Exploring_Small_Language_Models.ipynb

{
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      "source": [
        "<a href=\"https://colab.research.google.com/github/jaycrossler/ai-training/blob/main/Exploring_Small_Language_Models.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4FBtpG6moXn_"
      },
      "source": [
        "# Exploring Small Language Models (SLMs) that run locally\n",
        "## Basic Language Model queries and functions\n",
        "\n",
        "Install a [small DistiliBERT](https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english) (250Mb) model, and perform basic queries and AI language tricks on it, like sentence classification or translation.\n",
        "\n",
        "Launch this within Google Colab (you will need a Gmail account and to sign up) to run this on a cloud server for free.  Click the [  ] box on the left of each code block to run those commands on the server.  The server will auto-shutdown after a few minutes of inactivity.\n",
        "\n",
        "*(modified from a [Hugging Face tutorial](https://colab.research.google.com/github/huggingface/notebooks/blob/main/course/en/chapter1/section3.ipynb))*\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8gppusWYoXoA"
      },
      "source": [
        "Install the Transformers, Datasets, and Evaluate libraries to run this notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x4ByaG2xoXoA",
        "outputId": "47ca54d0-13e6-4b89-8298-efa3f203c91c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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        }
      ],
      "source": [
        "!pip install datasets evaluate transformers[sentencepiece]\n",
        "\n",
        "#If you are running this notebook locally (not in Google Colab), might need to also download torch:\n",
        "#!pip install datasets evaluate torch torchvision torchaudio \"transformers[sentencepiece]\""
      ]
    },
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        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to distilbert/distilbert-base-uncased-finetuned-sst-2-english and revision 714eb0f (https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n",
            "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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        },
        {
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          "data": {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Device set to use cpu\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'POSITIVE', 'score': 0.9047655463218689}]"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "# Note that this will default to the basic distilbert model - other notebooks will show how to use more complex models\n",
        "\n",
        "from transformers import pipeline\n",
        "\n",
        "classifier = pipeline(\"sentiment-analysis\")\n",
        "classifier(\"I've been waiting for my whole life to join the Air Force.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Pf5IYALzoXoB",
        "outputId": "d0e8644e-a89f-4c02-983f-96d5041c5e9e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'NEGATIVE', 'score': 0.9840741157531738},\n",
              " {'label': 'POSITIVE', 'score': 0.9973541498184204},\n",
              " {'label': 'POSITIVE', 'score': 0.9981622099876404}]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "classifier(\n",
        "    [\"I love 2 hour briefings. Not.\", \"My boss made me attend\", \"I for one welcome our new AI overlords.\"]\n",
        ")"
      ]
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            "871debc5d33e4447b358427d6c7ba929"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to facebook/bart-large-mnli and revision d7645e1 (https://huggingface.co/facebook/bart-large-mnli).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.15k [00:00<?, ?B/s]"
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/1.63G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "480cc81cf11143baab4302ea0a395f0d"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e737d9ae48854060999a5bb51587f00c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0c10addb1eae4c9e98896b5699bae2c2"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "65110d2b8aae461d9fbc299419881fab"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "aa16a642de7546afa33523ac87a00af0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Device set to use cpu\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'sequence': 'This is an online course about AI for use on Air Force systems',\n",
              " 'labels': ['military', 'technology', 'education', 'business', 'politics'],\n",
              " 'scores': [0.5246004462242126,\n",
              "  0.4516729712486267,\n",
              "  0.01688522845506668,\n",
              "  0.004751493688672781,\n",
              "  0.00208978820592165]}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "classifier = pipeline(\"zero-shot-classification\")\n",
        "classifier(\n",
        "    \"This is an online course about AI for use on Air Force systems\",\n",
        "    candidate_labels=[\"education\", \"politics\", \"business\", \"technology\", \"military\"],\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PSlcvxUcoXoB",
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        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to openai-community/gpt2 and revision 607a30d (https://huggingface.co/openai-community/gpt2).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]"
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        {
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          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/548M [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "091fefd144784f72a8e6864942a7ad50"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
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          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3fc01210882647389158bcbf1dd33892"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "316bf59c46f04cd19bd6530091829b42"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c969d8cb3844492d9cc1e1d98170f0fd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4a78811dc254479982d44009a7a6bb33"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Device set to use cpu\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'generated_text': \"In this Air Force course, we will teach you how to build the fastest jet plane ever made using the latest supercomputers, advanced sensors, and innovative technologies.\\n\\nIt's called the Aircraft Airplane (AAS) in honor of the\"}]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "generator = pipeline(\"text-generation\")\n",
