notebooks/Distilling_Zero_Shot_multilingual_distilbert_sentiments_student.ipynb

{
 "cells": [
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eGgfoKVYnXPk",
    "outputId": "6d3e842d-e2e8-4a0a-cdf5-a0a6707d02ad"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "!git clone https://github.com/huggingface/transformers.git\n",
    "!pip install transformers\n",
    "!pip install datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kXRF4c7oRCAN",
    "outputId": "aa014d00-5876-441e-e30c-d0abd0424319",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: sentencepiece in ./env/lib/python3.10/site-packages (0.1.99)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1.2\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install sentencepiece"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install protobuf==3.20.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wMrUSGMI5MXZ"
   },
   "source": [
    "This notebook demonstrates how to use a script that provides a way to improve the speed and memory performance of a zero-shot classifier by training a more efficient student model from the zero-shot teacher's predictions over an unlabeled dataset.\n",
    "\n",
    "For a given sequence, the zero-shot classification pipeline requires each possible label to be fed through the large NLI model separately. This requirement slows results considerably, particularly for tasks with a large number of classes K.\n",
    "\n",
    "We'll use the `tyqiangz/multilingual-sentiments` dataset for this example."
   ]
  },
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      "5b159b85a351442d8d0f3fe9ba0cfc2f",
      "faadb68bf74349cb846b7b1aff6fc04c",
      "19ba05e98d7e45a4b17b1c73014cdd43",
      "0b7088fa1b8a41c4bddfe10628cba8dc",
      "6a0c6171d5b34795b5fd266381305f88",
      "053ef9d165d34e5cb2c23c1885b7a073",
      "f7ea62af13cc4d3489a435cf61a55d9f",
      "186d6566cd204cd2991ee9ac5432d7a8",
      "a3afa2670dde4643853de17074cdf514",
      "53c4c38b300f41599d2745de56243427",
      "cdc89981db774602a1e3ae3a9eb026f5",
      "5bcee21bc1474fdc8287f52a1e268061",
      "322c6a9631014dc6a06f1d9bcfa3574a",
      "140b71e1e16d45329a1f04fa2234c50a",
      "18e3e6d34b0346159d9913783c424e7f",
      "fa9391cc78b64f23ae99146e294032df",
      "e55d65f89e9449e3ba4cb3a225f32d4c",
      "def4f6e652934e988803dc12ce5ff489",
      "fb74be8502454d658fb1a27ae9093083"
     ]
    },
    "id": "gUCM704I5u6E",
    "outputId": "74d6d760-a5d8-40e3-f21b-067eb66801c7",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lxyuan/playground/llm/env/lib64/python3.10/site-packages/tqdm/auto.py:21: 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",
      "Found cached dataset multilingual-sentiments (/home/lxyuan/.cache/huggingface/datasets/tyqiangz___multilingual-sentiments/all/1.0.0/b7cdd8874d82679e59432edf79e074f595c4ad26d2e562eba4fb55f361691b07)\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 301.07it/s]\n"
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "train, test = load_dataset(\"tyqiangz/multilingual-sentiments\", \"all\", split=['train', 'test'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RBT9ulvsN9Fw",
    "outputId": "94b9a5b4-c79a-46ee-d2b5-0c9d000689e0",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['text', 'source', 'language', 'label'],\n",
       "    num_rows: 270399\n",
       "})"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xZvliTREQeFa",
    "outputId": "0db8888b-25c9-4d77-aefe-4441062aed3f",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['text', 'source', 'language', 'label'],\n",
       "    num_rows: 14465\n",
       "})"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "h3h7sckhOdGD",
    "outputId": "3221673b-55f4-4a45-8055-ac014f6ea8aa",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'arabic',\n",
       " 'chinese',\n",
       " 'english',\n",
       " 'french',\n",
       " 'german',\n",
       " 'hindi',\n",
       " 'indonesian',\n",
       " 'italian',\n",
       " 'japanese',\n",
       " 'malay',\n",
       " 'portuguese',\n",
       " 'spanish'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_lang = set(train[\"language\"])\n",
    "unique_lang"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17,
     "referenced_widgets": [
      "397cfe0008fe4cdaa6b53e5378714d33",
      "314cb9fb88f64caebbe3d777227487c7",
      "990723f3bdec470c83ff1c60e46c50ed",
      "1634ba55065b4feaaa98adde2e2a3538",
      "e055cfe8c1e046dcb32f1751341fd580",
      "64d11d9a90664c60948e115b7c8e7a45",
      "d4d5bbf9a9364cee85a25b9bc5c7a8f7",
      "75663ef45045407aa962440eebbd7820",
      "461430490e9846909c509b75ff3f1661",
      "969757cd4a9b431d931e548e7e88bcaa",
      "4ab2951242e84ac891b9cae7f4be31cf",
      "6fd0d9f95ed847bfae78dffbac40d710",
      "8d5d8e308c6847139e23405b2490e0fd",
      "fdd40489a2a54c1da74a5824b5138ca8",
      "a8657f3cfb1249318a07f2736eafb84f",
      "3d071e3f42b9441886ed5a165121d9f5",
      "c8b3adfbee994104a05ee178d653604c",
      "03d2136346004b738cdebba6645a1f36",
      "c865519d95c547c28dc0fd3237605868",
      "0c2af95e5064429581c886477f058faa",
      "1d5a65ca3a384c98b3d61fb7e75715ce",
      "6aebc424804548e7b390829a81fbd560"
     ]
    },
    "id": "Kx3rFWhPOB94",
    "outputId": "5a30a4ac-2d5e-4a57-a55e-7e13363f9916",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading cached processed dataset at /home/lxyuan/.cache/huggingface/datasets/tyqiangz___multilingual-sentiments/all/1.0.0/b7cdd8874d82679e59432edf79e074f595c4ad26d2e562eba4fb55f361691b07/cache-c1f1d041f22604ae.arrow\n",
      "Loading cached processed dataset at /home/lxyuan/.cache/huggingface/datasets/tyqiangz___multilingual-sentiments/all/1.0.0/b7cdd8874d82679e59432edf79e074f595c4ad26d2e562eba4fb55f361691b07/cache-1d835284274eae4f.arrow\n"
     ]
    }
   ],
   "source": [
    "filtered_train = train.filter(lambda example: example[\"language\"] not in [\"italian\", \"japanese\", \"portuguese\"])\n",
    "filtered_test = test.filter(lambda example: example[\"language\"] not in [\"italian\", \"japanese\", \"portuguese\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lw564R0uQKr7",
    "outputId": "1d8b8204-e7ed-477d-ed32-18de3949d2a8",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['text', 'source', 'language', 'label'],\n",
       "    num_rows: 146721\n",
       "})"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XVaaMwEwQbh1",
    "outputId": "54288b7f-89dc-4045-805b-6710fb9be846",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['text', 'source', 'language', 'label'],\n",
       "    num_rows: 9725\n",
       "})"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "G349Ul8NQs6L",
    "outputId": "577e8f2e-eebc-4f38-c16e-e0096786b654",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ClassLabel(names=['positive', 'neutral', 'negative'], id=None)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_train.features[\"label\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SMZcx0dz61ef"
   },
   "source": [
    "### 🤗 Zero-shot classification pipeline\n",
    "\n",
    "The [zero-shot classification pipeline](https://huggingface.co/transformers/main_classes/pipelines.html#transformers.ZeroShotClassificationPipeline) is a tool withing 🤗 Transformers that can be used to classify text sequences out of the box, provided only a list of possible class names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZbaEZ1nA69_8",
    "outputId": "d3ac1c82-e4ee-4d8a-c4fa-d0102edb249b",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lxyuan/playground/llm/env/lib64/python3.10/site-packages/transformers/convert_slow_tokenizer.py:454: UserWarning: The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option which is not implemented in the fast tokenizers. In practice this means that the fast version of the tokenizer can produce unknown tokens whereas the sentencepiece version would have converted these unknown tokens into a sequence of byte tokens matching the original piece of text.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from transformers import pipeline\n",
    "zero_shot_classifier = pipeline('zero-shot-classification', model=\"MoritzLaurer/mDeBERTa-v3-base-mnli-xnli\", device=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "y8_z-awr9LEb",
