03_getting-started-with-transformers.ipynb

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      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/education-toolkit/blob/main/03_getting-started-with-transformers.ipynb)\n",
        "\n",
        "💡 **Welcome!**\n",
        "\n",
        "We’ve assembled a toolkit that university instructors and organizers can use to easily prepare labs, homework, or classes. The content is designed in a self-contained way such that it can easily be incorporated into the existing curriculum. This content is free and uses widely known Open Source technologies (`transformers`, `gradio`, etc).\n",
        "\n",
        "Alternatively, you can request for someone on the Hugging Face team to run the tutorials for your class via the [ML demo.cratization tour](https://huggingface2.notion.site/ML-Demo-cratization-tour-with-66847a294abd4e9785e85663f5239652) initiative!\n",
        "\n",
        "You can find all the tutorials and resources we’ve assembled [here](https://huggingface2.notion.site/Education-Toolkit-7b4a9a9d65ee4a6eb16178ec2a4f3599). "
      ]
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      "source": [
        "# Tutorial: Getting Started with Transformers"
      ]
    },
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      "metadata": {
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      "source": [
        "**Learning goals:** The goal of this tutorial is to learn how:\n",
        "\n",
        "1. Transformer neural networks can be used to tackle a wide range of tasks in natural language processing and beyond.\n",
        "3. Transfer learning allows one to adapt Transformers to specific tasks.\n",
        "2. The `pipeline()` function from the `transformers` library can be used to run inference with models from the [Hugging Face Hub](https://huggingface.co/models).\n",
        "\n",
        "This tutorial is based on the first of our O'Reilly book [_Natural Language Processing with Transformers_](https://transformersbook.com/) - check it out if you want to dive deeper into the topic!\n",
        "\n",
        "**Duration**: 30-45 minutes\n",
        "\n",
        "**Prerequisites:** Knowledge of Python and basic familiarity with machine learning \n",
        "\n",
        "\n",
        "**Author**: [Lewis Tunstall](https://twitter.com/_lewtun) (feel free to ping me with any questions about this tutorial)\n",
        "\n",
        "All of these steps can be done for free! All you need is an Internet browser and a place where you can write Python 👩‍💻"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "## 0. Why Transformers?"
      ]
    },
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      "source": [
        "Deep learning is currently undergoing a period of rapid progress across a wide variety of domains, including: \n",
        "\n",
        "* 📖 Natural language processing\n",
        "* 👀 Computer vision\n",
        "* 🔊 Audio\n",
        "* 🧬 Biology\n",
        "* and many more!\n",
        "\n",
        "The main driver of these breakthroughs is the **Transformer** -- a novel **neural network** developed by Google researchers in 2017. In short, if you’re into deep learning, you need Transformers!\n",
        "\n",
        "Here's a few examples of what Transformers can do:\n",
        "\n",
        "* 💻 They can **generate code** as in products like [GitHub Copilot](https://copilot.github.com/), which is based on OpenAI's family of [GPT models](https://huggingface.co/gpt2?text=My+name+is+Clara+and+I+am).\n",
        "* ❓ They can be used for **improve search engines**, like [Google did](https://www.blog.google/products/search/search-language-understanding-bert/) with a Transformer called [BERT](https://huggingface.co/bert-base-uncased).\n",
        "* 🗣️ They can **process speech in multiple languages** to perform speech recognition, speech translation, and language identification. For example, Facebook's [XLS-R model](https://huggingface.co/spaces/facebook/XLS-R-2B-22-16) can automatically transcribe audio in one language to another!\n",
        "\n",
        "Training these models **from scratch** involves **a lot of resources**: you need large amounts of compute, data, and days to train for 😱.\n",
        "\n",
        "Fortunately, you don't need to do this in most cases! Thanks to a technique known as **transfer learning**, it is possible to adapt a model that has been trained from scratch (usually called a **pretrained model**), to a variety of downstream tasks. This process is called **fine-tuning** and can typically be carried with a single GPU and a dataset of the size that you're like to find in your university or company.\n",
        "\n",
        "The models that we'll be looking at in this tutorial are all examples of fine-tuned models, and you can learn more about the transfer learning process in the video below:\n"
      ]
    },
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",
            "text/html": [
              "\n",
              "        <iframe\n",
              "            width=\"400\"\n",
              "            height=\"300\"\n",
              "            src=\"https://www.youtube.com/embed/BqqfQnyjmgg\"\n",
              "            frameborder=\"0\"\n",
              "            allowfullscreen\n",
              "        ></iframe>\n",
              "        "
            ],
            "text/plain": [
