FLAN-T5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "870e8279",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "\n",
    "import torch\n",
    "import tqdm\n",
    "\n",
    "from transformers import T5Tokenizer, T5ForConditionalGeneration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "677130fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = json.load(open(\"Complete_dataset/test.json\"))\n",
    "files = os.listdir(\"Complete_dataset/CT json/\")\n",
    "files.remove(\".DS_Store\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d5df4e00",
   "metadata": {},
   "outputs": [],
   "source": [
    "files_data = {file[:-5]:json.load(open(f\"Complete_dataset/CT json/{file}\")) for file in files}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "07636837",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_expanded = []\n",
    "for _id, value in data.items():\n",
    "    temp = {}\n",
    "    temp[\"id\"] = _id\n",
    "    p_nctid = value[\"Primary_id\"]\n",
    "    s_nctid = value.get(\"Secondary_id\")\n",
    "    section_id = value[\"Section_id\"]\n",
    "    statement = value[\"Statement\"]\n",
    "    primary_evidence = files_data[p_nctid][section_id]\n",
    "    temp[\"statement\"] = statement\n",
    "    temp[\"primary_evidence\"] = primary_evidence\n",
    "    # temp[\"label\"] = value[\"Label\"]\n",
    "    \n",
    "    if s_nctid is not None:\n",
    "        secondary_evidence = files_data[s_nctid][section_id]\n",
    "        temp[\"secondary_evidence\"] = secondary_evidence\n",
    "    \n",
    "    data_expanded.append(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e0a6c139",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = T5Tokenizer.from_pretrained(\"output_dir_xxl_3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dce56a86",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = T5ForConditionalGeneration.from_pretrained(\"output_dir_xxl_3\", device_map=\"auto\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ab1349c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_input_text(premise, hypothesis):\n",
    "    options_prefix = \"OPTIONS:\\n- \"\n",
    "    separator = \"\\n- \"\n",
    "    options_ = options_prefix + f\"{separator}\".join([\"Entailment\",\"Contradiction\"])\n",
    "    return f\"{premise} \\n Question: Does this imply that {hypothesis}? {options_}\"\n",
    "    # return f\"Classification: {premise} \\n Question: Does this imply that {hypothesis}? Entailment or Contradiction?Answer:\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a307dd6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "samples = []\n",
    "for sample in data_expanded:\n",
    "    primary_evidence = \"\".join(sample['primary_evidence'])\n",
    "    sentence = f\"Primary trial evidence are {primary_evidence}\"\n",
    "    secondary_evidence = sample.get(\"secondary_evidence\")\n",
    "    if secondary_evidence:\n",
    "        secondary_evidence = \"\".join(sample['secondary_evidence'])\n",
    "        sentence = f\"{sentence} and Secondary trial evidence are {secondary_evidence}\"\n",
    "    input_text = get_input_text(sentence, sample['statement'])\n",
    "    # temp = {\"text\":input_text, \"label\":sample['label']}\n",
    "    temp = {\"text\":input_text, \"label\":0}\n",
    "    samples.append(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f5d62610",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 200/200 [02:44<00:00,  1.21it/s]\n"
     ]
    }
   ],
   "source": [
    "labels = []\n",
    "pred = []\n",
    "with torch.inference_mode():\n",
    "    for sample in tqdm.tqdm(samples):\n",
    "        labels.append(sample[\"label\"])\n",
    "        input_ids = tokenizer(sample[\"text\"], return_tensors=\"pt\",).input_ids.to(\"cuda\")\n",
    "        outputs = model.generate(input_ids)\n",
    "        pred.append(tokenizer.decode(outputs[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "708cd824",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
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       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
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       " '<pad> Contradiction</s>',\n",
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       " '<pad> Entailment</s>',\n",
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       " '<pad> Contradiction</s>',\n",
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       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Contradiction</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Entailment</s>',\n",
       " '<pad> Contradiction</s>']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "32f3b8fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = [p[5:][:-4].strip() for p in pred]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5d349880",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'Contradiction': 95, 'Entailment': 105})"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(pred)\n",
    "from collections import Counter\n",
    "Counter(pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6fbbb1d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_dict = {}\n",
    "for _id,pred_x in zip(data, pred):\n",
