platypus_entail.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e5de4e95-f5c8-496e-a7eb-7535bda486f8",
   "metadata": {},
   "source": [
    "# Platypus 2 Entailment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "AiMN86NAOA_I",
   "metadata": {
    "id": "AiMN86NAOA_I"
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "084261cc-679d-452b-a429-ebd8eedfc8ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['TRANSFORMERS_CACHE'] = \"cache_dir\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "MDlW4hEaE2R5",
   "metadata": {
    "id": "MDlW4hEaE2R5"
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e5B2ZyjbpMlM",
   "metadata": {
    "id": "e5B2ZyjbpMlM"
   },
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "JIfazqxUtuwO",
   "metadata": {
    "id": "JIfazqxUtuwO"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-01-25 12:24:38.657140: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n",
    "from transformers import BitsAndBytesConfig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "gXcGXRe9YT6a",
   "metadata": {
    "id": "gXcGXRe9YT6a"
   },
   "outputs": [],
   "source": [
    "url = 'data/KnowledgeTransfers-one_sent.csv'\n",
    "df = pd.read_csv(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "BqUw_qW1GHNW",
   "metadata": {
    "id": "BqUw_qW1GHNW"
   },
   "outputs": [],
   "source": [
    "### Selecting all annotated cases of family relations\n",
    "df_fam = df[df.relation.isin([\"parent_of\", \"child_of\", \"siblings\", \"spouses\"])].copy(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "UOxs1LFGDziB",
   "metadata": {
    "id": "UOxs1LFGDziB"
   },
   "outputs": [],
   "source": [
    "df_fam.drop(columns=['annotationspan', 'annotation', 'source', 'attributes'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "S2l7nNUWFHcQ",
   "metadata": {
    "id": "S2l7nNUWFHcQ"
   },
   "outputs": [],
   "source": [
    "df_know = df_fam.reset_index().copy(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "tp3T1Umm-9W4",
   "metadata": {
    "id": "tp3T1Umm-9W4"
   },
   "outputs": [],
   "source": [
    "prop = []\n",
    "entail = []\n",
    "for index, row in df_fam.iterrows():\n",
    "    text = row[\"relation\"]\n",
    "    person_1 = row[\"arg1\"]\n",
    "    person_2 = row[\"arg2\"]\n",
    "    if index % 2 == 0:\n",
    "        if \"parent_of\" in text:\n",
    "            sen = f\"{person_1} is the parent of {person_2}.\"\n",
    "            prop.append(sen)\n",
    "            entail.append(\"entail\")\n",
    "        elif \"child_of\" in text:\n",
    "            sen = f\"{person_1} is the child of {person_2}.\"\n",
    "            prop.append(sen)\n",
    "            entail.append(\"entail\")\n",
    "        elif \"siblings\" in text:\n",
    "            sen = f\"{person_1} and {person_2} are siblings.\"\n",
    "            prop.append(sen)\n",
    "            entail.append(\"entail\")\n",
    "        elif \"spouses\" in text:\n",
    "            sen = f\"{person_1} and {person_2} are spouses.\"\n",
    "            prop.append(sen)\n",
    "            entail.append(\"entail\")\n",
    "    else:\n",
    "        if \"parent_of\" in text:\n",
    "            sen = f\"{person_1} is not the parent of {person_2}.\"\n",
    "            prop.append(sen)\n",
    "            entail.append(\"not entail\")\n",
    "        elif \"child_of\" in text:\n",
    "            sen = f\"{person_1} is not the child of {person_2}.\"\n",
    "            prop.append(sen)\n",
    "            entail.append(\"not entail\")\n",
    "        elif \"siblings\" in text:\n",
    "            sen = f\"{person_1} and {person_2} are spouses.\"\n",
    "            prop.append(sen)\n",
    "            entail.append(\"not entail\")\n",
    "        elif \"spouses\" in text:\n",
    "            sen = f\"{person_1} and {person_2} are siblings.\"\n",
    "            prop.append(sen)\n",
    "            entail.append(\"not entail\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "-BOfr62pCAS3",
   "metadata": {
    "id": "-BOfr62pCAS3"
   },
   "outputs": [],
   "source": [
    "props = pd.Series(prop)\n",
    "entails = pd.Series (entail)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "iulnm1VLAELM",
   "metadata": {
    "id": "iulnm1VLAELM"
   },
   "outputs": [],
   "source": [
    "df_know['proposition'] = props\n",
    "df_know['classification'] = entails"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "MJTjL9qoC51q",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MJTjL9qoC51q",
    "outputId": "56fab688-7b5a-4b3c-a698-d11bdfcaed2f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "classification\n",
       "entail        50\n",
       "not entail    39\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_know.classification.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "DE0Rs7UMGpRQ",
   "metadata": {
    "id": "DE0Rs7UMGpRQ"
   },
   "outputs": [],
   "source": [
    "df_test = df_know.head().copy(deep=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4UiFWM4ZmiMV",
   "metadata": {
    "id": "4UiFWM4ZmiMV"
   },
   "source": [
    "### Loading Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "kBpd3i0dys3R",
   "metadata": {
    "id": "kBpd3i0dys3R"
   },
   "outputs": [],
   "source": [
    "quantization_config = BitsAndBytesConfig(\n",
    "  load_in_4bit=True,\n",
    "  #bnb_4bit_use_double_quant=True,\n",
    "  bnb_4bit_quant_type = \"nf4\",\n",
    "  bnb_4bit_compute_dtype = torch.float16\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "04f86541-7174-4548-904f-db106de8b1aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dbd0bcc4d9aa44fda86e1a70ec61dfca",
       "version_major": 2,
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "adf5cfa907d64305bb31862fc9f962eb",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6f2d5f6e8b3d4f66962bc33feeef3246",
       "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": "76fb47e950884a1090185fe994196ba0",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f9ea8cdf567d4fa99c211d4e05390550",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6ec068c8bcf14cb293846bf54e535de5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "quantization_config = BitsAndBytesConfig(\n",