              "<IPython.lib.display.YouTubeVideo at 0x7f9dea02e4d0>"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from IPython.display import YouTubeVideo\n",
        "\n",
        "YouTubeVideo('BqqfQnyjmgg')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NOcZex3OwFcF"
      },
      "source": [
        "Now, Transformers are coolest kids in town, but how can we use them? If only there was a library that could help us ... oh wait, there is! The [Hugging Face Transformers library](https://github.com/huggingface/transformers) provides a unified API across dozens of Transformer architectures, as well as the means to train models and run inference with them. So to get started, let's install the library with the following command:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QKWO9iE_y8Vs"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "%pip install transformers[sentencepiece]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-2-8iQfS1C66"
      },
      "source": [
        "Now that we've installed the library, let's take a look at some applications!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lJJQ_HFuDvqV",
        "tags": []
      },
      "source": [
        "## 1. Pipelines for Transformers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BSZqyAh_DvqU"
      },
      "source": [
        "The fastest way to learn what Transformers can do is via the `pipeline()` function. This function loads a model from the Hugging Face Hub and takes care of all the preprocessing and postprocessing steps that are needed to convert inputs into predictions:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G67RTb2dDvqV"
      },
      "source": [
        "<img src=\"https://github.com/huggingface/workshops/blob/main/nlp-zurich/images/pipeline.png?raw=1\" alt=\"Alt text that describes the graphic\" title=\"Title text\" width=800>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yx8Hn2ND2VSd"
      },
      "source": [
        "In the next few sections we'll see how these steps are combined for different applications. If you want to learn more about what is happening under the hood, then check out the video below:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "8r-qIToMDvqW",
        "outputId": "ec79ef2a-ea5a-44f5-eb4b-763439de70de"
      },
      "outputs": [
        {
          "data": {
            "image/jpeg": 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            "text/html": [
              "\n",
              "        <iframe\n",
              "            width=\"400\"\n",
              "            height=\"300\"\n",
              "            src=\"https://www.youtube.com/embed/1pedAIvTWXk\"\n",
              "            frameborder=\"0\"\n",
              "            allowfullscreen\n",
              "        ></iframe>\n",
              "        "
            ],
            "text/plain": [
              "<IPython.lib.display.YouTubeVideo at 0x7f99be71bd50>"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "YouTubeVideo('1pedAIvTWXk')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mXAlr2u76bkg",
        "jp-MarkdownHeadingCollapsed": true,
        "tags": []
      },
      "source": [
        "## 2. Text classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "amrlJNI9DvqZ"
      },
      "source": [
        "Let's start with one of the most common tasks in NLP: text classification. We need a snippet of text for our models to analyze, so let's use the following (fictious!) customer feedback about a certain online order:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G1RROa_I39OK"
      },
      "outputs": [],
      "source": [
        "text = \"\"\"Dear Amazon, last week I ordered an Optimus Prime action figure \\\n",
        "from your online store in Germany. Unfortunately, when I opened the package, \\\n",
        "I discovered to my horror that I had been sent an action figure of Megatron \\\n",
        "instead! As a lifelong enemy of the Decepticons, I hope you can understand my \\\n",
        "dilemma. To resolve the issue, I demand an exchange of Megatron for the \\\n",
        "Optimus Prime figure I ordered. Enclosed are copies of my records concerning \\\n",
        "this purchase. I expect to hear from you soon. Sincerely, Bumblebee.\"\"\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KDUFC-x1BtQw"
      },
      "source": [
        "While we're at it, let's create a simple wrapper so that we can pretty print out texts:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PTC-NycTByIP",
        "outputId": "320f7d74-9411-4b01-d88e-2123c50e65f9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dear Amazon, last week I ordered an Optimus Prime action figure from your online\n",
            "store in Germany. Unfortunately, when I opened the package, I discovered to my\n",
            "horror that I had been sent an action figure of Megatron instead! As a lifelong\n",
            "enemy of the Decepticons, I hope you can understand my dilemma. To resolve the\n",
            "issue, I demand an exchange of Megatron for the Optimus Prime figure I ordered.\n",
            "Enclosed are copies of my records concerning this purchase. I expect to hear\n",
            "from you soon. Sincerely, Bumblebee.\n"
          ]
        }
      ],
      "source": [
        "import textwrap\n",
        "\n",
        "wrapper = textwrap.TextWrapper(width=80, break_long_words=False, break_on_hyphens=False)\n",
