SentimentalAnalysisWithDistilbert.ipynb

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            "\u001b[?25h"
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            "fa0b0c30b6e440e6a8e431fee716073a",
            "dfa9b6fee68844769cbf6a59cdc1548f",
            "5a87701c0f074237a467b3d426b8fea8",
            "e0222afa3a5049c5bad78d9c4f22007b",
            "5c101d9d49b3496abde1588520e651d5",
            "28f982b2802f40d5810faaa82861fe13",
            "6e906899594640c8958a44fba4824d09",
            "25aeded203ca4eb194b30cefe5f0838b",
            "0c31d739c1c14937a2174e228b8309df",
            "7cd8253ee3c748e498ab09efd46f17b2"
          ]
        },
        "id": "OXj7HfPX4dy2",
        "outputId": "142f4786-aaf7-4fd7-e574-28d7e4e6398b"
      },
      "source": [
        "from datasets import load_dataset\n",
        "emotions = load_dataset(\"emotion\")"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2e655fa10cd34f5e9a8d37cda903023a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1600.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8c53fb0d096d486ea8d318d31f023587",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1649.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Using custom data configuration default\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Downloading and preparing dataset emotion/default (download: 1.97 MiB, generated: 2.07 MiB, post-processed: Unknown size, total: 4.05 MiB) to /root/.cache/huggingface/datasets/emotion/default/0.0.0/6e4212efe64fd33728549b8f0435c73081391d543b596a05936857df98acb681...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1658616.0, style=ProgressStyle(descript…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=204240.0, style=ProgressStyle(descripti…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=206760.0, style=ProgressStyle(descripti…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bdf6dc7a67764a2c815827836e234d92",
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5a87701c0f074237a467b3d426b8fea8",
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rDataset emotion downloaded and prepared to /root/.cache/huggingface/datasets/emotion/default/0.0.0/6e4212efe64fd33728549b8f0435c73081391d543b596a05936857df98acb681. Subsequent calls will reuse this data.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "geh_UBlk6XeI"
      },
      "source": [
        "import torch\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AZm-Ve9i5_oL"
      },
      "source": [
        "## Step2. Preprocess Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lbKr76Pu6hYT",
        "outputId": "f9942709-ce77-444c-8279-f7a2a7685c2c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215,
          "referenced_widgets": [
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            "0ff0164694ad404e8adc1ab640b82436",
            "3bac0483c3ad49a2bf6f5a47fa561ce7",
            "eae5905346b44281b3d60cf7f6114b5f",
            "692c4320488540518cb8ef912ff7f76f",
            "20cfdd75d625427589bbaf6ef3e89719",
            "daee3aac126b45ce9c94f67d8dd4ec0e",
            "8793d001580d4d87ba1b7a4e8d54f281",
            "ac78084f9a7f47418cacf1093f24c3ad",
            "fa75edcefa984881bb4b684db9db3e64",
            "139bf8a5d298408c8fd65a0e0d045a28",
            "2fb93c8b34104ac4b7e702b2c836638a"
          ]
        }
      },
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "model_name = \"bert-base-uncased\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "\n",
        "def tokenize(batch):\n",
        "    return tokenizer(batch[\"text\"], padding=True, truncation=True)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=570.0, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0e3a69d5faf64cf59ad3916ba8b1d800",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=231508.0, style=ProgressStyle(descripti…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bdfd3041523548eb8b2030097da51a33",
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=466062.0, style=ProgressStyle(descripti…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "692c4320488540518cb8ef912ff7f76f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=28.0, style=ProgressStyle(description_w…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166,
          "referenced_widgets": [
            "c3a6a9da6a5346b282de3ac8d4961d76",
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            "506d9a7118a44517bfa77cda3c71f574",
            "19bd2b92cd9e4dc791a1ced494785bf2",
            "245a4fb3fd564673aaa887cdc29967a5",
            "0627a4513f674efa90c8ceb65e8da517",
            "8e446da7ae2a4c69a08c7b4f36d0452b",
            "adce132c666e4637a3429bda2981b224",
            "bde67b8b3b0e4b9db9ea2ccffdbc2ce7",
            "c30891a65a3344b9b742e77d826f98e6",
            "4e65c8efaeda4d2f959ac85c6fa8cbfd",
