOXUZH", 1786 | "outputId": "fd3bf6a7-0f79-49ad-c5b8-fcb067501f63" 1787 | }, 1788 | "execution_count": 2, 1789 | "outputs": [ 1790 | { 1791 | "output_type": "stream", 1792 | "name": "stdout", 1793 | "text": [ 1794 | "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", 1795 | "Collecting transformers\n", 1796 | " Downloading transformers-4.25.1-py3-none-any.whl (5.8 MB)\n", 1797 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.8/5.8 MB\u001b[0m \u001b[31m31.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 1798 | "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.8/dist-packages (from transformers) (4.64.1)\n", 1799 | "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/dist-packages (from transformers) (21.3)\n", 1800 | "Requirement already satisfied: filelock in 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1845 | "execution_count": 3, 1846 | "outputs": [] 1847 | }, 1848 | { 1849 | "cell_type": "markdown", 1850 | "source": [ 1851 | "Put datapair" 1852 | ], 1853 | "metadata": { 1854 | "id": "KfKGHuv7LfF5" 1855 | } 1856 | }, 1857 | { 1858 | "cell_type": "code", 1859 | "source": [ 1860 | "few_src = [] \n", 1861 | "few_tgt = []\n", 1862 | "\n", 1863 | "\n", 1864 | "def put_datapair(src, tgt):\n", 1865 | " few_src.append(src)\n", 1866 | " few_tgt.append(tgt)\n", 1867 | "\n", 1868 | "\n", 1869 | "# k=4\n", 1870 | "'''\n", 1871 | "put_datapair(\"k\", \"x\")\n", 1872 | "put_datapair(\"kk\", \"xx\")\n", 1873 | "put_datapair(\"kkk\", \"xxx\")\n", 1874 | "put_datapair(\"kkkk\", \"xxxx\")\n", 1875 | "'''\n", 1876 | "\n", 1877 | "\n", 1878 | "# k=4 -> Manual sample: Text style transfer\n", 1879 | "put_datapair(\"South Korea's Capital is Seoul\", \"Seoul -> South Korea\")\n", 1880 | "put_datapair(\"Mexico's Capital is Mexico City\", \"Mexico City -> Mexico\")\n", 1881 | "put_datapair(\"Germany's Capital is Berlin\", \"Berlin -> Germany\")\n", 1882 | "put_datapair(\"Uganda's Capital is Kampala\", \"Kampala -> Uganda\")\n", 1883 | "\n", 1884 | "\n", 1885 | "\n", 1886 | "print(few_src)\n", 1887 | "print(few_tgt)\n" 1888 | ], 1889 | "metadata": { 1890 | "id": "0Ki7stDebE_D", 1891 | "colab": { 1892 | "base_uri": "https://localhost:8080/" 1893 | }, 1894 | "outputId": "2c41e7b1-310d-4011-b9c9-6eedd6c39c26" 1895 | }, 1896 | "execution_count": 4, 1897 | "outputs": [ 1898 | { 1899 | "output_type": "stream", 1900 | "name": "stdout", 1901 | "text": [ 1902 | "[\"South Korea's Capital is Seoul\", \"Mexico's Capital is Mexico City\", \"Germany's Capital is Berlin\", \"Uganda's Capital is Kampala\"]\n", 1903 | "['Seoul -> South Korea', 'Mexico City -> Mexico', 'Berlin -> Germany', 'Kampala -> Uganda']\n" 1904 | ] 1905 | } 1906 | ] 1907 | }, 1908 | { 1909 | "cell_type": "markdown", 1910 | "source": [ 1911 | "### Prompt\n", 1912 | "\n", 1913 | "***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/)***" 1914 | ], 1915 | "metadata": { 1916 | "id": "ZwMZvIpCKOiP" 1917 | } 1918 | }, 1919 | { 1920 | "cell_type": "code", 1921 | "source": [ 1922 | "in_context_text = ''\n", 1923 | "\n", 1924 | "for i in range(len(few_src)):\n", 1925 | " 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", 1926 | "\n", 1927 | "print(in_context_text)" 1928 | ], 1929 | "metadata": { 1930 | "colab": { 1931 | "base_uri": "https://localhost:8080/" 1932 | }, 1933 | "id": "F5LM18a7f_FQ", 1934 | "outputId": "198de336-5edb-474e-951a-2ee846a9001f" 1935 | }, 1936 | "execution_count": 5, 1937 | "outputs": [ 1938 | { 1939 | "output_type": "stream", 1940 | "name": "stdout", 1941 | "text": [ 1942 | "Here is some text: South Korea's Capital is Seoul. Here is a rewrite of the text, which is more simple: Seoul -> South Korea\n", 1943 | "Here is some text: Mexico's Capital is Mexico City. Here is a rewrite of the text, which is more simple: Mexico City -> Mexico\n", 1944 | "Here is some text: Germany's Capital is Berlin. Here is a rewrite of the text, which is more simple: Berlin -> Germany\n", 1945 | "Here is some text: Uganda's Capital is Kampala. Here is a rewrite of the text, which is more simple: Kampala -> Uganda\n", 1946 | "\n" 1947 | ] 1948 | } 1949 | ] 1950 | }, 1951 | { 1952 | "cell_type": "markdown", 1953 | "source": [ 1954 | "transformers model" 1955 | ], 1956 | "metadata": { 1957 | "id": "N8qXu5MTKStT" 1958 | } 1959 | }, 1960 | { 1961 | "cell_type": "code", 1962 | "source": [ 1963 | "from transformers import AutoTokenizer\n", 1964 | "from transformers import AutoModelForCausalLM, AutoModelForSeq2SeqLM\n", 1965 | "from transformers import pipeline\n", 1966 | "\n", 1967 | "\n", 1968 | "# GPT-2 (This is the smallest version of GPT-2, with 124M parameters.) = \"gpt2\"\n", 1969 | "# GPT-2 (XL) (1.5B parameters.) = \"gpt2-xl\"\n", 1970 | "# T5 (base, with 220 million parameters.) = \"t5-base\"\n", 1971 | "# T5 (3B parameters.) = \"t5-3b\"\n", 1972 | "# T5 (11B parameters.) = \"t5-11b\" -> 45.2GB (Colab may not cover this size..)\n", 1973 | "# GPT-J (6B parameters.) = \"EleutherAI/gpt-j-6B\"\n", 1974 | "\n", 1975 | "# In-context learning work well at least 1 Billions parameters. -eunchan-\n", 1976 | "\n", 1977 | "model_zoo = [\"gpt2\",\"gpt2-xl\",\"t5-base\",\"t5-3b\",\"t5-11b\",\"EleutherAI/gpt-j-6B\"]\n", 1978 | "\n", 1979 | "\n", 1980 | "\n", 1981 | "model_name = model_zoo[1]\n", 1982 | "\n", 1983 | "\n", 1984 | "if model_name.find('t5') > -1: #model = t5\n", 1985 | " tokenizer = AutoTokenizer.from_pretrained(model_name)\n", 1986 | " model = AutoModelForSeq2SeqLM.from_pretrained(model_name)\n", 1987 | " nlg_pipeline = pipeline('text2text-generation',model=model, tokenizer=tokenizer)\n", 1988 | "\n", 1989 | "else: #model = gpt\n", 1990 | " tokenizer = AutoTokenizer.from_pretrained(model_name)\n", 1991 | " model = AutoModelForCausalLM.from_pretrained(model_name)\n", 1992 | " #nlg_pipeline = pipeline(model=model, tokenizer=tokenizer)" 1993 | ], 1994 | "metadata": { 1995 | "colab": { 1996 | "base_uri": "https://localhost:8080/", 1997 | "height": 177, 1998 | "referenced_widgets": [ 1999 | "b539d7fa50874c5cb353742acc38d035", 2000 | "63ea3da5f9ec47a9856fd878ff9653ed", 2001 | "0fed3654a5e5479e933a65aa41ea0556", 2002 | "6299de1200494cb0ac2e54ff7c16d2bf", 2003 | "6f763e8210ac4e0992317f24f1fdb546", 2004 | "dd9cf398e6604ab9953d372cc833fd75", 2005 | "a61ac8da58ad42a2bd7329d2d339d07c", 2006 | "17dbb761d1664bae8bb96915e8c23bec", 2007 | "9ca215a28e914c8abccb1c5d3385775f", 2008 | "f58a5bcefd394896a052075108e9a1b3", 2009 | "eadd4f803c3949ef908873b191ddaf99", 2010 | "1dd2fc24b5884aadae9e0b9995ca55f0", 2011 | "8caea121390c4267b01fde27f3afe6b3", 2012 | "1760b31dbdf843099341a67608c1464d", 2013 | "62ec2b2f26594d57ad78947f5710dd8d", 2014 | "6721a3f8ce98467497272204722f3c6a", 2015 | "f0b867f5fb1a4f898702c4f413bcc78e", 2016 | "baa787efb0c24407b8ab0948fc1f92e3", 2017 | "ca734695bf1b4ab5a94bc2c6b4e193dc", 2018 | "07239082bbb04095b8f5b53311d523fb", 2019 | "8b32bf66fdd6450f96d4829d44d360a5", 2020 | "95930beda3aa4d8ca2a3ad077cdf7767", 2021 | "dbdd3dea8dcd4d16b07012222e74a6ec", 2022 | "e6706518f93d43a3922af38f33950175", 2023 | "0233c7dcc0744465a12dabead48e3e97", 2024 | "3cac992c4ab64f50a5ab47ebbbcbc482", 2025 | "a6910a37ffa94c6e9381a31c2d96a81c", 2026 | "874c31e0b254426ab6741f39a20d1ffe", 2027 | "56ec677b71664909a54b0aa07948f642", 2028 | "1a9df2a53870436889a8884b38a9ce92", 2029 | "5648468189ce45d5b524fc1c1c3da74b", 2030 | "e14806aac33b49fd9107ca41f4ae54c1", 2031 | "71e38be035e940c4a3e85b30a58b8bd2", 2032 | "ab0aad4ff0364e2f9c669787f4fa2325", 2033 | "835cfc81f94f4e67a703de2a95ec0d8b", 2034 | "70d0301df2ce492f8f74fdcd4b8948da", 2035 | "74e66cf643634539a7a19c44837c6f25", 2036 | "b12381d1d85247b7bb909a62a9ea4c50", 2037 | "4a6bc061051d4200bb539c18dbd4efde", 2038 | "bdd729c2cc4c4b6ab7cca979ee93634a", 2039 | "71356ac8813f443bba51f96e1afeca12", 2040 | "8bcac4b50d1d498eaf47f31c9f02851a", 2041 | "7d17b7f55a0d4d19889784a738cdc80d", 2042 | "79dac273081d4da7a09edd7c4ced0e06", 2043 | "8fc8ad820a3c4e268fd6fd487becd5f9", 2044 | "b40392c4052b49ca8561972f1bfcdfa7", 2045 | "3d8545b43fad4a2189be7be4c0905c98", 2046 | "ff617d0462d34d8187550b382692075f", 2047 | "dd0561c26ae046ed95ba59c0f9839dff", 2048 | "2d2f1b226c9943a48b6642e197f2fed8", 2049 | "b44d3c3603af4867a53be7ef6fa8e168", 2050 | "4cc37601897e4f1d96974fe48cc0ac68", 2051 | "45b89a44d72c43c28d364d8b6dfe67c6", 2052 | "b9af6361d0c54e6dbebdbc42d0e05547", 2053 | "a6eb35e7bbeb4de8bde2ad5c4c8bd1d1" 2054 | ] 2055 | }, 2056 | "id": "QVf1QNumiPlC", 2057 | "outputId": "860bdbf9-a63c-4812-a7f5-f7cc1f2ff950" 2058 | }, 2059 | "execution_count": 6, 2060 | "outputs": [ 2061 | { 2062 | "output_type": "display_data", 2063 | "data": { 2064 | "text/plain": [ 2065 | "Downloading: 0%| | 0.00/689 [00:00 -1: #model = T5\n", 2153 | " def generate_text(pipe, text, num_return_sequences=5, max_length=512):\n", 2154 | " text = f\"{text}\"\n", 2155 | " out = pipe(text, num_return_sequences=num_return_sequences, max_length=max_length, num_beams=5, no_repeat_ngram_size=2,)\n", 2156 | " return [x['generated_text'] for x in out]\n", 2157 | "\n", 2158 | " #target_text = 'kjkjkjkj'\n", 2159 | " 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", 2160 | "\n", 2161 | " print(\"Input text:\", src_text)\n", 2162 | " test_output_text = generate_text(nlg_pipeline, src_text, num_return_sequences=1, max_length=test_output_length)\n", 2163 | "\n", 2164 | "\n", 2165 | " #you can cook this output anything you want! \n", 2166 | " print(test_output_text)\n", 2167 | "\n", 2168 | "\n", 2169 | "\n", 2170 | "else: #model = GPT\n", 2171 | " 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", 2172 | " tokens = tokenizer.encode(src_text, return_tensors='pt')\n", 2173 | " gen_tokens = model.generate(tokens, do_sample=True, temperature=0.8, max_length=len(tokens[0])+test_output_length)\n", 2174 | " generated = tokenizer.batch_decode(gen_tokens)[0]\n", 2175 | " \n", 2176 | " test_output_text = generated[generated.rfind('more simple:')+12:]\n", 2177 | " print(generated)\n", 2178 | "\n", 2179 | " #you can cook this output anything you want!