with_BERT_from_scratch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "source": [
    "# !pip install transformers datasets evaluate"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FAghcFF0wqcw",
    "outputId": "44f68004-0278-441b-90c3-7e0c00925cdd"
   },
   "execution_count": 1,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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      "Installing collected packages: tokenizers, xxhash, multidict, frozenlist, dill, charset-normalizer, async-timeout, yarl, responses, multiprocess, huggingface-hub, aiosignal, transformers, aiohttp, datasets, evaluate\n",
      "Successfully installed aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 charset-normalizer-3.1.0 datasets-2.10.1 dill-0.3.6 evaluate-0.4.0 frozenlist-1.3.3 huggingface-hub-0.13.2 multidict-6.0.4 multiprocess-0.70.14 responses-0.18.0 tokenizers-0.13.2 transformers-4.27.0 xxhash-3.2.0 yarl-1.8.2\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "import transformers\n",
    "from datasets import load_dataset\n",
    "from transformers import BertTokenizer, BertForSequenceClassification, Trainer, TrainingArguments, DataCollatorWithPadding\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import evaluate\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ],
   "metadata": {
    "id": "WGQ1kGMVwpro"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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       "model_id": "49e79057125f4bd9a543856344f51721"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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       "model_id": "a042981860044d05baba8a27769fbb69"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading readme:   0%|          | 0.00/8.78k [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "7be8ccc058bb4944adc4cc6e57f7ea08"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Downloading and preparing dataset emotion/split to /root/.cache/huggingface/datasets/dair-ai___emotion/split/1.0.0/cca5efe2dfeb58c1d098e0f9eeb200e9927d889b5a03c67097275dfb5fe463bd...\n"
     ]
    },
    {
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      ],
      "application/vnd.jupyter.widget-view+json": {
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      }
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     "metadata": {}
    },
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      }
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     "metadata": {}
    },
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      "application/vnd.jupyter.widget-view+json": {
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     "metadata": {}
    },
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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       "model_id": "a6a867f536554b608e74dc0cf5b16ee6"
      }
     },
     "metadata": {}
    },
    {
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "bc975e54b8f246049b8f035648e3f0be"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "1fe61ba078fa4744ba7c424eb77c7054"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Dataset emotion downloaded and prepared to /root/.cache/huggingface/datasets/dair-ai___emotion/split/1.0.0/cca5efe2dfeb58c1d098e0f9eeb200e9927d889b5a03c67097275dfb5fe463bd. Subsequent calls will reuse this data.\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "WARNING:datasets.builder:Found cached dataset emotion (/root/.cache/huggingface/datasets/dair-ai___emotion/split/1.0.0/cca5efe2dfeb58c1d098e0f9eeb200e9927d889b5a03c67097275dfb5fe463bd)\n",
      "WARNING:datasets.builder:Found cached dataset emotion (/root/.cache/huggingface/datasets/dair-ai___emotion/split/1.0.0/cca5efe2dfeb58c1d098e0f9eeb200e9927d889b5a03c67097275dfb5fe463bd)\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "size of train: 16000, validation: 2000, test: 2000\n"
     ]
    }
   ],
   "source": [
    "train = load_dataset('dair-ai/emotion', 'split', split='train')\n",
    "valid = load_dataset('dair-ai/emotion', 'split', split='validation')\n",
    "test = load_dataset('dair-ai/emotion', 'split', split='test')\n",
    "print('size of train: {}, validation: {}, test: {}'.format(len(train), len(valid), len(test)))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "id": "TfIhtdvTwprq",
    "outputId": "573e96c2-a1b0-42a6-90f4-cf8da90583b3"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "In practice, uneven sample sizes could also happen."
