transformers.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/hrushikute/DataAnalytics/blob/master/transformers.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lYyRJCxCS8Sw",
        "outputId": "caefb350-e9c5-482a-86ae-f3ac9f3b39db"
      },
      "outputs": [
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          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.7/dist-packages (4.16.2)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.10.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.62.3)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.1.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.4.0)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,>=0.10.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.11.4)\n",
            "Requirement already satisfied: sacremoses in /usr/local/lib/python3.7/dist-packages (from transformers) (0.0.47)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.4.2)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (3.10.0.2)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.7)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.7.0)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2021.10.8)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.1.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n",
            "\u001b[K     |████████████████████████████████| 48 kB 2.8 MB/s \n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[K     |████████████████████████████████| 788 kB 14.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 53 kB 1.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.2 MB 43.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 19.7 MB 1.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 981 kB 49.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 63 kB 1.5 MB/s \n",
            "\u001b[?25h  Building wheel for gdown (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for mpld3 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for overrides (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for sqlitedict (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for langdetect (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for wikipedia-api (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "markdown 3.3.6 requires importlib-metadata>=4.4; python_version < \"3.10\", but you have importlib-metadata 3.10.1 which is incompatible.\n",
            "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.27.1 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "!pip install --upgrade transformers\n",
        "\n",
        "# Full Development Version\n",
        "!pip install -q --upgrade transformers[sentencepiece]\n",
        "\n",
        "# Install Flair NLP library - https://github.com/flairNLP/flair\n",
        "!pip install -q --upgrade flair"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import pipeline\n",
        "from transformers import pipeline\n",
        "import tensorflow as tf\n",
        "\n",
        "# Instantiate the pipeline\n",
        "\n",
        "classifier = pipeline(\"sentiment-analysis\")\n",
        "\n",
        "print(classifier([\n",
        "                 \"Beyond business, he was passionate about community service and was a great conversationalist. Pained by his demise. Condolences to his family and friends\",\n",
        "                 \"RIP to all bank nifty gap down pandits\"\n",
        "                 ])\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7mhVwii9VAU7",
        "outputId": "db2daab2-5ac3-447d-c8bc-671ae9bcfee7"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to distilbert-base-uncased-finetuned-sst-2-english (https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'label': 'POSITIVE', 'score': 0.9980022311210632}, {'label': 'NEGATIVE', 'score': 0.9914584755897522}]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        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)\n"
      ],
      "metadata": {
        "id": "E11RjcQdTvP2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "PreProcessing with Tokenizers"
      ],
      "metadata": {
        "id": "3xzViBe5Tyib"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
        "\n",
        "raw_input = [\n",
        "                 \"Beyond business, he was passionate about community service and was a great conversationalist. Pained by his demise. Condolences to his family and friends\",\n",
        "                 \"RIP to all bank nifty gap down pandits\"\n",
        "                 ]\n",
        "\n",
        "print(\"=================================\\n     TOKENIZER \\n=================================\")\n",
        "print(tokenizer)\n",
        "print(\"=================================\\n     RAW INPUT \\n=================================\")\n",
        "print(raw_input)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_f51HX5QT662",
        "outputId": "45adf0d0-c591-4ed0-baf2-205917453795"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=================================\n",
            "     TOKENIZER \n",
            "=================================\n",
            "PreTrainedTokenizerFast(name_or_path='distilbert-base-uncased-finetuned-sst-2-english', vocab_size=30522, model_max_len=512, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'unk_token': '[UNK]', 'sep_token': '[SEP]', 'pad_token': '[PAD]', 'cls_token': '[CLS]', 'mask_token': '[MASK]'})\n",
            "=================================\n",
            "     RAW INPUT \n",
            "=================================\n",
            "['Beyond business, he was passionate about community service and was a great conversationalist. Pained by his demise. Condolences to his family and friends', 'RIP to all bank nifty gap down pandits']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf_inputs = tokenizer(raw_input, padding=True, truncation=True,  return_tensors=\"tf\")\n",
        "print(\"=================================\\n     tf inputs \\n=================================\")\n",
        "print(tf_inputs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6g2EgEHVUGEU",
        "outputId": "fa45db91-a4b3-4c10-9366-f761b5a1bb95"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=================================\n",
            "     tf inputs \n",
            "=================================\n",
            "{'input_ids': <tf.Tensor: shape=(2, 32), dtype=int32, numpy=\n",
