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"id": "Pex5DLhrTub_" }, "outputs": [], "source": [ "# ============================================\n", "# 0) Install Required Python Libraries\n", "# ============================================\n", "\n", "# !pip:\n", "# This is a command that installs Python packages.\n", "# In Google Colab, we use \"!\" to run shell commands.\n", "\n", "# -q:\n", "# Stands for \"quiet\".\n", "# It reduces installation log output to keep the notebook clean.\n", "\n", "# transformers:\n", "# Library by HuggingFace.\n", "# Provides pretrained transformer models (BERT, GPT, etc.)\n", "# and tools to fine-tune them.\n", "\n", "# datasets:\n", "# Library for easily loading and processing NLP datasets.\n", "# Includes many benchmark datasets.\n", "\n", "# evaluate:\n", "# Library for computing evaluation metrics such as accuracy and F1.\n", "\n", "# accelerate:\n", "# Library that helps training run efficiently on GPU or multi-GPU.\n", "# Used internally by HuggingFace Trainer.\n", "\n", "# scikit-learn:\n", "# Classical machine learning library.\n", "# We'll use it for Logistic Regression and evaluation tools.\n", "\n", "!pip -q install transformers datasets evaluate accelerate scikit-learn\n" ] }, { "cell_type": "code", "source": [ "# ============================================\n", "# 1) Import Required Libraries\n", "# ============================================\n", "\n", "# os:\n", "# Used for interacting with the operating system (paths, environment).\n", "import os\n", "\n", "# random:\n", "# Python’s built-in random number generator.\n", "# Used for reproducibility.\n", "import random\n", "\n", "# numpy:\n", "# Numerical computing library.\n", "# Used for arrays and mathematical operations.\n", "import numpy as np\n", "\n", "# torch:\n", "# PyTorch deep learning framework.\n", "# Transformers are built on top of PyTorch.\n", "import torch\n" ], "metadata": { "id": "-TQSlwYjWicf" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# ============================================\n", "# 2) Check if GPU is available\n", "# ============================================\n", "\n", "# torch.cuda.is_available():\n", "# Returns True if a GPU is available in Colab.\n", "print(\"Is GPU available?\", torch.cuda.is_available())\n", "\n", "# If GPU exists, print its name\n", "if torch.cuda.is_available():\n", " print(\"GPU Name:\", torch.cuda.get_device_name(0))\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "8cQ0u2siYTAQ", "outputId": "eb638644-46f2-41ae-ac57-ff97607cee3a" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Is GPU available? True\n", "GPU Name: NVIDIA H100 80GB HBM3\n" ] } ] }, { "cell_type": "code", "source": [ "# ============================================\n", "# 3) Set Random Seed for Reproducibility\n", "# ============================================\n", "\n", "# SEED:\n", "# A fixed number used to initialize randomness.\n", "# This ensures results are reproducible.\n", "# Without this, two runs may produce slightly different results.\n", "SEED = 42\n", "\n", "# Set Python random seed\n", "random.seed(SEED)\n", "\n", "# Set NumPy random seed\n", "np.random.seed(SEED)\n", "\n", "# Set PyTorch seed (CPU)\n", "torch.manual_seed(SEED)\n", "\n", "# Set PyTorch seed (GPU)\n", "torch.cuda.manual_seed_all(SEED)\n" ], "metadata": { "id": "kPuzeafzYV7B" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# ============================================\n", "# 4) Load Dataset\n", "# ============================================\n", "\n", "# load_dataset:\n", "# Function from HuggingFace \"datasets\" library.\n", "# Downloads and loads a dataset by name.\n", "from datasets import load_dataset\n", "\n", "# \"emotion\":\n", "# A dataset with short text sentences labeled with 6 emotions.