NPL_course.ipynb

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      "metadata": {
        "id": "j9bkdzlVmiL2"
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      "source": [
        "from transformers import pipeline"
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        "classifier = pipeline(\"sentiment-analysis\")"
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        "outputId": "24511357-f9a1-47a0-9695-48cb89e6d8fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to distilbert/distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (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/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: 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",
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              "config.json:   0%|          | 0.00/629 [00:00<?, ?B/s]"
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        {
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        {
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classifier(\"I've been waiting for a HuggingFace course my whole life.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hdF-D-iXm60T",
        "outputId": "4b9b36cf-8b07-45bc-abdc-6fe6bad72298"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'POSITIVE', 'score': 0.9598048329353333}]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classifier(\n",
        "    [\"I've been waiting for a HuggingFace course my whole life.\", \"I hate this so much!\"]\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_HxXtOn1m-8g",
        "outputId": "b3f6b116-848d-4f48-d8a6-a8703efc275b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'POSITIVE', 'score': 0.9598048329353333},\n",
              " {'label': 'NEGATIVE', 'score': 0.9994558691978455}]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "phl06IULovM4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Some of the currently **[available pipelines](https://huggingface.co/transformers/main_classes/pipelines)** are:\n",
        "\n",
        "- `feature-extraction` (get the vector representation of a text)\n",
        "- `fill-mask`\n",
        "- `ner` (named entity recognition)\n",
        "- `question-answering`\n",
        "- `sentiment-analysis`\n",
        "- `summarization`\n",
        "- `text-generation`\n",
        "- `translation`\n",
        "- `zero-shot-classification`"
      ],
      "metadata": {
        "id": "fzJG2UMao5hV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Zero-shot classification\n",
        "We’ll start by tackling a more challenging task where we need to classify texts that haven’t been labelled. This is a common scenario in real-world projects because annotating text is usually time-consuming and requires domain expertise. For this use case, the zero-shot-classification pipeline is very powerful: it allows you to specify which labels to use for the classification, so you don’t have to rely on the labels of the pretrained model. You’ve already seen how the model can classify a sentence as positive or negative using those two labels — but it can also classify the text using any other set of labels you like."
      ],
      "metadata": {
        "id": "Ua_VkmJ6pBy0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "classifier = pipeline(\"zero-shot-classification\")\n",
        "classifier(\n",
        "    \"This is a course about the Transformers library\",\n",
        "    candidate_labels=[\"education\", \"politics\", \"business\"],\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316,
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        },
        "id": "8N9TzW92nR6v",
        "outputId": "97042685-a868-4f85-cd73-e31ba3f8d95c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        },
        {
          "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": {
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              "tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'sequence': 'This is a course about the Transformers library',\n",
              " 'labels': ['education', 'business', 'politics'],\n",
              " 'scores': [0.8445991277694702, 0.11197404563426971, 0.043426841497421265]}"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Using any model from the Hub in a pipeline\n",
        "The previous examples used the default model for the task at hand, but you can also choose a particular model from the Hub to use in a pipeline for a specific task — say, text generation. Go to the Model Hub and click on the corresponding tag on the left to display only the supported models for that task. You should get to a page like this one.\n",
        "\n",
        "Let’s try the distilgpt2 model! Here’s how to load it in the same pipeline as before:"
      ],
      "metadata": {
        "id": "Oc7_o19JqxFS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "generator = pipeline(\"text-generation\", model=\"distilgpt2\")\n",
        "generator(\n",
        "    \"In this course, we will teach you how to\",\n",
        "    max_length=30,\n",
        "    num_return_sequences=2,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365,
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      "execution_count": null,
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        {
          "output_type": "stream",
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          "text": [
            "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 course, we will teach you how to use the tool to create and share images using the tool and get the results you want with it.'},\n",
              " {'generated_text': 'In this course, we will teach you how to learn the basics of cryptography and can only use this as an introduction.\\n\\n\\nFor more information'}]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Using transformers\n",
        "three steps of the pipeline: preprocessing with tokenizers,\n",
        "passing the inputs through the model,\n",
        "and postprocessing!\n",
        "# Processing with a tokenizer"
      ],
      "metadata": {
        "id": "A-egxSROcGhe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(checkpoint)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237,
          "referenced_widgets": [
            "d4fc9fd8f18844c2a140a21ed6f97873",
            "31615b6057da440cb30b0f81551458bd",
            "798b05b857264daeb25f6f1a3af29009",
