examples/notebooks/03-active-learning-with-setfit.ipynb

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    "# Use SetFit for Active Learning in Small-Text\n",
    "*Notebook 3*  \n",
    "In this tutorial we demonstrate how to use [SetFit](https://github.com/huggingface/setfit) as a classifier for active learning.\n",
    "\n",
    "----\n",
    "\n",
    "## Overview\n",
    "\n",
    "- [Part I: Installation](#nb3-part1-installation)\n",
    "- [Part II: Data](#nb3-part2-data)\n",
    "- [Part III: Active Learning](#nb3-part3-active-learning)\n",
    "- [Part IV: Comparison](#nb3-part4-comparison)\n",
    "\n",
    "----\n",
    "\n",
    "<a id=\"nb3-part1-installation\"></a>\n",
    "## I. Installation\n",
    "\n",
    "The small-text wrapper for SetFit is a part of the transformers integration, so the installation procedure is the same as for every other example that shows small-text with transformers. "
   ]
  },
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     "text": [
      "Collecting git+https://github.com/webis-de/small-text\n",
      "  Cloning https://github.com/webis-de/small-text to /tmp/pip-req-build-mvso96bh\n",
      "  Running command git clone --filter=blob:none --quiet https://github.com/webis-de/small-text /tmp/pip-req-build-mvso96bh\n",
      "  Resolved https://github.com/webis-de/small-text to commit 9b3377732c32fadafbce63d98fb9ce6448d818f3\n",
      "  Installing build dependencies ... \u001b[?25ldone\n",
      "\u001b[?25h  Getting requirements to build wheel ... \u001b[?25ldone\n",
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      "Note: you may need to restart the kernel to use updated packages.\n",
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      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "%pip install small-text[transformers]==1.3.0\n",
    "\n",
    "# setfit is an optional dependency and must be installed as well\n",
    "%pip install setfit>=0.5.0\n",
    "%pip install datasets\n",
    "%pip install matplotlib\n",
    "\n",
    "import logging\n",
    "\n",
    "import datasets\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rc('figure', titlesize=22)\n",
    "plt.rc('axes', titlesize=22, labelsize=20, linewidth=1.2)\n",
    "plt.rc('xtick', labelsize=14)\n",
    "plt.rc('ytick', labelsize=14)\n",
    "plt.rc('legend', fontsize=16)\n",
    "plt.rc('lines', linewidth=2)\n",
    "\n",
    "datasets.logging.set_verbosity_error()\n",
    "\n",
    "# disables the progress bar for notebooks: https://github.com/huggingface/datasets/issues/2651\n",
    "datasets.logging.get_verbosity = lambda: logging.NOTSET\n",
    "\n",
    "for logger_name in ['setfit.modeling', 'setfit.trainer']:\n",
    "    logger = logging.getLogger(logger_name)\n",
    "    logger.setLevel(logging.ERROR)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdf5fd19",
   "metadata": {},
   "source": [
    "<a id=\"nb3-part2-data\"></a>\n",
    "## II. Data\n",
    "\n",
    "We use the \"AG's News\" dataset for this demonstration. It consists of news articles which are categorized into four mutually exclusive classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "754e5b56",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1134.52it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 10 training samples:\n",
      "\n",
      "2   Wall St. Bears Claw Back Into the Black (Reuters) Reuters - Short-sellers, Wall Street's dwindling\\band of ultra-cynics, are seeing green again.\n",
      "2   Carlyle Looks Toward Commercial Aerospace (Reuters) Reuters - Private investment firm Carlyle Group,\\which has a reputation for making well-timed and occasionally\\controversial plays in the defense industry, has quietly placed\\its bets on another part of the market.\n",
      "2   Oil and Economy Cloud Stocks' Outlook (Reuters) Reuters - Soaring crude prices plus worries\\about the economy and the outlook for earnings are expected to\\hang over the stock market next week during the depth of the\\summer doldrums.\n",
      "2   Iraq Halts Oil Exports from Main Southern Pipeline (Reuters) Reuters - Authorities have halted oil export\\flows from the main pipeline in southern Iraq after\\intelligence showed a rebel militia could strike\\infrastructure, an oil official said on Saturday.\n",