        "print(wrapper.fill(text))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LkrhsqmRDvqa"
      },
      "source": [
        "Now suppose that we'd like to predict the _sentiment_ of this text, i.e. whether the feedback is positive or negative. This is a special type of text classification that is often used in industry to aggregate customer feedback across products or services. The example below shows how a Transformer like BERT converts the inputs into atomic chunks called **tokens** which are then fed through the network to produce a single prediction:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T4Rb3oe-Dvqb"
      },
      "source": [
        "<img src=\"https://github.com/huggingface/workshops/blob/main/nlp-zurich/images/clf_arch.png?raw=1\" alt=\"Alt text that describes the graphic\" title=\"Title text\" width=600>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MM2ZZoerDvqb"
      },
      "source": [
        "To load a Transformer model for this task is quite simple. We just need to specify the task in the `pipeline()` function as follows;"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "K3ckzsscpPUh",
        "outputId": "9654e5b2-d9d8-4088-cba8-91faac0387ec"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No model was supplied, defaulted to distilbert-base-uncased-finetuned-sst-2-english (https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english)\n"
          ]
        },
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              "Downloading:   0%|          | 0.00/629 [00:00<?, ?B/s]"
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        {
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              "Downloading:   0%|          | 0.00/255M [00:00<?, ?B/s]"
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          },
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        },
        {
          "data": {
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              "model_id": "eefdca12862d4462be330cd02ecd88c1",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
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        },
        {
          "data": {
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              "model_id": "0e962104723b480c876e9ed0220e18ed",
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              "version_minor": 0
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              "Downloading:   0%|          | 0.00/226k [00:00<?, ?B/s]"
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "sentiment_pipeline = pipeline('text-classification')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4-JvPV-IDvqc"
      },
      "source": [
        "When you run this code, you'll see a message about which Hub model is being used by default. In this case, the `pipeline()` function loads the `distilbert-base-uncased-finetuned-sst-2-english` model, which is a small BERT variant trained on [SST-2](https://paperswithcode.com/sota/sentiment-analysis-on-sst-2-binary) which is a sentiment analysis dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "14-9I6cRDvqc"
      },
      "source": [
        "💡 The first time you execute the code, the model will be automatically downloaded from the Hub and cached for later use! "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G3qkCt07Dvqd"
      },
      "source": [
        "Now we are ready to run our example through pipeline and look at some predictions:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uWEfVEll5qgu",
        "outputId": "f1739c7a-47bc-4924-a127-2f47671b7be7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[{'label': 'NEGATIVE', 'score': 0.9015457034111023}]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sentiment_pipeline(text)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xyKN1JDYDvqd"
      },
      "source": [
        "The model predicts negative sentiment with a high confidence which makes sense given that we have a disgruntled customer. You can also see that the pipeline returns a list of Python dictionaries with the predictions. We can also pass several texts at the same time in which case we would get several dicts in the list for each text one."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pIuYr7X269F2"
      },
      "source": [
        "⚡ **Your turn!** Feed a list of texts with different types of sentiment to the `sentiment_pipeline` object. Do the predictions always make sense?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M0c9r_ZC66rC",
        "jp-MarkdownHeadingCollapsed": true,
        "tags": []
      },
      "source": [
        "## 3. Named entity recognition"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XjB4_YBT8JoB"
      },
      "source": [
        "Let's now do something a little more sophisticated. Instead of just finding the overall sentiment, let's see if we can extract **entities** such as organizations, locations, or individuals from the text. This task is called named entity recognition, or NER for short. Instead of predicting just a class for the whole text **a class is predicted for each token**, as shown in the example below:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I4tR3C7_Dvqe"
      },
      "source": [
        "<img src=\"https://github.com/huggingface/workshops/blob/main/nlp-zurich/images/ner_arch.png?raw=1\" alt=\"Alt text that describes the graphic\" title=\"Title text\" width=600>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sPvWZmU1Dvqf"
