t81_558_class_11_04_hf_train.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3CF2edFAI4Uj"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_11_04_hf_train.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RvbM-RwHI4Ul"
   },
   "source": [
    "# T81-558: Applications of Deep Neural Networks\n",
    "**Module 11: Natural Language Processing with Hugging Face**\n",
    "* Instructor: [Jeff Heaton](https://sites.wustl.edu/jeffheaton/), McKelvey School of Engineering, [Washington University in St. Louis](https://engineering.wustl.edu/Programs/Pages/default.aspx)\n",
    "* For more information visit the [class website](https://sites.wustl.edu/jeffheaton/t81-558/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5dE7A-0aI4Ul"
   },
   "source": [
    "# Module 11 Material\n",
    "\n",
    "* Part 11.1: Introduction to Hugging Face [[Video]](https://www.youtube.com/watch?v=1IHXSbz02XM&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_11_01_huggingface.ipynb)\n",
    "* Part 11.2: Hugging Face Tokenizers [[Video]](https://www.youtube.com/watch?v=U-EGU1RyChg&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_11_02_tokenizers.ipynb)\n",
    "* Part 11.3: Hugging Face Datasets [[Video]](https://www.youtube.com/watch?v=Mq5ODegT17M&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_11_03_hf_datasets.ipynb)\n",
    "* **Part 11.4: Training Hugging Face Models** [[Video]](https://www.youtube.com/watch?v=https://www.youtube.com/watch?v=l69ov6b7DOM&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_11_04_hf_train.ipynb)\n",
    "* Part 11.5: What are Embedding Layers in Keras [[Video]](https://www.youtube.com/watch?v=OuNH5kT-aD0list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN&index=58) [[Notebook]](https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_11_05_embedding.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9Z4A091yI4Um"
   },
   "source": [
    "# Google CoLab Instructions\n",
    "\n",
    "The following code ensures that Google CoLab is running the correct version of TensorFlow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RJmzbge9I4Um",
    "outputId": "f4465798-0648-4311-9f71-b4ad74293413"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: using Google CoLab\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    %tensorflow_version 2.x\n",
    "    COLAB = True\n",
    "    print(\"Note: using Google CoLab\")\n",
    "except:\n",
    "    print(\"Note: not using Google CoLab\")\n",
    "    COLAB = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vdNN6e45I4Un"
   },
   "source": [
    "# Part 11.4: Training Hugging Face Models\n",
    "\n",
    "Up to this point, we've used data and models from the Hugging Face hub unmodified. In this section, we will transfer and train a Hugging Face model. We will use Hugging Face data sets, tokenizers, and pretrained models to achieve this training.\n",
    "\n",
    "We begin by installing Hugging Face if needed. It is also essential to install Hugging Face datasets.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3jGET2abMjcl",
    "outputId": "7676ef61-20e7-4932-a450-dd3a0cab8535"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Installing collected packages: sentencepiece\n",
      "Successfully installed sentencepiece-0.1.96\n",
      "Collecting datasets\n",
      "  Downloading datasets-2.0.0-py3-none-any.whl (325 kB)\n",
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      "Installing collected packages: multidict, frozenlist, yarl, urllib3, asynctest, async-timeout, aiosignal, fsspec, aiohttp, xxhash, responses, datasets\n",
      "  Attempting uninstall: urllib3\n",
      "    Found existing installation: urllib3 1.24.3\n",
      "    Uninstalling urllib3-1.24.3:\n",
      "      Successfully uninstalled urllib3-1.24.3\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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     ]
    }
   ],
   "source": [
    "# HIDE OUTPUT\n",
    "!pip install transformers\n",
    "!pip install transformers[sentencepiece]\n",
    "!pip install datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4QgmlpyezhMy"
   },
   "source": [
    "We begin by loading the emotion data set from the Hugging Face hub. Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. The following code loads the emotion data set from the Hugging Face hub."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 281,
     "referenced_widgets": [
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    },
    "id": "3rY9MS_LZSNs",
    "outputId": "b9f0cf9d-d00a-4df4-b6c6-29fc41d6d0b4"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "85b9006eefef46bbafcb5e53614b43c4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading builder script:   0%|          | 0.00/1.66k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "662a51efbbc64e98b906f76ecc37a3ca",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading metadata:   0%|          | 0.00/1.61k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using custom data configuration default\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset emotion/default (download: 1.97 MiB, generated: 2.07 MiB, post-processed: Unknown size, total: 4.05 MiB) to /root/.cache/huggingface/datasets/emotion/default/0.0.0/348f63ca8e27b3713b6c04d723efe6d824a56fb3d1449794716c0f0296072705...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e2f4995dc607491585d77fe4664b381e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "72eb9608b206403c93c9693b71a3be50",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "628a2616cdc340c08f9cd46f969b424f",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "41c3b9c1576c4aca9c2fd0ac746a2012",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split:   0%|          | 0/16000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "3801f3993e46452fa24c508c580f1a67",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "3036e65da00946208b4dae8fc7fc5038",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating test split:   0%|          | 0/2000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset emotion downloaded and prepared to /root/.cache/huggingface/datasets/emotion/default/0.0.0/348f63ca8e27b3713b6c04d723efe6d824a56fb3d1449794716c0f0296072705. Subsequent calls will reuse this data.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cef0883c350441349e827577f72f6f67",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# HIDE OUTPUT\n",
    "from datasets import load_dataset\n",
    "\n",
    "emotions = load_dataset(\"emotion\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C7jkei-jLn5B"
   },
   "source": [
    "You can see a single observation from the training data set here. This observation includes both the text sample and the assigned emotion label. The label is a numeric index representing the assigned emotion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ngTHE2qwGKRF",
    "outputId": "83f25e0d-56b2-4a54-df59-29b313b1f362"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'label': 3, 'text': 'im grabbing a minute to post i feel greedy wrong'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotions['train'][2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3ezW7eIcLxdQ"
   },
   "source": [
    "We can display the labels in order of their index labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "slGcHMCJGZKe",
    "outputId": "b3b7cc68-4015-46e4-cf4a-3ba0c54f82cf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'label': ClassLabel(num_classes=6, names=['sadness', 'joy', 'love', 'anger', 'fear', 'surprise'], id=None),\n",
       " 'text': Value(dtype='string', id=None)}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotions['train'].features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JnOtiJTzL12w"
   },
   "source": [
    "Next, we utilize Hugging Face tokenizers and data sets together. The following code tokenizes the entire emotion data set. You can see below that the code has transformed the training set into subword tokens that are now ready to be used in conjunction with a transformer for either inference or training."