      "2   Oil prices soar to all-time record, posing new menace to US economy (AFP) AFP - Tearaway world oil prices, toppling records and straining wallets, present a new economic menace barely three months before the US presidential elections.\n",
      "2   Stocks End Up, But Near Year Lows (Reuters) Reuters - Stocks ended slightly higher on Friday\\but stayed near lows for the year as oil prices surged past  #36;46\\a barrel, offsetting a positive outlook from computer maker\\Dell Inc. (DELL.O)\n",
      "2   Money Funds Fell in Latest Week (AP) AP - Assets of the nation's retail money market mutual funds fell by  #36;1.17 billion in the latest week to  #36;849.98 trillion, the Investment Company Institute said Thursday.\n",
      "2   Fed minutes show dissent over inflation (USATODAY.com) USATODAY.com - Retail sales bounced back a bit in July, and new claims for jobless benefits fell last week, the government said Thursday, indicating the economy is improving from a midsummer slump.\n",
      "2   Safety Net (Forbes.com) Forbes.com - After earning a PH.D. in Sociology, Danny Bazil Riley started to work as the general manager at a commercial real estate firm at an annual base salary of  #36;70,000. Soon after, a financial planner stopped by his desk to drop off brochures about insurance benefits available through his employer. But, at 32, \"buying insurance was the furthest thing from my mind,\" says Riley.\n",
      "2   Wall St. Bears Claw Back Into the Black  NEW YORK (Reuters) - Short-sellers, Wall Street's dwindling  band of ultra-cynics, are seeing green again.\n"
     ]
    }
   ],
   "source": [
    "import datasets\n",
    "import logging\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "# disables the progress bar for notebooks: https://github.com/huggingface/datasets/issues/2651\n",
    "datasets.logging.get_verbosity = lambda: logging.NOTSET\n",
    "\n",
    "raw_dataset = datasets.load_dataset('ag_news')\n",
    "num_classes = np.unique(raw_dataset['train']['label']).shape[0]\n",
    "\n",
    "print('First 10 training samples:\\n')\n",
    "for i in range(10):\n",
    "    print(raw_dataset['train']['label'][i], ' ', raw_dataset['train']['text'][i])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1742f7d7",
   "metadata": {},
   "source": [
    "### Preparing the Data\n",
    "\n",
    "The SetFit Classifier uses the `TextData` structure as input. Such datasets can be easily created from raw text and labels using `TextDataset.from_arrays()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "be151ce8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages/small_text/utils/annotations.py:67: ExperimentalWarning: The function from_arrays is experimental and maybe subject to change soon.\n",
      "  warnings.warn(f'The {subject} {func_or_class.__name__} is experimental '\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from small_text import TextDataset\n",
    "\n",
    "num_classes = raw_dataset['train'].features['label'].num_classes\n",
    "\n",
    "target_labels = np.arange(num_classes)\n",
    "\n",
    "train = TextDataset.from_arrays(raw_dataset['train']['text'], np.array(raw_dataset['train']['label']), target_labels=target_labels)\n",
    "test = TextDataset.from_arrays(raw_dataset['test']['text'], np.array(raw_dataset['test']['label']), target_labels=target_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3db26811",
   "metadata": {},
   "source": [
    "<a id=\"nb3-part3-active-learning\"></a>\n",
    "## III. Active Learning \n",
    "\n",
    "### Configuring the Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2184e4b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from small_text.integrations.transformers.classifiers.setfit import SetFitModelArguments\n",
    "from small_text.integrations.transformers.classifiers.factories import SetFitClassificationFactory\n",
    "\n",
    "\n",
    "num_classes = 4\n",
    "\n",
    "sentence_transformer_model_name = 'sentence-transformers/paraphrase-mpnet-base-v2'\n",
    "setfit_model_args = SetFitModelArguments(sentence_transformer_model_name)\n",
    "clf_factory = SetFitClassificationFactory(setfit_model_args, \n",
    "                                          num_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "017ebf3d",
   "metadata": {},
   "source": [