\n", "dataset = load_dataset(\"emotion\")\n", "\n", "# Print dataset structure\n", "print(dataset)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "CPxhetsUYZXd", "outputId": "6e57c13f-2106-455e-8e3f-9b8328f3ac78" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "DatasetDict({\n", " train: Dataset({\n", " features: ['text', 'label'],\n", " num_rows: 16000\n", " })\n", " validation: Dataset({\n", " features: ['text', 'label'],\n", " num_rows: 2000\n", " })\n", " test: Dataset({\n", " features: ['text', 'label'],\n", " num_rows: 2000\n", " })\n", "})\n" ] } ] }, { "cell_type": "code", "source": [ "# ============================================\n", "# 5) Inspect Label Names\n", "# ============================================\n", "\n", "# dataset[\"train\"].features[\"label\"].names:\n", "# Returns list of class names.\n", "label_names = dataset[\"train\"].features[\"label\"].names\n", "\n", "# Number of classes\n", "num_labels = len(label_names)\n", "\n", "print(\"Labels:\", label_names)\n", "print(\"Number of labels:\", num_labels)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "R9AfSPaZYj7i", "outputId": "f89d3e89-8067-4214-d8ac-825fd0df6886" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Labels: ['sadness', 'joy', 'love', 'anger', 'fear', 'surprise']\n", "Number of labels: 6\n" ] } ] }, { "cell_type": "code", "source": [ "# ============================================\n", "# 6) Load Tokenizer\n", "# ============================================\n", "\n", "# AutoTokenizer:\n", "# Automatically selects correct tokenizer for a model checkpoint.\n", "from transformers import AutoTokenizer\n", "\n", "# CHECKPOINT:\n", "# The name of the pretrained model we want to use.\n", "# DistilBERT is smaller and faster than BERT-base.\n", "CHECKPOINT = \"distilbert-base-uncased\"\n", "\n", "# Load tokenizer\n", "tokenizer = AutoTokenizer.from_pretrained(CHECKPOINT)\n" ], "metadata": { "id": "TzBR8nilYoSN" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# ============================================\n", "# 6) Load Tokenizer\n", "# ============================================\n", "\n", "# AutoTokenizer:\n", "# Automatically selects correct tokenizer for a model checkpoint.\n", "from transformers import AutoTokenizer\n", "\n", "# CHECKPOINT:\n", "# The name of the pretrained model we want to use.\n", "# DistilBERT is smaller and faster than BERT-base.\n", "CHECKPOINT = \"distilbert-base-uncased\"\n", "\n", "# Load tokenizer\n", "tokenizer = AutoTokenizer.from_pretrained(CHECKPOINT)\n" ], "metadata": { "id": "cp-xOmYvYtd3" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# ============================================\n", "# 7) Tokenization Function\n", "# ============================================\n", "\n", "def tokenize_function(example):\n", " \"\"\"\n", " example:\n", " A dictionary containing a single text entry.\n", "\n", " truncation=True:\n", " If text is longer than model maximum length,\n", " cut it to avoid errors.\n", " \"\"\"\n", " return tokenizer(example[\"text\"], truncation=True)\n", "\n", "# Apply tokenization to entire dataset\n", "tokenized_dataset = dataset.map(tokenize_function, batched=True)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 113, "referenced_widgets": [ "d72d1b5d07de4f9299feea23cd50a567", "e835e303b84040298a187b3dc8e15761", "c76718aaa099463c88cd785e7bdd299f", "46d6ca8225904b5a82c5eb808a5e1414", "c060186ad9d3464eb433764a00ceb82a", "48109eae184d49fd8a0000978be512a9", "f831cc73dd084ed3942f02dc0387098a", "b5d8bfb217234cf780d4f08f63d14177", "59341ce3b2cf47f99cf4466b0367d179", "59f96da93b354b9ab0ffc1fc94f97b13", "26656d54440b49629aaa3026655bf6d1", "4de6cfe12e1e4e69ae01bd1e24df53d8", "0e64cb9233274efbb2f9a4799e8e6129", "7f97994fdade4fa8baf0c759a3e935c8", "3c9f207ecf6548c68141bc5880035ff0", "b182ab5691574c0fbcd52d46523a44d0", "bee264399b964ef5acd9c0a53c2e8071", "cd783cb61fe449149303c9fdf3e560a4", "7e7604fede5d4065bc4799d3461bcacc", "7a959a8cc863442f9b5f4e870d564bd0", "305c9a89e94d439bb947b0ec2a77ac78", "d4183b472b0148fcb9f6a36c061a67a5", "caeef6d3f1594b22a1a8710141cbd5c4", "d760555b5e4149a091ae6d573d19eda5", "fa69165f0a004e0aa0622f61770eb877", "a34ef9b450b54797bcca35adaa505327", "9beb6d857ba84f80a456117d7af4dc14", "479a7f06f7fd4c938942bbaab4085df5", "18e79989aa234f3da9da503394b16cf6", "fa51be2eb10c48c0ac0805547cd87836", "bf4a45efba41481b817c8f4bdd7c55ac", "2c750896c3294a34bc3a2b2141aaf159", "b5f77cf430f145029e5e040d76a507fc" ] }, "id": "V1tuA8pPYyo4", "outputId": "00abdf64-71e5-451c-a585-b159ee806fde" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Map: 0%| | 0/16000 [00:00 [hidden_dim]\n", "\n", "def mean_pooling(last_hidden_state, attention_mask):\n", " \"\"\"\n", " last_hidden_state:\n", " Tensor of shape [batch_size, seq_len, hidden_dim]\n", " This is the embedding for each token in each sentence.