   ]
  },
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    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "018ce05bff8d4007b486e608f6c456d7",
       "version_major": 2,
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      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      ]
     },
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    },
    {
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    },
    {
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     },
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    },
    {
     "data": {
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     },
     "metadata": {},
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    },
    {
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   ],
   "source": [
    "# HIDE OUTPUT\n",
    "from transformers import AutoTokenizer\n",
    "\n",
    "\n",
    "def tokenize(rows):\n",
    "    return tokenizer(rows['text'], padding=\"max_length\", truncation=True)\n",
    "\n",
    "\n",
    "model_ckpt = \"distilbert-base-uncased\"\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_ckpt)\n",
    "\n",
    "emotions.set_format(type=None)\n",
    "\n",
    "tokenized_datasets = emotions.map(tokenize, batched=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TqZAt1gKOf9M"
   },
   "source": [
    "We will utilize the Hugging Face **DefaultDataCollator** to transform the emotion data set into TensorFlow type data that we can use to finetune a neural network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "mjbt7ysHFFqh"
   },
   "outputs": [],
   "source": [
    "from transformers import DefaultDataCollator\n",
    "\n",
    "data_collator = DefaultDataCollator(return_tensors=\"tf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "duVS5rGnOgsi"
   },
   "source": [
    "Now we generate a shuffled training and evaluation data set."
   ]
  },
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   "execution_count": null,
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   "source": [
    "small_train_dataset = tokenized_datasets[\"train\"].shuffle(seed=42)\n",
    "small_eval_dataset = tokenized_datasets[\"test\"].shuffle(seed=42)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "We can now generate the TensorFlow data sets. We specify which columns should map to the input features and labels. We do not need to shuffle because we previously shuffled the data."
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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    "tf_train_dataset = small_train_dataset.to_tf_dataset(\n",
    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
    "    label_cols=[\"labels\"],\n",
    "    shuffle=True,\n",
    "    collate_fn=data_collator,\n",
    "    batch_size=8,\n",
    ")\n",
    "\n",
    "tf_validation_dataset = small_eval_dataset.to_tf_dataset(\n",
    "    columns=[\"attention_mask\", \"input_ids\", \"token_type_ids\"],\n",
    "    label_cols=[\"labels\"],\n",
    "    shuffle=False,\n",
    "    collate_fn=data_collator,\n",
    "    batch_size=8,\n",
    ")\n"
   ]
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   "source": [
    "We will now load the distilbert model for classification. We will adjust the pretrained weights to predict the emotions of text lines."
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     "text": [
      "Some layers from the model checkpoint at distilbert-base-uncased were not used when initializing TFDistilBertForSequenceClassification: ['vocab_layer_norm', 'vocab_transform', 'vocab_projector', 'activation_13']\n",
      "- This IS expected if you are initializing TFDistilBertForSequenceClassification 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 TFDistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some layers of TFDistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier', 'classifier', 'dropout_19']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
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    "# HIDE OUTPUT\n",
    "import tensorflow as tf\n",
    "from transformers import TFAutoModelForSequenceClassification\n",
    "\n",
    "model = TFAutoModelForSequenceClassification.from_pretrained(\\\n",
    "    \"distilbert-base-uncased\", num_labels=6) "
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     "text": [
      "Epoch 1/5\n",
      "2000/2000 [==============================] - 360s 174ms/step - loss: 0.3720 - sparse_categorical_accuracy: 0.8669 - val_loss: 0.1728 - val_sparse_categorical_accuracy: 0.9180\n",
      "Epoch 2/5\n",
      "2000/2000 [==============================] - 347s 174ms/step - loss: 0.1488 - sparse_categorical_accuracy: 0.9338 - val_loss: 0.1496 - val_sparse_categorical_accuracy: 0.9295\n",
      "Epoch 3/5\n",
      "2000/2000 [==============================] - 347s 173ms/step - loss: 0.1253 - sparse_categorical_accuracy: 0.9420 - val_loss: 0.1617 - val_sparse_categorical_accuracy: 0.9245\n",
      "Epoch 4/5\n",
      "2000/2000 [==============================] - 346s 173ms/step - loss: 0.1092 - sparse_categorical_accuracy: 0.9486 - val_loss: 0.1654 - val_sparse_categorical_accuracy: 0.9295\n",
      "Epoch 5/5\n",
      "2000/2000 [==============================] - 347s 173ms/step - loss: 0.0960 - sparse_categorical_accuracy: 0.9585 - val_loss: 0.1830 - val_sparse_categorical_accuracy: 0.9220\n"
     ]
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    {
     "data": {
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    "model.compile(\n",
    "    optimizer=tf.keras.optimizers.Adam(learning_rate=5e-5),\n",
    "    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "    metrics=tf.metrics.SparseCategoricalAccuracy(),\n",
    ")\n",
    "\n",
    "model.fit(tf_train_dataset, validation_data=tf_validation_dataset,\n",
    "          epochs=5)\n"
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