{
 "nbformat": 4,
 "nbformat_minor": 0,
 "metadata": {
  "colab": {
   "provenance": [],
   "collapsed_sections": []
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3"
  },
  "language_info": {
   "name": "python"
  },
  "accelerator": "GPU"
 },
 "cells": [
  {
   "cell_type": "code",
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "apMnCeBi0kAa",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1668340169194,
     "user_tz": -210,
     "elapsed": 26751,
     "user": {
      "displayName": "Mohammad Ebrahimi",
      "userId": "10407139745331958037"
     }
    },
    "outputId": "852beb1d-3b81-4f1e-8b41-511ee62a6458"
   },
   "execution_count": 2,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "juTvp3cXy0vT",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1668340312085,
     "user_tz": -210,
     "elapsed": 2,
     "user": {
      "displayName": "Mohammad Ebrahimi",
      "userId": "10407139745331958037"
     }
    },
    "outputId": "76fc9722-9631-4573-b68c-f2d460ebaf96"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "/content/drive/MyDrive/OpenNRE-master\n"
     ]
    }
   ],
   "source": [
    "%cd /content/drive/MyDrive/OpenNRE-master"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "!pip install -r requirements.txt"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Zw-L_bBWy3q6",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1668340295967,
     "user_tz": -210,
     "elapsed": 119014,
     "user": {
      "displayName": "Mohammad Ebrahimi",
      "userId": "10407139745331958037"
     }
    },
    "outputId": "c73fdd46-9d49-4643-a588-3021e6395c32"
   },
   "execution_count": 5,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR: Could not find a version that satisfies the requirement torch==1.6.0 (from versions: 1.7.1, 1.8.0, 1.8.1, 1.9.0, 1.9.1, 1.10.0, 1.10.1, 1.10.2, 1.11.0, 1.12.0, 1.12.1, 1.13.0, 1.13.1)\n",
      "ERROR: No matching distribution found for torch==1.6.0\n",
      "\n",
      "[notice] A new release of pip available: 22.3.1 -> 23.0\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pip in e:\\hamed\\work\\5\\opennre-master\\venv\\lib\\site-packages (22.3.1)\n",
      "Collecting pip\n",
      "  Using cached pip-23.0-py3-none-any.whl (2.1 MB)\n",
      "Installing collected packages: pip\n",
      "  Attempting uninstall: pip\n",
      "    Found existing installation: pip 22.3.1\n",
      "    Uninstalling pip-22.3.1:\n",
      "      Successfully uninstalled pip-22.3.1\n",
      "Successfully installed pip-23.0\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# %%shell\n",
    "!python train_supervised_bert.py \\\n",
    "    --pretrain_path HooshvareLab/bert-base-parsbert-uncased \\\n",
    "    --dataset none \\\n",
    "    --train_file ./Perlex/Perlex_train.txt \\\n",
    "    --val_file ./Perlex/Perlex_val.txt \\\n",
    "    --test_file ./Perlex/Perlex_test.txt \\\n",
    "    --rel2id_file ./Perlex/Perlex_rel2id.json \\\n",