   ],
   "metadata": {
    "collapsed": false,
    "id": "h3CuZ3vywprr"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Preprocessing"
   ],
   "metadata": {
    "collapsed": false,
    "id": "hhAg4XWOwprr"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'text': 'i didnt feel humiliated', 'label': 0}"
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "train[0]"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mEA9Nf7uwprs",
    "outputId": "f441b78b-cb35-4e04-c85c-db26729fa431"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "3a09b23900b24b65bf114fc166881697"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "77159abe88694b148c5dc63c21449e80"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)lve/main/config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "e01d7eeb41b140b3af13abb76048d91e"
      }
     },
     "metadata": {}
    }
   ],
   "source": [
    "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 113,
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    },
    "id": "ICrIpLMEwprs",
    "outputId": "b5dd9fbd-fbfd-41df-8293-bbab27ddd480"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Map:   0%|          | 0/16000 [00:00<?, ? examples/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "0cee0636e9a8480ea4c3ec9c1b347909"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Map:   0%|          | 0/2000 [00:00<?, ? examples/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "ae335b3fccf942ec9cc41a8a6750655b"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Map:   0%|          | 0/2000 [00:00<?, ? examples/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "c9f1d75cb0f34583bc1e021eb0acdbe4"
      }
     },
     "metadata": {}
    }
   ],
   "source": [
    "def tokenizing(record):\n",
    "    return tokenizer(record['text'], truncation=True, max_length=300)\n",
    "\n",
    "train_tokenized = train.map(tokenizing, batched=True)\n",
    "valid_tokenized = valid.map(tokenizing, batched=True)\n",
    "test_tokenized = test.map(tokenizing, batched=True)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17,
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     ]
    },
    "id": "bCsQl91Swprt",
    "outputId": "ec8a3ddb-310a-4292-94d1-2f2cae1cb3a0"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'text': 'i didnt feel humiliated',\n",
       " 'label': 0,\n",
       " 'input_ids': [101, 1045, 2134, 2102, 2514, 26608, 102],\n",
       " 'token_type_ids': [0, 0, 0, 0, 0, 0, 0],\n",
       " 'attention_mask': [1, 1, 1, 1, 1, 1, 1]}"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "train_tokenized[0]"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "w5QJJtw9wprt",
    "outputId": "19be8f9b-74ad-4de8-9760-c29d5a06d174"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Define metrics"
   ],
   "metadata": {
    "collapsed": false,
    "id": "vhiyEdfVwpru"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading builder script:   0%|          | 0.00/4.20k [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "5333bec4fc974b2d91423f53d38dde3e"
      }
     },
     "metadata": {}
    }
   ],
   "source": [
    "accuracy = evaluate.load('accuracy')\n",
    "f1 = evaluate.load('f1')\n",
    "precision = evaluate.load('precision')\n",
    "recall = evaluate.load('recall')\n",
    "def metrics(pred):\n",
    "    predictions, labels = pred\n",
    "    predictions = np.argmax(predictions, axis=1)\n",
    "    ret = dict()\n",
    "    ret.update(accuracy.compute(predictions=predictions, references=labels))\n",
    "    ret.update(f1.compute(predictions=predictions, references=labels, average=\"macro\"))\n",
    "    ret.update(precision.compute(predictions=predictions, references=labels, average='macro'))\n",
    "    ret.update(recall.compute(predictions=predictions, references=labels, average='macro'))\n",
    "    return ret"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "5333bec4fc974b2d91423f53d38dde3e",
      "bbc42c1ee68d44a5b65c84d359e2028a",
      "e335188539a94abeafc94e7d1e57dca9",
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      "450691e7c1994cf79bd0afe9edca5ec5",