    "    prediction_dict[str(_id)] = {\"Prediction\":pred_x}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "317efb29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8780487804878048"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from sklearn.metrics import f1_score\n",
    "# uuid_list = list(prediction_dict.keys())\n",
    "# results_pred = []\n",
    "# gold_labels = []\n",
    "# for i in range(len(uuid_list)):\n",
    "#     if prediction_dict[uuid_list[i]][\"Prediction\"] in [\"Entailment\", \"Yes\"]:\n",
    "#         results_pred.append(1)\n",
    "#     else:\n",
    "#         results_pred.append(0)\n",
    "#     if data[uuid_list[i]][\"Label\"] in [\"Entailment\", \"No\"]:\n",
    "#         gold_labels.append(1)\n",
    "#     else:\n",
    "#         gold_labels.append(0)\n",
    "# f1_score(gold_labels,results_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fdfcff67",
   "metadata": {},
   "outputs": [],
   "source": [
    "json.dump(prediction_dict, open(\"results.json\",'w'),indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e87cce1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  adding: results.json (deflated 73%)\n"
     ]
    }
   ],
   "source": [
    "!zip results_xxl_3_only_train.zip results.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "37a17ffd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'9f978634-637c-472f-a588-6f4bb2fb121f': {'Prediction': 'Contradiction'},\n",
       " '20b34e62-97c2-4ca0-bb1d-7824dab0b8bb': {'Prediction': 'Entailment'},\n",
       " '893a5337-2aa9-4a87-a020-4c2f03cd4aea': {'Prediction': 'Entailment'},\n",
       " 'd401affc-f081-4eee-bd61-d109cc88f6de': {'Prediction': 'Entailment'},\n",
       " '791790a6-187b-4e4b-be5f-9e5304e9ec2c': {'Prediction': 'Contradiction'},\n",
       " 'b95b7438-ec16-4d4d-826d-5891e7982b36': {'Prediction': 'Entailment'},\n",
       " '4988cb16-7dbb-4847-84e0-4a7957b32c72': {'Prediction': 'Entailment'},\n",
       " 'e244fc3a-53b3-4158-99c5-a45afc726af6': {'Prediction': 'Contradiction'},\n",
       " '56530063-b408-47f2-8421-6be825f5559c': {'Prediction': 'Entailment'},\n",
       " 'd3379655-55b7-4e58-88c2-c3cd3e8cb557': {'Prediction': 'Contradiction'},\n",
       " '7bf988b4-5e6f-41c4-bef6-7b3549dd58d9': {'Prediction': 'Contradiction'},\n",
       " '84915e35-a8c9-4d26-ad09-4b1df48a6df8': {'Prediction': 'Entailment'},\n",
       " '64df451c-f868-49e6-9b4e-a325e62ce837': {'Prediction': 'Entailment'},\n",
       " 'dc4765a3-6168-4594-969b-3ce5ea7dc02a': {'Prediction': 'Contradiction'},\n",
       " '9c2ca035-d531-4d44-876e-6ed9f2d3f76b': {'Prediction': 'Entailment'},\n",
       " '7238ddd4-eade-4b88-8933-0c3df5be9875': {'Prediction': 'Contradiction'},\n",
       " 'aeddc485-09b1-4ddc-9a45-3a8bf0bec2c3': {'Prediction': 'Contradiction'},\n",
       " '204d5293-d6e5-452a-8961-3b76d6be35c9': {'Prediction': 'Entailment'},\n",
       " '4ed1c5fd-58e5-4fe8-97b1-59ed509ccb10': {'Prediction': 'Entailment'},\n",
       " '6d0812b9-f9bd-4cd6-82aa-9c272eba23be': {'Prediction': 'Contradiction'},\n",
       " 'fd0fa999-2f1c-400d-977d-2d45dd9c358c': {'Prediction': 'Contradiction'},\n",
       " '571d4650-e64b-4cb2-b719-0d7064e51745': {'Prediction': 'Contradiction'},\n",
       " '4437aa5b-4b4e-4eb4-a258-e7f17dc8f363': {'Prediction': 'Contradiction'},\n",
       " 'd7abbd7d-a767-49c0-bc99-1c28074eac21': {'Prediction': 'Contradiction'},\n",
       " '54c7e880-a1bf-4206-9573-1c994932ba15': {'Prediction': 'Entailment'},\n",
       " 'f9f0ba74-16e0-4790-afa0-b7616b256e5e': {'Prediction': 'Entailment'},\n",
       " '412583db-ce19-451d-a804-3837e4dcaea3': {'Prediction': 'Contradiction'},\n",
       " 'bca4c94d-4a49-4e05-82ed-0d5c9707685b': {'Prediction': 'Contradiction'},\n",
       " '97acf304-2707-4a1b-81e7-339fde2ed0ee': {'Prediction': 'Contradiction'},\n",
       " '21a5f296-5b3c-4860-be64-77ffb8f76501': {'Prediction': 'Entailment'},\n",
       " '8d27bb53-e66e-4eab-bea4-0597fd5760b5': {'Prediction': 'Entailment'},\n",
       " 'dc60304d-6fd2-4b12-9108-b5ce78454d42': {'Prediction': 'Contradiction'},\n",
       " '328417f9-8f45-4114-82d7-9396b6bc578e': {'Prediction': 'Contradiction'},\n",
       " '40d84c7b-e126-4d6f-9588-eb9c385ca716': {'Prediction': 'Contradiction'},\n",
       " 'b685be77-9c55-4948-99cc-2a249739a233': {'Prediction': 'Contradiction'},\n",
       " '5fbee4f6-43fc-45a7-88c4-3dcd5ff21053': {'Prediction': 'Entailment'},\n",
       " '8127d46a-3a48-4173-8d00-75e949755a74': {'Prediction': 'Contradiction'},\n",
       " '576967ff-d2bb-4d1c-b2a1-8516ceb7a519': {'Prediction': 'Contradiction'},\n",
       " 'fd334e71-2bbe-4138-b578-2ab848686045': {'Prediction': 'Contradiction'},\n",
       " 'a72ddea5-ee4b-4895-bd8c-10b4a143c3f8': {'Prediction': 'Contradiction'},\n",
       " '6c29e8c1-36ca-4e6e-b4dc-f1e91b98e546': {'Prediction': 'Contradiction'},\n",
       " 'd1118512-9c0b-4480-ab67-d4e0c7de390d': {'Prediction': 'Contradiction'},\n",
       " 'd35e9b96-20fc-4778-a7db-e87c861c4938': {'Prediction': 'Contradiction'},\n",
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       " '8b50bdc1-1722-403b-a854-adeaacd02acb': {'Prediction': 'Contradiction'}}"
      ]
     },
     "execution_count": 16,
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