        "generator(\"In this Air Force course, we will teach you how to\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KCr-V7o_oXoB",
        "outputId": "e8eb4748-fa16-4ccd-df83-84db192caf3f",
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      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/762 [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/353M [00:00<?, ?B/s]"
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              "model_id": "d0d990fff0714a78bab06fa545207eb5"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6543578d28214d9c8b8b335b861bf2be"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f14b3700be5743c09b93d88e45067326"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3bc0c0b5acde47248fe350e79f388811"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "727df8497b5f4dd39c53b8fbb4738d20"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Device set to use cpu\n",
            "Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'generated_text': 'In this Air Force course, we will teach you how to use your skills to improve your life and ensure that you can contribute to the economy through your'},\n",
              " {'generated_text': 'In this Air Force course, we will teach you how to write.”\\n\\n\\nIn my talk we will take a long long look into'}]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "generator = pipeline(\"text-generation\", model=\"distilgpt2\")\n",
        "generator(\n",
        "    \"In this Air Force course, we will teach you how to\",\n",
        "    max_length=30,\n",
        "    num_return_sequences=2,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-5ZD4W34oXoB",
        "outputId": "5fea3c04-2dc1-48a2-a5b9-7807def1f8cb",
        "colab": {
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          ]
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to distilbert/distilroberta-base and revision fb53ab8 (https://huggingface.co/distilbert/distilroberta-base).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/480 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "993520a7c3c74ae7a8bb5d2f46df2c56"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/331M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b3ece439cb104fe8966c93b954859c83"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at distilbert/distilroberta-base were not used when initializing RobertaForMaskedLM: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n",
            "- This IS expected if you are initializing RobertaForMaskedLM 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 RobertaForMaskedLM 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": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/25.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "80f7d35160ab4c63922f9705894df4c4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "56431d274f734ff49cf91ac510cbe6b9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "013ced4526e44e60b9f2e72a62118507"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c6f8e75f791641eea09fb10603496b5b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Device set to use cpu\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'score': 0.10436581820249557,\n",
              "  'token': 3054,\n",
              "  'token_str': ' aircraft',\n",
              "  'sequence': 'This course will teach you all about aircraft models and their use in Air Force mission systems.'},\n",
              " {'score': 0.04549553990364075,\n",
              "  'token': 16847,\n",
              "  'token_str': ' airplane',\n",
              "  'sequence': 'This course will teach you all about airplane models and their use in Air Force mission systems.'}]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "unmasker = pipeline(\"fill-mask\")\n",
        "unmasker(\"This course will teach you all about <mask> models and their use in Air Force mission systems.\", top_k=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Zf5rGwr0oXoB",
        "outputId": "323e845c-4551-4966-b3ac-34d76305af91",
        "colab": {
          "base_uri": "https://localhost:8080/",
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        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to dbmdz/bert-large-cased-finetuned-conll03-english and revision 4c53496 (https://huggingface.co/dbmdz/bert-large-cased-finetuned-conll03-english).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/998 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "11ff7f77069f49c499734242bb798389"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/1.33G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c9f748815d1742a484306570b0511b4d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at dbmdz/bert-large-cased-finetuned-conll03-english were not used when initializing BertForTokenClassification: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight']\n",
            "- This IS expected if you are initializing BertForTokenClassification 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 BertForTokenClassification 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": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/60.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ff34984932b14e3abe519929b98e0540"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/213k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "aabd6977cbca473e82c5ef96af794401"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Device set to use cpu\n",
            "/usr/local/lib/python3.11/dist-packages/transformers/pipelines/token_classification.py:170: UserWarning: `grouped_entities` is deprecated and will be removed in version v5.0.0, defaulted to `aggregation_strategy=\"AggregationStrategy.SIMPLE\"` instead.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'entity_group': 'PER',\n",
              "  'score': 0.9994412,\n",
              "  'word': 'Jay',\n",
              "  'start': 11,\n",