\n", "\n", " attention_mask:\n", " Tensor of shape [batch_size, seq_len]\n", " 1 = real token\n", " 0 = padding token\n", "\n", " Goal:\n", " Compute the average embedding across REAL tokens only\n", " (ignore padding tokens)\n", " \"\"\"\n", "\n", " # attention_mask.unsqueeze(-1):\n", " # Changes shape from [batch, seq_len] to [batch, seq_len, 1]\n", " # so it can multiply with embeddings\n", " mask = attention_mask.unsqueeze(-1).float()\n", "\n", " # Multiply embeddings by mask:\n", " # - real tokens keep their value (mask=1)\n", " # - padding tokens become 0 (mask=0)\n", " masked_embeddings = last_hidden_state * mask\n", "\n", " # Sum across tokens (dim=1 means sum over seq_len)\n", " sum_embeddings = masked_embeddings.sum(dim=1)\n", "\n", " # Count how many real tokens each sentence has\n", " # clamp(min=1e-9) avoids division by zero\n", " token_counts = mask.sum(dim=1).clamp(min=1e-9)\n", "\n", " # Average = sum / count\n", " sentence_embeddings = sum_embeddings / token_counts\n", "\n", " return sentence_embeddings\n" ], "metadata": { "id": "_eiNCfJcZpat" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# ============================================\n", "# A4) Create DataLoader for batching\n", "# ============================================\n", "\n", "# Collator dynamically pads each batch\n", "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n", "\n", "def make_dataloader(split_name, batch_size=64):\n", " \"\"\"\n", " split_name:\n", " \"train\", \"validation\", or \"test\"\n", "\n", " batch_size:\n", " Number of examples processed at once.\n", " Larger = faster (if GPU memory allows).\n", " \"\"\"\n", "\n", " # We keep only the columns we need for the model:\n", " # input_ids, attention_mask, label\n", " split_dataset = tokenized_dataset[split_name].remove_columns(\n", " [col for col in tokenized_dataset[split_name].column_names\n", " if col not in [\"input_ids\", \"attention_mask\", \"label\"]]\n", " )\n", "\n", " # Convert dataset into PyTorch tensors\n", " split_dataset.set_format(type=\"torch\")\n", "\n", " # Create DataLoader for batching\n", " loader = DataLoader(\n", " split_dataset,\n", " batch_size=batch_size,\n", " shuffle=False, # no shuffle because we just extract embeddings\n", " collate_fn=data_collator # handles padding\n", " )\n", "\n", " return loader\n" ], "metadata": { "id": "Lje3lMhJZ0nO" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# ============================================\n", "# A5) Extract embeddings from frozen transformer\n", "# ============================================\n", "\n", "@torch.no_grad()\n", "def extract_split_embeddings(split_name, batch_size=64):\n", " \"\"\"\n", " @torch.no_grad():\n", " Disables gradient computation.\n", " This saves memory and makes inference faster.\n", " Very important because we are NOT training the transformer.\n", "\n", " Returns:\n", " X: numpy array of sentence embeddings [N, hidden_dim]\n", " y: numpy array of labels [N]\n", " \"\"\"\n", "\n", " # Make a DataLoader for this split\n", " loader = make_dataloader(split_name, batch_size=batch_size)\n", "\n", " # Lists to store embeddings and labels\n", " all_embeddings = []\n", " all_labels = []\n", "\n", " # Loop over each batch\n", " for batch in loader:\n", "\n", " # Move batch tensors to GPU/CPU\n", " input_ids = batch[\"input_ids\"].to(device)\n", " attention_mask = batch[\"attention_mask\"].to(device)\n", " # Fix: Access 'labels' (plural) instead of 'label'\n", " labels = batch[\"labels\"].to(device)\n", "\n", " # Pass through transformer backbone\n", " outputs = backbone_model(input_ids=input_ids, attention_mask=attention_mask)\n", "\n", " # outputs.last_hidden_state:\n", " # token embeddings of shape [batch, seq_len, hidden_dim]\n", " token_embeddings = outputs.last_hidden_state\n", "\n", " # Pool