      },
      "source": [
        "Again, we just load a pipeline for NER without specifying a model. This will load a default BERT model that has been trained on the [CoNLL-2003](https://huggingface.co/datasets/conll2003) dataset:"
      ]
    },
    {
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      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No model was supplied, defaulted to dbmdz/bert-large-cased-finetuned-conll03-english (https://huggingface.co/dbmdz/bert-large-cased-finetuned-conll03-english)\n"
          ]
        },
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        {
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        {
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          "metadata": {},
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      ],
      "source": [
        "ner_pipeline = pipeline('ner')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-YWdgXyODvqf"
      },
      "source": [
        "When we pass our text through the model,  we now get a long list of Python dictionaries, where each dictionary corresponds to one detected entity. Since multiple tokens can correspond to a a single entity ,we can apply an aggregation strategy that merges entities if the same class appears in consequtive tokens:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Q1Ns7TXt0Y4",
        "outputId": "03114ed6-aeb3-402d-f855-28f5d8805600"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[{'entity_group': 'ORG', 'score': 0.8790103, 'word': 'Amazon', 'start': 5, 'end': 11}, {'entity_group': 'MISC', 'score': 0.9908588, 'word': 'Optimus Prime', 'start': 36, 'end': 49}, {'entity_group': 'LOC', 'score': 0.9997547, 'word': 'Germany', 'start': 90, 'end': 97}, {'entity_group': 'MISC', 'score': 0.5565697, 'word': 'Mega', 'start': 208, 'end': 212}, {'entity_group': 'PER', 'score': 0.59025633, 'word': '##tron', 'start': 212, 'end': 216}, {'entity_group': 'ORG', 'score': 0.6696923, 'word': 'Decept', 'start': 253, 'end': 259}, {'entity_group': 'MISC', 'score': 0.49834913, 'word': '##icons', 'start': 259, 'end': 264}, {'entity_group': 'MISC', 'score': 0.77536184, 'word': 'Megatron', 'start': 350, 'end': 358}, {'entity_group': 'MISC', 'score': 0.98785394, 'word': 'Optimus Prime', 'start': 367, 'end': 380}, {'entity_group': 'PER', 'score': 0.81209636, 'word': 'Bumblebee', 'start': 502, 'end': 511}]\n"
          ]
        }
      ],
      "source": [
        "entities = ner_pipeline(text, aggregation_strategy=\"simple\")\n",
        "print(entities)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Su1xNy1zDvqg"
      },
      "source": [
        "This isn't very easy to read, so let's clean up the outputs a bit:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WSffmwPn5kVT",
        "outputId": "df37abbb-9f20-4e32-ae07-344768869956"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Amazon: ORG (0.88)\n",
            "Optimus Prime: MISC (0.99)\n",
            "Germany: LOC (1.00)\n",
            "Mega: MISC (0.56)\n",
            "##tron: PER (0.59)\n",
            "Decept: ORG (0.67)\n",
            "##icons: MISC (0.50)\n",
            "Megatron: MISC (0.78)\n",
            "Optimus Prime: MISC (0.99)\n",
            "Bumblebee: PER (0.81)\n"
          ]
        }
      ],
      "source": [
        "for entity in entities:\n",
        "    print(f\"{entity['word']}: {entity['entity_group']} ({entity['score']:.2f})\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xZ7RX6OXDvqg"
      },
      "source": [
        "That's much better! It seems that the model found most of the named entities but was confused about \"Megatron\" andn \"Decepticons\", which are characters in the transformers franchise. This is no surprise since the original dataset probably did not contain many transformer characters. For this reason it makes sense to further fine-tune a model on your on dataset!\n",
        "\n",
        "Now that we've seen an example of text and token classification using Transformers, let's look at an interesting application called **question answering**."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xzqKbiD87A8v",
        "jp-MarkdownHeadingCollapsed": true,
        "tags": []
      },
      "source": [
        "## 4. Question answering"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l--riwHrDvqh"
      },
      "source": [
        "In this task, the model is given a **question** and a **context** and needs to find the answer to the question within the context. This problem can be rephrased as a classification problem: For each token the model needs to predict whether it is the start or the end of the answer. In the end we can extract the answer by looking at the span between the token with the highest start probability and highest end probability:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R-7QA_cFDvqh"
      },
      "source": [
        "<img src=\"https://github.com/huggingface/workshops/blob/main/nlp-zurich/images/qa_arch.png?raw=1\" alt=\"Alt text that describes the graphic\" title=\"Title text\" width=600>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NY1ZYoR6Dvqh"
      },
      "source": [
        "You can imagine that this requires quite a bit of pre- and post-processing logic. Good thing that the pipeline takes care of all that! As usual, we load the model by specifying the task in the `pipeline()` function:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195,
          "referenced_widgets": [