              "  'end': 14},\n",
              " {'entity_group': 'ORG',\n",
              "  'score': 0.98143977,\n",
              "  'word': 'The MITRE Corporation',\n",
              "  'start': 30,\n",
              "  'end': 51},\n",
              " {'entity_group': 'LOC',\n",
              "  'score': 0.999054,\n",
              "  'word': 'DC',\n",
              "  'start': 59,\n",
              "  'end': 61},\n",
              " {'entity_group': 'LOC',\n",
              "  'score': 0.9989548,\n",
              "  'word': 'Boston',\n",
              "  'start': 91,\n",
              "  'end': 97},\n",
              " {'entity_group': 'LOC',\n",
              "  'score': 0.9993248,\n",
              "  'word': 'New York',\n",
              "  'start': 99,\n",
              "  'end': 107},\n",
              " {'entity_group': 'LOC',\n",
              "  'score': 0.999363,\n",
              "  'word': 'LA',\n",
              "  'start': 113,\n",
              "  'end': 115}]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "ner = pipeline(\"ner\", grouped_entities=True)\n",
        "ner(\"My name is Jay and I work for The MITRE Corporation in the DC area. I frequently travel to Boston, New York, and LA.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "od0X0ZTJoXoB",
        "outputId": "db875aae-9320-43d0-ffb7-a6fbfc1e69a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322,
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            "13b44984fd13486c91ef68c112d50c53",
            "9764948c883c4ad1ae769a7b17d04bc4"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to distilbert/distilbert-base-cased-distilled-squad and revision 564e9b5 (https://huggingface.co/distilbert/distilbert-base-cased-distilled-squad).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/473 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b535c659e8914eca849a9ea14542a83c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/261M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4ff985f788b6494996f4b2ec9066e9ec"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/49.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6c64c73c59b742b687e6c7d2fe78b2aa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/213k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "tokenizer.json:   0%|          | 0.00/436k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9ffdf48e446b4cf7ae6fd9ed2bd50460"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Device set to use cpu\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'score': 0.3822679817676544,\n",
              " 'start': 30,\n",
              " 'end': 51,\n",
              " 'answer': 'The MITRE Corporation'}"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "question_answerer = pipeline(\"question-answering\")\n",
        "question_answerer(\n",
        "    question=\"Where do I work?\",\n",
        "    context=\"My name is Jay and I work for The MITRE Corporation in the DC area. I frequently travel to Boston, New York, and LA.\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rhhKWxieoXoB",
        "outputId": "48a64ccb-f514-4c53-e1ac-63bbfed00cc9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to sshleifer/distilbart-cnn-12-6 and revision a4f8f3e (https://huggingface.co/sshleifer/distilbart-cnn-12-6).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n",
            "Device set to use cpu\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'summary_text': ' This document presents a consensus of low-side software development best practices collected from government agencies . DevSecOps is an organization’s software engineering culture and practice that aims at unifying software development, security, and operations . Continuous Monitoring involves automating security and IT operations processes to consistently verify and check code, tools, apps, and configurations for vulnerabilities . Continuous Integration/Continuous Delivery environments are attractive targets for malicious cyber actors .'}]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "summarizer = pipeline(\"summarization\")\n",
        "summarizer(\n",
        "    \"\"\"\n",
        "    Background\n",
        "\n",
        "Our mission systems are increasingly more complex, and the time required to respond to world events is decreasing on average, while more tools are needed, and less budget is available. This leads to system solutions of higher complexity, faster delivery, and lower cost.  As evidenced by increasing compromises over time, software supply chains and Continuous Integration/Continuous Delivery (CI/CD) environments are attractive targets for malicious cyber actors.\n",
        "Purpose and Methodology\n",
        "\n",
        "This document captures the DevSecOps best practices commonly employed by government organizations for secure development performed in an unclassified environment.  Although this document focuses on secure development in the unclassified system; the tools, software, and resources under consideration should also operate securely in higher security domains.\n",
        "\n",
        "This document presents a consensus of low-side software development best practices collected from government agencies.\n",
        "\n",
        "Setting the context – Best Practices revolve around these Key Terms\n",
        "\n",
        "There definitions will be used within the document, and based on feedback from the DevSecOps Community of Practice.\n",
        "\n",
        "-       DevSecOps is an organization’s software engineering culture and practice that aims at unifying software development, security, and operations. The purpose of DevSecOps is to automate, monitor, and apply security at all phases of the software lifecycle (plan, develop, build, test, release, deliver, deploy, operate, and monitor). DevSecOps is the cornerstone of reducing risk in modern software development and deployment.  It relies on strong Configuration Management (CM) to trace and manage all software artifacts.\n",