token embeddings into a single sentence embedding per example\n", " sentence_embeddings = mean_pooling(token_embeddings, attention_mask)\n", "\n", " # Move to CPU and convert to numpy for scikit-learn\n", " all_embeddings.append(sentence_embeddings.cpu().numpy())\n", " all_labels.append(labels.cpu().numpy())\n", "\n", " # Combine batches into one big array\n", " X = np.concatenate(all_embeddings, axis=0)\n", " y = np.concatenate(all_labels, axis=0)\n", "\n", " return X, y\n", "\n", "# Extract embeddings for train, validation, test\n", "X_train, y_train = extract_split_embeddings(\"train\", batch_size=64)\n", "X_val, y_val = extract_split_embeddings(\"validation\", batch_size=64)\n", "X_test, y_test = extract_split_embeddings(\"test\", batch_size=64)\n", "\n", "print(\"Train embeddings shape:\", X_train.shape)\n", "print(\"Validation embeddings shape:\", X_val.shape)\n", "print(\"Test embeddings shape:\", X_test.shape)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "yYtfbpMKZ8x-", "outputId": "737975f6-79e3-4d35-a98a-233bbdfcfcc7" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Train embeddings shape: (16000, 768)\n", "Validation embeddings shape: (2000, 768)\n", "Test embeddings shape: (2000, 768)\n" ] } ] }, { "cell_type": "code", "source": [ "# ============================================\n", "# A6) Train a classic ML classifier on embeddings\n", "# ============================================\n", "\n", "# LogisticRegression:\n", "# A classic linear classifier.\n", "# It learns a linear decision boundary in embedding space.\n", "from sklearn.linear_model import LogisticRegression\n", "\n", "# Create Logistic Regression model\n", "clf_frozen = LogisticRegression(\n", " max_iter=2000, # maximum training iterations (increase if it doesn't converge)\n", " C=1.0, # regularization strength (smaller C = stronger regularization)\n", " n_jobs=-1 # use all CPU cores for faster training\n", ")\n", "\n", "# Train classifier on train embeddings\n", "clf_frozen.fit(X_train, y_train)\n", "\n", "# Predict on test embeddings\n", "pred_test_frozen = clf_frozen.predict(X_test)\n" ], "metadata": { "id": "drpuMDiaZ_Pq" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# ============================================\n", "# A7) Evaluate the frozen feature extraction approach\n", "# ============================================\n", "\n", "from sklearn.metrics import accuracy_score, f1_score, classification_report, confusion_matrix, ConfusionMatrixDisplay\n", "import matplotlib.pyplot as plt\n", "\n", "# Accuracy:\n", "# fraction of correct predictions\n", "acc_frozen = accuracy_score(y_test, pred_test_frozen)\n", "\n", "# Macro F1:\n", "# average F1 score across classes (treats all classes equally)\n", "f1_frozen = f1_score(y_test, pred_test_frozen, average=\"macro\")\n", "\n", "print(\"FROZEN Feature Extraction Results\")\n", "print(\"Accuracy:\", round(acc_frozen, 4))\n", "print(\"Macro F1:\", round(f1_frozen, 4))\n", "\n", "# Full classification report (precision, recall, F1 per class)\n", "print(\"\\nClassification Report:\")\n", "print(classification_report(y_test, pred_test_frozen, target_names=label_names))\n", "\n", "# Confusion Matrix (shows which labels get confused)\n", "cm = confusion_matrix(y_test, pred_test_frozen)\n", "\n", "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=label_names)\n", "disp.plot(xticks_rotation=45)\n", "plt.title(\"Confusion Matrix - Frozen Feature Extraction\")\n", "plt.show()\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 826 }, "id": "dxCUwJN_aghW", "outputId": "a268276b-c552-4c5a-ad20-e9cada92c5f5" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "FROZEN Feature Extraction Results\n", "Accuracy: 0.6655\n", "Macro F1: 0.5703\n", "\n", "Classification Report:\n", " precision recall f1-score support\n", "\n", " sadness 0.68 0.72 0.70 581\n", " joy 0.74 0.81 0.77 695\n", " love 0.50 0.35 0.41 159\n", " anger 0.59 0.51 0.54 275\n", " fear 0.59 0.56 0.58 224\n", " surprise 0.49 0.36 0.42 66\n", "\n", " accuracy 0.67 2000\n", " macro avg 0.60 0.55 0.57 2000\n", "weighted avg 0.66 0.67 0.66 2000\n", "\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "\n", "# 