Bias_and_limitations.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lrnV8iUEIXas"
      },
      "source": [
        "# Bias and limitations"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K5tDCr7qIXau"
      },
      "source": [
        "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "roUGncWjIXau",
        "outputId": "db387150-00cf-4a92-f912-6c9b7f90aeee"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
            "Installing collected packages: tokenizers, sentencepiece, safetensors, xxhash, dill, responses, multiprocess, huggingface-hub, transformers, datasets, evaluate\n",
            "Successfully installed datasets-2.14.5 dill-0.3.7 evaluate-0.4.0 huggingface-hub-0.17.2 multiprocess-0.70.15 responses-0.18.0 safetensors-0.3.3 sentencepiece-0.1.99 tokenizers-0.13.3 transformers-4.33.2 xxhash-3.3.0\n"
          ]
        }
      ],
      "source": [
        "!pip install datasets evaluate transformers[sentencepiece]"
      ]
    },
    {
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        },
        "id": "0ReXEVLYIXav",
        "outputId": "4a5864f8-cbdc-40da-dd5b-c0c0ab4dcd66"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
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        {
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          "data": {
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            ],
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMaskedLM: ['cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'bert.pooler.dense.bias', 'bert.pooler.dense.weight']\n",
            "- This IS expected if you are initializing BertForMaskedLM 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 BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "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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            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['carpenter', 'lawyer', 'farmer', 'businessman', 'doctor']\n",
            "['nurse', 'maid', 'teacher', 'waitress', 'prostitute']\n"
          ]
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "unmasker = pipeline(\"fill-mask\", model=\"bert-base-uncased\")\n",
        "result = unmasker(\"This man works as a [MASK].\")\n",
        "print([r[\"token_str\"] for r in result])\n",
        "\n",
        "result = unmasker(\"This woman works as a [MASK].\")\n",
        "print([r[\"token_str\"] for r in result])"
      ]
    }
  ],
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