            "c6081a585d1d4145b2287540538937ec",
            "556bc8b35ca74c21afa4c87f0b21d1dd",
            "535e65167b394b798655099b27f756d6",
            "cfb46e03df704fa9a37e9c94977a93a4",
            "91059ce49af5470dae505a2b1dc0745d",
            "675992fd5f2040a2b92187fbdd5c189f",
            "ffaf1845afa54921bb134fe5d6da82d2",
            "0b354d1c333b4563af826f298c63199d",
            "45467de730c24e6fb10b7930a73f7534"
          ]
        },
        "id": "QGl8lWiU5R4O",
        "outputId": "97593cae-8644-4f8c-a860-a4d894b3d4e1"
      },
      "source": [
        "emotions_encoded = emotions.map(tokenize, batched=True, batch_size=None)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c3a6a9da6a5346b282de3ac8d4961d76",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "245a4fb3fd564673aaa887cdc29967a5",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "556bc8b35ca74c21afa4c87f0b21d1dd",
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              "HBox(children=(FloatProgress(value=0.0, max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174,
          "referenced_widgets": [
            "3fa47443825548b3a077abcbd1e5e3a3",
            "058abe41dcd24392b1711f0241d3503c",
            "2c2824ea34d6470b8618cc7484851a32",
            "816731b7226048a596e1349e39ed52c4",
            "81f1bf8b2b3040ba89df0429e8b8d9f3",
            "f686feff88ad41ce8234e813f0951186",
            "de5dc462698146eda2210c754df708d0",
            "28dc719f816d45289341b63094ffae0e"
          ]
        },
        "id": "U0HoQZnD5iOa",
        "outputId": "9a247717-e81f-4e35-a9a5-139cb1feedf7"
      },
      "source": [
        "from transformers import AutoModelForSequenceClassification\n",
        "num_labels = 6\n",
        "model = (AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=num_labels).to(device))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3fa47443825548b3a077abcbd1e5e3a3",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=440473133.0, style=ProgressStyle(descri…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.transform.dense.weight']\n",
            "- This IS expected if you are initializing BertForSequenceClassification 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 BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OJmzWQ117AOJ",
        "outputId": "426e66ea-75b6-4548-f85a-1e464fe3b739"
      },
      "source": [
        "emotions_encoded[\"train\"].features"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'attention_mask': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None),\n",
              " 'input_ids': Sequence(feature=Value(dtype='int32', id=None), length=-1, id=None),\n",
              " 'label': ClassLabel(num_classes=6, names=['sadness', 'joy', 'love', 'anger', 'fear', 'surprise'], names_file=None, id=None),\n",
              " 'text': Value(dtype='string', id=None),\n",
              " 'token_type_ids': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None)}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FXhQn19v7O42",
        "outputId": "34d285fb-f446-4156-9999-482aea8796e5"
      },
      "source": [
        "emotions_encoded.set_format(\"torch\", columns=[\"input_ids\", \"attention_mask\", \"label\"])\n",
        "emotions_encoded[\"train\"].features"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'attention_mask': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None),\n",
              " 'input_ids': Sequence(feature=Value(dtype='int32', id=None), length=-1, id=None),\n",
              " 'label': ClassLabel(num_classes=6, names=['sadness', 'joy', 'love', 'anger', 'fear', 'surprise'], names_file=None, id=None),\n",
              " 'text': Value(dtype='string', id=None),\n",
              " 'token_type_ids': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None)}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U0UvJsUK7c1f"
      },
      "source": [
        "from sklearn.metrics import accuracy_score, f1_score\n",
        "\n",
        "def compute_metrics(pred):\n",
        "    labels = pred.label_ids\n",
        "    preds = pred.predictions.argmax(-1)\n",
        "    f1 = f1_score(labels, preds, average=\"weighted\")\n",
        "    acc = accuracy_score(labels, preds)\n",
        "    return {\"accuracy\": acc, \"f1\": f1}"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pMXplrNj7lgi"
      },
      "source": [
        "from transformers import Trainer, TrainingArguments\n",
        "\n",
        "batch_size = 64\n",
        "logging_steps = len(emotions_encoded[\"train\"]) // batch_size\n",
        "training_args = TrainingArguments(output_dir=\"results\",\n",
        "                                  num_train_epochs=8,\n",
        "                                  learning_rate=2e-5,\n",
        "                                  per_device_train_batch_size=batch_size,\n",