📊 Classification Metrics\n", "\n", "| Metric | What It Measures | Formula (Conceptually) | When It Is Important |\n", "| ------------- | ----------------------------------------------------------- | ----------------------------------------------- | ---------------------------------------------------------------- |\n", "| **Accuracy** | Overall proportion of correct predictions | (Correct Predictions) / (Total Predictions) | When classes are balanced and all errors have similar importance |\n", "| **Precision** | Of all predictions made for a class, how many were correct? | TP / (TP + FP) | Important when false positives are costly |\n", "| **Recall** | Of all true instances of a class, how many did we detect? | TP / (TP + FN) | Important when false negatives are costly |\n", "| **F1-score** | Harmonic mean of precision and recall | 2 × (Precision × Recall) / (Precision + Recall) | When we want balance between precision and recall |\n", "| **Support** | Number of true instances of a class in the dataset | Count of samples in that class | Shows how many real examples exist per class |\n", "\n", "---\n", "\n", "# 🧠 Examples\n", "\n", "### 🔹 Accuracy\n", "\n", "* Measures how many predictions were correct overall.\n", "* Good when dataset is balanced.\n", "* Misleading if one class dominates.\n", "\n", "Example:\n", "If 90% of data is “happy”, predicting always “happy” gives 90% accuracy — but it’s useless.\n", "\n", "---\n", "\n", "### 🔹 Precision\n", "\n", "* Out of everything the model predicted as class X, how many were actually X?\n", "* Measures prediction quality.\n", "\n", "Example:\n", "If model predicts 100 texts as “anger” but only 70 are truly anger:\n", "Precision = 70 / 100 = 0.70\n", "\n", "High precision → Few false alarms.\n", "\n", "---\n", "\n", "### 🔹 Recall\n", "\n", "* Out of all real class X examples, how many did the model find?\n", "* Measures detection ability.\n", "\n", "Example:\n", "If there are 100 real anger texts and model detects 60:\n", "Recall = 60 / 100 = 0.60\n", "\n", "High recall → Few missed cases.\n", "\n", "---\n", "\n", "### 🔹 F1-score\n", "\n", "* Balances precision and recall.\n", "* Useful when we care about both types of error.\n", "\n", "Important:\n", "It is the harmonic mean, not arithmetic mean — so low precision or low recall reduces F1 strongly.\n", "\n", "---\n", "\n", "### 🔹 Support\n", "\n", "* Simply the number of true examples of that class.\n", "* It does NOT measure performance.\n", "* It tells us class distribution.\n", "\n", "If support is small:\n", "\n", "* Metrics may be unstable.\n", "* Model may struggle due to limited data.\n", "\n", "---\n", "\n", "# 🎯 Extra: Macro vs Weighted F1 (important for your class)\n", "\n", "| Type | Meaning |\n", "| --------------- | ------------------------------------------------------ |\n", "| **Macro F1** | Average F1 across classes, treats all classes equally |\n", "| **Weighted F1** | Average F1 weighted by number of samples in each class |\n", "\n", "Macro F1 is better when:\n", "\n", "* You care about minority classes.\n", "\n", "Weighted F1 is influenced by dominant classes.\n", "\n", "\n" ], "metadata": { "id": "aKo2ETXrbQA2" } }, { "cell_type": "code", "source": [ "# ============================================\n", "# A8) Error analysis: show examples that were misclassified\n", "# ============================================\n", "\n", "# Get original test texts\n", "test_texts = dataset[\"test\"][\"text\"]\n", "\n", "# Find indices where model is wrong\n", "wrong_indices = np.where(pred_test_frozen != y_test)[0]\n", "\n", "print(\"Number of wrong predictions:\", len(wrong_indices))\n", "\n", "# Show a few wrong examples\n", "for idx in wrong_indices[:10]:\n", " # Convert numpy.int64 to standard Python int for indexing\n", " text = test_texts[int(idx)]\n", " true_label = label_names[y_test[idx]]\n", " pred_label = label_names[pred_test_frozen[idx]]\n", "\n", " print(\"\\n-----------------------------------\")\n", " print(\"TEXT:\", text)\n", " print(\"TRUE