            "array([[  101,  3458,  2449,  1010,  2002,  2001, 13459,  2055,  2451,\n",
            "         2326,  1998,  2001,  1037,  2307,  4512, 13911,  2102,  1012,\n",
            "        22295,  2011,  2010, 13614,  1012, 25805, 22717,  2015,  2000,\n",
            "         2010,  2155,  1998,  2814,   102],\n",
            "       [  101, 10973,  2000,  2035,  2924,  9152,  6199,  2100,  6578,\n",
            "         2091, 29331,  2015,   102,     0,     0,     0,     0,     0,\n",
            "            0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
            "            0,     0,     0,     0,     0]], dtype=int32)>, 'attention_mask': <tf.Tensor: shape=(2, 32), dtype=int32, numpy=\n",
            "array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
            "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
            "       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=int32)>}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Going Through Model "
      ],
      "metadata": {
        "id": "6TiluXwSXzlx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TFAutoModel\n",
        "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
        "# Load the model\n",
        "\n",
        "model = TFAutoModel.from_pretrained(checkpoint)\n",
        "print(\"=================================\\n     Model \\n=================================\")\n",
        "print(model)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225,
          "referenced_widgets": [
            "1a93fc556d3d4351a8ba00e1a7baf9dd",
            "69595f12193c48f8a477099c9ba6d961",
            "ff97fc5be28b426f8132f548d5af5bb1",
            "096a1c52866f41219dfb3559be1507f6",
            "257b44d6cfd84ce487d2b0205376fb7c",
            "1e580f60218d484b9c778ce3afe99d73",
            "34d27dc4290f449e8144c969e5a1d230",
            "a49c8dc341054e67a4e10aa0b0921577",
            "7e913005cd034966b3a09f0f415bab60",
            "c40cea51ebe749fdba46eb53b56088ea",
            "403171011733421081172a9ad064b349"
          ]
        },
        "id": "2LBuYfYDZBLw",
        "outputId": "ffb654e0-5b9f-4b67-f8e3-09305496b109"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1a93fc556d3d4351a8ba00e1a7baf9dd",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/256M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some layers from the model checkpoint at distilbert-base-uncased-finetuned-sst-2-english were not used when initializing TFDistilBertModel: ['classifier', 'dropout_19', 'pre_classifier']\n",
            "- This IS expected if you are initializing TFDistilBertModel 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 TFDistilBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "All the layers of TFDistilBertModel were initialized from the model checkpoint at distilbert-base-uncased-finetuned-sst-2-english.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFDistilBertModel for predictions without further training.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=================================\n",
            "     Model \n",
            "=================================\n",
            "<transformers.models.distilbert.modeling_tf_distilbert.TFDistilBertModel object at 0x7f8afa5afc50>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Vector output of transformer model\n",
        "\n",
        "The vector output by the Transformer module is usually large. It generally has three dimensions:\n",
        "\n",
        "\n",
        "\n",
        "*   Batch Size: The number of sequences processed at a time (2 in our example).\n",
        "*   Sequence Length: The length of the numerical representation of the sequence (16 in our example).\n",
        "\n",
        "*   Hidden Size: The vector dimension of each model input\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        ".\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "_kMUrNpKZiCu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tf_outputs = model(tf_inputs)\n",
        "print(\"=================================\\n   Raw TF output \\n=================================\")\n",
        "print(tf_outputs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LPYjm5hkadUN",
        "outputId": "dedcafd3-3e52-4129-b5f1-5885964dfca1"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=================================\n",
            "   Raw TF output \n",
            "=================================\n",
            "TFBaseModelOutput(last_hidden_state=<tf.Tensor: shape=(2, 32, 768), dtype=float32, numpy=\n",
            "array([[[ 0.33690408,  0.1769116 ,  0.2587504 , ...,  0.5565776 ,\n",
            "          0.53172517, -0.27726588],\n",
            "        [ 0.5929333 ,  0.5393171 , -0.33838904, ..., -0.02571315,\n",
            "          1.1667072 , -0.33906192],\n",
            "        [ 0.50029397,  0.4612086 , -0.69921046, ..., -0.55066997,\n",
            "          0.6008497 , -0.37206405],\n",
            "        ...,\n",
            "        [ 0.7049812 ,  0.8557627 ,  0.8748533 , ...,  0.3800969 ,\n",
            "          0.34265214, -0.3487924 ],\n",
            "        [ 0.12011801,  0.5697107 ,  0.79569614, ...,  0.51953113,\n",
            "          0.2179576 , -0.38870358],\n",
            "        [ 0.97776014,  0.164042  ,  0.41255245, ...,  0.8867819 ,\n",
            "          0.14614522, -0.565903  ]],\n",
            "\n",
            "       [[-0.5708207 ,  0.4735319 ,  0.06376486, ..., -0.08832987,\n",
            "          0.15053229, -0.12497143],\n",
            "        [-0.3993901 ,  0.46335846,  0.10603127, ..., -0.03396523,\n",
            "          0.22005509, -0.2221143 ],\n",
            "        [-0.6526674 ,  0.3135463 , -0.05378269, ..., -0.14175129,\n",
            "          0.19185974,  0.15437408],\n",
            "        ...,\n",
            "        [-0.51969916,  0.34770036,  0.06588435, ..., -0.07127576,\n",
            "          0.04979548,  0.0390133 ],\n",
            "        [-0.40820658,  0.2648358 ,  0.02728896, ..., -0.02298091,\n",
            "          0.10348023,  0.04581314],\n",
            "        [-0.44416922,  0.20820396,  0.02378832, ..., -0.01668724,\n",