      "7a5bf7b593644221aaa2bb892d0d58b4"
     ]
    },
    "id": "d-ayZ-Jzwpru",
    "outputId": "e31da044-ae90-437e-f424-0f1fc62e6d2b"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Define model"
   ],
   "metadata": {
    "collapsed": false,
    "id": "w-7yVtPpwprv"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "vocab_size = len(tokenizer.get_vocab())\n",
    "model_cfg = transformers.BertConfig(\n",
    "    vocab_size=vocab_size,\n",
    "    num_labels=6\n",
    ")\n",
    "MyBert = BertForSequenceClassification(model_cfg)"
   ],
   "metadata": {
    "id": "2DfM516-wprv"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Set training arguments"
   ],
   "metadata": {
    "collapsed": false,
    "id": "aQmD9kHgwprv"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [],
   "source": [
    "args = TrainingArguments(\n",
    "    output_dir='./output',\n",
    "    learning_rate=2e-5,\n",
    "    per_device_train_batch_size=128,\n",
    "    per_device_eval_batch_size=128,\n",
    "    optim=\"adamw_torch\",\n",
    "    num_train_epochs=10,\n",
    "    weight_decay=0.2,\n",
    "    evaluation_strategy='epoch',\n",
    "    save_strategy='epoch',\n",
    "    logging_strategy='epoch'\n",
    ")\n",
    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors='pt')\n",
    "trainer = Trainer(\n",
    "    model=MyBert,\n",
    "    args=args,\n",
    "    tokenizer=tokenizer,\n",
    "    data_collator=data_collator,\n",
    "    train_dataset=train_tokenized,\n",
    "    eval_dataset=valid_tokenized,\n",
    "    compute_metrics=metrics\n",
    ")"
   ],
   "metadata": {
    "id": "JjfhGD6fwprv"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Train"
   ],
   "metadata": {
    "collapsed": false,
    "id": "OsWBH2Bhwprw"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ],
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='1250' max='1250' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [1250/1250 26:30, Epoch 10/10]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>No log</td>\n",
       "      <td>1.106419</td>\n",
       "      <td>0.595500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.676108</td>\n",
       "      <td>0.771000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.348282</td>\n",
       "      <td>0.877500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.682600</td>\n",
       "      <td>0.319398</td>\n",
       "      <td>0.887500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.682600</td>\n",
       "      <td>0.282032</td>\n",
       "      <td>0.891000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.682600</td>\n",
       "      <td>0.274547</td>\n",
       "      <td>0.897000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.682600</td>\n",
       "      <td>0.270564</td>\n",
       "      <td>0.899000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.181900</td>\n",
       "      <td>0.278040</td>\n",
       "      <td>0.901000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.181900</td>\n",
       "      <td>0.277550</td>\n",
       "      <td>0.899000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.181900</td>\n",
       "      <td>0.275750</td>\n",
       "      <td>0.898500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "TrainOutput(global_step=1250, training_loss=0.36957623443603516, metrics={'train_runtime': 1591.545, 'train_samples_per_second': 100.531, 'train_steps_per_second': 0.785, 'total_flos': 4956400949520384.0, 'train_loss': 0.36957623443603516, 'epoch': 10.0})"
      ]
     },
     "metadata": {},
     "execution_count": 17
    }
   ],
   "source": [
    "trainer.train()"
   ],
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    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 443
    },
    "id": "b-4ljwB2wprw",
    "outputId": "2191f21d-1ee0-437d-fd7f-d59ff672bfc6"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [],
   "source": [
    "# model_trained = BertForSequenceClassification.from_pretrained('./output/checkpoint-1000/')"
   ],