\n", 2180 | " print(test_output_text)\n" 2181 | ], 2182 | "metadata": { 2183 | "colab": { 2184 | "base_uri": "https://localhost:8080/" 2185 | }, 2186 | "id": "n71_yzKjz-gL", 2187 | "outputId": "c4e060a7-3e4c-4339-c8f6-42cdb0588535" 2188 | }, 2189 | "execution_count": 10, 2190 | "outputs": [ 2191 | { 2192 | "output_type": "stream", 2193 | "name": "stderr", 2194 | "text": [ 2195 | "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n", 2196 | "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n" 2197 | ] 2198 | }, 2199 | { 2200 | "output_type": "stream", 2201 | "name": "stdout", 2202 | "text": [ 2203 | "Here is some text: South Korea's Capital is Seoul. Here is a rewrite of the text, which is more simple: Seoul -> South Korea\n", 2204 | "Here is some text: Mexico's Capital is Mexico City. Here is a rewrite of the text, which is more simple: Mexico City -> Mexico\n", 2205 | "Here is some text: Germany's Capital is Berlin. Here is a rewrite of the text, which is more simple: Berlin -> Germany\n", 2206 | "Here is some text: Uganda's Capital is Kampala. Here is a rewrite of the text, which is more simple: Kampala -> Uganda\n", 2207 | "Here is some text: Italy's Capital is Roma. Here is a rewrite of the text, which is more simple:  Roma -> Italy\n", 2208 | "Here is\n", 2209 | "  Roma -> Italy\n", 2210 | "Here is\n" 2211 | ] 2212 | } 2213 | ] 2214 | }, 2215 | { 2216 | "cell_type": "code", 2217 | "source": [], 2218 | "metadata": { 2219 | "id": "Akfyhhiw0fym" 2220 | }, 2221 | "execution_count": 7, 2222 | "outputs": [] 2223 | } 2224 | ] 2225 | } -------------------------------------------------------------------------------- /Fewshot_OPT_PROMPTING.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "attachments": {}, 5 | "cell_type": "markdown", 6 | "metadata": { 7 | "id": "nr5CTloRas9w" 8 | }, 9 | "source": [ 10 | "### Few-shot Prompting for Compositionality+Reasoning with Large Language Models \n", 11 | "\n", 12 | "\n", 13 | "**[ Code from ]** [Eunchan Lee of UNIST-LIL-LAB](https://github.com/purang2)\n", 14 | "\n", 15 | "\n", 16 | "Option\n", 17 | "```(python)\n", 18 | "- LLM : \"Meta AI/opt-6.7b\"\n", 19 | "- Number of data: 2개\n", 20 | "\n", 21 | "- Data [0]\n", 22 | " - Q: '[A man on a bike] [vs] [a bike on a man]. Please Explain what is more natural.', \n", 23 | " - 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", 24 | "- Data [1]\n", 25 | " - Q: '[The grass is eating the horse] [vs] [The horse is eating the grass.] Please Explain what is more natural.'\n", 26 | " - 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", 27 | "```" 28 | ] 29 | }, 30 | { 31 | "attachments": {}, 32 | "cell_type": "markdown", 33 | "metadata": {}, 34 | "source": [ 35 | "install\n", 36 | "- torch\n", 37 | "- pip install transformers" 38 | ] 39 | }, 40 | { 41 | "cell_type": "code", 42 | "execution_count": 1, 43 | "metadata": {}, 44 | "outputs": [ 45 | { 46 | "name": "stdout", 47 | "output_type": "stream", 48 | "text": [ 49 | "True\n" 50 | ] 51 | } 52 | ], 53 | "source": [ 54 | "# GPU 사용 가능 -> True, GPU 사용 불가 -> False\n", 55 | "import os\n", 56 | "import torch\n", 57 | "print(torch.cuda.is_available())\n", 58 | "os.environ[\"CUDA_VISIBLE_DEVICES\"]= \"0\"" 59 | ] 60 | }, 61 | { 62 | "attachments": {}, 63 | "cell_type": "markdown", 64 | "metadata": { 65 | "id": "KfKGHuv7LfF5" 66 | }, 67 | "source": [ 68 | "Put datapair" 