    "outputId": "e0b85041-1977-4005-83a7-729abc44c813"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sequence': 'I love this movie and i would watch it again and again!',\n",
       " 'labels': ['positive', 'neutral', 'negative'],\n",
       " 'scores': [0.9610546827316284, 0.029341870918869972, 0.009603399783372879]}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sequence = \"I love this movie and i would watch it again and again!\"\n",
    "class_names = [\"positive\", \"neutral\", \"negative\"]\n",
    "zero_shot_classifier(sequence, class_names, hypothesis_template=\"The sentiment of this text is {}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "R0MFPz4_RlQO",
    "outputId": "64fba6a2-881a-40bf-df70-98046a649c40"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sequence': '我喜欢这部电影，我会一遍又一遍地看！',\n",
       " 'labels': ['positive', 'neutral', 'negative'],\n",
       " 'scores': [0.9576952457427979, 0.031247487291693687, 0.011057236231863499]}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sequence = \"我喜欢这部电影，我会一遍又一遍地看！\"\n",
    "class_names = [\"positive\", \"neutral\", \"negative\"]\n",
    "zero_shot_classifier(sequence, class_names, hypothesis_template=\"The sentiment of this text is {}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FMtvm8ZyTkfY",
    "outputId": "56a34883-605b-49da-8461-a68e65e79a72"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "278811651"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zero_shot_classifier.model.num_parameters() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AB-0v4ep84vv"
   },
   "source": [
    "This method serves as a convenient out-of-the-box classifier. Unfortunately, the method is by necessity somewhat slow. This is partially due to the large underlying model being used, but more important is the fact that for this method to work, every possible sequence / class name pair must be fed through the model together. So in order to classify `N` sequences into `K` classes, the model has to be called `N*K` times (whereas a typical classifier would only be called `N` times). This makes the method comparatively slow, especially for settings with a large number of classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7h2uvu0F90_9",
    "outputId": "48e2778a-4812-46b0-bee4-5615d7c8da50"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1070: 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": [
      "CPU times: user 1min 44s, sys: 363 ms, total: 1min 44s\n",
      "Wall time: 1min 46s\n"
     ]
    }
   ],
   "source": [
    "# classify 1600 examples with K=4 classes\n",
    "%%time\n",
    "for _ in range(100):\n",
    "    zero_shot_classifier([sequence] * 16, class_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5-rBuO1_973y",
    "outputId": "fe2930ef-0f89-4036-b1a5-1fbccf7bfd2f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3min 56s, sys: 624 ms, total: 3min 57s\n",
      "Wall time: 4min 4s\n"
     ]
    }
   ],
   "source": [
    "# classify 1600 examples with K=8 classes\n",
    "%%time\n",
    "expanded_class_names = class_names + [\"politics\", \"health\", \"food\", \"weather\"]\n",
    "for _ in range(100):\n",
    "    zero_shot_classifier([sequence] * 16, expanded_class_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yTnrdYNI-zR5"
   },
   "source": [
    "As we can see, increasing the number of classes from `K=4` to `K=8` approximately doubles the inference time. This classification method is extremely useful, but ideally we'd like to speed up inference."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QwQz_FPB_K4j"
   },
   "source": [
    "### Distilling a more efficient student model\n",
    "\n",
    "The best way to speed up inference is to **train a more efficient student model on the zero-shot classifier's predictions** over an unlabeled dataset. This can be done with the [`distill_classifier.py`](https://github.com/huggingface/transformers/blob/master/examples/research_projects/zero-shot-distillation/distill_classifier.py) script provided in the `transformers` repo.\n",
    "\n",
    "Given (1) an unlabeled corpus and (2) a set of `K` class names, this script allows a user to train a standard classification head with `K` output dimensions. The script generates a softmax distribution for the provided data & class names, and a student classifier is then fine-tuned on these proxy labels. The resulting student model can be used for classifying novel text instances into the previously specified `K` classes with an order-of-magnitude boost in inference speed plus decreased memory usage.\n",
    "\n",
    "Let's see how to do this with the [tyqiangz/multilingual-sentiments](https://huggingface.co/datasets/tyqiangz/multilingual-sentiments)[AG's News](https://huggingface.co/datasets/ag_news) topic classification dataset. The first thing we need is an unlabeled dataset (in reality the multilingual-sentiment dataset is annotated of course, but we'll pretend and ignore the annotations for the sake of example). Let's put the sequences from the train set into a `txt` file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "xykweYIWoy-6",
    "tags": []
   },
   "outputs": [],
   "source": [
    "!mkdir multilingual-sentiments\n",
    "with open(\"multilingual-sentiments/train_unlabeled.txt\", 'w') as f:\n",
    "    for seq in filtered_train[\"text\"]:\n",
    "        f.write(seq + '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B8zXcKPqApoG"
   },
   "source": [
    "The other thing the script needs is the names of the classes. We'll put these into their own newline-delimitted `txt` as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "6Gn5PidvAkHQ",
    "tags": []
   },
   "outputs": [],
   "source": [
    "class_names = ['positive', 'neutral', 'negative']\n",
    "with open(\"multilingual-sentiments/class_names.txt\", 'w') as f:\n",
    "    for label in class_names:\n",
    "        f.write(label + '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d14RrB4iTMsn",
    "outputId": "2bbded7a-9f3e-4d1c-add0-97169068ac97",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yang memerlukan pemerhatian dan tindakan serius\n",
      "sentiasa memikirkan dan merancang inisiatif bagi menambah baik sistem penyampaian kerajaan kepada rakyat\n",
      "Kita akan tengok daripada pelbagai aspek supaya akhirnya hak rakyat dapat dikembalikan, itu fokus kita pada tahun ini selaras dengan arahan pucuk pimpinan SPRM\n",
      "justeru asean perlu mengambil tindakan sebagaimana dianjurkan oleh malaysia\n",
      "@_Niiar_ Jangan punah dulu, aku belum ke labuan bajo\n",
      "cat: write error: Broken pipe\n"
     ]
    }
   ],
   "source": [
    "!cat multilingual-sentiments/train_unlabeled.txt | head -5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mDhpWtGlTQey",
    "outputId": "771110c5-e49b-4a20-f75f-937dc1f87e54",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "positive\n",
      "neutral\n",
      "negative\n"
     ]
    }
   ],
   "source": [
    "!cat multilingual-sentiments/class_names.txt | head -5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0A11PttrBZgf"
   },
   "source": [
    "Now we can run the script. First the zero-shot model will loop through the data and generate (soft) proxy-labels, and then a student `Multilinguai DistilBert` model will be fine-tuned on these predictions. The student will then be saved in `./distilbert-base-multilingual-cased-sentiments-student/`. See the [script readme](https://github.com/huggingface/transformers/blob/master/examples/research_projects/zero-shot-distillation/README.md) for more information about the available script arguments.\n",
    "\n",
    "On a single P100, this will take about ~2 hours with the full training set of 130K examples. On a V100 with mixed precision (just pass `--fp16`), it will take ~30 minutes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pkRwzNUZma_H"
   },
   "source": [
    "---\n",
    "Code changes:\n",
    "\n",
    "###### modify L78 to disable fast tokenizer \n",
    "```python\n",
    "default=False,\n",
    "```\n",
    "\n",