    "### Setting up the Active Learner\n",
    "\n",
    "Here, we construct a `PoolBasedActiveLearner` instance which requires a classifier factory, a query strategy, and the train dataset.\n",
    "\n",
    "To obtain a first model, we initialize the active learner by providing the true labels for 20 sentences. This corresponds to an initial labeling the real-world setting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "66774521",
   "metadata": {},
   "outputs": [],
   "source": [
    "from small_text import (\n",
    "    PoolBasedActiveLearner, \n",
    "    random_initialization_balanced,\n",
    "    BreakingTies,\n",
    "    SubsamplingQueryStrategy\n",
    ")\n",
    "\n",
    "# define a query strategy and initialize a pool-based active learner\n",
    "query_strategy = SubsamplingQueryStrategy(BreakingTies())\n",
    "# suppress progress bars in jupyter notebook\n",
    "setfit_train_kwargs = {'show_progress_bar': False}\n",
    "active_learner = PoolBasedActiveLearner(clf_factory, query_strategy, train, fit_kwargs={'setfit_train_kwargs': setfit_train_kwargs})\n",
    "\n",
    "\n",
    "# simulate a warm start\n",
    "def initialize_active_learner(active_learner, y_train):\n",
    "\n",
    "    x_indices_initial = random_initialization_balanced(y_train, n_samples=20)\n",
    "    y_initial = y_train[x_indices_initial]\n",
    "\n",
    "    active_learner.initialize_data(x_indices_initial, y_initial)\n",
    "\n",
    "    return x_indices_initial\n",
    "\n",
    "\n",
    "initial_indices = initialize_active_learner(active_learner, train.y)\n",
    "labeled_indices = initial_indices"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8888591",
   "metadata": {},
   "source": [
    "### Active Learning Loop\n",
    "\n",
    "The main active learning loop queries the unlabeled pool and thereby decides which documents are labeled next.\n",
    "We then provide the labels for those documents and the active learner retrains the model.\n",
    "After each query, we evaluate the current model against the test set and save the result.\n",
    "\n",
    "\n",
    "Note: This is active learning as it is done in a scientific simulation. In reality, the label feedback would have been given by human annotators."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aacd866c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy: 1.00\n",
      "Test accuracy: 0.74\n",
      "---------------\n",
      "Iteration #0 (40 samples)\n",
      "Train accuracy: 1.00\n",
      "Test accuracy: 0.84\n",
      "---------------\n",
      "Iteration #1 (60 samples)\n",
      "Train accuracy: 1.00\n",
      "Test accuracy: 0.87\n",
      "---------------\n",
      "Iteration #2 (80 samples)\n",
      "Train accuracy: 0.99\n",
      "Test accuracy: 0.86\n",
      "---------------\n",
      "Iteration #3 (100 samples)\n",
      "Train accuracy: 1.00\n",
      "Test accuracy: 0.88\n",
      "---------------\n",
      "Iteration #4 (120 samples)\n",
      "Train accuracy: 0.99\n",
      "Test accuracy: 0.88\n",
      "---------------\n",
      "Iteration #5 (140 samples)\n",
      "Train accuracy: 0.99\n",
      "Test accuracy: 0.88\n",
      "---------------\n",
      "Iteration #6 (160 samples)\n",
      "Train accuracy: 1.00\n",
      "Test accuracy: 0.88\n",
      "---------------\n",
      "Iteration #7 (180 samples)\n",
      "Train accuracy: 1.00\n",
      "Test accuracy: 0.89\n",
      "---------------\n",
      "Iteration #8 (200 samples)\n",
      "Train accuracy: 1.00\n",
      "Test accuracy: 0.90\n",
      "---------------\n",
      "Iteration #9 (220 samples)\n",
      "Train accuracy: 1.00\n",
      "Test accuracy: 0.89\n"
     ]
    }
   ],
   "source": [
    "import gc\n",
    "import torch\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "\n",
    "num_queries = 10\n",
    "\n",
    "\n",
    "def evaluate(active_learner, train, test):\n",
    "    y_pred = active_learner.classifier.predict(train)\n",
    "    y_pred_test = active_learner.classifier.predict(test)\n",
    "    \n",
    "    test_acc = accuracy_score(y_pred_test, test.y)\n",
    "\n",
    "    print('Train accuracy: {:.2f}'.format(accuracy_score(y_pred, train.y)))\n",
    "    print('Test accuracy: {:.2f}'.format(test_acc))\n",
    "    \n",
    "    return test_acc\n",
    "\n",
    "\n",
