notebooks/Transformers_example_simple_text_classification.ipynb

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      "source": [
        "# Transformer Implementation Example for Text Classification\n",
        "\n",
        "**(C) 2024 by [Damir Cavar](http://damir.cavar.me/)**\n",
        "\n",
        "\n",
        "**Download:** This and various other Jupyter notebooks are available from my [GitHub repo](https://github.com/dcavar/python-tutorial-for-ipython).\n",
        "\n",
        "\n",
        "Based on [Transformer Text Classification by Eijaz Allibhai](https://github.com/jaz-alli/transformers-text-classification/blob/main/transformers_text_classification.ipynb).\n"
      ]
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      "outputs": [],
      "source": [
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Import the `transformer` pipeline:"
      ]
    },
    {
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      "execution_count": 1,
      "metadata": {
        "id": "yVCWxFA_a1Cc"
      },
      "outputs": [
        {
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          "text": [
            "2024-10-23 17:10:52.468340: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
            "2024-10-23 17:10:52.477833: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
            "2024-10-23 17:10:52.489025: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
            "2024-10-23 17:10:52.492373: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
            "2024-10-23 17:10:52.501195: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
            "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
            "2024-10-23 17:10:53.195914: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
          ]
        }
      ],
      "source": [
        "from transformers import pipeline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "See the [documentation for the pipeline](https://huggingface.co/docs/transformers/en/main_classes/pipelines) on the website."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "In the following we use the zero-shot-classification approach. This is predicting a class for some text that wasn't seen by the model during training. The pre-trained language model is used in that task without tuning or additional training."
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "Setting the device parameter to 0 will activate GPU-based processing, which might improve the processing time:\n",
        "\n",
        "    classifier = pipeline(\"zero-shot-classification\", device=0)\n"
      ]
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          "text": [
            "/home/damir/.local/lib/python3.12/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n",
            "Hardware accelerator e.g. GPU is available in the environment, but no `device` argument is passed to the `Pipeline` object. Model will be on CPU.\n"
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        "classifier = pipeline(\"zero-shot-classification\", model=\"facebook/bart-large-mnli\")"
      ]
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      "source": [
        "We can specify some text and potential labels (or keywords) for it:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "sequence = \"We like to use Code for Python, but vim for C.\"\n",
        "labels = [\"automotive\", \"programming\", \"politics\", \"economy\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The `classifier` will generate a list of labels and a list of scores. The scores in this case sum up to 1 and represent probabilities."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
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        "outputId": "a2ea5747-f90f-4bd1-ff01-ba6e67265541"
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      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'sequence': 'We like to use Code for Python, but vim for C.',\n",
              " 'labels': ['programming', 'economy', 'automotive', 'politics'],\n",
              " 'scores': [0.8942243456840515,\n",
              "  0.07695267349481583,\n",
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          "execution_count": 4,
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      ],
      "source": [
        "classifier(sequence, labels)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We can also specify a list of texts to label using the model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "l2Mcyt4pmJUw"
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        "sequences = [\"An increase in travel demand is one of the causes of an oil price increase\",\n",
        "            \"As expected, polls show that the party in power will lose seats in Congress during the midterm elections.\",\n",
        "            \"The party in power may lose seats in the next election due to inflation and recession concerns\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The `classifier` will return a list of text, label, and score tuples."
      ]
    },
    {
      "cell_type": "code",
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      "metadata": {
        "colab": {
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              "[{'sequence': 'An increase in travel demand is one of the causes of an oil price increase',\n",
              "  'labels': ['economy', 'automotive', 'programming', 'politics'],\n",
              "  'scores': [0.39332109689712524,\n",
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              " {'sequence': 'As expected, polls show that the party in power will lose seats in Congress during the midterm elections.',\n",
              "  'labels': ['politics', 'economy', 'programming', 'automotive'],\n",
              "  'scores': [0.9058505892753601,\n",
              "   0.039337169378995895,\n",
              "   0.0312370453029871,\n",
              "   0.023575183004140854]},\n",
              " {'sequence': 'The party in power may lose seats in the next election due to inflation and recession concerns',\n",
              "  'labels': ['economy', 'politics', 'programming', 'automotive'],\n",
              "  'scores': [0.5537724494934082,\n",
              "   0.4306282103061676,\n",
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      "source": [
        "classifier(sequences, labels)"
      ]
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      "source": [
        "It is also possible to use multi-labels for the text, i.e., each label receives a score for how goot it fits the corresponding text. The user needs to specify a threshold of exclusion based on the scores."
      ]
    },
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              "[{'sequence': 'An increase in travel demand is one of the causes of an oil price increase',\n",
              "  'labels': ['automotive', 'economy', 'programming', 'politics'],\n",
              "  'scores': [0.009671766310930252,\n",
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              " {'sequence': 'As expected, polls show that the party in power will lose seats in Congress during the midterm elections.',\n",
              "  'labels': ['politics', 'economy', 'programming', 'automotive'],\n",
              "  'scores': [0.9895076751708984,\n",
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              "   0.04091533273458481]},\n",
              " {'sequence': 'The party in power may lose seats in the next election due to inflation and recession concerns',\n",
              "  'labels': ['politics', 'economy', 'programming', 'automotive'],\n",
              "  'scores': [0.9844666719436646,\n",
              "   0.9779921174049377,\n",
              "   0.009205055423080921,\n",
              "   0.0031888692174106836]}]"
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          "execution_count": 9,
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      "source": [
        "classifier(sequences, labels, multi_label=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "(C) 2024 by [Damir Cavar](http://damir.cavar.me/)"
      ]
    }
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