69 | ] 70 | }, 71 | { 72 | "cell_type": "code", 73 | "execution_count": 11, 74 | "metadata": { 75 | "colab": { 76 | "base_uri": "https://localhost:8080/" 77 | }, 78 | "id": "0Ki7stDebE_D", 79 | "outputId": "2c41e7b1-310d-4011-b9c9-6eedd6c39c26" 80 | }, 81 | "outputs": [ 82 | { 83 | "name": "stdout", 84 | "output_type": "stream", 85 | "text": [ 86 | "['[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", 87 | "['[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" 88 | ] 89 | } 90 | ], 91 | "source": [ 92 | "few_src = [] \n", 93 | "few_tgt = []\n", 94 | "\n", 95 | "\n", 96 | "def put_datapair(src, tgt):\n", 97 | " few_src.append(src)\n", 98 | " few_tgt.append(tgt)\n", 99 | "\n", 100 | "\n", 101 | "#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", 102 | "#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", 103 | "#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", 104 | "\n", 105 | "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", 106 | "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", 107 | "\n", 108 | "\n", 109 | "print(few_src)\n", 110 | "print(few_tgt)\n" 111 | ] 112 | }, 113 | { 114 | "attachments": {}, 115 | "cell_type": "markdown", 116 | "metadata": { 117 | "id": "ZwMZvIpCKOiP" 118 | }, 119 | "source": [ 120 | "### Prompt\n", 121 | "\n", 122 | "***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/)***" 123 | ] 124 | }, 125 | { 126 | "cell_type": "code", 127 | "execution_count": 12, 128 | "metadata": { 129 | "colab": { 130 | "base_uri": "https://localhost:8080/" 131 | }, 132 | "id": "F5LM18a7f_FQ", 133 | "outputId": "198de336-5edb-474e-951a-2ee846a9001f" 134 | }, 135 | "outputs": [ 136 | { 137 | "name": "stdout", 138 | "output_type": "stream", 139 | "text": [ 140 | "Q: [A man on a bike] [vs] [a bike on a man]. Please Explain what is more natural.. \n", 141 | "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", 142 | "\n", 143 | "Q: [The grass is eating the horse] [vs] [The horse is eating the grass.] Please Explain what is more natural.. \n", 144 | "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", 145 | "\n", 146 | "\n" 147 | ] 148 | } 149 | ], 150 | "source": [ 151 | "in_context_text = ''\n", 152 | "\n", 153 | "for i in range(len(few_src)):\n", 154 | " #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", 155 | " in_context_text += f'Q: {few_src[i]}. \\nA: {few_tgt[i]}\\n\\n'\n", 156 | " \n", 157 | "print(in_context_text)" 158 | ] 159 | }, 160 | { 161 | "attachments": {}, 162 | "cell_type": "markdown", 163 | "metadata": { 164 | "id": "N8qXu5MTKStT" 165 | }, 166 | "source": [ 167 | "transformers model" 168 | ] 169 | }, 170 | { 171 | "cell_type": "code", 172 | "execution_count": 6, 173 | "metadata": { 174 | "colab": { 175 | "base_uri": "https://localhost:8080/", 176 | "height": 177, 177 | "referenced_widgets": [ 178 | "b539d7fa50874c5cb353742acc38d035", 179 | "63ea3da5f9ec47a9856fd878ff9653ed", 180 | "0fed3654a5e5479e933a65aa41ea0556", 181 | "6299de1200494cb0ac2e54ff7c16d2bf", 182 | "6f763e8210ac4e0992317f24f1fdb546", 183 | "dd9cf398e6604ab9953d372cc833fd75", 184 | "a61ac8da58ad42a2bd7329d2d339d07c", 185 | "17dbb761d1664bae8bb96915e8c23bec", 186 | "9ca215a28e914c8abccb1c5d3385775f", 187 | "f58a5bcefd394896a052075108e9a1b3", 188 | "eadd4f803c3949ef908873b191ddaf99", 189 | "1dd2fc24b5884aadae9e0b9995ca55f0", 190 | "8caea121390c4267b01fde27f3afe6b3", 191 | "1760b31dbdf843099341a67608c1464d", 192 | "62ec2b2f26594d57ad78947f5710dd8d", 193 | "6721a3f8ce98467497272204722f3c6a", 194 | "f0b867f5fb1a4f898702c4f413bcc78e", 195 | "baa787efb0c24407b8ab0948fc1f92e3", 196 | "ca734695bf1b4ab5a94bc2c6b4e193dc", 197 | "07239082bbb04095b8f5b53311d523fb", 198 | "8b32bf66fdd6450f96d4829d44d360a5", 199 | "95930beda3aa4d8ca2a3ad077cdf7767", 200 | "dbdd3dea8dcd4d16b07012222e74a6ec", 201 | "e6706518f93d43a3922af38f33950175", 202 | "0233c7dcc0744465a12dabead48e3e97", 203 | "3cac992c4ab64f50a5ab47ebbbcbc482", 204 | "a6910a37ffa94c6e9381a31c2d96a81c", 205 | "874c31e0b254426ab6741f39a20d1ffe", 206 | "56ec677b71664909a54b0aa07948f642", 207 | "1a9df2a53870436889a8884b38a9ce92", 208 | "5648468189ce45d5b524fc1c1c3da74b", 209 | "e14806aac33b49fd9107ca41f4ae54c1", 210 | "71e38be035e940c4a3e85b30a58b8bd2", 211 | "ab0aad4ff0364e2f9c669787f4fa2325", 212 | "835cfc81f94f4e67a703de2a95ec0d8b", 213 | "70d0301df2ce492f8f74fdcd4b8948da", 214 | "74e66cf643634539a7a19c44837c6f25", 215 | "b12381d1d85247b7bb909a62a9ea4c50", 216 | "4a6bc061051d4200bb539c18dbd4efde", 217 | "bdd729c2cc4c4b6ab7cca979ee93634a", 218 | "71356ac8813f443bba51f96e1afeca12", 219 | "8bcac4b50d1d498eaf47f31c9f02851a", 220 | "7d17b7f55a0d4d19889784a738cdc80d", 221 | "79dac273081d4da7a09edd7c4ced0e06", 222 | "8fc8ad820a3c4e268fd6fd487becd5f9", 223 | "b40392c4052b49ca8561972f1bfcdfa7", 224 | "3d8545b43fad4a2189be7be4c0905c98", 225 | "ff617d0462d34d8187550b382692075f", 226 | "dd0561c26ae046ed95ba59c0f9839dff", 227 | "2d2f1b226c9943a48b6642e197f2fed8", 228 | "b44d3c3603af4867a53be7ef6fa8e168", 229 | "4cc37601897e4f1d96974fe48cc0ac68", 230 | "45b89a44d72c43c28d364d8b6dfe67c6", 231 | "b9af6361d0c54e6dbebdbc42d0e05547", 232 | "a6eb35e7bbeb4de8bde2ad5c4c8bd1d1" 233 | ] 234 | }, 235 | "id": "QVf1QNumiPlC", 236 | "outputId": "860bdbf9-a63c-4812-a7f5-f7cc1f2ff950" 237 | }, 238 | "outputs": [ 239 | { 240 | "name": "stderr", 241 | "output_type": "stream", 242 | "text": [ 243 | "Loading checkpoint shards: 100%|██████████| 2/2 [00:14<00:00, 7.12s/it]\n" 244 | ] 245 | } 246 | ], 247 | "source": [ 248 | "from transformers import AutoTokenizer\n", 249 | "from transformers import AutoModelForCausalLM, AutoModelForSeq2SeqLM\n", 250 | "from transformers import pipeline\n", 251 | "\n", 252 | "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", 253 | "\n", 254 | "model_name = model_zoo[5]\n", 255 | "\n", 256 | "\n", 257 | "if model_name.find('t5') > -1 or model_name.lower().find('t0') > -1: #model = t5, t0 [encoder-decoder]\n", 258 | " tokenizer = AutoTokenizer.from_pretrained(model_name)\n", 259 | " model = AutoModelForSeq2SeqLM.from_pretrained(model_name)\n", 260 | " nlg_pipeline = pipeline('text2text-generation',model=model, tokenizer=tokenizer)\n", 261 | "\n", 262 | "else: #model = gpt,etc [decoder-only]\n", 263 | " tokenizer = AutoTokenizer.from_pretrained(model_name)\n", 264 | " model = AutoModelForCausalLM.from_pretrained(model_name)\n", 265 | " #nlg_pipeline = pipeline(model=model, tokenizer=tokenizer)" 266 | ] 267 | }, 268 | { 269 | "attachments": {}, 270 | "cell_type": "markdown", 271 | "metadata": { 272 | "id": "1mfaqHtbKWYD" 273 | }, 274 | "source": [ 275 | "model inference" 276 | ] 277 | }, 278 | { 279 | "cell_type": "code", 280 | "execution_count": 13, 281 | "metadata": { 282 | "colab": { 283 | "base_uri": "https://localhost:8080/" 284 | }, 285 | "id": "n71_yzKjz-gL", 286 | "outputId": "c4e060a7-3e4c-4339-c8f6-42cdb0588535" 287 | }, 288 | "outputs": [ 289 | { 290 | "name": "stdout", 291 | "output_type": "stream", 292 | "text": [ 293 | "OUTPUT\n", 294 | "\n", 295 | " \n", 296 | "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.