    "    --max_epoch 20"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aEiTLWpPzV3J",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1665308620117,
     "user_tz": -210,
     "elapsed": 7519388,
     "user": {
      "displayName": "arian ebrahimi",
      "userId": "00418818321983401320"
     }
    },
    "outputId": "90c72478-d09d-4f96-9417-7c2ad805a66b"
   },
   "execution_count": 3,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"E:\\Hamed\\Work\\5\\OpenNRE-master\\train_supervised_bert.py\", line 2, in <module>\n",
      "    import torch\n",
      "ModuleNotFoundError: No module named 'torch'\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "%%shell\n",
    "python train_supervised_bert.py \\\n",
    "    --pretrain_path HooshvareLab/bert-base-parsbert-uncased \\\n",
    "    --dataset none \\\n",
    "    --train_file ./Perlex/Perlex_train.txt \\\n",
    "    --val_file ./Perlex/Perlex_val.txt \\\n",
    "    --test_file ./Perlex/Perlex_test.txt \\\n",
    "    --rel2id_file ./Perlex/Perlex_rel2id.json \\\n",
    "    --max_epoch 20"
   ],
   "metadata": {
    "id": "zhYjakhv13c8",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1665318000439,
     "user_tz": -210,
     "elapsed": 7490397,
     "user": {
      "displayName": "arian ebrahimi",
      "userId": "00418818321983401320"
     }
    },
    "outputId": "d2d9d494-0222-425f-ff3a-50e01b33419f"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "100% 242/242 [05:39<00:00,  1.40s/it, acc=0.285, loss=2.38]\n",
      "100% 47/47 [00:25<00:00,  1.82it/s, acc=0.627]\n",
      "100% 242/242 [05:46<00:00,  1.43s/it, acc=0.643, loss=1.2]\n",
      "100% 47/47 [00:25<00:00,  1.82it/s, acc=0.718]\n",
      "100% 242/242 [05:46<00:00,  1.43s/it, acc=0.739, loss=0.848]\n",
      "100% 47/47 [00:25<00:00,  1.82it/s, acc=0.742]\n",
      "100% 242/242 [05:46<00:00,  1.43s/it, acc=0.797, loss=0.675]\n",
      "100% 47/47 [00:25<00:00,  1.83it/s, acc=0.742]\n",
      "100% 242/242 [05:46<00:00,  1.43s/it, acc=0.842, loss=0.544]\n",
      "100% 47/47 [00:25<00:00,  1.82it/s, acc=0.752]\n",
      "100% 242/242 [05:46<00:00,  1.43s/it, acc=0.872, loss=0.452]\n",
      "100% 47/47 [00:25<00:00,  1.82it/s, acc=0.742]\n",
      "100% 242/242 [05:45<00:00,  1.43s/it, acc=0.901, loss=0.354]\n",
      "100% 47/47 [00:25<00:00,  1.82it/s, acc=0.749]\n",
      "100% 242/242 [05:46<00:00,  1.43s/it, acc=0.926, loss=0.292]\n",
      "100% 47/47 [00:25<00:00,  1.82it/s, acc=0.746]\n",
      "100% 242/242 [05:46<00:00,  1.43s/it, acc=0.941, loss=0.236]\n",
      "100% 47/47 [00:25<00:00,  1.81it/s, acc=0.747]\n",
      "100% 242/242 [05:46<00:00,  1.43s/it, acc=0.951, loss=0.195]\n",
      "100% 47/47 [00:25<00:00,  1.82it/s, acc=0.748]\n",
      "100% 242/242 [05:46<00:00,  1.43s/it, acc=0.962, loss=0.162]\n",
      "100% 47/47 [00:25<00:00,  1.83it/s, acc=0.746]\n",
      "100% 242/242 [05:46<00:00,  1.43s/it, acc=0.969, loss=0.14]\n",
      "100% 47/47 [00:25<00:00,  1.83it/s, acc=0.744]\n",
      "100% 242/242 [05:46<00:00,  1.43s/it, acc=0.974, loss=0.118]\n",