    "results_setfit = []\n",
    "results_setfit.append(evaluate(active_learner, train[labeled_indices], test))\n",
    "\n",
    "    \n",
    "for i in range(num_queries):\n",
    "    # ...where each iteration consists of labelling 20 samples\n",
    "    q_indices = active_learner.query(num_samples=20)\n",
    "\n",
    "    # Simulate user interaction here. Replace this for real-world usage.\n",
    "    y = train.y[q_indices]\n",
    "\n",
    "    # Return the labels for the current query to the active learner.\n",
    "    active_learner.update(y)\n",
    "    \n",
    "    # memory fix: https://github.com/UKPLab/sentence-transformers/issues/1793\n",
    "    gc.collect()\n",
    "    torch.cuda.empty_cache()\n",
    "\n",
    "    labeled_indices = np.concatenate([q_indices, labeled_indices])\n",
    "\n",
    "    print('---------------')\n",
    "    print('Iteration #{:d} ({} samples)'.format(i, len(labeled_indices)))\n",
    "    results_setfit.append(evaluate(active_learner, train[labeled_indices], test))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5eeb9a63",
   "metadata": {},
   "source": [
    "## Plotting the Results\n",
    "\n",
    "With the previous results we can plot a [learning curve](https://en.wikipedia.org/wiki/Learning_curve_(machine_learning)) to visualize the performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "278c1d13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages/matplotlib/pyplot.py'>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize=(12, 8))\n",
    "ax = plt.axes()\n",
    "\n",
    "ax.plot(np.arange(num_queries+1), results_setfit, label='SetFit')\n",
    "ax.legend()\n",
    "\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('num queries')\n",
    "\n",
    "display(plt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b6f8f75",
   "metadata": {},
   "source": [
    "<a id=\"nb3-part4-comparison\"></a>\n",
    "## IV. Comparing against BERT\n",
    "\n",
    "We perform the same steps again for BERT."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0c64d809",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages/small_text/utils/annotations.py:67: ExperimentalWarning: The function from_arrays is experimental and maybe subject to change soon.\n",
      "  warnings.warn(f'The {subject} {func_or_class.__name__} is experimental '\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "from small_text import TransformersDataset\n",
    "from transformers import AutoTokenizer\n",
    "\n",
    "\n",
    "transformer_model_name = 'bert-base-uncased'\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\n",
    "    transformer_model_name\n",
    ")\n",
    "\n",
    "num_classes = raw_dataset['train'].features['label'].num_classes\n",
    "\n",
    "target_labels = np.arange(num_classes)\n",
    "max_length = 128\n",
    "\n",
    "\n",
    "train = TransformersDataset.from_arrays(raw_dataset['train']['text'], \n",
    "                                        np.array(raw_dataset['train']['label']), \n",
    "                                        tokenizer,\n",
    "                                        target_labels=target_labels,\n",
    "                                        max_length=max_length)\n",
    "test = TransformersDataset.from_arrays(raw_dataset['test']['text'], \n",
    "                                       np.array(raw_dataset['test']['label']), \n",
    "                                       tokenizer,\n",
    "                                       target_labels=target_labels,\n",
    "                                       max_length=max_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b8b78fc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from small_text import TransformerBasedClassificationFactory, TransformerModelArguments\n",
    "\n",
    "setfit_model_args = TransformerModelArguments(transformer_model_name)\n",
    "clf_factory = TransformerBasedClassificationFactory(setfit_model_args, \n",
    "                                                    num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a2d93583",
   "metadata": {},
   "outputs": [],
   "source": [
    "from small_text import (\n",
    "    PoolBasedActiveLearner, \n",
    "    random_initialization_balanced,\n",
    "    BreakingTies,\n",
    "    SubsamplingQueryStrategy\n",