\n" 297 | ] 298 | } 299 | ], 300 | "source": [ 301 | "\n", 302 | "test_input_text = \"[The coffee is drinking a boy] [vs] [A boy is drinkng the coffee.] Please Explain what is more natural\"\n", 303 | "#test_input_text = \"Italy's Capital is Roma\"\n", 304 | "test_output_length = 100 #token length\n", 305 | "\n", 306 | "\n", 307 | "if model_name.find('t5') > -1 or model_name.lower().find('t0') > -1: #model = t5, t0 [encoder-decoder]\n", 308 | " def generate_text(pipe, text, num_return_sequences=5, max_length=512):\n", 309 | " text = f\"{text}\"\n", 310 | " out = pipe(text, num_return_sequences=num_return_sequences, max_length=max_length, num_beams=5, no_repeat_ngram_size=2,)\n", 311 | " return [x['generated_text'] for x in out]\n", 312 | "\n", 313 | " #target_text = 'kjkjkjkj'\n", 314 | " #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", 315 | " src_text = in_context_text + f\"Q: {test_input_text} \\nA: \"\n", 316 | " #src_text = f\"Q: {test_input_text}. \\nA: \"\n", 317 | " \n", 318 | "\n", 319 | " print(\"Input text:\", src_text)\n", 320 | " test_output_text = generate_text(nlg_pipeline, src_text, num_return_sequences=1, max_length=test_output_length)\n", 321 | "\n", 322 | "\n", 323 | " #you can cook this output anything you want! \n", 324 | " print(test_output_text)\n", 325 | "\n", 326 | "\n", 327 | "\n", 328 | "else: #model = GPT, ETC\n", 329 | " src_text = in_context_text + f\"Q: {test_input_text} \\nA:\"\n", 330 | " tokens = tokenizer.encode(src_text, return_tensors='pt')\n", 331 | " gen_tokens = model.generate(tokens, do_sample=True, temperature=0.8, max_length=len(tokens[0])+test_output_length)\n", 332 | " generated = tokenizer.batch_decode(gen_tokens)[0]\n", 333 | " \n", 334 | " test_output_text = generated[len(src_text):]\n", 335 | " 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6.43G/6.43G [01:54<00:00, 60.5MB/s]" 2085 | } 2086 | } 2087 | } 2088 | } 2089 | }, 2090 | "nbformat": 4, 2091 | "nbformat_minor": 0 2092 | } 2093 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2022 Eunchan Lee 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /PPT-Sildes/LabSeminar_NLGwithPrompting.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/purang2/prompting-nlp/4463d46591d8b56607a92a17c6d64f3e2b2562cd/PPT-Sildes/LabSeminar_NLGwithPrompting.pdf -------------------------------------------------------------------------------- /PPT-Sildes/LabSeminar_NLGwithPrompting.pptx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/purang2/prompting-nlp/4463d46591d8b56607a92a17c6d64f3e2b2562cd/PPT-Sildes/LabSeminar_NLGwithPrompting.pptx -------------------------------------------------------------------------------- /PPT-Sildes/readme.