   "metadata": {
    "id": "rSWS9m8pwprw"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Evaluate on Test set"
   ],
   "metadata": {
    "collapsed": false,
    "id": "2OYNnz5Gwprw"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ],
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     },
     "metadata": {}
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    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "PredictionOutput(predictions=array([[ 7.0165949e+00, -1.6746609e+00, -1.8239363e+00,  1.5467220e-03,\n",
       "        -1.2399427e+00, -2.0806143e+00],\n",
       "       [ 6.6876230e+00, -1.1944501e+00, -2.4416435e+00, -1.0156672e+00,\n",
       "        -8.3823971e-02, -1.7264107e+00],\n",
       "       [ 6.7442532e+00, -1.7427825e+00, -1.5358291e+00,  7.6650566e-01,\n",
       "        -1.1692830e+00, -2.9643319e+00],\n",
       "       ...,\n",
       "       [-2.6119847e+00,  7.1800365e+00, -9.8436677e-01, -1.6442600e+00,\n",
       "        -2.0482655e+00, -4.8150644e-01],\n",
       "       [-1.4948800e+00,  7.2474575e+00, -5.2126676e-01, -2.0270472e+00,\n",
       "        -2.2380035e+00, -1.4791368e+00],\n",
       "       [-6.8568510e-01, -2.6536300e+00, -1.7572117e+00, -2.1163754e+00,\n",
       "         4.0461621e+00,  3.7217915e+00]], dtype=float32), label_ids=array([0, 0, 0, ..., 1, 1, 4]), metrics={'test_loss': 0.25778356194496155, 'test_accuracy': 0.892, 'test_runtime': 6.606, 'test_samples_per_second': 302.756, 'test_steps_per_second': 2.422})"
      ]
     },
     "metadata": {},
     "execution_count": 19
    }
   ],
   "source": [
    "pred = trainer.predict(test_tokenized)\n",
    "pred"
   ],
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    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 285
    },
    "id": "Ok8mQzznwprw",
    "outputId": "b0c61b6b-c493-4b6e-fc02-120af16b8d90"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "pred.predictions[:10]"
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   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ITKWzDnu5l2M",
    "outputId": "cde6fd35-d85d-4045-f6e7-9a6db481b2b6"
   },
   "execution_count": 27,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[ 7.0165949e+00, -1.6746609e+00, -1.8239363e+00,  1.5467220e-03,\n",
       "        -1.2399427e+00, -2.0806143e+00],\n",
       "       [ 6.6876230e+00, -1.1944501e+00, -2.4416435e+00, -1.0156672e+00,\n",
       "        -8.3823971e-02, -1.7264107e+00],\n",
       "       [ 6.7442532e+00, -1.7427825e+00, -1.5358291e+00,  7.6650566e-01,\n",
       "        -1.1692830e+00, -2.9643319e+00],\n",
       "       [-1.8422629e+00,  7.0467124e+00, -1.0968795e+00, -1.4894482e+00,\n",
       "        -1.3538961e+00, -2.1228695e+00],\n",
       "       [ 6.8569651e+00, -1.4998029e+00, -2.2594230e+00, -7.6733273e-01,\n",
       "        -4.9236500e-01, -1.5739446e+00],\n",
       "       [-1.1266652e+00,  5.5088532e-01, -2.6890302e+00, -1.3966659e+00,\n",
       "         5.7976651e+00, -1.1004949e+00],\n",
       "       [ 1.4859027e+00, -1.9868534e+00, -2.2421050e+00,  5.8768916e+00,\n",
       "        -1.6447313e+00, -2.1945164e+00],\n",
       "       [-1.8021313e+00,  4.6182923e+00,  3.0606413e+00, -5.6828356e-01,\n",
       "        -3.1741593e+00, -3.0398793e+00],\n",
       "       [-2.1408687e+00,  7.1573124e+00, -1.1566513e+00, -1.4426515e+00,\n",
       "        -2.3147523e+00, -7.1305311e-01],\n",
       "       [-1.2842572e+00,  6.8302363e-01, -7.6827891e-02,  4.9819326e+00,\n",
       "        -1.6998527e+00, -3.4395730e+00]], dtype=float32)"
      ]
     },
     "metadata": {},
     "execution_count": 27
    }
   ]
  },
  {
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   "execution_count": 30,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "tensor([0, 0, 0, 1, 0, 4, 3, 1, 1, 3, 4, 0, 0, 1, 2, 0, 1, 0, 3, 1])"
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     "execution_count": 30
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     "execution_count": 31
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