      "100% 47/47 [00:25<00:00,  1.82it/s, acc=0.746]\n",
      "100% 242/242 [05:46<00:00,  1.43s/it, acc=0.978, loss=0.102]\n",
      "100% 47/47 [00:25<00:00,  1.82it/s, acc=0.746]\n",
      "100% 242/242 [05:46<00:00,  1.43s/it, acc=0.981, loss=0.0912]\n",
      "100% 47/47 [00:25<00:00,  1.82it/s, acc=0.74]\n",
      "100% 242/242 [05:46<00:00,  1.43s/it, acc=0.985, loss=0.081]\n",
      "100% 47/47 [00:25<00:00,  1.83it/s, acc=0.742]\n",
      "100% 242/242 [05:46<00:00,  1.43s/it, acc=0.986, loss=0.0736]\n",
      "100% 47/47 [00:25<00:00,  1.83it/s, acc=0.74]\n",
      "100% 242/242 [05:46<00:00,  1.43s/it, acc=0.986, loss=0.0724]\n",
      "100% 47/47 [00:25<00:00,  1.81it/s, acc=0.742]\n",
      "100% 242/242 [05:46<00:00,  1.43s/it, acc=0.99, loss=0.065]\n",
      "100% 47/47 [00:25<00:00,  1.82it/s, acc=0.742]\n",
      "100% 242/242 [05:46<00:00,  1.43s/it, acc=0.99, loss=0.0624]\n",
      "100% 47/47 [00:25<00:00,  1.82it/s, acc=0.739]\n",
      "100% 47/47 [00:25<00:00,  1.82it/s, acc=0.744]\n",
      "Test set results:\n",
      "Accuracy: 0.7444963308872582\n",
      "Micro precision: 0.7831612390786339\n",
      "Micro recall: 0.7919678714859437\n",
      "Micro F1: 0.7875399361022364\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": []
     },
     "metadata": {},
     "execution_count": 3
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "%%shell\n",
    "python train_supervised_bert.py \\\n",
    "    --pretrain_path HooshvareLab/bert-base-parsbert-uncased \\\n",
    "    --dataset none \\\n",
    "    --train_file ./Perlex/Perlex_train.txt \\\n",
    "    --val_file ./Perlex/Perlex_val.txt \\\n",
    "    --test_file ./Perlex/Perlex_test.txt \\\n",
    "    --rel2id_file ./Perlex/Perlex_rel2id.json \\\n",
    "    --max_epoch 20 \\\n",
    "    --lr 15e-6"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "bcdc6f55-f9b9-40b0-a661-777145267ede",
    "id": "zUa7CDEeFBGJ"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "100% 242/242 [05:38<00:00,  1.40s/it, acc=0.444, loss=1.84]\n",
      "100% 47/47 [00:25<00:00,  1.83it/s, acc=0.704]\n",
      "100% 242/242 [05:46<00:00,  1.43s/it, acc=0.733, loss=0.842]\n",
      "100% 47/47 [00:25<00:00,  1.82it/s, acc=0.753]\n",
      "100% 242/242 [05:46<00:00,  1.43s/it, acc=0.871, loss=0.432]\n",
      "100% 47/47 [00:25<00:00,  1.83it/s, acc=0.764]\n",
      "100% 242/242 [05:46<00:00,  1.43s/it, acc=0.942, loss=0.208]\n",
      "100% 47/47 [00:25<00:00,  1.82it/s, acc=0.747]\n",
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      "100% 47/47 [00:25<00:00,  1.81it/s, acc=0.75]\n",
      "100% 242/242 [05:46<00:00,  1.43s/it, acc=0.991, loss=0.0488]\n",
      "100% 47/47 [00:25<00:00,  1.81it/s, acc=0.737]\n",
      "100% 242/242 [05:46<00:00,  1.43s/it, acc=0.998, loss=0.0243]\n",
      "100% 47/47 [00:25<00:00,  1.82it/s, acc=0.746]\n",
      "100% 242/242 [05:45<00:00,  1.43s/it, acc=0.999, loss=0.0157]\n",
      "100% 47/47 [00:25<00:00,  1.82it/s, acc=0.747]\n",
