demo/SBert example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qYJbw_-Fv1TH"
   },
   "source": [
    "## Download & install packages and models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "executionInfo": {
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     "status": "ok",
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      "userId": "17569698098126794188"
     },
     "user_tz": -120
    },
    "id": "EiZKkBZXvbzk"
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('..')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "executionInfo": {
     "elapsed": 7130,
     "status": "ok",
     "timestamp": 1650389583933,
     "user": {
      "displayName": "Ulf Hamster",
      "userId": "17569698098126794188"
     },
     "user_tz": -120
    },
    "id": "sTjeYBFGuvwM"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/uh/projects/paper-293/sentence-embedding-evaluation-german/.venv/lib/python3.7/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from typing import List\n",
    "import sentence_embedding_evaluation_german as seeg\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "executionInfo": {
     "elapsed": 9064,
     "status": "ok",
     "timestamp": 1650389592953,
     "user": {
      "displayName": "Ulf Hamster",
      "userId": "17569698098126794188"
     },
     "user_tz": -120
    },
    "id": "af7fcl2HvsjY"
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "!pip install sentence_transformers"
   ]
  },
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    "executionInfo": {
     "elapsed": 24721,
     "status": "ok",
     "timestamp": 1650389617624,
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      "displayName": "Ulf Hamster",
      "userId": "17569698098126794188"
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    "id": "kww3gDvUwXDE",
    "outputId": "475143d1-4f12-4c1b-db6f-91ed7f2ce78b"
   },
   "outputs": [],
   "source": [
    "import sentence_transformers as sbert\n",
    "model_sbert = sbert.SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bLKzlBlrv7fW"
   },
   "source": [
    "## Specify & run sentence embedding evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "D0rKxkCCv0gj"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2h 30min 45s, sys: 8min 27s, total: 2h 39min 13s\n",
      "Wall time: 2h 31min 27s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# (1) Instantiate your Embedding model\n",
    "model_sbert = sbert.SentenceTransformer(\n",
    "    'paraphrase-multilingual-MiniLM-L12-v2')\n",
    "\n",
    "# (2) Specify the preprocessing\n",
    "def preprocesser(batch: List[str], params: dict=None) -> List[List[float]]:\n",
    "    \"\"\" Specify your embedding or pretrained encoder here\n",
    "    Paramters:\n",
    "    ----------\n",
    "    params : dict\n",
    "        The params dictionary\n",
    "    batch : List[str]\n",
    "        A list of sentence as string\n",
    "    Returns:\n",
    "    --------\n",
    "    List[List[float]]\n",
    "        A list of embedding vectors\n",
    "    \"\"\"\n",
    "    features = model_sbert.encode(batch)\n",
    "    return features\n",
    "\n",
    "# (3) Training settings\n",
    "params = {\n",
    "    'datafolder': '../datasets',\n",
    "    'bias': True,\n",
    "    'balanced': True,\n",
    "    'batch_size': 128, \n",
    "    'num_epochs': 500,\n",
    "    # 'early_stopping': True,\n",
    "    # 'split_ratio': 0.1,  # if early_stopping=True\n",
    "    # 'patience': 10,  # if early_stopping=True\n",
    "}\n",
    "\n",
    "# (4) Specify downstream tasks\n",
    "downstream_tasks = ['FCLAIM', 'VMWE', 'OL19-C', 'ABSD-2', 'MIO-P', 'ARCHI', 'LSDC']\n",
    "\n",
    "# (5) Run experiments\n",
    "results = seeg.evaluate(downstream_tasks, preprocesser, **params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "Uc91u4dZ0qWn"
   },
   "outputs": [],
   "source": [
    "# save results\n",
    "import json\n",
    "with open(\"seeg-results-sbert.json\", 'w') as fp:\n",
    "    json.dump(results, fp, indent=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pLr2z5YEyCd0"
   },
   "source": [
    "## Display the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "eUomhnL1wz8J"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Task | Epochs | N train | N test\n",