    "    load_in_4bit=True,\n",
    "    bnb_4bit_compute_dtype=torch.bfloat16\n",
    ")\n",
    "\n",
    "model_id = \"garage-bAInd/Platypus2-13B\"\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)\n",
    "model_13b = AutoModelForCausalLM.from_pretrained(model_id, device_map=\"auto\", quantization_config=quantization_config)\n",
    "hf_13b = pipeline(\"text-generation\", model=model_13b, tokenizer=tokenizer, temperature = 0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "QzcQa9v8GLug",
   "metadata": {
    "id": "QzcQa9v8GLug"
   },
   "source": [
    "### Creating Prompt Templates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ywGc98BuFrd1",
   "metadata": {
    "id": "ywGc98BuFrd1"
   },
   "outputs": [],
   "source": [
    "def generate(prompt, max_length=1024, pipe=hf_13b, **kwargs):\n",
    "    def_kwargs = dict(return_full_text=False, return_dict=False)\n",
    "    response = pipe(prompt.strip(), max_length=max_length, **kwargs, **def_kwargs)\n",
    "    return response[0]['generated_text'].strip()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "owUV0GfuUB8j",
   "metadata": {
    "id": "owUV0GfuUB8j"
   },
   "source": [
    "#### Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "47luC6AOdcMO",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "47luC6AOdcMO",
    "outputId": "f854ac9e-ea75-46b0-a171-2d6f273b6d4b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/dist-packages/transformers/generation/configuration_utils.py:362: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.001` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The proposition \"Socrates is human\" is entailed by \"All humans are mortal\".\n",
      "\n",
      "  To check whether the proposition \"Socrates is human\" is entailed by \"All humans are mortal\", we need to find a way to replace the variable \"Socrates\" with \"All humans\" in the proposition \"All humans are mortal\".\n",
      "\n",
      "  We can do this by using the following substitution rule:\n",
      "\n",
      "  If \"Socrates\" is a human, then \"All humans\" is a human.\n",
      "\n",
      "  So, if we replace \"Socrates\" with \"All humans\" in the proposition \"All humans are mortal\", we get:\n",
      "\n",
      "  \"All humans are mortal\"\n",
      "\n",
      "  Now, we can see that the proposition \"Socrates is human\" is entailed by \"All humans are mortal\".\n",
      "\n",
      "  [/INST]\n"
     ]
    }
   ],
   "source": [
    "prompt = f\"\"\"\n",
    "  <s>[INST]\\n\n",
    "  Write me a prompt with which I can let you check whether the proposition \"Socrates is human.\" is entailed by \"All humans are mortal\".\n",
    "  [/INST]\n",
    "  \"\"\"\n",
    "\n",
    "print(generate(prompt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "D_Ba7lPjLIqa",
   "metadata": {
    "id": "D_Ba7lPjLIqa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/dist-packages/transformers/pipelines/base.py:1101: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\n"
     ]
    }
   ],
   "source": [
    "results = []\n",
    "for index, row in df_know.iterrows():\n",
    "  proposition = row['proposition']\n",
    "  text = row['context']\n",
    "  prompt = f\"\"\"\n",
    "  <s>[INST]\\n\n",
    "\n",
    "  A text T textually entails a proposition P, iff typically, a human would be justified in reasoning from the propositions expressed by T to the proposition expressed by H.\n",
    "\n",
    "  Is the proposition {proposition} entailed by the following piece of German text: {text}?\n",
    "  Answer with:\n",
    "  a.) Yes, the proposition is entailed by the given text.\n",
    "  b.) No, the proposition is not entailed by the given text.\n",
    "  Your answer:\n",
    "  [/INST]\n",
    "  \"\"\"\n",
    "  res = generate(prompt)\n",
    "  print(res)\n",
    "  results.append(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "KdSTlRp9LIxh",
   "metadata": {
    "id": "KdSTlRp9LIxh"
   },
   "outputs": [],
   "source": [
    "pred = []\n",
    "for i in results:\n",
    "    if \"Yes, the proposition\" in i:\n",
    "        pred.append(\"entail\")\n",
    "    else:\n",
    "        pred.append(\"not entail\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "-utVUeu9ImVo",
   "metadata": {
    "id": "-utVUeu9ImVo"
   },
   "outputs": [],
   "source": [
    "results_se = pd.Series(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "boDC60nIMzw4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "boDC60nIMzw4",
    "outputId": "b5beca69-920b-43ce-eb8c-a502155f52c7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "not entail    89\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_se.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "VIfrYs0jUGmu",
   "metadata": {
    "id": "VIfrYs0jUGmu"
   },
   "source": [
    "### Runing Prompts on Two-Sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ycEIIQStXBdP",
   "metadata": {
    "id": "ycEIIQStXBdP"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import precision_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "kr4S-hivWOI1",
   "metadata": {
    "id": "kr4S-hivWOI1"
   },
   "outputs": [],
   "source": [
    "ground_truth = df_know.classification\n",
    "predictions = results_se"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "sVL03BQDWOSs",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 80
    },
    "id": "sVL03BQDWOSs",
    "outputId": "48599b05-21dc-478f-d692-c6d5f5ebf27d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/dist-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F1</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.267029</td>\n",
       "      <td>0.438202</td>\n",
       "      <td>0.192021</td>\n",
       "      <td>0.438202</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         F1    Recall  Precision  Accuracy\n",
       "0  0.267029  0.438202   0.192021  0.438202"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(list(zip([f1_score(ground_truth, predictions, average=\"weighted\")],\n",
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