    "###### update dataset map part at L313\n",
    "```python\n",
    "dataset = dataset.map(tokenizer, input_columns=\"text\", fn_kwargs={\"padding\": \"max_length\", \"truncation\": True, \"max_length\": 512})\n",
    "```\n",
    "\n",
    "###### add following lines to L213\n",
    "```python\n",
    "del model\n",
    "print(f\"Manually deleted Teacher model\")\n",
    "```\n",
    "\n",
    "###### add following lines to L337\n",
    "```python\n",
    "trainer.push_to_hub()\n",
    "tokenizer.push_to_hub(\"distilbert-base-multilingual-cased-sentiments-student\")\n",
    "```\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 145,
     "referenced_widgets": [
      "34ff1db6ebe948e9a85a4b5152c0efe8",
      "708ed7a8ab88484289f239cbe06411f9",
      "79d71872ae0242f3bc7abe870a9f2c36",
      "35a80cf83c014549933c645562ed747d",
      "04af288659384f7db6533afcbbfadac7",
      "00254196e0a54c428a4c5cc6a3dca664",
      "89e568b4558f4197952006ebd6b105a9",
      "7fa04f07899c4e7882e7467e284ccb96",
      "9fad2f57efc14aada79588df0660d85b",
      "3897f6abea4a4ddeb33fa740e94eeed6",
      "fb6ce2f22e254616b12de859915d4fd8",
      "31d84ec1bdd34954877518f2acd93fb9",
      "6e9cd99d3d6f40f5a1c4c84358a7a472",
      "f26c8705126a495eab83a2eb1efd22ab",
      "4e3d2ee9567847e4ad90586b89d818f2",
      "c9a4f32fe94b43b7ab0891e51e5cbbaa",
      "8b05c65a7cf24ecd8c0f68f8206a400b",
      "56fdf245239e4d4bb41075dfd952d28e",
      "b038a651013c4b478d730c45059c6b7a",
      "16efd5e998274315a032189220dc0d61",
      "4aa3b85d3d3e400386932d006e71b07b",
      "7e346a2f5394439889abd8bcc7ca76ba",
      "7b3149ce1e0a4c13ab825f249d40f342",
      "2df31fdb2f714a77a64a242513ad09ac",
      "ebf2e2a87e214ea59f40e14bc4d45df3",
      "86274b2682414bcd8ed625569efc0616",
      "80908e617bbc421abff92bc95fb02a65",
      "41b2bb70cd1a4cf8806c2b931329f405",
      "dbf6e0302e9a44148310cf84258ce6ad",
      "c7a0d1a14de847b29aea9b36866f98f1",
      "aa0f22c0be7d436f8a1b00df4552b729",
      "f45242bee48b4bfc87003a52a1dfbb57"
     ]
    },
    "id": "rSlJKVo2oXPP",
    "outputId": "09140326-8c9e-4284-b6b6-d053c5b12b82"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "34ff1db6ebe948e9a85a4b5152c0efe8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from huggingface_hub import notebook_login\n",
    "\n",
    "notebook_login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "i2wRLxIsowzY",
    "outputId": "faa940ea-9523-46e9-d2cb-63b0d95f098b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading package lists... Done\n",
      "Building dependency tree       \n",
      "Reading state information... Done\n",
      "git-lfs is already the newest version (2.9.2-1).\n",
      "0 upgraded, 0 newly installed, 0 to remove and 24 not upgraded.\n"
     ]
    }
   ],
   "source": [
    "!apt install git-lfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ECt06ndcnpyb",
    "outputId": "6a978fad-1cb1-4bf0-cf72-e2adb8935f35",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "05/06/2023 10:05:30 - WARNING - __main__ - Process rank: -1, device: cuda:0, n_gpu: 1distributed training: False, 16-bits training: True\n",
      "05/06/2023 10:05:30 - INFO - __main__ - Training/evaluation parameters DistillTrainingArguments(\n",
      "_n_gpu=1,\n",
      "adafactor=False,\n",
      "adam_beta1=0.9,\n",
      "adam_beta2=0.999,\n",
      "adam_epsilon=1e-08,\n",
      "auto_find_batch_size=False,\n",
      "bf16=False,\n",
      "bf16_full_eval=False,\n",
      "data_seed=None,\n",
      "dataloader_drop_last=False,\n",
      "dataloader_num_workers=0,\n",
      "dataloader_pin_memory=True,\n",
      "ddp_bucket_cap_mb=None,\n",
      "ddp_find_unused_parameters=None,\n",
      "ddp_timeout=1800,\n",
      "debug=[],\n",
      "deepspeed=None,\n",
      "disable_tqdm=False,\n",
      "do_eval=True,\n",
      "do_predict=False,\n",
      "do_train=True,\n",
      "eval_accumulation_steps=None,\n",
      "eval_delay=0,\n",
      "eval_steps=None,\n",
      "evaluation_strategy=no,\n",
      "fp16=True,\n",
      "fp16_backend=auto,\n",
      "fp16_full_eval=False,\n",
      "fp16_opt_level=O1,\n",
      "fsdp=[],\n",
      "fsdp_config={'fsdp_min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False},\n",
      "fsdp_min_num_params=0,\n",
      "fsdp_transformer_layer_cls_to_wrap=None,\n",
      "full_determinism=False,\n",
      "gradient_accumulation_steps=1,\n",
      "gradient_checkpointing=False,\n",
      "greater_is_better=None,\n",
      "group_by_length=False,\n",
      "half_precision_backend=auto,\n",
      "hub_model_id=None,\n",
      "hub_private_repo=False,\n",
      "hub_strategy=every_save,\n",
      "hub_token=<HUB_TOKEN>,\n",
      "ignore_data_skip=False,\n",
      "include_inputs_for_metrics=False,\n",
      "jit_mode_eval=False,\n",
      "label_names=None,\n",
      "label_smoothing_factor=0.0,\n",
      "learning_rate=5e-05,\n",
      "length_column_name=length,\n",
      "load_best_model_at_end=False,\n",
      "local_rank=-1,\n",
      "log_level=passive,\n",
      "log_level_replica=warning,\n",
      "log_on_each_node=True,\n",
      "logging_dir=./distilbert-base-multilingual-cased-sentiments-student/runs/May06_10-05-30_sota,\n",
      "logging_first_step=False,\n",
      "logging_nan_inf_filter=True,\n",
      "logging_steps=500,\n",
      "logging_strategy=steps,\n",
      "lr_scheduler_type=linear,\n",
      "max_grad_norm=1.0,\n",
      "max_steps=-1,\n",
      "metric_for_best_model=None,\n",
      "mp_parameters=,\n",
      "no_cuda=False,\n",
      "num_train_epochs=1.0,\n",
      "optim=adamw_hf,\n",
      "optim_args=None,\n",
      "output_dir=./distilbert-base-multilingual-cased-sentiments-student,\n",
      "overwrite_output_dir=False,\n",
      "past_index=-1,\n",
      "per_device_eval_batch_size=128,\n",
      "per_device_train_batch_size=16,\n",
      "prediction_loss_only=False,\n",
      "push_to_hub=False,\n",
      "push_to_hub_model_id=None,\n",
      "push_to_hub_organization=None,\n",
      "push_to_hub_token=<PUSH_TO_HUB_TOKEN>,\n",
      "ray_scope=last,\n",
      "remove_unused_columns=True,\n",
      "report_to=[],\n",
      "resume_from_checkpoint=None,\n",
      "run_name=./distilbert-base-multilingual-cased-sentiments-student,\n",
      "save_on_each_node=False,\n",
      "save_safetensors=False,\n",
      "save_steps=500,\n",
      "save_strategy=steps,\n",
      "save_total_limit=0,\n",
      "seed=42,\n",
      "sharded_ddp=[],\n",
      "skip_memory_metrics=True,\n",
      "tf32=None,\n",
      "torch_compile=False,\n",
      "torch_compile_backend=None,\n",
      "torch_compile_mode=None,\n",
      "torchdynamo=None,\n",
      "tpu_metrics_debug=False,\n",
      "tpu_num_cores=None,\n",
      "use_ipex=False,\n",
      "use_legacy_prediction_loop=False,\n",
      "use_mps_device=False,\n",
      "warmup_ratio=0.0,\n",
      "warmup_steps=0,\n",
      "weight_decay=0.0,\n",
      "xpu_backend=None,\n",
      ")\n",
      "05/06/2023 10:05:30 - INFO - __main__ - Generating predictions from zero-shot teacher model\n",
      "[INFO|configuration_utils.py:668] 2023-05-06 10:05:31,043 >> loading configuration file config.json from cache at /home/lxyuan/.cache/huggingface/hub/models--MoritzLaurer--mDeBERTa-v3-base-mnli-xnli/snapshots/9086d78af18a62546cf05a768934d291684d6309/config.json\n",
      "[INFO|configuration_utils.py:720] 2023-05-06 10:05:31,057 >> Model config DebertaV2Config {\n",
      "  \"_name_or_path\": \"MoritzLaurer/mDeBERTa-v3-base-mnli-xnli\",\n",
      "  \"architectures\": [\n",
      "    \"DebertaV2ForSequenceClassification\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 768,\n",
      "  \"id2label\": {\n",
      "    \"0\": \"entailment\",\n",
      "    \"1\": \"neutral\",\n",
      "    \"2\": \"contradiction\"\n",
      "  },\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 3072,\n",
      "  \"label2id\": {\n",
      "    \"contradiction\": 2,\n",
      "    \"entailment\": 0,\n",
      "    \"neutral\": 1\n",
      "  },\n",
      "  \"layer_norm_eps\": 1e-07,\n",
      "  \"max_position_embeddings\": 512,\n",
      "  \"max_relative_positions\": -1,\n",
      "  \"model_type\": \"deberta-v2\",\n",