LABEL:\", true_label)\n", " print(\"PREDICTED:\", pred_label)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "hFdceajJapXB", "outputId": "84b1b44c-6df6-41b7-d2c5-fe7862971d38" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Number of wrong predictions: 669\n", "\n", "-----------------------------------\n", "TEXT: i was feeling a little vain when i did this one\n", "TRUE LABEL: sadness\n", "PREDICTED: anger\n", "\n", "-----------------------------------\n", "TEXT: i explain why i clung to a relationship with a boy who was in many ways immature and uncommitted despite the excitement i should have been feeling for getting accepted into the masters program at the university of virginia\n", "TRUE LABEL: joy\n", "PREDICTED: sadness\n", "\n", "-----------------------------------\n", "TEXT: i jest i feel grumpy tired and pre menstrual which i probably am but then again its only been a week and im about as fit as a walrus on vacation for the summer\n", "TRUE LABEL: anger\n", "PREDICTED: sadness\n", "\n", "-----------------------------------\n", "TEXT: i don t feel particularly agitated\n", "TRUE LABEL: fear\n", "PREDICTED: joy\n", "\n", "-----------------------------------\n", "TEXT: i feel beautifully emotional knowing that these women of whom i knew just a handful were holding me and my baba on our journey\n", "TRUE LABEL: sadness\n", "PREDICTED: love\n", "\n", "-----------------------------------\n", "TEXT: i pay attention it deepens into a feeling of being invaded and helpless\n", "TRUE LABEL: fear\n", "PREDICTED: sadness\n", "\n", "-----------------------------------\n", "TEXT: i just feel extremely comfortable with the group of people that i dont even need to hide myself\n", "TRUE LABEL: joy\n", "PREDICTED: fear\n", "\n", "-----------------------------------\n", "TEXT: i find myself in the odd position of feeling supportive of\n", "TRUE LABEL: love\n", "PREDICTED: sadness\n", "\n", "-----------------------------------\n", "TEXT: im not sure the feeling of loss will ever go away but it may dull to a sweet feeling of nostalgia at what i shared in this life with my dad and the luck i had to have a dad for years\n", "TRUE LABEL: sadness\n", "PREDICTED: joy\n", "\n", "-----------------------------------\n", "TEXT: i don t feel guilty like i m not going to be able to cook for him\n", "TRUE LABEL: sadness\n", "PREDICTED: joy\n" ] } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "tmJPsndnauaj" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# 🔵 PART B — FULL FINE-TUNING" ], "metadata": { "id": "VNdA2vUicaYG" } }, { "cell_type": "code", "source": [ "# ============================================\n", "# B1) Import tools for Fine-Tuning\n", "# ============================================\n", "\n", "# AutoModelForSequenceClassification:\n", "# Loads a pretrained transformer WITH a classification head on top.\n", "# This head outputs logits for each class.\n", "from transformers import AutoModelForSequenceClassification\n", "\n", "# TrainingArguments:\n", "# Defines all training hyperparameters.\n", "from transformers import TrainingArguments\n", "\n", "# Trainer:\n", "# High-level training loop manager.\n", "# Handles forward pass, backpropagation, evaluation, saving.\n", "from transformers import Trainer\n", "\n", "# evaluate:\n", "# Used to compute evaluation metrics.\n", "import evaluate\n" ], "metadata": { "id": "8ZWl_ybXcbln" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# ============================================\n", "# B2) Load Pretrained Model for Classification\n", "# ============================================\n", "\n", "# CHECKPOINT:\n", "# Same pretrained model used before (DistilBERT).\n", "# Now we add a classification head.\n", "\n", "model_ft = AutoModelForSequenceClassification.from_pretrained(\n", " CHECKPOINT,\n", " num_labels=num_labels # Number of output classes (6 emotions)\n", ")\n", "\n", "# Move model to GPU or CPU\n", "model_ft.to(device)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 962, "referenced_widgets": [ "3495d6e1fc73455187f36e951dbf53a7", "de4793f2244c4401825fc0e5f596db1d", "4d20471a7032402b91ae6342c5b0a435", "fa81caf7a9764774919fd51d9674041e", "89cb8e5fb6ae481face66961c7be7b43", "771704f277274795a9d749a868abc944", "ffaf95bb13004cdebf8a5db0dee328f7", "4b19075425c84806878b01cb0fea6f87", "0d7083375f504890813b34b7e3dffc08", "942c0dbfef1044afac74ba8f3b171e03", "e66f1684821c4392a74d176152699a0d" ] }, "id": "OysYvGhKcdxk", "outputId": "1e8b828f-c40c-470a-8192-1af22ac73ce0" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/100 [00:00 The numerical IDs of tokens in the vocabulary.