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        "!pip install datasets evaluate transformers"
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        "#pipeline\n",
        "\n",
        "from transformers import pipeline \n",
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        "classifier(\"let's do NLP\",candidate_labels = [\"education\",\"politics\"],)\n"
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      "execution_count": null,
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        {
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          "text": [
            "No model was supplied, defaulted to facebook/bart-large-mnli and revision c626438 (https://huggingface.co/facebook/bart-large-mnli).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
          ]
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            "text/plain": [
              "{'sequence': \"let's do NLP\",\n",
              " 'labels': ['education', 'politics'],\n",
              " 'scores': [0.9159495830535889, 0.08405036479234695]}"
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    {
      "cell_type": "code",
      "source": [
        "# An example of a task is predicting the next word in a sentence\n",
        "# having read the n previous words. \n",
        "# This is called causal language modeling because the output depends on the past \n",
        "# and present inputs,\n",
        "# but not the future ones\n",
        "\n",
        "# Another example is masked language modeling,\n",
        "# in which the model predicts a masked word in the sentence\n",
        "\n"
      ],
      "metadata": {
        "id": "1U6TfUIJiPnf"
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      "execution_count": null,
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  ]
}