      "  \"norm_rel_ebd\": \"layer_norm\",\n",
      "  \"num_attention_heads\": 12,\n",
      "  \"num_hidden_layers\": 12,\n",
      "  \"pad_token_id\": 0,\n",
      "  \"pooler_dropout\": 0,\n",
      "  \"pooler_hidden_act\": \"gelu\",\n",
      "  \"pooler_hidden_size\": 768,\n",
      "  \"pos_att_type\": [\n",
      "    \"p2c\",\n",
      "    \"c2p\"\n",
      "  ],\n",
      "  \"position_biased_input\": false,\n",
      "  \"position_buckets\": 256,\n",
      "  \"relative_attention\": true,\n",
      "  \"share_att_key\": true,\n",
      "  \"torch_dtype\": \"float16\",\n",
      "  \"transformers_version\": \"4.28.1\",\n",
      "  \"type_vocab_size\": 0,\n",
      "  \"vocab_size\": 251000\n",
      "}\n",
      "\n",
      "[INFO|modeling_utils.py:2534] 2023-05-06 10:05:31,077 >> loading weights file pytorch_model.bin from cache at /home/lxyuan/.cache/huggingface/hub/models--MoritzLaurer--mDeBERTa-v3-base-mnli-xnli/snapshots/9086d78af18a62546cf05a768934d291684d6309/pytorch_model.bin\n",
      "[INFO|modeling_utils.py:3190] 2023-05-06 10:05:32,714 >> All model checkpoint weights were used when initializing DebertaV2ForSequenceClassification.\n",
      "\n",
      "[INFO|modeling_utils.py:3198] 2023-05-06 10:05:32,714 >> All the weights of DebertaV2ForSequenceClassification were initialized from the model checkpoint at MoritzLaurer/mDeBERTa-v3-base-mnli-xnli.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use DebertaV2ForSequenceClassification for predictions without further training.\n",
      "[INFO|tokenization_utils_base.py:1809] 2023-05-06 10:05:33,953 >> loading file spm.model from cache at /home/lxyuan/.cache/huggingface/hub/models--MoritzLaurer--mDeBERTa-v3-base-mnli-xnli/snapshots/9086d78af18a62546cf05a768934d291684d6309/spm.model\n",
      "[INFO|tokenization_utils_base.py:1809] 2023-05-06 10:05:33,953 >> loading file added_tokens.json from cache at /home/lxyuan/.cache/huggingface/hub/models--MoritzLaurer--mDeBERTa-v3-base-mnli-xnli/snapshots/9086d78af18a62546cf05a768934d291684d6309/added_tokens.json\n",
      "[INFO|tokenization_utils_base.py:1809] 2023-05-06 10:05:33,953 >> loading file special_tokens_map.json from cache at /home/lxyuan/.cache/huggingface/hub/models--MoritzLaurer--mDeBERTa-v3-base-mnli-xnli/snapshots/9086d78af18a62546cf05a768934d291684d6309/special_tokens_map.json\n",
      "[INFO|tokenization_utils_base.py:1809] 2023-05-06 10:05:33,953 >> loading file tokenizer_config.json from cache at /home/lxyuan/.cache/huggingface/hub/models--MoritzLaurer--mDeBERTa-v3-base-mnli-xnli/snapshots/9086d78af18a62546cf05a768934d291684d6309/tokenizer_config.json\n",
      "[INFO|tokenization_utils.py:426] 2023-05-06 10:05:34,665 >> Adding [MASK] to the vocabulary\n",
      "100%|█████████████████████████████████████| 13756/13756 [16:05<00:00, 14.25it/s]\n",
      "Manually deleted Teacher model\n",
      "05/06/2023 10:21:40 - INFO - __main__ - Initializing student model\n",
      "[INFO|configuration_utils.py:668] 2023-05-06 10:21:41,176 >> loading configuration file config.json from cache at /home/lxyuan/.cache/huggingface/hub/models--distilbert-base-multilingual-cased/snapshots/9e90d6dd84b6a2e4d65e4d751baed6cd56578fd3/config.json\n",
      "[INFO|configuration_utils.py:720] 2023-05-06 10:21:41,176 >> Model config DistilBertConfig {\n",
      "  \"_name_or_path\": \"distilbert-base-multilingual-cased\",\n",
      "  \"activation\": \"gelu\",\n",
      "  \"architectures\": [\n",
      "    \"DistilBertForMaskedLM\"\n",
      "  ],\n",
      "  \"attention_dropout\": 0.1,\n",
      "  \"dim\": 768,\n",
      "  \"dropout\": 0.1,\n",
      "  \"hidden_dim\": 3072,\n",
      "  \"id2label\": {\n",
      "    \"0\": \"LABEL_0\",\n",
      "    \"1\": \"LABEL_1\",\n",
      "    \"2\": \"LABEL_2\"\n",
      "  },\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"label2id\": {\n",
      "    \"LABEL_0\": 0,\n",
      "    \"LABEL_1\": 1,\n",
      "    \"LABEL_2\": 2\n",
      "  },\n",
      "  \"max_position_embeddings\": 512,\n",
      "  \"model_type\": \"distilbert\",\n",
      "  \"n_heads\": 12,\n",
      "  \"n_layers\": 6,\n",
      "  \"output_past\": true,\n",
      "  \"pad_token_id\": 0,\n",
      "  \"qa_dropout\": 0.1,\n",
      "  \"seq_classif_dropout\": 0.2,\n",
      "  \"sinusoidal_pos_embds\": false,\n",
      "  \"tie_weights_\": true,\n",
      "  \"transformers_version\": \"4.28.1\",\n",
      "  \"vocab_size\": 119547\n",
      "}\n",
      "\n",
      "[INFO|modeling_utils.py:2534] 2023-05-06 10:21:41,178 >> loading weights file pytorch_model.bin from cache at /home/lxyuan/.cache/huggingface/hub/models--distilbert-base-multilingual-cased/snapshots/9e90d6dd84b6a2e4d65e4d751baed6cd56578fd3/pytorch_model.bin\n",
      "[WARNING|modeling_utils.py:3180] 2023-05-06 10:21:42,151 >> Some weights of the model checkpoint at distilbert-base-multilingual-cased were not used when initializing DistilBertForSequenceClassification: ['vocab_projector.bias', 'vocab_transform.bias', 'vocab_layer_norm.bias', 'vocab_transform.weight', 'vocab_layer_norm.weight', 'vocab_projector.weight']\n",
      "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "[WARNING|modeling_utils.py:3192] 2023-05-06 10:21:42,151 >> Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-multilingual-cased and are newly initialized: ['pre_classifier.bias', 'classifier.bias', 'pre_classifier.weight', 'classifier.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "[INFO|configuration_utils.py:668] 2023-05-06 10:21:42,391 >> loading configuration file config.json from cache at /home/lxyuan/.cache/huggingface/hub/models--distilbert-base-multilingual-cased/snapshots/9e90d6dd84b6a2e4d65e4d751baed6cd56578fd3/config.json\n",
      "[INFO|configuration_utils.py:720] 2023-05-06 10:21:42,391 >> Model config DistilBertConfig {\n",
      "  \"_name_or_path\": \"distilbert-base-multilingual-cased\",\n",
      "  \"activation\": \"gelu\",\n",
      "  \"architectures\": [\n",
      "    \"DistilBertForMaskedLM\"\n",
      "  ],\n",
      "  \"attention_dropout\": 0.1,\n",
      "  \"dim\": 768,\n",
      "  \"dropout\": 0.1,\n",
      "  \"hidden_dim\": 3072,\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"max_position_embeddings\": 512,\n",
      "  \"model_type\": \"distilbert\",\n",
      "  \"n_heads\": 12,\n",
      "  \"n_layers\": 6,\n",
      "  \"output_past\": true,\n",
      "  \"pad_token_id\": 0,\n",
      "  \"qa_dropout\": 0.1,\n",
      "  \"seq_classif_dropout\": 0.2,\n",
      "  \"sinusoidal_pos_embds\": false,\n",
      "  \"tie_weights_\": true,\n",
      "  \"transformers_version\": \"4.28.1\",\n",
      "  \"vocab_size\": 119547\n",
      "}\n",
      "\n",
      "[INFO|tokenization_utils_base.py:1809] 2023-05-06 10:21:42,392 >> loading file vocab.txt from cache at /home/lxyuan/.cache/huggingface/hub/models--distilbert-base-multilingual-cased/snapshots/9e90d6dd84b6a2e4d65e4d751baed6cd56578fd3/vocab.txt\n",
      "[INFO|tokenization_utils_base.py:1809] 2023-05-06 10:21:42,392 >> loading file added_tokens.json from cache at None\n",
      "[INFO|tokenization_utils_base.py:1809] 2023-05-06 10:21:42,392 >> loading file special_tokens_map.json from cache at None\n",
      "[INFO|tokenization_utils_base.py:1809] 2023-05-06 10:21:42,392 >> loading file tokenizer_config.json from cache at /home/lxyuan/.cache/huggingface/hub/models--distilbert-base-multilingual-cased/snapshots/9e90d6dd84b6a2e4d65e4d751baed6cd56578fd3/tokenizer_config.json\n",
      "[INFO|configuration_utils.py:668] 2023-05-06 10:21:42,392 >> loading configuration file config.json from cache at /home/lxyuan/.cache/huggingface/hub/models--distilbert-base-multilingual-cased/snapshots/9e90d6dd84b6a2e4d65e4d751baed6cd56578fd3/config.json\n",
      "[INFO|configuration_utils.py:720] 2023-05-06 10:21:42,392 >> Model config DistilBertConfig {\n",
      "  \"_name_or_path\": \"distilbert-base-multilingual-cased\",\n",
      "  \"activation\": \"gelu\",\n",
      "  \"architectures\": [\n",
      "    \"DistilBertForMaskedLM\"\n",
      "  ],\n",
      "  \"attention_dropout\": 0.1,\n",
      "  \"dim\": 768,\n",
      "  \"dropout\": 0.1,\n",