            "c503b67876e547e78dea44fb22b5576f",
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      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No model was supplied, defaulted to distilbert-base-cased-distilled-squad (https://huggingface.co/distilbert-base-cased-distilled-squad)\n"
          ]
        },
        {
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              "Downloading:   0%|          | 0.00/473 [00:00<?, ?B/s]"
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        {
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        {
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        {
          "data": {
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          "metadata": {},
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        }
      ],
      "source": [
        "qa_pipeline = pipeline(\"question-answering\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KkQPviRTDvqi"
      },
      "source": [
        "This default model is trained on the famous [SQuAD dataset](https://huggingface.co/datasets/squad). Let's see if we can ask it what the customer wants:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C85OWgOu5coO",
        "outputId": "7e186e9a-a70d-44d2-8d64-32f022ab363d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'answer': 'an exchange of Megatron',\n",
              " 'end': 358,\n",
              " 'score': 0.6312924027442932,\n",
              " 'start': 335}"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "question = \"What does the customer want?\"\n",
        "\n",
        "outputs = qa_pipeline(question=question, context=text)\n",
        "outputs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dZudlnD9Dvqj"
      },
      "source": [
        "Awesome, that sounds about right!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nXf6qViJ7FNk",
        "jp-MarkdownHeadingCollapsed": true,
        "tags": []
      },
      "source": [
        "## 5. Text summarization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gOwy0ncrDvqj"
      },
      "source": [
        "Let's see if we can go beyond these natural language understanding tasks (NLU) where BERT excels and delve into the generative domain. Note that generation is much more computationally demanding since we usually generate one token at a time and need to run this several times. An example for how this process works is shown below:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eFKqI9NRDvqj"
      },
      "source": [
        "<img src=\"https://github.com/huggingface/workshops/blob/main/nlp-zurich/images/gen_steps.png?raw=1\" alt=\"Alt text that describes the graphic\" title=\"Title text\" width=600>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NUVru-mzDvqk"
      },
      "source": [
        "A popular task involving generation is summarization. Let's see if we can use a transformer to generate a summary for us:"
      ]
    },
    {
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      "execution_count": null,
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        "colab": {
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      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No model was supplied, defaulted to sshleifer/distilbart-cnn-12-6 (https://huggingface.co/sshleifer/distilbart-cnn-12-6)\n"
          ]
        },
        {
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          },
          "metadata": {},
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        },
        {
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        {
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              "Downloading:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"
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          "metadata": {},
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        },
        {
          "data": {
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              "Downloading:   0%|          | 0.00/878k [00:00<?, ?B/s]"
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          },
          "metadata": {},
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        },
        {
          "data": {
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          "metadata": {},
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      ],
      "source": [
        "summarization_pipeline = pipeline(\"summarization\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iC536PrZDvqk"
      },
      "source": [
        "This model is trained was trained on the [CNN/Dailymail dataset](https://huggingface.co/datasets/cnn_dailymail) to summarize news articles."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BYjuIjL1vDYP",
        "outputId": "426d0d41-6ec8-4943-87a5-3884359c0c54"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Your min_length=56 must be inferior than your max_length=45.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " Bumblebee ordered an Optimus Prime action figure from your online store in\n",
            "Germany. Unfortunately, when I opened the package, I discovered to my horror\n",
            "that I had been sent an action figure of Megatron instead.\n"
          ]
        }
      ],
      "source": [
        "outputs = summarization_pipeline(text, max_length=45, clean_up_tokenization_spaces=True)\n",
        "print(wrapper.fill(outputs[0]['summary_text']))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "txmfrvK1CP03"
