day_3/day_3a.ipynb

{
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   "cell_type": "markdown",
   "source": [
    "# Day 3a\n",
    "In this notebook we will explore zero-shot and few-shot classification using a pre-trained LLM in the context of political bias classification. The goal will be to classify the tweets as either `\"neutral\"` or `\"partisan\"` using different classification techniques (zero-shot, few-shot, and feature extraction).  We will make use of a [dataset](https://data.world/crowdflower/classification-of-pol-social) of tweets containing four columns:\n",
    "\n",
    "1. `\"author\"`: The author of the tweet.\n",
    "2. `\"text\"`: The text of the tweet.\n",
    "3. `\"bias\"`:  The political bias of the tweet. This can be either `\"neutral\"` or `\"partisan\"`.\n",
    "4. `\"type\"`:  The type of tweet.\n",
    "\n",
    "By the end of this notebook, you will be able to:\n",
    "- Load a pre-trained causal LLM for text generation and run it on a GPU. \n",
    "- Implement zero-shot and few-shot classification and understand the difference between the two."
   ],
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  {
   "cell_type": "markdown",
   "source": [
    "## Environment Setup\n",
    "Make sure to set your runtime to use a GPU by going to `Runtime` -> `Change runtime type` -> `Hardware accelerator` -> `T4 GPU`"
   ],
   "metadata": {
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   "id": "initial_id",
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    "id": "initial_id",
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    },
    "outputId": "75d0aaf5-47d9-4b3b-ecba-e8b03b543a35"
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   "source": [
    "import sys\n",
    "if 'google.colab' in sys.modules:  # If in Google Colab environment\n",
    "    # Mount google drive to enable access to data files\n",
    "    from google.colab import drive\n",
    "    drive.mount('/content/drive')\n",
    "    \n",
    "    # Installing requisite packages\n",
    "    !pip install transformers accelerate &> /dev/null\n",
    "\n",
    "    # Change working directory to day_3\n",
    "    %cd /content/drive/MyDrive/LLM4BeSci_GSERM2024/day_3"
   ]
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer\n",
    "import torch\n",
    "import seaborn as sns\n",
    "from tqdm.notebook import tqdm_notebook as tqdm"
   ],
   "metadata": {
    "id": "5ee02d097856ee15"
   },
   "id": "5ee02d097856ee15",
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Zero-shot Classification\n",
    "We begin by loading the dataset as a `pandas.DataFrame`:"
   ],
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   "cell_type": "code",
   "outputs": [],
   "source": [
    "media_bias_test = pd.read_csv('media_bias_test.csv')\n",
    "media_bias_test"
   ],
   "metadata": {
    "id": "619109c58f993657",
    "colab": {
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   "cell_type": "markdown",
   "source": [
    "The code next loads the causal LLM and its corresponding tokenizer. We will use [`\"microsoft/Phi-3-mini-128k-instruct\"`](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct), a recent model trained by Microsoft which shows impressive performance given its relatively small size. The smaller size has the main advantage that it can be run on the freely available GPUs on Google Colab. \n",
    "\n",
    "The code begins by setting the random seed. This helps ensure the reproducibility of the often stochastic processes involved in training and running LLMs. The code then loads the model and tokenizer. The model is loaded onto the GPU via `device_map=\"cuda\"` and the model is set to use half-precision via `torch_dtype=torch.float16` to save memory (RAM). The `trust_remote_code=True` argument is used to trust the remote code, and `attn_implementation='eager'` is used for faster inference on the T4 GPUs available on Google Colab."
   ],
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    "torch.random.manual_seed(42) # For reproducibility\n",
    "\n",
    "# Load the model \n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    \"microsoft/Phi-3-mini-128k-instruct\",\n",
    "    device_map=\"cuda\", # Use GPU\n",
    "    torch_dtype=torch.float16, # Use half-precision\n",
    "    trust_remote_code=True, \n",
    "    attn_implementation='eager' # For faster inference on T4 GPUs\n",
    ")\n",
    "\n",
    "# Load the tokenizer\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"microsoft/Phi-3-mini-128k-instruct\")"
   ],
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   "source": [
    "The code next initializes a `transformers` pipeline for text generation. This is a high-level API that allows for easy text generation using the pre-trained models. We will use this pipeline to classify the tweets as either `\"neutral\"` or `\"partisan\"` using zero-shot classification. The pipeline takes two arguments in addition to the task (`\"text-generation\"`): \n",
    "\n",
    "1. `model`: The model to use for text generation.\n",
    "2. `tokenizer`: The tokenizer to use for text generation."
