examples/scoring_example.ipynb

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   "metadata": {
    "id": "UnvyQbdBJemf"
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   "source": [
    "!pip install -U transformers datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%cd ../"
   ]
  },
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   "metadata": {
    "id": "4RE2mA8ALDFS"
   },
   "source": [
    "## An example for scoring an encoder LM on RuBLiMP\n",
    "\n",
    "Here, we show how to score an encoder and decoder LM not decontaminated with RuBLiMP (that is, an external LM of interest) using a dataset sample.\n"
   ]
  },
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      "text/plain": [
       "       id                        source_sentence  \\\n",
       "0  132968  Руками обхватила широкие плечи Окота.   \n",
       "1  211801        Под левый сосок в междуреберье.   \n",
       "2  168527         Спуск темною ночью в Никиту...   \n",
       "\n",
       "                            target_sentence source_word target_word  \\\n",
       "0  Руками обхватила широкие плечники Окота.       плечи    плечники   \n",
       "1        Под левый сосокщик в междуреберье.       сосок    сосокщик   \n",
       "2        Спуск темоватною ночью в Никиту...      темною  темоватною   \n",
       "\n",
       "        level      phenomenon             PID         subtype    domain  \\\n",
       "0  Morphology  Word Formation  add_new_suffix  add_new_suffix  librusec   \n",
       "1  Morphology  Word Formation  add_new_suffix  add_new_suffix  librusec   \n",
       "2  Morphology  Word Formation  add_new_suffix  add_new_suffix  librusec   \n",
       "\n",
       "   tree_length  \n",
       "0            2  \n",
       "1            2  \n",
       "2            3  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "\n",
    "add_new_suffix = load_dataset(\"RussianNLP/rublimp\", \"add_new_suffix\")\n",
    "add_new_suffix = add_new_suffix[\"train\"].to_pandas()\n",
    "add_new_suffix.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "j2hZbT5zLeCD"
   },
   "outputs": [],
   "source": [
    "from src.scorer import *\n",
    "\n",
    "\n",
    "encoder_model_name = \"DeepPavlov/rubert-base-cased\"\n",
    "encoder_scorer = Scorer(model_name=encoder_model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "XRsR_95DL9Py"
   },
   "outputs": [],
   "source": [
    "# scoring a dataset sample for illustration\n",
    "sample = encoder_scorer.run(pool=add_new_suffix.sample(25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9Q5HJY-1MM5L",
    "outputId": "4dcbf793-13c2-4404-f72e-dc3f856c1947"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.84"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# accuracy on the dataset sample\n",
    "(\n",
    "    sample[\"DeepPavlov-rubert-base-cased-ppl-s\"]\n",
    "    > sample[\"DeepPavlov-rubert-base-cased-ppl-t\"]\n",
    ").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "_tNrvcvrKIHi"
   },
   "outputs": [],
   "source": [
    "decoder_model_name = \"Vikhrmodels/Vikhr-7b-0.1\"\n",
    "decoder_scorer = Scorer(model_name=decoder_model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "yNsU2ssPNvS0"
   },
   "outputs": [],
   "source": [
    "# scoring a dataset sample for illustration\n",
    "sample = decoder_scorer.run(pool=add_new_suffix.sample(25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BeZr5vbVO1zx",
    "outputId": "eecfc2a8-08bd-49dd-8847-987f41681989"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# accuracy on the dataset sample\n",
    "(\n",
    "    sample[\"Vikhrmodels-Vikhr-7b-0.1-ppl-s\"] < sample[\"Vikhrmodels-Vikhr-7b-0.1-ppl-t\"]\n",
    ").mean()"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "gpuType": "A100",
   "machine_shape": "hm",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  },
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}