      },
      "source": [
        "That's not too bad! We can see the model was able to get the main gist of the customer feedback and even identified the author as \"Bumblebee\"."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "COD8oN7d7Ic4",
        "jp-MarkdownHeadingCollapsed": true,
        "tags": []
      },
      "source": [
        "## 6. Translation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ioRsVpbHDvql"
      },
      "source": [
        "But what if there is no model in the language of my data? You can still try to translate the text. The [Helsinki NLP team](https://huggingface.co/models?pipeline_tag=translation&sort=downloads&search=Helsinkie-NLP) has provided over 1,000 language pair models for translation 🤯. Here we load one that translates English to German:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cMF1FRfWvYrG"
      },
      "outputs": [],
      "source": [
        "translator = pipeline(\"translation_en_to_de\", model=\"Helsinki-NLP/opus-mt-en-de\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s2Ge5c10Dvqm"
      },
      "source": [
        "Let's translate the our text to German:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Aijh7j_Gvyeu",
        "outputId": "df437719-667e-42a8-b3db-a3dfb99f6626"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Sehr geehrter Amazon, letzte Woche habe ich eine Optimus Prime Action Figur aus\n",
            "Ihrem Online-Shop in Deutschland bestellt. Leider, als ich das Paket öffnete,\n",
            "entdeckte ich zu meinem Entsetzen, dass ich stattdessen eine Action Figur von\n",
            "Megatron geschickt worden war! Als lebenslanger Feind der Decepticons, Ich\n",
            "hoffe, Sie können mein Dilemma verstehen. Um das Problem zu lösen, Ich fordere\n",
            "einen Austausch von Megatron für die Optimus Prime Figur habe ich bestellt.\n",
            "Anbei sind Kopien meiner Aufzeichnungen über diesen Kauf. Ich erwarte, bald von\n",
            "Ihnen zu hören. Aufrichtig, Bumblebee.\n"
          ]
        }
      ],
      "source": [
        "outputs = translator(text, clean_up_tokenization_spaces=True, min_length=100)\n",
        "print(wrapper.fill(outputs[0]['translation_text']))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q-lP6QUQDvqm"
      },
      "source": [
        "We can see that the text is clearly not perfectly translated, but the core meaning stays the same. Another cool application of translation models is data augmentation via backtranslation!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8-miZB1m8YVM",
        "jp-MarkdownHeadingCollapsed": true,
        "tags": []
      },
      "source": [
        "## 7. Zero-shot classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XaBDCa0PDvqn"
      },
      "source": [
        "As a last example let's have a look at a cool application showing the versatility of transformers: zero-shot classification. In zero-shot classification the model receives a text and a list of candidate labels and determines which labels are compatible with the text. Instead of having fixed classes this allows for flexible classification without any labelled data! Usually this is a good first baseline!"
      ]
    },
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      ],
      "source": [
        "zero_shot_classifier = pipeline(\"zero-shot-classification\",\n",
        "                                model=\"vicgalle/xlm-roberta-large-xnli-anli\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7z01wJrvDvqn"
      },
      "source": [
        "Let's have a look at an example:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SjMXSBx5897m"
      },
      "outputs": [],
      "source": [
        "text = 'Dieser Tutorial ist großartig! Ich hoffe, dass jemand von Hugging Face meine Universität besuchen wird :)'\n",
        "classes = ['Treffen', 'Arbeit', 'Digital', 'Reisen']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l6U71HAL9oBG",
        "outputId": "ce8bd955-1d36-4184-c876-30753fea9f36"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'labels': ['Digital', 'Treffen', 'Arbeit', 'Reisen'],\n",
              " 'scores': [0.7863982915878296,\n",
              "  0.5954463481903076,\n",
              "  0.4407258629798889,\n",
              "  0.0017183449817821383],\n",
              " 'sequence': 'Dieser Tutorial ist großartig! Ich hoffe, dass jemand von Hugging Face meine Universität besuchen wird :)'}"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "zero_shot_classifier(text, classes, multi_label=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LGwLNx0PDvqo"
      },
      "source": [
        "This seems to have worked really well on this short example. Naturally, for longer and more domain specific examples this approach might suffer."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WGnElSPY7ta7",
        "tags": []
      },
      "source": [
        "## 8. Going beyond text"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F-jmgxDyDvqo"
      },
      "source": [
        "As mentioned at the start of this tutorial, Transformers can also be used for domains other than NLP! For these domains, there are many more pipelines that you can experiment with. Look at the following list for an overview:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m28XOn4t5YlM",