\n", "\n", "Every model (BERT, RoBERTa, etc.) has a vocabulary dictionary like:\n", "\n", "```\n", "\"love\" → 2293\n", "\"NLP\" → 17953\n", "```\n", "\n", "---\n", "\n", "# 🔹 What is `attention_mask`?\n", "\n", "Transformers need fixed-length inputs. If max length = 10 but sentence has 6 tokens:\n", "\n", "```\n", "Real tokens: [101, 1045, 2293, 17953, 102]\n", "Padding: [0, 0, 0, 0, 0]\n", "```\n", "\n", "Now the model must ignore padding.\n", "\n", "So we create: `attention_mask`\n", "\n", "```\n", "[1, 1, 1, 1, 1, 0, 0, 0, 0, 0]\n", "```\n", "\n", "Meaning:\n", "\n", "* 1 → real token\n", "* 0 → padding (ignore this)\n", "\n", "Without it, the model would attend to padding tokens and mess up learning.\n", "\n", "\n", "\n", "\n", "> For one example, your dataset now looks like:\n", "\n", "\n", "\n", "```python\n", "{\n", " \"input_ids\": [101, 1045, 2293, 17953, 102, 0, 0, 0],\n", " \"attention_mask\": [1,1,1,1,1,0,0,0],\n", " \"label\": 1\n", "}\n", "```\n", "\n" ], "metadata": { "id": "znLjpa5h-gXJ" } }, { "cell_type": "code", "source": [ "# ============================================\n", "# B6) Create Trainer Object\n", "# ============================================\n", "\n", "trainer = Trainer(\n", " model=model_ft,\n", " args=training_args,\n", " train_dataset=train_dataset,\n", " eval_dataset=val_dataset,\n", " data_collator=data_collator, # Use the data_collator defined previously\n", " compute_metrics=compute_metrics\n", ")\n" ], "metadata": { "id": "ULRiX_DbgK_y" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# ============================================\n", "# B7) Train (Fine-Tune) the Model\n", "# ============================================\n", "\n", "trainer.train()\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 357, "referenced_widgets": [ "1cb10b6e7b824bb0a084edc7efe98fad", "c7660ced7b194e6a835601db41fa883e", "b65a02fed38d44899fb91fe90e829ac0", "cd18e945605041749fca05654b58df3c", "49edb5f8d23d45c5b684cad617494e07", "348915fb6bd741689e2ba92bd1d00b36", "edc18177cc0f4b74a49133b6e494471c", "b7e07cc94ee94fa3998d5e121a6d8831", "1b44c2d0fd524827b2d47591a6dabde5", "be999dea2bd04a36b0d0d60e2f96827d", "b0354f0cdf71467880548a8b5d7516ef", "3b184d5a9d9b492f9f971d50ce183d3f", "a2676a3e94fc469cb41360f27a1b91e3", "0d995e3691734b2585e06ec707f51638", "589f8dce22844a41886af704c9a829a4", "e7362c336c4a4d2eb8f18cc85bd9ce25", "a7f160aeb9c54546be28529c33721872", "d7c1fd7f478240a7b06ad59b16db979e", "ce8ad4585e9447d5ba554338b1ad9145", "6637a9d9311344fbb0f79d0bd13e9e64", "14e45c53560c4a2089a6f24a52ac3d08", "0e0f275438964da9bceacb1f27ee18fd", "2782d2dec61240b6b41cc754b5b9323a", "3d248a9df6b64ae7881c52ff43ca44c4", "4a214411c71144248da65d0c2f745957", "69dd43000f2444909d75ac09060207f1", "75710aad902b4d34bb55f23b8c325731", "6f72bc9f84bc4a1b8ebb20cdd2627945", "ef1d1e6baeef41528f7190b94525b183", "bf91b3fe347846d58735f1aa401df649", "9bcede8ecf85403f8509708c870ed004", "1391f543a0d24070a095cbf80c12d35f", "98152c837db54945b9e437935b337373" ] }, "id": "LBeCJsyogSCG", "outputId": "aa85aaad-675e-45f0-eed8-3b2f33dfd9c3" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
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EpochTraining LossValidation LossAccuracyMacro F1
10.2369460.1971730.9335000.909894
20.1212110.1766100.9350000.908507
30.0750090.1575600.9385000.913226

