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        "import pandas as pd\n",
        "import json\n",
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              "      <td>edit  standardize  or make changes to material...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Technical writer</td>\n",
              "      <td>select photographs  drawings  sketches  diagra...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Technical writer</td>\n",
              "      <td>interview production and engineering personnel...</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['encoded_cat'] = df['category'].astype('category').cat.codes\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "4dp6V2sc-Yy0",
        "outputId": "3801fb3f-3e7a-482c-9798-2026a6c96cf3"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           category                                               text  \\\n",
              "0  Technical writer  organize material and complete writing assignm...   \n",
              "1  Technical writer   maintain records and files of work and revisions   \n",
              "2  Technical writer  edit  standardize  or make changes to material...   \n",
              "3  Technical writer  select photographs  drawings  sketches  diagra...   \n",
              "4  Technical writer  interview production and engineering personnel...   \n",
              "\n",
              "   encoded_cat  \n",
              "0           55  \n",
              "1           55  \n",
              "2           55  \n",
              "3           55  \n",
              "4           55  "
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              "                                                     [key], {});\n",
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              "\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_texts = df[\"text\"].to_list() # Features (not-tokenized yet)\n",
        "data_labels = df[\"encoded_cat\"].to_list() # Lables"
      ],
      "metadata": {
        "id": "nwha3xMv-bWE"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split Train and Validation data\n",
        "train_texts, val_texts, train_labels, val_labels = train_test_split(data_texts, data_labels, test_size=0.2, random_state=1000)"
      ],
      "metadata": {
        "id": "V0L894Ah-f4Y"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')\n",
        "train_encodings = tokenizer(train_texts, truncation=True, padding=True)\n",
        "val_encodings = tokenizer(val_texts, truncation=True, padding=True)"
      ],
      "metadata": {
        "colab": {
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      "execution_count": 7,
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
          "data": {
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              "Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
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              "Downloading (…)lve/main/config.json:   0%|          | 0.00/483 [00:00<?, ?B/s]"
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    {
      "cell_type": "code",
      "source": [
        "train_dataset = tf.data.Dataset.from_tensor_slices((\n",
        "    dict(train_encodings),\n",
        "    train_labels\n",
        "))\n",
        "val_dataset = tf.data.Dataset.from_tensor_slices((dict(val_encodings), val_labels ))"
      ],
      "metadata": {
        "id": "QNzTP8Oc-m2_"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = TFDistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=21)\n",
        "\n",
        "optimizer = tf.keras.optimizers.Adam(learning_rate=5e-5)\n",
        "model.compile(optimizer=optimizer, loss=model.compute_loss, metrics=['accuracy'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tf_model.h5:   0%|          | 0.00/363M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some layers from the model checkpoint at distilbert-base-uncased were not used when initializing TFDistilBertForSequenceClassification: ['vocab_transform', 'activation_13', 'vocab_projector', 'vocab_layer_norm']\n",
            "- This IS expected if you are initializing TFDistilBertForSequenceClassification 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 TFDistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some layers of TFDistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['dropout_19', 'pre_classifier', 'classifier']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(train_dataset.shuffle(100).batch(16), epochs=5, batch_size=16, validation_data=val_dataset.shuffle(100).batch(16))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q8fjF_A1-sP2",
        "outputId": "fe931082-28db-4a23-c470-3a237545219e"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "62/62 [==============================] - 8s 123ms/step - loss: 1.5237 - accuracy: 0.5237 - val_loss: 2.2847 - val_accuracy: 0.3024\n",
            "Epoch 2/5\n",
            "62/62 [==============================] - 7s 105ms/step - loss: 1.4421 - accuracy: 0.5378 - val_loss: 2.3720 - val_accuracy: 0.2823\n",
            "Epoch 3/5\n",
            "62/62 [==============================] - 7s 106ms/step - loss: 1.3973 - accuracy: 0.5439 - val_loss: 2.4879 - val_accuracy: 0.2742\n",
            "Epoch 4/5\n",
            "62/62 [==============================] - 7s 105ms/step - loss: 1.3523 - accuracy: 0.5610 - val_loss: 2.4525 - val_accuracy: 0.2944\n",
            "Epoch 5/5\n",
            "62/62 [==============================] - 7s 111ms/step - loss: 1.3322 - accuracy: 0.5469 - val_loss: 2.5269 - val_accuracy: 0.2944\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fb236ff79d0>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "save_directory = \"/saved_models\" # change this to your preferred location\n",
        "\n",
        "model.save_pretrained(save_directory)\n",