        "outputId": "7015ad9b-8951-4197-bfd7-38e4bb536404"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "audio-classification\n",
            "automatic-speech-recognition\n",
            "feature-extraction\n",
            "text-classification\n",
            "token-classification\n",
            "question-answering\n",
            "table-question-answering\n",
            "fill-mask\n",
            "summarization\n",
            "translation\n",
            "text2text-generation\n",
            "text-generation\n",
            "zero-shot-classification\n",
            "conversational\n",
            "image-classification\n",
            "image-segmentation\n",
            "object-detection\n"
          ]
        }
      ],
      "source": [
        "from transformers import pipelines\n",
        "for task in pipelines.SUPPORTED_TASKS:\n",
        "    print(task)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V0Fl9HbdDvqp"
      },
      "source": [
        "Let's have a look at an application involving images!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MElFihO0Dvqp",
        "tags": []
      },
      "source": [
        "### Computer vision"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5JartRCsDvqq"
      },
      "source": [
        "Recently, transformer models have also entered computer vision. Check out the DETR model on the [Hub](https://huggingface.co/facebook/detr-resnet-101-dc5):"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5wwkfybM7MZU"
      },
      "source": [
        "<img src=\"https://github.com/huggingface/workshops/blob/main/nlp-zurich/images/object_detection.png?raw=1\" alt=\"Alt text that describes the graphic\" title=\"Title text\" width=400>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ObzbVHFnL0hv",
        "tags": []
      },
      "source": [
        "### Audio"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aSdPHGNxDvqr"
      },
      "source": [
        "Another promising area is audio processing. Especially Speech2Text there have been some promising advancements recently. See for example the [wav2vec2 model](https://huggingface.co/facebook/wav2vec2-base-960h):"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J_bZrjQUDvqr"
      },
      "source": [
        "<img src=\"https://github.com/huggingface/workshops/blob/main/nlp-zurich/images/speech2text.png?raw=1\" alt=\"Alt text that describes the graphic\" title=\"Title text\" width=400>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nZtrfoydDvqr",
        "tags": []
      },
      "source": [
        "### Table QA"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L4wR2N0RDvqr"
      },
      "source": [
        "Finally, a lot of real world data is still in form of tables. Being able to query tables is very useful and with [TAPAS](https://huggingface.co/google/tapas-large-finetuned-wtq) you can do tabular question-answering:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rfmSOzcNDvqr"
      },
      "source": [
        "<img src=\"https://github.com/huggingface/workshops/blob/main/nlp-zurich/images/tapas.png?raw=1\" alt=\"Alt text that describes the graphic\" title=\"Title text\" width=400>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dXHCk79KDvqs"
      },
      "source": [
        "## 9. Where to next?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E5WWCw_jmpGg"
      },
      "source": [
        "Hopefully this tutorial has given you a taste of what Transformers can do and you're now excited to learn more! Here's a few resources you can use to dive deeper into the topic and the Hugging Face ecosystem:\n",
        "\n",
        "🤗 **A Tour through the Hugging Face Hub**\n",
        "\n",
        "In this tutorial, you get to:\n",
        "- Explore the over 30,000 models shared in the Hub.\n",
        "- Learn efficient ways to find the right model and datasets for your own task.\n",
        "- Learn how to contribute and work collaboratively in your ML workflows\n",
        "\n",
        "***Duration: 20-40 minutes***\n",
        "\n",
        "👉 [click here to access the tutorial](https://www.notion.so/Workshop-A-Tour-through-the-Hugging-Face-Hub-2098e4bae9ba4288857e85c87ff1c851)\n",
        "\n",
        "✨ **Build and Host Machine Learning Demos with Gradio & Hugging Face**\n",
        "\n",
        "In this tutorial, you get to:\n",
        "- Explore ML demos created by the community.\n",
        "- Build a quick demo for your machine learning model in Python using the `gradio` library\n",
        "- Host the demos for free with Hugging Face Spaces\n",
        "- Add your demo to the Hugging Face org for your class or conference\n",
        "\n",
        "***Duration: 20-40 minutes***\n",
        "\n",
        "👉 [click here to access the tutorial](https://colab.research.google.com/github.com/huggingface/education-toolkit/tree/main/02_ml-demos-with-gradio.ipynb)\n",
        "\n",
        "🎓 **The Hugging Face Course**\n",
        "\n",
        "This course teaches you about applying Transformers to various tasks in natural language processing and beyond. Along the way, you'll learn how to use the Hugging Face ecosystem — 🤗 Transformers, 🤗 Datasets, 🤗 Tokenizers, and 🤗 Accelerate — as well as the Hugging Face Hub. It's completely free too!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "m47Kc-rYoY9o",
        "outputId": "a4556638-2111-401e-982f-75a075dd025e"
      },
      "outputs": [
        {
          "data": {
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