      "  \"hidden_dim\": 3072,\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"max_position_embeddings\": 512,\n",
      "  \"model_type\": \"distilbert\",\n",
      "  \"n_heads\": 12,\n",
      "  \"n_layers\": 6,\n",
      "  \"output_past\": true,\n",
      "  \"pad_token_id\": 0,\n",
      "  \"qa_dropout\": 0.1,\n",
      "  \"seq_classif_dropout\": 0.2,\n",
      "  \"sinusoidal_pos_embds\": false,\n",
      "  \"tie_weights_\": true,\n",
      "  \"transformers_version\": \"4.28.1\",\n",
      "  \"vocab_size\": 119547\n",
      "}\n",
      "\n",
      "[INFO|trainer.py:621] 2023-05-06 10:22:48,664 >> Using cuda_amp half precision backend\n",
      "05/06/2023 10:22:48 - INFO - __main__ - Training student model on teacher predictions\n",
      "[INFO|trainer.py:762] 2023-05-06 10:22:48,664 >> The following columns in the training set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: text. If text are not expected by `DistilBertForSequenceClassification.forward`,  you can safely ignore this message.\n",
      "/home/lxyuan/playground/llm/env/lib64/python3.10/site-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  warnings.warn(\n",
      "[INFO|trainer.py:1769] 2023-05-06 10:22:48,668 >> ***** Running training *****\n",
      "[INFO|trainer.py:1770] 2023-05-06 10:22:48,668 >>   Num examples = 146,721\n",
      "[INFO|trainer.py:1771] 2023-05-06 10:22:48,668 >>   Num Epochs = 1\n",
      "[INFO|trainer.py:1772] 2023-05-06 10:22:48,668 >>   Instantaneous batch size per device = 16\n",
      "[INFO|trainer.py:1773] 2023-05-06 10:22:48,668 >>   Total train batch size (w. parallel, distributed & accumulation) = 16\n",
      "[INFO|trainer.py:1774] 2023-05-06 10:22:48,668 >>   Gradient Accumulation steps = 1\n",
      "[INFO|trainer.py:1775] 2023-05-06 10:22:48,668 >>   Total optimization steps = 9,171\n",
      "[INFO|trainer.py:1776] 2023-05-06 10:22:48,668 >>   Number of trainable parameters = 135,326,979\n",
      "{'loss': 0.7855, 'learning_rate': 4.7279467887907535e-05, 'epoch': 0.05}        \n",
      "  5%|██▏                                     | 500/9171 [01:46<30:53,  4.68it/s][INFO|trainer.py:2868] 2023-05-06 10:24:34,910 >> Saving model checkpoint to ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-500\n",
      "[INFO|configuration_utils.py:457] 2023-05-06 10:24:34,911 >> Configuration saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-500/config.json\n",
      "[INFO|modeling_utils.py:1847] 2023-05-06 10:24:35,546 >> Model weights saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-500/pytorch_model.bin\n",
      "[INFO|tokenization_utils_base.py:2171] 2023-05-06 10:24:35,547 >> tokenizer config file saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-500/tokenizer_config.json\n",
      "[INFO|tokenization_utils_base.py:2178] 2023-05-06 10:24:35,547 >> Special tokens file saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-500/special_tokens_map.json\n",
      "{'loss': 0.7081, 'learning_rate': 4.455348380765457e-05, 'epoch': 0.11}         \n",
      " 11%|████▎                                  | 1000/9171 [03:35<29:16,  4.65it/s][INFO|trainer.py:2868] 2023-05-06 10:26:24,156 >> Saving model checkpoint to ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-1000\n",
      "[INFO|configuration_utils.py:457] 2023-05-06 10:26:24,157 >> Configuration saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-1000/config.json\n",
      "[INFO|modeling_utils.py:1847] 2023-05-06 10:26:24,805 >> Model weights saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-1000/pytorch_model.bin\n",
      "[INFO|tokenization_utils_base.py:2171] 2023-05-06 10:26:24,805 >> tokenizer config file saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-1000/tokenizer_config.json\n",
      "[INFO|tokenization_utils_base.py:2178] 2023-05-06 10:26:24,805 >> Special tokens file saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-1000/special_tokens_map.json\n",
      "{'loss': 0.6885, 'learning_rate': 4.1827499727401595e-05, 'epoch': 0.16}        \n",
      " 16%|██████▍                                | 1500/9171 [05:24<27:34,  4.64it/s][INFO|trainer.py:2868] 2023-05-06 10:28:13,612 >> Saving model checkpoint to ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-1500\n",
      "[INFO|configuration_utils.py:457] 2023-05-06 10:28:13,613 >> Configuration saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-1500/config.json\n",
      "[INFO|modeling_utils.py:1847] 2023-05-06 10:28:14,259 >> Model weights saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-1500/pytorch_model.bin\n",
      "[INFO|tokenization_utils_base.py:2171] 2023-05-06 10:28:14,259 >> tokenizer config file saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-1500/tokenizer_config.json\n",
      "[INFO|tokenization_utils_base.py:2178] 2023-05-06 10:28:14,260 >> Special tokens file saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-1500/special_tokens_map.json\n",
      "{'loss': 0.6656, 'learning_rate': 3.910151564714862e-05, 'epoch': 0.22}         \n",
      " 22%|████████▌                              | 2000/9171 [07:14<25:39,  4.66it/s][INFO|trainer.py:2868] 2023-05-06 10:30:03,190 >> Saving model checkpoint to ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-2000\n",
      "[INFO|configuration_utils.py:457] 2023-05-06 10:30:03,191 >> Configuration saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-2000/config.json\n",
      "[INFO|modeling_utils.py:1847] 2023-05-06 10:30:03,836 >> Model weights saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-2000/pytorch_model.bin\n",
      "[INFO|tokenization_utils_base.py:2171] 2023-05-06 10:30:03,836 >> tokenizer config file saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-2000/tokenizer_config.json\n",
      "[INFO|tokenization_utils_base.py:2178] 2023-05-06 10:30:03,836 >> Special tokens file saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-2000/special_tokens_map.json\n",
      "{'loss': 0.6635, 'learning_rate': 3.637553156689565e-05, 'epoch': 0.27}         \n",
      " 27%|██████████▋                            | 2500/9171 [09:04<23:56,  4.64it/s][INFO|trainer.py:2868] 2023-05-06 10:31:52,890 >> Saving model checkpoint to ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-2500\n",
      "[INFO|configuration_utils.py:457] 2023-05-06 10:31:52,891 >> Configuration saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-2500/config.json\n",
      "[INFO|modeling_utils.py:1847] 2023-05-06 10:31:53,539 >> Model weights saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-2500/pytorch_model.bin\n",
      "[INFO|tokenization_utils_base.py:2171] 2023-05-06 10:31:53,540 >> tokenizer config file saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-2500/tokenizer_config.json\n",
      "[INFO|tokenization_utils_base.py:2178] 2023-05-06 10:31:53,540 >> Special tokens file saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-2500/special_tokens_map.json\n",
      "{'loss': 0.654, 'learning_rate': 3.364954748664268e-05, 'epoch': 0.33}          \n",
      " 33%|████████████▊                          | 3000/9171 [10:53<22:05,  4.66it/s][INFO|trainer.py:2868] 2023-05-06 10:33:42,452 >> Saving model checkpoint to ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-3000\n",
      "[INFO|configuration_utils.py:457] 2023-05-06 10:33:42,453 >> Configuration saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-3000/config.json\n",
      "[INFO|modeling_utils.py:1847] 2023-05-06 10:33:43,120 >> Model weights saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-3000/pytorch_model.bin\n",
      "[INFO|tokenization_utils_base.py:2171] 2023-05-06 10:33:43,120 >> tokenizer config file saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-3000/tokenizer_config.json\n",
      "[INFO|tokenization_utils_base.py:2178] 2023-05-06 10:33:43,121 >> Special tokens file saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-3000/special_tokens_map.json\n",
      "{'loss': 0.6492, 'learning_rate': 3.092901537455021e-05, 'epoch': 0.38}         \n",
      " 38%|██████████████▉                        | 3500/9171 [12:43<20:22,  4.64it/s][INFO|trainer.py:2868] 2023-05-06 10:35:32,016 >> Saving model checkpoint to ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-3500\n",