        "                                  per_device_eval_batch_size=batch_size,\n",
        "                                  load_best_model_at_end=True,\n",
        "                                  metric_for_best_model=\"f1\",\n",
        "                                  weight_decay=0.01,\n",
        "                                  evaluation_strategy=\"epoch\",\n",
        "                                  save_strategy=\"no\",\n",
        "                                  disable_tqdm=False)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "gHmZ0uCz7z6c",
        "outputId": "56d44e3c-80af-42c5-e50d-16bbfea6d6d0"
      },
      "source": [
        "from transformers import Trainer\n",
        "\n",
        "trainer = Trainer(model=model, args=training_args,\n",
        "                  compute_metrics=compute_metrics,\n",
        "                  train_dataset=emotions_encoded[\"train\"],\n",
        "                  eval_dataset=emotions_encoded[\"validation\"])\n",
        "trainer.train();"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2000' max='2000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2000/2000 35:25, Epoch 8/8]\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",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.222840</td>\n",
              "      <td>0.921000</td>\n",
              "      <td>0.920150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.453100</td>\n",
              "      <td>0.168043</td>\n",
              "      <td>0.934500</td>\n",
              "      <td>0.934587</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.453100</td>\n",
              "      <td>0.133198</td>\n",
              "      <td>0.938500</td>\n",
              "      <td>0.938607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.109100</td>\n",
              "      <td>0.148102</td>\n",
              "      <td>0.938000</td>\n",
              "      <td>0.937897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.109100</td>\n",
              "      <td>0.157691</td>\n",
              "      <td>0.940500</td>\n",
              "      <td>0.940592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.068100</td>\n",
              "      <td>0.181132</td>\n",
              "      <td>0.937000</td>\n",
              "      <td>0.937110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.068100</td>\n",
              "      <td>0.189021</td>\n",
              "      <td>0.936500</td>\n",
              "      <td>0.936228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.042600</td>\n",
              "      <td>0.189509</td>\n",
              "      <td>0.937500</td>\n",
              "      <td>0.937500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160
        },
        "id": "FkfjSLOM786Y",
        "outputId": "c7f39c33-2767-4d10-93f3-1c982f1ff6e0"
      },
      "source": [
        "results = trainer.evaluate()\n",
        "results"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='32' max='32' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [32/32 00:09]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'epoch': 8.0,\n",
              " 'eval_accuracy': 0.9405,\n",
              " 'eval_f1': 0.9405920712282673,\n",
              " 'eval_loss': 0.15769127011299133,\n",
              " 'eval_runtime': 10.2399,\n",
              " 'eval_samples_per_second': 195.315,\n",
              " 'eval_steps_per_second': 3.125}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "hz4WJIg1FDhq",
        "outputId": "0700c947-1239-49bf-ec84-3e9483c47ab1"
      },
      "source": [
        "preds_output = trainer.predict(emotions_encoded[\"validation\"])\n",
        "preds_output.metrics"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='64' max='32' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [32/32 00:26]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'test_accuracy': 0.9405,\n",
              " 'test_f1': 0.9405920712282673,\n",
              " 'test_loss': 0.15769127011299133,\n",
              " 'test_runtime': 10.5179,\n",
              " 'test_samples_per_second': 190.152,\n",
              " 'test_steps_per_second': 3.042}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "BgTZbwmIFIQx",
        "outputId": "641aefc8-e89c-4f34-960b-483310a49d42"
      },
      "source": [
        "import numpy as np\n",
        "from sklearn.metrics import plot_confusion_matrix\n",
        "y_valid = np.array(emotions_encoded[\"validation\"][\"label\"])\n",
        "y_preds = np.argmax(preds_output.predictions, axis=1)\n",
        "labels = ['sadness', 'joy', 'love', 'anger', 'fear', 'surprise']\n",