            "          0.01182801,  0.0798308 ]]], dtype=float32)>, hidden_states=None, attentions=None)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "### **We will need a model with a sequence classification head (to be able to classify the sentences as positive or negative)**\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "tkD0GWk8atVF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TFAutoModelForSequenceClassification\n",
        "\n",
        "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
        "\n",
        "tf_model = TFAutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
        "\n",
        "tf_output = tf_model(tf_inputs)\n",
        "\n",
        "print(\"\\n\\n================================= TF OUTPUT LOGITS' SHAPE =================================\\n\")\n",
        "print(tf_output.logits.shape)\n",
        "\n",
        "print(\"\\n\\n\\n================================= TF RAW OUTPUTS =================================\\n\")\n",
        "print(tf_output)\n",
        "tf_output = tf.convert_to_tensor(tf_output)\n",
        "print(tf_output)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 747
        },
        "id": "3jjmNLu3b8IV",
        "outputId": "0ca546ef-6bd2-4631-87f1-bd6dfafc9d87"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some layers from the model checkpoint at distilbert-base-uncased-finetuned-sst-2-english 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 distilbert-base-uncased-finetuned-sst-2-english and are newly initialized: ['dropout_78']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "================================= TF OUTPUT LOGITS' SHAPE =================================\n",
            "\n",
            "(2, 2)\n",
            "\n",
            "\n",
            "\n",
            "================================= TF RAW OUTPUTS =================================\n",
            "\n",
            "TFSequenceClassifierOutput(loss=None, logits=<tf.Tensor: shape=(2, 2), dtype=float32, numpy=\n",
            "array([[-3.0124366,  3.2012906],\n",
            "       [ 2.564236 , -2.1900017]], dtype=float32)>, hidden_states=None, attentions=None)\n"
          ]
        },
        {
          "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-24-6065ed06a444>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\n\\n\\n================================= TF RAW OUTPUTS =================================\\n\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf_output\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mtf_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_to_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf_output\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     15\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf_output\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    151\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 153\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    154\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m       \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/constant_op.py\u001b[0m in \u001b[0;36mconvert_to_eager_tensor\u001b[0;34m(value, ctx, dtype)\u001b[0m\n\u001b[1;32m    104\u001b[0m       \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_datatype_enum\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m   \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\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--> 106\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEagerTensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\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    107\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Attempt to convert a value (TFSequenceClassifierOutput(loss=None, logits=<tf.Tensor: shape=(2, 2), dtype=float32, numpy=\narray([[-3.0124366,  3.2012906],\n       [ 2.564236 , -2.1900017]], dtype=float32)>, hidden_states=None, attentions=None)) with an unsupported type (<class 'transformers.modeling_tf_outputs.TFSequenceClassifierOutput'>) to a Tensor."
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## POST-PROCESSING THE OUTPUT\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "nGvJO4_hcoEq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "tf_predictions = tf.math.softmax(tf.convert_to_tensor(tf_output))\n",
        "print(\"\\n\\n\\n================================= TF Predictions =================================\\n\")\n",
        "print(tf_predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "TwkuwPHhc5Cz",
        "outputId": "7962d5db-15e8-42b0-d24d-80d8e22d4906"
      },
      "execution_count": 23,
      "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-23-13508525bc86>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtf_predictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msoftmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_to_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf_output\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[0m\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\n\\n\\n================================= TF Predictions =================================\\n\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf_predictions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    151\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 153\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    154\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m       \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/constant_op.py\u001b[0m in \u001b[0;36mconvert_to_eager_tensor\u001b[0;34m(value, ctx, dtype)\u001b[0m\n\u001b[1;32m    104\u001b[0m       \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_datatype_enum\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m   \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\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--> 106\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEagerTensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\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    107\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Attempt to convert a value (TFSequenceClassifierOutput(loss=None, logits=<tf.Tensor: shape=(2, 2), dtype=float32, numpy=\narray([[-3.0124366,  3.2012906],\n       [ 2.564236 , -2.1900017]], dtype=float32)>, hidden_states=None, attentions=None)) with an unsupported type (<class 'transformers.modeling_tf_outputs.TFSequenceClassifierOutput'>) to a Tensor."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "zn5D-Q8JdjGJ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}