      "100% 242/242 [05:45<00:00,  1.43s/it, acc=1, loss=0.0098]\n",
      "100% 47/47 [00:25<00:00,  1.83it/s, acc=0.753]\n",
      "100% 242/242 [05:45<00:00,  1.43s/it, acc=0.999, loss=0.00806]\n",
      "100% 47/47 [00:25<00:00,  1.82it/s, acc=0.751]\n",
      " 65% 157/242 [03:45<02:01,  1.43s/it, acc=0.999, loss=0.00759]"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "%%shell\n",
    "python train_supervised_bert.py \\\n",
    "    --pretrain_path HooshvareLab/bert-base-parsbert-uncased \\\n",
    "    --dataset none \\\n",
    "    --train_file ./Perlex/Perlex_train.txt \\\n",
    "    --val_file ./Perlex/Perlex_val.txt \\\n",
    "    --test_file ./Perlex/Perlex_test.txt \\\n",
    "    --rel2id_file ./Perlex/Perlex_rel2id.json \\\n",
    "    --max_epoch 20 \\\n",
    "    --lr 15e-6"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "spUwXoWnPchu",
    "outputId": "b1b389c8-c1b5-41bf-ee81-7dfc2cb5f8a0"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Downloading: 100% 654M/654M [00:09<00:00, 66.7MB/s]\n",
      "Downloading: 100% 1.22M/1.22M [00:01<00:00, 872kB/s]\n",
      "100% 242/242 [05:18<00:00,  1.32s/it, acc=0.444, loss=1.84]\n",
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      "100% 242/242 [05:29<00:00,  1.36s/it, acc=0.733, loss=0.842]\n",
      "100% 47/47 [00:23<00:00,  1.98it/s, acc=0.753]\n",
      "100% 242/242 [05:29<00:00,  1.36s/it, acc=0.871, loss=0.432]\n",
      "100% 47/47 [00:23<00:00,  1.99it/s, acc=0.764]\n",
      "100% 242/242 [05:29<00:00,  1.36s/it, acc=0.942, loss=0.208]\n",
      "100% 47/47 [00:23<00:00,  1.99it/s, acc=0.747]\n",
      "100% 242/242 [05:28<00:00,  1.36s/it, acc=0.979, loss=0.0991]\n",
      "100% 47/47 [00:23<00:00,  1.99it/s, acc=0.75]\n",
      "100% 242/242 [05:29<00:00,  1.36s/it, acc=0.991, loss=0.0488]\n",
      "100% 47/47 [00:23<00:00,  2.00it/s, acc=0.737]\n",
      " 31% 75/242 [01:43<03:47,  1.36s/it, acc=0.998, loss=0.0257]"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "%%shell\n",
    "python train_supervised_bert.py \\\n",
    "    --pretrain_path HooshvareLab/bert-base-parsbert-uncased \\\n",
    "    --dataset none \\\n",
    "    --train_file ./Perlex/Perlex_train.txt \\\n",
    "    --val_file ./Perlex/Perlex_val.txt \\\n",
    "    --test_file ./Perlex/Perlex_test.txt \\\n",
    "    --rel2id_file ./Perlex/Perlex_rel2id.json \\\n",
    "    --max_epoch 20 \\\n",
    "    --lr 15e-6"
   ],
   "metadata": {
    "id": "_lWuRi4EuuV4",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "status": "ok",
     "timestamp": 1668347083563,
     "user_tz": -210,
     "elapsed": 441647,
     "user": {
      "displayName": "Mohammad Ebrahimi",
      "userId": "10407139745331958037"
     }
    },
    "outputId": "4080fb9e-cf2c-498c-a412-a8ba1b44ad74"
   },
   "execution_count": 3,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "100% 101/101 [02:00<00:00,  1.20s/it, acc=0.278, loss=2.4]\n",
      "100% 25/25 [00:12<00:00,  2.06it/s, acc=0.541]\n",