   ],
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   "cell_type": "code",
   "source": [
    "# Initialize pipeline\n",
    "pipe = pipeline(\n",
    "    \"text-generation\",\n",
    "    model=model,\n",
    "    tokenizer=tokenizer,\n",
    ")"
   ],
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   "cell_type": "markdown",
   "source": [
    "Since we only want the model to output either `\"neutral\"` or `\"partisan\"`, the code provides a hard constraint on the generation by setting `\"max_new_tokens\": 10` and `\"do_sample\": False`. This is done in the `generation_args` dictionary, which is later feed into the text generation `pipe`. As a final piece of setup, we also add a soft constraint by prompting the model to `\"Strictly answer with only 'neutral' or 'partisan'\"`:"
   ],
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   "source": [
    "# Text generation arguments\n",
    "generation_args = {\n",
    "    \"max_new_tokens\": 10,\n",
    "    \"return_full_text\": False,\n",
    "    \"do_sample\": False,\n",
    "}\n",
    "\n",
    "zero_shot_prompt = \"Is this text neutral or partisan? Strictly answer with only 'neutral' or 'partisan':\\n\""
   ],
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   "source": [
    "The code then iterates through each tweet in the test set, and generates a classification label using the zero-shot prompt. For each tweet, it:\n",
    "\n",
    "1. Concatenates the zero-shot prompt with the tweet text.\n",
    "2. Passes the concatenated text to the pipeline in the required json format.\n",
    "3. Extracts the generated text from the output by checking for the presence of `\"neutral\"` or `\"partisan\"`."
   ],
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   "cell_type": "code",
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   "source": [
    "zero_shot_labels = []\n",
    "for tweet in tqdm(media_bias_test['text']):\n",
    "    # Concatenate zero-shot prompt with tweet\n",
    "    message = [{\"role\": \"user\", \"content\": zero_shot_prompt + tweet}]  \n",
    "    \n",
    "    # Generate text and access output at index 0 at key 'generated_text'    \n",
    "    output = pipe(message, **generation_args)[0]['generated_text'].lower() \n",
    "    \n",
    "    # Extract label and append to list\n",
    "    label = 'neutral' if 'neutral' in output else 'partisan' if 'partisan' in output else 'nan' # \n",
    "    zero_shot_labels.append(label)\n",
    "\n",
    "media_bias_test['zero_shot_label'] = zero_shot_labels\n",
    "media_bias_test"
   ],
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   "source": [
    "Prediction accuracy can now be evaluated by comparing the predicted labels with the actual labels:"
   ],
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   "source": [
    "# Comparing zero-shot and actual labels\n",
    "print(f'Zero-shot accuracy: {(media_bias_test[\"zero_shot_label\"] == media_bias_test[\"bias\"]).mean()}')"
   ],
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   "source": [
    "Impressively, without any task-specific training, the model achieves an accuracy of 66%. This compares to a baseline accuracy of 50% that could be achieved by randomly guessing between the two classes.\n",
    "\n",
    "The results can also visualised with a confusion matrix, which shows the number of true positives, true negatives, false positives, and false negatives. The confusion matrix can be used to identify where the model is making mistakes and to understand the types of errors."
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   "source": [
    "# Confusion matrix\n",
    "confusion = pd.crosstab(media_bias_test['bias'], media_bias_test['zero_shot_label'])\n",
    "sns.heatmap(confusion, annot=True)"
   ],
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    "When interpreting the confusion matrix, it is important to be aware of the fact that the number of `\"neutral\"` and `\"partisan\"` tweets are (intentionally) balanced in our dataset. As illustrated, the model incorrectly classifies neutral tweets as partisan (false positives) more often than it incorrectly classifies partisan tweets as neutral (false negatives). In other words, it is overly \"hasty\" in classifying tweets as partisan."