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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "0oG8XpMChJNz" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "oGx8_vR19dfg" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "UvKKbmH49dUd" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "![transformer.png](data:image/png;base64,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"\n", "---\n", "\n", "# 🔵 INPUT SIDE\n", "\n", "## 1️⃣ Embeddings\n", "\n", "This turns each word into a list of numbers (a vector).\n", "Computers don’t understand text, so we convert words into numerical representations.\n", "\n", "👉 Output: one vector per word.\n", "\n", "---\n", "\n", "## 2️⃣ Positional Encoding\n", "\n", "Because the Transformer has no sense of order by default, we add position information to each word vector.\n", "\n", "This tells the model:\n", "\n", "* which word comes first\n", "* which word comes next\n", "* how far words are from each other\n", "\n", "👉 Now each word knows both **what it is** and **where it is**.\n", "\n", "---\n", "\n", "# 🔴 ENCODER (Understanding the Input)\n", "\n", "The encoder is repeated several times (layers). Each layer has two main parts.\n", "\n", "---\n", "\n", "## 🔹 Multi-Head Attention (Self-Attention)\n", "\n", "This is the “understanding” mechanism.\n", "\n", "Each word:\n", "\n", "* looks at every other word in the sentence\n", "* decides which ones are important\n", "* builds a better understanding based on context\n", "\n", "Example:\n", "In “The animal didn’t cross the road because it was tired”\n", "The word **“it”** learns that it probably refers to **“animal”**.\n", "\n", "Multiple heads mean:\n", "\n", "* the model looks at different types of relationships at the same time\n", " (grammar, meaning, long-distance dependencies, etc.)\n", "\n", "👉 Output: context-aware word representations.\n", "\n", "---\n", "\n", "## 🔹 Add & Norm\n", "\n", "This helps stabilize learning.\n", "\n", "It:\n", "\n", "* keeps the original information (skip connection)\n", "* slightly adjusts it\n", "* keeps training stable\n", "\n", "Think of it like:\n", "\n", "> “Don’t forget what you already knew, just improve it.”\n", "\n", "---\n", "\n", "## 🔹 Feed Forward (MLP)\n", "\n", "This is a small neural network applied to each word separately.\n", "\n", "It:\n", "\n", "* refines the meaning\n", "* makes the representation richer\n", "* adds non-linearity (so the model can learn complex patterns)\n", "\n", "After several encoder layers:\n", "👉 Each word becomes deeply contextual and meaningful.\n", "\n", "---\n", "\n", "# 🟢 DECODER (Generating Output)\n", "\n", "The decoder writes the output sentence **one word at a time**.\n", "\n", "It has three parts in each layer.\n", "\n", "---\n", "\n", "## 🔹 1. Masked Self-Attention\n", "\n", "The decoder looks at:\n", "\n", "* words it has already generated\n", "* but NOT future words\n", "\n", "This prevents cheating.\n", "\n", "Example:\n", "When predicting the next word, it can only look at previous words.\n", "\n", "---\n", "\n", "## 🔹 2. Cross Attention\n", "\n", "Now the decoder looks at the encoder output.\n", "\n", "This means:\n", "\n", "* while generating, it constantly refers back to the input sentence\n", "* it decides which input words are relevant right now\n", "\n", "Example in translation:\n", "When generating a Spanish word, it focuses on the corresponding English word.\n", "\n", "---\n", "\n", "## 🔹 3. Feed Forward (MLP)\n", "\n", "Same as encoder:\n", "\n", "* further refines meaning\n", "* prepares output for prediction\n", "\n", "---\n", "\n", "# 🟡 FINAL STEP\n", "\n", "## Linear + Softmax\n", "\n", "The decoder:\n", "\n", "* converts its final representation into probabilities over all vocabulary words\n", "* picks the most likely next word\n", "\n", "Then repeats the process until the sentence is finished.\n", "\n", "---\n", "\n", "# 🧠 In One Simple Flow\n", "\n", "1. Convert words to vectors\n", "2. Add position info\n", "3. Encoder builds deep contextual understanding\n", "4. Decoder generates output word-by-word\n", "5. Each new word is based on:\n", "\n", " * previous generated words\n", " * full input understanding\n", "\n", "---\n", "\n", "# 🚀 In One Sentence\n", "\n", "The Transformer:\n", "\n", "> Lets every word look at every other word to understand context, then generates output step-by-step using that global understanding.\n", "\n" ], "metadata": { "id": "lyuVhNRH9eFJ" } }, { "cell_type": "code", "source": [], "metadata": { "id": "vBtxw7S-9mq5" }, "execution_count": null, "outputs": [] } ] }