      "100% 101/101 [02:11<00:00,  1.30s/it, acc=0.667, loss=1.15]\n",
      "100% 25/25 [00:12<00:00,  1.97it/s, acc=0.615]\n",
      "100% 101/101 [02:12<00:00,  1.31s/it, acc=0.784, loss=0.721]\n",
      "100% 25/25 [00:12<00:00,  1.99it/s, acc=0.66]\n",
      "100% 101/101 [02:12<00:00,  1.31s/it, acc=0.879, loss=0.422]\n",
      "100% 25/25 [00:12<00:00,  2.00it/s, acc=0.656]\n",
      "100% 101/101 [02:12<00:00,  1.31s/it, acc=0.947, loss=0.209]\n",
      "100% 25/25 [00:12<00:00,  1.98it/s, acc=0.659]\n",
      "100% 101/101 [02:12<00:00,  1.31s/it, acc=0.981, loss=0.0976]\n",
      "100% 25/25 [00:12<00:00,  1.97it/s, acc=0.655]\n",
      "100% 101/101 [02:12<00:00,  1.31s/it, acc=0.99, loss=0.0528]\n",
      "100% 25/25 [00:12<00:00,  1.98it/s, acc=0.655]\n",
      "100% 101/101 [02:12<00:00,  1.31s/it, acc=0.997, loss=0.0308]\n",
      "100% 25/25 [00:12<00:00,  1.96it/s, acc=0.659]\n",
      "100% 101/101 [02:12<00:00,  1.31s/it, acc=0.998, loss=0.0199]\n",
      "100% 25/25 [00:12<00:00,  1.98it/s, acc=0.657]\n",
      "100% 101/101 [02:12<00:00,  1.31s/it, acc=0.999, loss=0.0145]\n",
      "100% 25/25 [00:12<00:00,  1.93it/s, acc=0.659]\n",
      "100% 101/101 [02:12<00:00,  1.31s/it, acc=1, loss=0.0111]\n",
      "100% 25/25 [00:12<00:00,  1.97it/s, acc=0.653]\n",
      "100% 101/101 [02:12<00:00,  1.31s/it, acc=1, loss=0.00921]\n",
      "100% 25/25 [00:12<00:00,  1.98it/s, acc=0.658]\n",
      "100% 101/101 [02:12<00:00,  1.31s/it, acc=1, loss=0.00797]\n",
      "100% 25/25 [00:13<00:00,  1.89it/s, acc=0.659]\n",
      "100% 101/101 [02:12<00:00,  1.31s/it, acc=1, loss=0.00623]\n",
      "100% 25/25 [00:12<00:00,  1.97it/s, acc=0.655]\n",
      "100% 101/101 [02:12<00:00,  1.31s/it, acc=1, loss=0.00641]\n",
      "100% 25/25 [00:12<00:00,  1.97it/s, acc=0.65]\n",
      "100% 101/101 [02:11<00:00,  1.31s/it, acc=1, loss=0.00551]\n",
      "100% 25/25 [00:12<00:00,  1.99it/s, acc=0.655]\n",
      "100% 101/101 [02:12<00:00,  1.31s/it, acc=1, loss=0.00564]\n",
      "100% 25/25 [00:12<00:00,  1.99it/s, acc=0.655]\n",
      "100% 101/101 [02:12<00:00,  1.31s/it, acc=1, loss=0.00474]\n",
      "100% 25/25 [00:12<00:00,  1.93it/s, acc=0.654]\n",
      "100% 101/101 [02:12<00:00,  1.31s/it, acc=0.999, loss=0.00528]\n",
      "100% 25/25 [00:12<00:00,  1.95it/s, acc=0.655]\n",
      "100% 101/101 [02:11<00:00,  1.31s/it, acc=1, loss=0.00506]\n",
      "100% 25/25 [00:12<00:00,  1.98it/s, acc=0.653]\n",
      "100% 43/43 [00:21<00:00,  2.04it/s, acc=0.725]\n",
      "Test set results:\n",
      "Accuracy: 0.7245949926362297\n",
      "Micro precision: 0.7602262837249782\n",
      "Micro recall: 0.7723253757736517\n",
      "Micro F1: 0.7662280701754387\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": []
     },
     "metadata": {},
     "execution_count": 3
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [],
   "metadata": {
    "id": "K8ArBYUg1gmV"
   },
   "execution_count": null,
   "outputs": []
  }
 ]
}