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | ## [Text Generation with Prompting /PPT, 2023-Feb version](https://github.com/purang2/prompting-nlp/blob/main/PPT-Sildes/LabSeminar_NLGwithPrompting.pdf) 4 | 5 | Text Generation with Prompting [PPT Slides](https://github.com/purang2/prompting-nlp/blob/main/PPT-Sildes/LabSeminar_NLGwithPrompting.pdf) ***by [Eunchan Lee ( UNIST AI )](https://github.com/purang2)*** 6 | 7 | 8 | 9 | 10 | ### Outline ***(You can check it on the PPT)*** 11 | Step 1: Text Generation 12 | ( Before & After GPT ) 13 | 14 | Step 2: GPT-3 & 15 | In-Context Learning (ICL) 16 | 17 | Step 3: Prompting 18 | 19 | Step 4: What makes ICL work? 20 | 21 | Step 5: Variants of Prompting (Chain-of-Thought Prompting 22 | / Prompting for Multimodal) 23 | 24 | Step 6: Limitations & Future Directions of Prompting 25 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # prompting-nlp 2 | About, prompting few-shot learning 3 | 4 | 5 | Prompting language models (LMs) with training examples and task descriptions has been 6 | seen as critical to recent successes in few-shot 7 | learning. 8 | 9 | 10 | 11 | ## List of Papers for Studying Prompt-based Few-shot Learning 12 | 13 | - [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing (Liu et al., ACM Survey, 2021)](https://openreview.net/forum?id=nlZ-VeHtoCG) 14 | 15 | - [Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models (Logan IV et al., ACL 2022)](https://aclanthology.org/2022.findings-acl.222.pdf) 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | -------------------------------------------------------------------------------- /Textchat2Prompt/NMT_KO/readme.md: -------------------------------------------------------------------------------- 1 | # NMT_KO 2 | 3 | Kor -> Eng Ipython notebook 4 | -------------------------------------------------------------------------------- /Textchat2Prompt/only_paper_pdf.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/purang2/prompting-nlp/4463d46591d8b56607a92a17c6d64f3e2b2562cd/Textchat2Prompt/only_paper_pdf.pdf -------------------------------------------------------------------------------- /Textchat2Prompt/paper_pdf.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/purang2/prompting-nlp/4463d46591d8b56607a92a17c6d64f3e2b2562cd/Textchat2Prompt/paper_pdf.pdf -------------------------------------------------------------------------------- /Textchat2Prompt/readme.md: -------------------------------------------------------------------------------- 1 | # Official Repository for Textchat2Prompt (HCLT'22, Eunchan Lee and Sangtae Ahn) 2 | 3 | 4 | ## Paper 5 | 6 | 7 | [[List of Contents + Paper, Link]](https://github.com/purang2/prompting-nlp/blob/main/Textchat2Prompt/paper_pdf.pdf) 8 | 9 | [[⭐Paper Only, Link]](https://github.com/purang2/prompting-nlp/blob/main/Textchat2Prompt/only_paper_pdf.pdf) 10 | 11 | ![illustration_prompt_FINAL (1)](https://user-images.githubusercontent.com/46081500/200230268-29f62191-20d2-4c4c-9748-ac69f28c18ad.jpg) 12 | 13 | 14 | ## HCLT 2022 (The 34th Annual Conference on Human & Cognitive Language Technology, ) 15 | 16 | 17 | 18 | ![image](https://user-images.githubusercontent.com/46081500/200228615-42d32c8c-4930-463b-97e1-930533355eda.png) 19 | 20 | 21 | https://sites.google.com/view/hclt2022 22 | -------------------------------------------------------------------------------- /notebook/readme.md: -------------------------------------------------------------------------------- 1 | Colab Notebook Code 2 | --------------------------------------------------------------------------------