      "[INFO|configuration_utils.py:457] 2023-05-06 10:35:32,016 >> Configuration saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-3500/config.json\n",
      "[INFO|modeling_utils.py:1847] 2023-05-06 10:35:32,674 >> Model weights saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-3500/pytorch_model.bin\n",
      "[INFO|tokenization_utils_base.py:2171] 2023-05-06 10:35:32,674 >> tokenizer config file saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-3500/tokenizer_config.json\n",
      "[INFO|tokenization_utils_base.py:2178] 2023-05-06 10:35:32,674 >> Special tokens file saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-3500/special_tokens_map.json\n",
      "{'loss': 0.645, 'learning_rate': 2.8203031294297243e-05, 'epoch': 0.44}         \n",
      " 44%|█████████████████                      | 4000/9171 [14:33<18:32,  4.65it/s][INFO|trainer.py:2868] 2023-05-06 10:37:21,728 >> Saving model checkpoint to ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-4000\n",
      "[INFO|configuration_utils.py:457] 2023-05-06 10:37:21,729 >> Configuration saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-4000/config.json\n",
      "[INFO|modeling_utils.py:1847] 2023-05-06 10:37:22,377 >> Model weights saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-4000/pytorch_model.bin\n",
      "[INFO|tokenization_utils_base.py:2171] 2023-05-06 10:37:22,377 >> tokenizer config file saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-4000/tokenizer_config.json\n",
      "[INFO|tokenization_utils_base.py:2178] 2023-05-06 10:37:22,377 >> Special tokens file saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-4000/special_tokens_map.json\n",
      "{'loss': 0.6303, 'learning_rate': 2.547704721404427e-05, 'epoch': 0.49}         \n",
      " 49%|███████████████████▏                   | 4500/9171 [16:22<16:45,  4.64it/s][INFO|trainer.py:2868] 2023-05-06 10:39:11,239 >> Saving model checkpoint to ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-4500\n",
      "[INFO|configuration_utils.py:457] 2023-05-06 10:39:11,239 >> Configuration saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-4500/config.json\n",
      "[INFO|modeling_utils.py:1847] 2023-05-06 10:39:11,885 >> Model weights saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-4500/pytorch_model.bin\n",
      "[INFO|tokenization_utils_base.py:2171] 2023-05-06 10:39:11,885 >> tokenizer config file saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-4500/tokenizer_config.json\n",
      "[INFO|tokenization_utils_base.py:2178] 2023-05-06 10:39:11,885 >> Special tokens file saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-4500/special_tokens_map.json\n",
      "{'loss': 0.6289, 'learning_rate': 2.2751063133791302e-05, 'epoch': 0.55}        \n",
      " 55%|█████████████████████▎                 | 5000/9171 [18:12<14:58,  4.64it/s][INFO|trainer.py:2868] 2023-05-06 10:41:01,086 >> Saving model checkpoint to ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-5000\n",
      "[INFO|configuration_utils.py:457] 2023-05-06 10:41:01,086 >> Configuration saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-5000/config.json\n",
      "[INFO|modeling_utils.py:1847] 2023-05-06 10:41:01,734 >> Model weights saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-5000/pytorch_model.bin\n",
      "[INFO|tokenization_utils_base.py:2171] 2023-05-06 10:41:01,734 >> tokenizer config file saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-5000/tokenizer_config.json\n",
      "[INFO|tokenization_utils_base.py:2178] 2023-05-06 10:41:01,734 >> Special tokens file saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-5000/special_tokens_map.json\n",
      "{'loss': 0.6317, 'learning_rate': 2.002507905353833e-05, 'epoch': 0.6}          \n",
      " 60%|███████████████████████▍               | 5500/9171 [20:02<13:11,  4.64it/s][INFO|trainer.py:2868] 2023-05-06 10:42:50,694 >> Saving model checkpoint to ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-5500\n",
      "[INFO|configuration_utils.py:457] 2023-05-06 10:42:50,695 >> Configuration saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-5500/config.json\n",
      "[INFO|modeling_utils.py:1847] 2023-05-06 10:42:51,359 >> Model weights saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-5500/pytorch_model.bin\n",
      "[INFO|tokenization_utils_base.py:2171] 2023-05-06 10:42:51,359 >> tokenizer config file saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-5500/tokenizer_config.json\n",
      "[INFO|tokenization_utils_base.py:2178] 2023-05-06 10:42:51,359 >> Special tokens file saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-5500/special_tokens_map.json\n",
      "{'loss': 0.6194, 'learning_rate': 1.730454694144586e-05, 'epoch': 0.65}         \n",
      " 65%|█████████████████████████▌             | 6000/9171 [21:51<11:31,  4.58it/s][INFO|trainer.py:2868] 2023-05-06 10:44:40,517 >> Saving model checkpoint to ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-6000\n",
      "[INFO|configuration_utils.py:457] 2023-05-06 10:44:40,518 >> Configuration saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-6000/config.json\n",
      "[INFO|modeling_utils.py:1847] 2023-05-06 10:44:41,171 >> Model weights saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-6000/pytorch_model.bin\n",
      "[INFO|tokenization_utils_base.py:2171] 2023-05-06 10:44:41,171 >> tokenizer config file saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-6000/tokenizer_config.json\n",
      "[INFO|tokenization_utils_base.py:2178] 2023-05-06 10:44:41,171 >> Special tokens file saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-6000/special_tokens_map.json\n",
      "{'loss': 0.6234, 'learning_rate': 1.4578562861192891e-05, 'epoch': 0.71}        \n",
      " 71%|███████████████████████████▋           | 6500/9171 [23:41<09:40,  4.60it/s][INFO|trainer.py:2868] 2023-05-06 10:46:30,285 >> Saving model checkpoint to ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-6500\n",
      "[INFO|configuration_utils.py:457] 2023-05-06 10:46:30,286 >> Configuration saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-6500/config.json\n",
      "[INFO|modeling_utils.py:1847] 2023-05-06 10:46:30,956 >> Model weights saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-6500/pytorch_model.bin\n",
      "[INFO|tokenization_utils_base.py:2171] 2023-05-06 10:46:30,956 >> tokenizer config file saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-6500/tokenizer_config.json\n",
      "[INFO|tokenization_utils_base.py:2178] 2023-05-06 10:46:30,956 >> Special tokens file saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-6500/special_tokens_map.json\n",
      "{'loss': 0.6156, 'learning_rate': 1.185257878093992e-05, 'epoch': 0.76}         \n",
      " 76%|█████████████████████████████▊         | 7000/9171 [25:31<07:48,  4.63it/s][INFO|trainer.py:2868] 2023-05-06 10:48:19,972 >> Saving model checkpoint to ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-7000\n",
      "[INFO|configuration_utils.py:457] 2023-05-06 10:48:19,972 >> Configuration saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-7000/config.json\n",
      "[INFO|modeling_utils.py:1847] 2023-05-06 10:48:20,618 >> Model weights saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-7000/pytorch_model.bin\n",
      "[INFO|tokenization_utils_base.py:2171] 2023-05-06 10:48:20,618 >> tokenizer config file saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-7000/tokenizer_config.json\n",
      "[INFO|tokenization_utils_base.py:2178] 2023-05-06 10:48:20,618 >> Special tokens file saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-7000/special_tokens_map.json\n",
      "{'loss': 0.6191, 'learning_rate': 9.126594700686949e-06, 'epoch': 0.82}         \n",
      " 82%|███████████████████████████████▉       | 7500/9171 [27:21<05:59,  4.65it/s][INFO|trainer.py:2868] 2023-05-06 10:50:09,758 >> Saving model checkpoint to ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-7500\n",
      "[INFO|configuration_utils.py:457] 2023-05-06 10:50:09,759 >> Configuration saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-7500/config.json\n",