    ")\n",
    "\n",
    "# define a query strategy and initialize a pool-based active learner\n",
    "query_strategy = SubsamplingQueryStrategy(BreakingTies())\n",
    "active_learner = PoolBasedActiveLearner(clf_factory, query_strategy, train)\n",
    "\n",
    "\n",
    "# use the same initial set as above\n",
    "active_learner.initialize_data(initial_indices, train.y[initial_indices])\n",
    "labeled_indices = initial_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e4252f63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy: 0.35\n",
      "Test accuracy: 0.25\n",
      "---------------\n",
      "Iteration #0 (40 samples)\n",
      "Train accuracy: 0.95\n",
      "Test accuracy: 0.58\n",
      "---------------\n",
      "Iteration #1 (60 samples)\n",
      "Train accuracy: 0.93\n",
      "Test accuracy: 0.60\n",
      "---------------\n",
      "Iteration #2 (80 samples)\n",
      "Train accuracy: 0.99\n",
      "Test accuracy: 0.77\n",
      "---------------\n",
      "Iteration #3 (100 samples)\n",
      "Train accuracy: 0.94\n",
      "Test accuracy: 0.73\n",
      "---------------\n",
      "Iteration #4 (120 samples)\n",
      "Train accuracy: 0.97\n",
      "Test accuracy: 0.78\n",
      "---------------\n",
      "Iteration #5 (140 samples)\n",
      "Train accuracy: 0.98\n",
      "Test accuracy: 0.86\n",
      "---------------\n",
      "Iteration #6 (160 samples)\n",
      "Train accuracy: 0.96\n",
      "Test accuracy: 0.81\n",
      "---------------\n",
      "Iteration #7 (180 samples)\n",
      "Train accuracy: 0.98\n",
      "Test accuracy: 0.87\n",
      "---------------\n",
      "Iteration #8 (200 samples)\n",
      "Train accuracy: 0.96\n",
      "Test accuracy: 0.87\n",
      "---------------\n",
      "Iteration #9 (220 samples)\n",
      "Train accuracy: 0.97\n",
      "Test accuracy: 0.88\n"
     ]
    }
   ],
   "source": [
    "results_bert = []\n",
    "results_bert.append(evaluate(active_learner, train[labeled_indices], test))\n",
    "\n",
    "    \n",
    "for i in range(num_queries):\n",
    "    # ...where each iteration consists of labelling 20 samples\n",
    "    q_indices = active_learner.query(num_samples=20)\n",
    "\n",
    "    # Simulate user interaction here. Replace this for real-world usage.\n",
    "    y = train.y[q_indices]\n",
    "\n",
    "    # Return the labels for the current query to the active learner.\n",
    "    active_learner.update(y)\n",
    "\n",
    "    labeled_indices = np.concatenate([q_indices, labeled_indices])\n",
    "    \n",
    "    print('---------------')\n",
    "    print('Iteration #{:d} ({} samples)'.format(i, len(labeled_indices)))\n",
    "    results_bert.append(evaluate(active_learner, train[labeled_indices], test))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b282d8bb",
   "metadata": {},
   "source": [
    "### Comparison\n",
    "\n",
    "This time we plot the learning curves of SetFit and BERT. Not only does SetFit run quite fast (albeit we did not measure the runtime here) but also its learning curve is quite steep.\n",
    "\n",
    "Do not draw any conclusions from this besides the impressive performance and runtime of SetFit. The comparison itself is not really fair and should be in favor of `bert-base-uncased` which has 12 layers and outputs embeddings of size 768 compared to `paraphrase-MiniLM-L3-v2` which has only three layers and outputs embeddings of size 384."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c2c39b7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/home/cschroeder/.local/share/virtualenvs/notebooks-R6UeU-dP/lib/python3.8/site-packages/matplotlib/pyplot.py'>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize=(12, 8))\n",
    "ax = plt.axes()\n",
    "\n",
    "ax.plot(np.arange(20, 240, 20), results_setfit, label=f'SetFit ({sentence_transformer_model_name})')\n",
    "ax.plot(np.arange(20, 240, 20), results_bert, label=f'BERT ({transformer_model_name})')\n",
    "ax.legend(loc='lower right')\n",
    "\n",
    "plt.xticks(np.arange(20, 240, 20))\n",
    "plt.ylim((0.25, 1.0))\n",
    "\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('number of instances')\n",
    "\n",
    "plt.title('AG\\'s News / Breaking Ties')\n",
    "\n",
    "\n",
    "display(plt)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}