        "plot_confusion_matrix(y_preds, y_valid, labels)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-18-c363f9a92547>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0my_preds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpreds_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'sadness'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'joy'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'love'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'anger'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'fear'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'surprise'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mplot_confusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_preds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_valid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_plot/confusion_matrix.py\u001b[0m in \u001b[0;36mplot_confusion_matrix\u001b[0;34m(estimator, X, y_true, labels, sample_weight, normalize, display_labels, include_values, xticks_rotation, values_format, cmap, ax)\u001b[0m\n\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_classifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 183\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"plot_confusion_matrix only supports classifiers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: plot_confusion_matrix only supports classifiers"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o1U2ULrR-CUX",
        "outputId": "5c8e4bc9-df9b-4edd-ece6-0a68c42d3508"
      },
      "source": [
        "model.save_pretrained('./model')\n",
        "tokenizer.save_pretrained('./model')"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('./model/tokenizer_config.json',\n",
              " './model/special_tokens_map.json',\n",
              " './model/vocab.txt',\n",
              " './model/added_tokens.json',\n",
              " './model/tokenizer.json')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5_8HsSu9MSvK",
        "outputId": "808c8335-bf99-44d6-90f4-eef546e19bb6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!transformers-cli login"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2021-06-17 18:24:14.398628: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n",
            "\n",
            "        _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "        _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "        _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "        _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "        _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "        \n",
            "Username: bhadresh-savani\n",
            "Password: \n",
            "Login successful\n",
            "Your token: USSrKekMAIdNYfcxwutSzdOSvEXmxQJcQWvJIQYlcxLUEOdLuBlwYFYRPUksnUhbBXDghVggoqQhdHoXexdlwHWVyytklJLRARIxPrkYfecZgatctBQwalZlMrMVWVyG \n",
            "\n",
            "Your token has been saved to /root/.huggingface/token\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f5oMGKOcBlPl",
        "outputId": "00a704c7-c42a-460f-c916-81a664aec944",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!sudo apt-get install git-lfs"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following NEW packages will be installed:\n",
            "  git-lfs\n",
            "0 upgraded, 1 newly installed, 0 to remove and 39 not upgraded.\n",
            "Need to get 2,129 kB of archives.\n",
            "After this operation, 7,662 kB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 git-lfs amd64 2.3.4-1 [2,129 kB]\n",
            "Fetched 2,129 kB in 1s (1,661 kB/s)\n",
            "debconf: unable to initialize frontend: Dialog\n",
            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 1.)\n",
            "debconf: falling back to frontend: Readline\n",
            "debconf: unable to initialize frontend: Readline\n",
            "debconf: (This frontend requires a controlling tty.)\n",
            "debconf: falling back to frontend: Teletype\n",
            "dpkg-preconfigure: unable to re-open stdin: \n",
            "Selecting previously unselected package git-lfs.\n",
            "(Reading database ... 160772 files and directories currently installed.)\n",
            "Preparing to unpack .../git-lfs_2.3.4-1_amd64.deb ...\n",
            "Unpacking git-lfs (2.3.4-1) ...\n",
            "Setting up git-lfs (2.3.4-1) ...\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Ubq1knsB8-l"
      },
      "source": [
        "!git config --global user.email \"bhadreshpsavani@gmail.com\"\n",
        "!git config --global user.name \"*****\"\n",
        "!git config --global user.password \"****\""
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OtbZdoPYBAPj"
      },
      "source": [
        "model.push_to_hub('bert-base-uncased-emotion')"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zny00Im7Bapy"
      },
      "source": [
        "tokenizer.push_to_hub('bert-base-uncased-emotion')"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8p8xWXMmCPor"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}