            "cd53645394d5439998bc130c84a1b8f5",
            "817f99ff9b0b4078b5080c2a0ad262c1",
            "04fc77938c3c40ecb69d784eb38fc690",
            "00cf2a9c278448469a980e139d298be5",
            "240652dac7734c75bc3438091558a321",
            "6be17979448b466fb66f0be6c0baf100",
            "fef9a27321e0459786b0638ec30a8385",
            "e126561c464d4b60a6b245dddaf1ec77",
            "84716287f42a4886a202df1a47b70a7e",
            "e61c156c0798445591678f4c2c953f2d",
            "dc236343647b484c923e114b5e3b37ea",
            "af6a7d2cf77142d5b6a7873cff1cb4ae",
            "15356ec8e847431cbb0812f551606b5b",
            "d8794912da644b94bf745239355cb779",
            "6aba3fceb71840b689186af4364a152e",
            "ae76035846524a16ab7a732632153c26",
            "6326a7ee4f924011b14e3f78d050c442",
            "60a000b2e3bc460daa488d539011495d",
            "98830240bcf04898a6eb9fe01bb2855c",
            "0543079c2d924003ba0e9ff30c03ff6f",
            "700ce4a9b52c47ab8046e6d9f03fdf3a",
            "319f6f9b82f94d30ada8edbbd2fd9628",
            "2ae32c8ce76646ac87b0fb6150424e31",
            "18f8c3b627664c0195170a07ee6d0deb",
            "8f663fa6f70a4aabba20f04ceeac61d0",
            "f6448a144e1941d9a870edb62c581ceb",
            "1bf22841ce594646af927b122c59aa98",
            "c57f2b21c7b4400e87ab3e8b5dcad4cb",
            "3134b3d8b0c84a86bea485a5d8f8de3f",
            "ab72422e49374c0fab1dbbf554bdf580"
          ]
        },
        "id": "-eMc8IC2cKCW",
        "outputId": "32159485-7a63-483a-f02c-c1a15d6864ba"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: 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": [
              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d4fc9fd8f18844c2a140a21ed6f97873"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/629 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "84716287f42a4886a202df1a47b70a7e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0543079c2d924003ba0e9ff30c03ff6f"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To specify the type of tensors we want to get back (PyTorch, TensorFlow, or plain NumPy), we use the return_tensors argument:"
      ],
      "metadata": {
        "id": "vDdnHzpXcoTp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "raw_inputs = [\n",
        "    \"I've been waiting for a HuggingFace course my whole life.\",\n",
        "    \"I hate this so much!\",\n",
        "]\n",
        "inputs = tokenizer(raw_inputs, padding=True, truncation=True, return_tensors=\"pt\")\n",
        "print(inputs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7BU9-GDQcUAH",
        "outputId": "353f6e39-0000-4575-92f6-66bbc071d5bd"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'input_ids': tensor([[  101,  1045,  1005,  2310,  2042,  3403,  2005,  1037, 17662, 12172,\n",
            "          2607,  2026,  2878,  2166,  1012,   102],\n",
            "        [  101,  1045,  5223,  2023,  2061,  2172,   999,   102,     0,     0,\n",
            "             0,     0,     0,     0,     0,     0]]), 'attention_mask': tensor([[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, 0, 0, 0, 0, 0, 0, 0, 0]])}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Going through the model"
      ],
      "metadata": {
        "id": "sFh6dHEBhiDn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModel\n",
        "\n",
        "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
        "model = AutoModel.from_pretrained(checkpoint)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "6496a58bce1542a0ba68921d3e253ec9",
            "de91498b83e44e90b4c50c2a797f28ba",
            "9383bd8f08c349d6a3cc2962bf400c07",
            "697ffc8b5fcf44548eac6aa66252539c",
            "f27e1c0182a4404498a7c2f782dc345c",
            "e9f36a6762934276934c09c51486f475",
            "a454da8d7cbc47668e628c5f3f2824f2",
            "b77e5a7c30174b6fa1f040b073ce7a0d",
            "7c014869d52f43f9afe862b41add97a0",
            "04a36dd6c1ea42b38818080d8444776e",
            "0c28fa2ff63143f2809701b29922558e"
          ]
        },
        "id": "gHXmL8rddBDA",
        "outputId": "435bfb74-ba1c-42b5-e637-a7d76d2c1f91"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6496a58bce1542a0ba68921d3e253ec9"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "outputs = model(**inputs)\n",
        "print(outputs.last_hidden_state.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nCOVDrFOdfX4",
        "outputId": "a7a39566-ebad-4a6f-dd27-da888052f948"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([2, 16, 768])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForSequenceClassification\n",
        "checkpoint = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
        "model = AutoModelForSequenceClassification.from_pretrained(checkpoint)\n",
        "outputs = model(**inputs)"
      ],
      "metadata": {
        "id": "Ka5h4apodgCJ"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(outputs.logits.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "66cMeEK8hBpg",
        "outputId": "8ca41742-7258-4395-dc16-0806fbdc8ea6"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([2, 2])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Postprocessing the output"
      ],
      "metadata": {
        "id": "P2y_658NhrPG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(outputs.logits)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1EwUuL1IhJ8W",
        "outputId": "134a55b3-941f-49c8-8fa7-11c4d07bc7f2"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[-1.5607,  1.6123],\n",
            "        [ 4.1692, -3.3464]], grad_fn=<AddmmBackward0>)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)\n",
        "print(predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vErTdV4fhxO6",
        "outputId": "2225842e-488b-4bef-db2d-ccc04c481f76"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[4.0195e-02, 9.5980e-01],\n",
            "        [9.9946e-01, 5.4418e-04]], grad_fn=<SoftmaxBackward0>)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.config.id2label"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vPBRMlv0h7lt",
        "outputId": "37d4c767-efd2-4c94-a520-26dc395cb334"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 'NEGATIVE', 1: 'POSITIVE'}"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MhL7T8bNh-LO"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}