   ],
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    "## Few-shot Classification\n",
    "We will now perform few-shot classification using the same dataset. Few-shot classification is a form of in-context learning where a model is trained on a small number of examples to learn a specific task. \n",
    "\n",
    "The code begins by taking 10 examples from the test set, 5 of each class. It will then use them as prompts to classify the remaining tweets. We will follow the same steps as in zero-shot classification, but with a few-shot adaptation:"
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    "# Defining the number of examples (shots)\n",
    "n_shots = 10\n",
    "\n",
    "# Taking equal number of neutral and partisan examples\n",
    "stratified_examples = pd.concat([media_bias_test.iloc[:n_shots//2], media_bias_test.iloc[-n_shots//2:]], )\n",
    "\n",
    "# Shuffling the examples so that the model does not learn an arbitrary order\n",
    "stratified_examples = stratified_examples.sample(frac=1, random_state=42)\n",
    "\n",
    "# Assigning the few-shot prompt\n",
    "few_shot_prompt = (\n",
    "        \"Based on the following texts and labels:\\n\\n\" +\n",
    "        '\\n'.join(('Text: ' + stratified_examples['text'] + '\\nLabel: ' + stratified_examples['bias']).iloc[:n_shots] + '\\n') +\n",
    "        \"\\n\\nClassify the following texts. Strictly answer with only the labels 'neutral' or 'partisan':\\n\\n\"\n",
    ")\n",
    "\n",
    "print(few_shot_prompt)"
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    "The code next classifies the remaining tweets using the few-shot prompt. First, we drop the few shot examples used to prompt the model from the test set. Next, the few-shot prompt is concatenated with each tweet text and saved to a new column of `media_bias_test` named `\"few_shot_text\"`. The model is then again used to generate the classification label for each tweet, this time using the few-shot prompt. The predicted labels are saved to a new column of `media_bias_test` named `\"few_shot_label\"`."
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    "# Dropping few_shot examples from test set\n",
    "media_bias_test = media_bias_test.iloc[n_shots//2: -n_shots//2]\n",
    "\n",
    "# Editing headlines to include few-shot prompt\n",
    "media_bias_test['few_shot_text'] = few_shot_prompt + media_bias_test['text']\n",
    "\n",
    "# Classify all news articles\n",
    "few_shot_label = []\n",
    "for tweet in tqdm(media_bias_test['few_shot_text']):\n",
    "    message = [{\"role\": \"user\", \"content\": tweet}]\n",
    "    output = pipe(message, **generation_args)[0]['generated_text'].lower()\n",
    "    label = 'neutral' if 'neutral' in output else 'partisan' if 'partisan' in output else 'nan'\n",
    "    few_shot_label.append(label)\n",
    "\n",
    "# Add few-shot labels to dataframe\n",
    "media_bias_test['few_shot_label'] = few_shot_label\n",
    "media_bias_test"
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    "The code now evaluates the few-shot classification accuracy by comparing the predicted labels with the actual labels. There are a couple of NaN labels that we will remove for convenience before calculating the accuracy (these would need to be investigated/imputed in a real-world scenario):"
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    "# Removing any nan labels\n",
    "media_bias_nan_removed = media_bias_test[media_bias_test['few_shot_label'] != 'nan']\n",
    "\n",
    "# Calculating few-shot accuracy\n",
    "few_shot_accuracy = (media_bias_nan_removed['few_shot_label'] == media_bias_nan_removed['bias']).mean()\n",
    "print('Few-shot accuracy:', few_shot_accuracy)"
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    "The few-shot accuracy is slightly higher than the zero-shot accuracy. It is important to note that this will [not always be the case](https://dl.acm.org/doi/abs/10.1145/3411763.3451760). We can also visualize the confusion matrix:"
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    "# Confusion matrix\n",
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    "sns.heatmap(confusion, annot=True)"
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    "As illustrated, the pattern of errors has changed slightly from the zero-shot classification. The model now incorrectly classifies partisan tweets as neutral (false negatives) more often than it incorrectly classifies neutral tweets as partisan (false positives). In other words, it is overly \"cautious\" in classifying tweets as partisan. \n",
    "\n",
    "**Task 1:** Try editing the few-shot setting to include different numbers for `n_shots` (e.g., `8`, `12`) and see how it affects the classification accuracy. \n",
    "\n",
    "**Task 2:** Try editing the few-shot prompt to include more information about the task and see how it affects the classification accuracy. For instance, explain what the model should look out for in neutral versus partisan tweets."
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