        "-       Continuous Integration (CI) involves developers frequently merging code changes into a central repository triggering automated builds and tests.  This represents a large process and cultural change where instead of a few large changes every few months, there are sometimes dozens of small changes per day to a system.  When done right, it reduces integration challenges and provides additional tracking of needs to work to functionality – which allows frequent updates and software more aligned to agile missions, along with business benefits of better tracking and predicting completion.\n",
        "-       Continuous Monitoring (ConMon, sometimes also called CM not to be confused with Configuration Management’s CM) involves automating security and IT operations processes to consistently verify and check code, tools, apps, and configurations for vulnerabilities, misconfigurations, or configuration drift.  This approach supports frequent checks, such as \"hourly\" rather than \"annually,\" which necessitates shifting to an active cyber defense strategy.  According to NIST SP 800-137, Information Security Continuous Monitoring (ISCM) for Federal Information Systems and Organizations, ISCM is defined as maintaining ongoing awareness of information security, vulnerabilities, and threats to support organizational risk management decisions.\n",
        "-       Continuous Delivery (CD) is a software development practice where code changes are automatically prepared for a release to production or automatically deployed to test or beta infrastructures.  Upon a manual compliance approval step, releases can be automatically deployed into operational IT infrastructure.  This explicit approval step might be waived resulting in Continuous Deployment.\n",
        "-       Software Supply Chain (SSC) is the combination of all the tools, libraries, and components used to develop, build, and publish software.  Sometimes adding one simple component can trigger the automatic inheritance of hundreds of other supporting components – a prime target for malicious cyber actors.\n",
        "-       Software Bill of Materials (SBOM) is a key building block in software supply chain risk management. “An SBOM is a formal, machine-readable inventory of software components and dependencies, information about those components, and their hierarchical relationships.  These inventories should be comprehensive – and should explicitly state where they could not be.  SBOMs may include open source or proprietary software and can be widely available or access restricted.”\n",
        "-       CI/CD pipelines and DevSecOps Platforms are the emerging sets of tools and techniques that automate, scan, and enable this modern system development.  These Platforms are massive force multipliers – though the more they are used, the greater targets they become.  These platforms should be embraced, while managing the risk.\n",
        "Mission Systems: Why System Risk Cannot Be Zero\n",
        "\n",
        "This document aims to help program offices and related stakeholders reduce risk by following tested best practices.\n",
        "\"\"\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "JsuehnoZoXoC",
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          "height": 440,
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      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.42k [00:00<?, ?B/s]"
            ],
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        },
        {
          "output_type": "display_data",
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              "pytorch_model.bin:   0%|          | 0.00/301M [00:00<?, ?B/s]"
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              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/293 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "40137667269343289f7377328e588bb4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/42.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "de70020a97094494b2058bebdb55281c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "source.spm:   0%|          | 0.00/802k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "target.spm:   0%|          | 0.00/778k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "34c3f07a88ea42e69e1d6b17c6f00064"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/1.34M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "7463bf10b4744cdc8eb36e424f505e76"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/transformers/models/marian/tokenization_marian.py:175: UserWarning: Recommended: pip install sacremoses.\n",
            "  warnings.warn(\"Recommended: pip install sacremoses.\")\n",
            "Device set to use cpu\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'translation_text': 'This course is hosted by MITRE - it was originally taught by HuggingFace.'}]"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "translator = pipeline(\"translation\", model=\"Helsinki-NLP/opus-mt-fr-en\")\n",
        "translator(\"Ce cours est hébergé par MITRE - il a été enseigné à l'origine par HuggingFace.\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "unmasker = pipeline(\"fill-mask\", model=\"bert-base-uncased\")\n",
        "result = unmasker(\"This Air Force Technical Seargeant works as a [MASK]. He is tall.\")\n",
        "print([r[\"token_str\"] for r in result])\n",
        "\n",
        "result = unmasker(\"This Air Force Technical Seargeant works as a [MASK]. She is tall.\")\n",
        "print([r[\"token_str\"] for r in result])"
      ],
      "metadata": {
        "id": "4flPT68HBUDV",
        "outputId": "64ea6091-6339-44f1-f95c-383fdc7dc50d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForMaskedLM: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.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",
            "Device set to use cpu\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['mechanic', 'pilot', 'waiter', 'teacher', 'salesman']\n",
            "['nurse', 'waitress', 'secretary', 'mechanic', 'model']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Beware of the **biases** inherent in the models! Much of this was trained from things like Reddit posts or dirty internet chat boards... and it shows."
      ],
      "metadata": {
        "id": "o-nFqoS1BosE"
      }
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "language_info": {
      "name": "python"
    },
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      "name": "python3",
      "display_name": "Python 3"
    },
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