      "[INFO|modeling_utils.py:1847] 2023-05-06 10:50:10,421 >> Model weights saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-7500/pytorch_model.bin\n",
      "[INFO|tokenization_utils_base.py:2171] 2023-05-06 10:50:10,421 >> tokenizer config file saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-7500/tokenizer_config.json\n",
      "[INFO|tokenization_utils_base.py:2178] 2023-05-06 10:50:10,421 >> Special tokens file saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-7500/special_tokens_map.json\n",
      "{'loss': 0.6173, 'learning_rate': 6.406062588594483e-06, 'epoch': 0.87}         \n",
      " 87%|██████████████████████████████████     | 8000/9171 [29:11<04:14,  4.60it/s][INFO|trainer.py:2868] 2023-05-06 10:51:59,735 >> Saving model checkpoint to ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-8000\n",
      "[INFO|configuration_utils.py:457] 2023-05-06 10:51:59,735 >> Configuration saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-8000/config.json\n",
      "[INFO|modeling_utils.py:1847] 2023-05-06 10:52:00,401 >> Model weights saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-8000/pytorch_model.bin\n",
      "[INFO|tokenization_utils_base.py:2171] 2023-05-06 10:52:00,402 >> tokenizer config file saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-8000/tokenizer_config.json\n",
      "[INFO|tokenization_utils_base.py:2178] 2023-05-06 10:52:00,402 >> Special tokens file saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-8000/special_tokens_map.json\n",
      "{'loss': 0.6102, 'learning_rate': 3.680078508341511e-06, 'epoch': 0.93}         \n",
      " 93%|████████████████████████████████████▏  | 8500/9171 [31:01<02:24,  4.63it/s][INFO|trainer.py:2868] 2023-05-06 10:53:50,120 >> Saving model checkpoint to ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-8500\n",
      "[INFO|configuration_utils.py:457] 2023-05-06 10:53:50,120 >> Configuration saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-8500/config.json\n",
      "[INFO|modeling_utils.py:1847] 2023-05-06 10:53:50,772 >> Model weights saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-8500/pytorch_model.bin\n",
      "[INFO|tokenization_utils_base.py:2171] 2023-05-06 10:53:50,772 >> tokenizer config file saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-8500/tokenizer_config.json\n",
      "[INFO|tokenization_utils_base.py:2178] 2023-05-06 10:53:50,773 >> Special tokens file saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-8500/special_tokens_map.json\n",
      "{'loss': 0.6048, 'learning_rate': 9.5409442808854e-07, 'epoch': 0.98}           \n",
      " 98%|██████████████████████████████████████▎| 9000/9171 [32:51<00:36,  4.65it/s][INFO|trainer.py:2868] 2023-05-06 10:55:39,870 >> Saving model checkpoint to ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-9000\n",
      "[INFO|configuration_utils.py:457] 2023-05-06 10:55:39,870 >> Configuration saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-9000/config.json\n",
      "[INFO|modeling_utils.py:1847] 2023-05-06 10:55:40,533 >> Model weights saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-9000/pytorch_model.bin\n",
      "[INFO|tokenization_utils_base.py:2171] 2023-05-06 10:55:40,534 >> tokenizer config file saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-9000/tokenizer_config.json\n",
      "[INFO|tokenization_utils_base.py:2178] 2023-05-06 10:55:40,534 >> Special tokens file saved in ./distilbert-base-multilingual-cased-sentiments-student/checkpoint-9000/special_tokens_map.json\n",
      "100%|██████████████████████████████████████▉| 9170/9171 [33:29<00:00,  4.64it/s][INFO|trainer.py:2039] 2023-05-06 10:56:18,554 >> \n",
      "\n",
      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
      "\n",
      "\n",
      "{'train_runtime': 2009.8864, 'train_samples_per_second': 73.0, 'train_steps_per_second': 4.563, 'train_loss': 0.6473459283913797, 'epoch': 1.0}\n",
      "100%|███████████████████████████████████████| 9171/9171 [33:29<00:00,  4.56it/s]\n",
      "[INFO|trainer.py:762] 2023-05-06 10:56:18,555 >> The following columns in the evaluation set don't have a corresponding argument in `DistilBertForSequenceClassification.forward` and have been ignored: text. If text are not expected by `DistilBertForSequenceClassification.forward`,  you can safely ignore this message.\n",
      "[INFO|trainer.py:3129] 2023-05-06 10:56:18,557 >> ***** Running Evaluation *****\n",
      "[INFO|trainer.py:3131] 2023-05-06 10:56:18,557 >>   Num examples = 146721\n",
      "[INFO|trainer.py:3134] 2023-05-06 10:56:18,557 >>   Batch size = 128\n",
      "100%|███████████████████████████████████████| 1147/1147 [08:59<00:00,  2.13it/s]\n",
      "05/06/2023 11:05:18 - INFO - __main__ - Agreement of student and teacher predictions: 88.29%\n",
      "[INFO|trainer.py:2868] 2023-05-06 11:05:18,251 >> Saving model checkpoint to ./distilbert-base-multilingual-cased-sentiments-student\n",
      "[INFO|configuration_utils.py:457] 2023-05-06 11:05:18,251 >> Configuration saved in ./distilbert-base-multilingual-cased-sentiments-student/config.json\n",
      "[INFO|modeling_utils.py:1847] 2023-05-06 11:05:18,905 >> Model weights saved in ./distilbert-base-multilingual-cased-sentiments-student/pytorch_model.bin\n",
      "[INFO|tokenization_utils_base.py:2171] 2023-05-06 11:05:18,905 >> tokenizer config file saved in ./distilbert-base-multilingual-cased-sentiments-student/tokenizer_config.json\n",
      "[INFO|tokenization_utils_base.py:2178] 2023-05-06 11:05:18,905 >> Special tokens file saved in ./distilbert-base-multilingual-cased-sentiments-student/special_tokens_map.json\n"
     ]
    }
   ],
   "source": [
    "#!python transformers/examples/research_projects/zero-shot-distillation/distill_classifier.py \\\n",
    "!python distill_classifier.py \\\n",
    "--data_file ./multilingual-sentiments/train_unlabeled.txt \\\n",
    "--class_names_file ./multilingual-sentiments/class_names.txt \\\n",
    "--hypothesis_template \"The sentiment of this text is {}.\" \\\n",
    "--teacher_name_or_path MoritzLaurer/mDeBERTa-v3-base-mnli-xnli \\\n",
    "--teacher_batch_size 32 \\\n",
    "--student_name_or_path distilbert-base-multilingual-cased \\\n",
    "--output_dir ./distilbert-base-multilingual-cased-sentiments-student \\\n",
    "--per_device_train_batch_size 16 \\\n",
    "--fp16"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3d2-ptlaF2M5"
   },
   "source": [
    "### Using the student model\n",
    "\n",
    "The resulting model can now be loaded and used like any other pre-trained model:\n",
    "\n",
    "(you can also use `\"lxyuan/distilbert-base-multilingual-cased-sentiments-student\"` to download this model from the hub if you want to try it without running whole script above)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "Hwh61MsAGdEw",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DistilBertConfig {\n",
       "  \"_name_or_path\": \"./distilbert-base-multilingual-cased-sentiments-student\",\n",
       "  \"activation\": \"gelu\",\n",
       "  \"architectures\": [\n",
       "    \"DistilBertForSequenceClassification\"\n",
       "  ],\n",
       "  \"attention_dropout\": 0.1,\n",
       "  \"dim\": 768,\n",
       "  \"dropout\": 0.1,\n",
       "  \"hidden_dim\": 3072,\n",
       "  \"id2label\": {\n",
       "    \"0\": \"positive\",\n",
       "    \"1\": \"neutral\",\n",
       "    \"2\": \"negative\"\n",
       "  },\n",
       "  \"initializer_range\": 0.02,\n",
       "  \"label2id\": {\n",
       "    \"negative\": 2,\n",
       "    \"neutral\": 1,\n",
       "    \"positive\": 0\n",
       "  },\n",
       "  \"max_position_embeddings\": 512,\n",
       "  \"model_type\": \"distilbert\",\n",
       "  \"n_heads\": 12,\n",
       "  \"n_layers\": 6,\n",
       "  \"output_past\": true,\n",
       "  \"pad_token_id\": 0,\n",
       "  \"qa_dropout\": 0.1,\n",
       "  \"seq_classif_dropout\": 0.2,\n",
       "  \"sinusoidal_pos_embds\": false,\n",
       "  \"tie_weights_\": true,\n",
       "  \"torch_dtype\": \"float32\",\n",