        "tokenizer.save_pretrained(save_directory)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QOgRMyjz_cdb",
        "outputId": "f41136b6-5abb-43ed-c414-b3df98013ce2"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('/saved_models/tokenizer_config.json',\n",
              " '/saved_models/special_tokens_map.json',\n",
              " '/saved_models/vocab.txt',\n",
              " '/saved_models/added_tokens.json')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loaded_tokenizer = DistilBertTokenizer.from_pretrained(save_directory)\n",
        "loaded_model = TFDistilBertForSequenceClassification.from_pretrained(save_directory)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5FoDCmk1_-MO",
        "outputId": "983d5b90-1c81-46e4-a113-7d63dbadb4f6"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some layers from the model checkpoint at /saved_models were not used when initializing TFDistilBertForSequenceClassification: ['dropout_19']\n",
            "- This IS expected if you are initializing TFDistilBertForSequenceClassification 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 TFDistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some layers of TFDistilBertForSequenceClassification were not initialized from the model checkpoint at /saved_models and are newly initialized: ['dropout_39']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install huggingface_hub"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-YpirwFG5IIR",
        "outputId": "0172d5d2-34f1-4c00-9d46-81c4709bef74"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.9/dist-packages (0.13.3)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.9/dist-packages (from huggingface_hub) (6.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from huggingface_hub) (2.27.1)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.9/dist-packages (from huggingface_hub) (23.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from huggingface_hub) (3.10.7)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.9/dist-packages (from huggingface_hub) (4.65.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.9/dist-packages (from huggingface_hub) (3.7.4.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->huggingface_hub) (2022.12.7)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests->huggingface_hub) (2.0.12)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->huggingface_hub) (1.26.15)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests->huggingface_hub) (3.4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387,
          "referenced_widgets": [
            "7aa5bc080dad4be6b0ac49fe0240ce89",
            "715119cd9a58493db5983d62d9d8bad5",
            "e350eee1c20348e598ca3707ebe2068b",
            "1ca3d1be0d464ca0a25c678c540d2cb4",
            "c37d1ccf56fc431e99924a9a978ec2ef",
            "50b3c2e37a0a430e9384316777a66abe",
            "b10679fb7d9f4ddbaf5638d8ee3caaa3",
            "dc47e25dfbcb406cae40caed3340d2cb",
            "8b706e4a73f8444b9242ffd9a6d51dcd",
            "74251c2768b5449dbc94a75ab2a4f20c",
            "a89885231e0e4df994a051fd07dd1b55",
            "c1ab0ff13c0f4c0b89b9c76c6fb7f661",
            "1cf5fd8e9b3a423bafc47e9fd5ec4fb8",
            "220ec08abd764f8c8b960f316fb653ed",
            "158d2eda521547f89bc645f34d9de3de",
            "4b4098c342c74ba08f6330313a0f1022",
            "08d9cc833707450d8743e49d8ac0dacf"
          ]
        },
        "id": "0_vOrKPy5VMX",
        "outputId": "cbe9c75d-14a9-49b1-c803-91f18d81321c"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Token is valid.\n",
            "Your token has been saved in your configured git credential helpers (store).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.push_to_hub(\"lfernandopg/Proyecto-Transformers\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "7baac729da904ceeacfdc3e87b524d66",
            "526d7f9597b844d399f48013d1c25471",
            "6392c9c107554f6da5329d647bf55656",
            "548acdc1bce6487a80e249dedb8a3064",
            "fb6e997a32f7459e995ffac7624af8a5",
            "9fa787762c594275ba9664b757618323",
            "493aac2bf5c340b9ac8ca3be2b39479f",
            "a8a7b0d2f00c4a04a12dcac14353bcd0",
            "c0d25439b2dc49788a9cd0967ef7e41e",
            "36e83b61ae7445a9b5a1dd91ecfc2f1a",
            "9bb6745fa7484698bf5e342bc75d7960",
            "16c3c53007eb4397a177bb9916e92359",
            "66f1907c4b064d40887540d2a6e82e7f",
            "09c59356ac5f438888e9693e5da5c27e",
            "23cb49268ac849668fd66e1cbd7839b4",
            "3f8b3354f3154c0180e01c9ebf2d4d03",
            "cede094415ac4c6c9226d4c244eba2ea",
            "6b175a405eeb4d9b9e0966c47d98d00e",
            "bd57483e4b254d038aef9d6acaf5c8e6",
            "6a7372d350ae45dd8f5844caf81c1590",
            "b69898022c7a485caf578ba7100ff658",
            "2f19e4fa46834e38a47153d8143f8cfd"
          ]
        },
        "id": "itffU8Ke6ET5",
        "outputId": "f3d96331-8117-475e-fbd6-e2aa5bee5f06"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tf_model.h5:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7baac729da904ceeacfdc3e87b524d66"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload 1 LFS files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "16c3c53007eb4397a177bb9916e92359"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.push_to_hub(\"lfernandopg/Proyecto-Transformers\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q2RzBT9lFobI",
        "outputId": "01a9590a-0659-4556-b79b-00b42a3f873c"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/lfernandopg/Proyecto-Transformers/commit/609ec9a9f028cf40873cd1180aa1776bdb0126b9', commit_message='Upload tokenizer', commit_description='', oid='609ec9a9f028cf40873cd1180aa1776bdb0126b9', pr_url=None, pr_revision=None, pr_num=None)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    }
  ]
}