      " FCLAIM:   500   3244    944\n",
      "   VMWE:   500   6652   1447\n",
      " OL19-C:   500   1921    930\n",
      " ABSD-2:   500  19432   2555\n",
      "  MIO-P:   500   4668   4668\n",
      "  ARCHI:   500  18809   4743\n",
      "   LSDC:   500  74140   8602\n"
     ]
    }
   ],
   "source": [
    "print(\"Task | Epochs | N train | N test\")\n",
    "for res in results:\n",
    "    print(f\"{res['task']:>7s}: {res['epochs']:5d} {res['train']['num']:6d} {res['test']['num']:6d}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "l8FEvnsZyUKB"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Task | train | test\n",
      " FCLAIM:  0.768  0.708\n",
      "   VMWE:  0.854  0.830\n",
      " OL19-C:  0.789  0.727\n",
      " ABSD-2:  0.664  0.633\n",
      "  MIO-P:  0.900  0.878\n",
      "  ARCHI:  0.476  0.377\n",
      "   LSDC:  0.580  0.504\n"
     ]
    }
   ],
   "source": [
    "metric = 'acc'  # 'acc', 'acc-balanced'\n",
    "print('  Task | train | test')\n",
    "for res in results:\n",
    "    print(f\"{res['task']:>7s}: {res['train'][metric]:6.3f} {res['test'][metric]:6.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Task | train | test\n",
      " FCLAIM:  0.765  0.672\n",
      "   VMWE:  0.826  0.800\n",
      " OL19-C:  0.815  0.685\n",
      " ABSD-2:  0.705  0.617\n",
      "  MIO-P:  0.918  0.884\n",
      "  ARCHI:  0.475  0.377\n",
      "   LSDC:  0.498  0.418\n"
     ]
    }
   ],
   "source": [
    "metric = 'acc-balanced'  # 'acc', 'acc-balanced'\n",
    "print('  Task | train | test')\n",
    "for res in results:\n",
    "    print(f\"{res['task']:>7s}: {res['train'][metric]:6.3f} {res['test'][metric]:6.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Task | train | test\n",
      " FCLAIM:  0.768  0.708\n",
      "   VMWE:  0.854  0.830\n",
      " OL19-C:  0.789  0.727\n",
      " ABSD-2:  0.664  0.633\n",
      "  MIO-P:  0.900  0.878\n",
      "  ARCHI:  0.476  0.377\n",
      "   LSDC:  0.580  0.504\n"
     ]
    }
   ],
   "source": [
    "metric = 'f1'  # 'f1', 'f1-balanced'\n",
    "print('  Task | train | test')\n",
    "for res in results:\n",
    "    print(f\"{res['task']:>7s}: {res['train'][metric]:6.3f} {res['test'][metric]:6.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "-9pvzgqmyPfj"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Task | train | test\n",
      " FCLAIM:  0.752  0.672\n",
      "   VMWE:  0.778  0.751\n",
      " OL19-C:  0.692  0.611\n",
      " ABSD-2:  0.584  0.521\n",
      "  MIO-P:  0.852  0.820\n",
      "  ARCHI:  0.474  0.374\n",
      "   LSDC:  0.367  0.396\n"
     ]
    }
   ],
   "source": [
    "metric = 'f1-balanced'  # 'f1', 'f1-balanced'\n",
    "print('  Task | train | test')\n",
    "for res in results:\n",
    "    print(f\"{res['task']:>7s}: {res['train'][metric]:6.3f} {res['test'][metric]:6.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "rg_m8DuZycuN"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FCLAIM 944\n",
      "{'0': '630', '1': '314'}\n",
      "{'0': '626', '1': '318'}\n",
      "VMWE 1447\n",
      "{'1': '256', '0': '1191'}\n",
      "{'1': '376', '0': '1071'}\n",
      "OL19-C 930\n",
      "{'0': '796', '1': '134'}\n",
      "{'0': '642', '1': '288'}\n",
      "ABSD-2 2555\n",
      "{'1': '1670', '0': '780', '2': '105'}\n",
      "{'0': '983', '1': '1189', '2': '383'}\n",
      "MIO-P 4668\n",
      "{'1': '812', '0': '3856'}\n",
      "{'1': '1208', '0': '3460'}\n",
      "ARCHI 4743\n",
      "{'2': '1177', '3': '1176', '1': '1199', '0': '1191'}\n",
      "{'2': '1442', '3': '1111', '1': '887', '0': '1303'}\n",
      "LSDC 8602\n",
      "{'13': '280', '6': '346', '12': '940', '3': '925', '2': '944', '1': '934', '11': '931', '0': '453', '10': '511', '5': '924', '4': '65', '8': '923', '9': '83', '7': '343'}\n",
      "{'8': '942', '1': '805', '3': '989', '12': '994', '2': '998', '0': '549', '10': '631', '11': '984', '5': '982', '13': '347', '9': '240', '7': '141'}\n"
     ]
    }
   ],
   "source": [
    "# class label distributions (inference)\n",
    "for res in results:\n",
    "    print(res['task'], res['test']['num'])\n",
    "    print(res['test']['distr-test'])\n",
    "    print(res['test']['distr-pred'])"
   ]
  },
  {
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