       "  \"transformers_version\": \"4.28.1\",\n",
       "  \"vocab_size\": 119547\n",
       "}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import AutoModelForSequenceClassification, AutoTokenizer\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"./distilbert-base-multilingual-cased-sentiments-student\")\n",
    "model = AutoModelForSequenceClassification.from_pretrained(\"./distilbert-base-multilingual-cased-sentiments-student\")\n",
    "model.config"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "glxrI7uXGhI6"
   },
   "source": [
    "and even used trivially with a `TextClassificationPipeline`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "id": "dAKVEuPEGXzF",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[{'label': 'positive', 'score': 0.6660619974136353},\n",
       "  {'label': 'neutral', 'score': 0.12189780175685883},\n",
       "  {'label': 'negative', 'score': 0.21204017102718353}]]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import TextClassificationPipeline\n",
    "distilled_classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True, device=0)\n",
    "distilled_classifier(train[\"text\"][2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Kita akan tengok daripada pelbagai aspek supaya akhirnya hak rakyat dapat dikembalikan, itu fokus kita pada tahun ini selaras dengan arahan pucuk pimpinan SPRM',\n",
       " 0)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 0: positive\n",
    "train[\"text\"][2], train[\"label\"][2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WoQ6hQe2Miu4"
   },
   "source": [
    "----\n",
    "\n",
    "Let's compare the speed & accuracy of the two methods.\n",
    "\n",
    "Original zero-shot model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "to7DOOp9LxlM",
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from time import time\n",
    "from tqdm.auto import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "to7DOOp9LxlM",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 453/453 [07:55<00:00,  1.05s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Teacher model accuracy: 58.87%\n",
      "Runtime:  475.53 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "start = time()\n",
    "batch_size = 32\n",
    "hypothesis_template = \"The sentiment of this text is {}.\"\n",
    "class_names = ['positive', 'neutral', 'negative']\n",
    "preds = []\n",
    "teacher_preds = []\n",
    "for i in tqdm(range(0, len(test), batch_size)):\n",
    "    examples = test[i:i+batch_size]['text']\n",
    "    outputs = zero_shot_classifier(examples, class_names, hypothesis_template=hypothesis_template)\n",
    "    preds += [class_names.index(o['labels'][0]) for o in outputs]\n",
    "    teacher_preds += [class_names.index(o['labels'][0]) for o in outputs]\n",
    "accuracy = np.mean(np.array(preds) == np.array(test['label']))\n",
    "print(f\"Teacher model accuracy: {accuracy*100:0.2f}%\")\n",
    "print(f\"Runtime: {time() - start : 0.2f} seconds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o74qNjt-MOCI"
   },
   "source": [
    "Distilled student model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "r4tuVf6VNLGg",
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 114/114 [00:46<00:00,  2.46it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distilled model accuracy: 54.26%\n",
      "Runtime:  46.37 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "start = time()\n",
    "batch_size = 128 # larger batch size bc distilled model is more memory efficient\n",
    "distilled_classifier.return_all_scores = False\n",
    "tokenizer_kwargs = {'padding':True,'truncation':True,'max_length':512}\n",
    "preds = []\n",
    "for i in tqdm(range(0, len(test), batch_size)):\n",
    "    examples = test[i:i+batch_size]['text']\n",
    "    outputs = distilled_classifier(examples, **tokenizer_kwargs)\n",
    "    preds += [class_names.index(o['label']) for o in outputs]\n",
    "accuracy = np.mean(np.array(preds) == np.array(test['label']))\n",
    "print(f\"Distilled model accuracy: {accuracy*100:0.2f}%\")\n",
    "print(f\"Runtime: {time() - start : 0.2f} seconds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text: Sepatutnya berbuat begitu  demi untuk menawarkan sesuatu yang lebih baik kepada rakyat.\n",
      "teacher_pred = 0  |  student_pred = 0  |  true = 0\n",
      "\n",
      "\n",
      "Text: Alhamdulillah, sama2 bantu kerajaan memerangi Covid19, menyelamatkan nyawa rakyat dan memulihkan semula negara. Buk https://t.co/A4uyo9BtXL\n",
      "teacher_pred = 0  |  student_pred = 0  |  true = 0\n",
      "\n",
      "\n",
      "Text: Biasanya bantuan disalurkan kepada sekolah berdaftar\n",
      "teacher_pred = 0  |  student_pred = 0  |  true = 0\n",
      "\n",
      "\n",
      "Text: Kerajaan wajar mengkaji semula had tunggakan cukai yang dinaikkan kepada RM10,000 untuk pembayar cukai individu dan RM50,000 untuk syarikat.\n",
      "teacher_pred = 2  |  student_pred = 0  |  true = 0\n",
      "\n",
      "\n",
      "Text: me; those everytime nak beli baju or seluar tak confidence dengan size sendiri\n",
      "teacher_pred = 2  |  student_pred = 2  |  true = 0\n",
      "\n",
      "\n",
      "Text: Apa pun yang berlaku, kami jalan terus.\n",
      "teacher_pred = 1  |  student_pred = 2  |  true = 0\n",
      "\n",
      "\n",
      "Text: hahahah betul2 tea ni..\n",
      "teacher_pred = 0  |  student_pred = 0  |  true = 1\n",
      "\n",
      "\n",
      "Text: PKR Perak hadiahkan YB Taki Kamus Dewan. https://t.co/Qbb8JaH2Zg Kalau saya pun suka dapat hadiah. Cuma rasa kel https://t.co/mSq14x4Qat\n",
      "teacher_pred = 1  |  student_pred = 0  |  true = 0\n",
      "\n",
      "\n",
      "Text: patut la selalu selamat melaka setiap musim .. kah kah kah ..\n",
      "teacher_pred = 0  |  student_pred = 0  |  true = 1\n",
      "\n",
      "\n",
      "Text: Tidak mengapa, ada ubi ada batas\n",
      "teacher_pred = 1  |  student_pred = 1  |  true = 0\n",
      "\n",
      "\n",
      "Text: Anggota PAS diajar bahawa pemimpin datang dan pergi, tetapi bahtera Islam yang diperjuangkan terus berlayar\n",
      "teacher_pred = 1  |  student_pred = 0  |  true = 0\n",
      "\n",
      "\n",
      "Text: Malah, soal kebajikan, pendidikan dan pembanguan ekonomi golongan ini sentiasa dititikeratkan dengan adanya pelbagai program yang diatur oleh kerajaan\n",
      "teacher_pred = 0  |  student_pred = 0  |  true = 0\n",
      "\n",
      "\n",
      "Text: Just posted a photo @ Klebang Besar, Melaka, Malaysia https://t.co/ph51B0IcvH\n",
      "teacher_pred = 0  |  student_pred = 0  |  true = 1\n",
      "\n",
      "\n",
      "Text: Pada masa sama, kerajaan negeri juga telah buat bayaran sebanyak RM700 kepada lebih 50,000 mangsa banjir.\n",
      "teacher_pred = 0  |  student_pred = 0  |  true = 0\n",
      "\n",
      "\n",
      "Text: kerajaan mengembalikan wang itu kepada rakyat dengan pelbagai pendekatan\n",
      "teacher_pred = 0  |  student_pred = 0  |  true = 0\n",
      "\n",
      "\n",
      "Text: Bukan Isapan Jempol, Ini Bukti Gus Dur Melarang Cak Imin Gunakan Foto dan Rekaman Suaranya https://t.co/LyEAHv7TbS\n",
      "teacher_pred = 1  |  student_pred = 0  |  true = 1\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# error analysis\n",
    "for idx, (student_pred, true, teacher_pred) in enumerate(zip(preds, test[\"label\"], teacher_preds)):\n",
    "    if idx >20:\n",
    "        break\n",
    "        \n",
    "    if pred != true:\n",
    "        print(f\"Text: {test['text'][idx]}\")\n",
    "        print(f\"{teacher_pred = }  |  {student_pred = }  |  {true = }\")\n",
    "        print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3Uhs--uSnuaf"
   },
   "source": [
    "As you can see, **the disitlled model gets similar accuracy on a held-out test set while running in 1/10th the time**. \n",
    "\n",
    "Note: The goal here is demostrate the idea of model distillation using zero-shot classification pipline. We aim to get a student model with similar performance as the teacher model but with smaller model size and more efficient inference spped.\n"
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