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Uses the 3/4 power to smooth frequencies. """ words = list(word_counts.keys()) frequencies = np.array([word_counts[w] for w in words]) # Apply power to flatten distribution adjusted_freqs = frequencies ** power # Normalize to probability distribution sampling_probs = adjusted_freqs / adjusted_freqs.sum() return words, sampling_probs def sample_negatives(positive_word, words, sampling_probs, k=5): """ Sample k negative words, excluding the positive context word. """ negatives = [] while len(negatives) < k: idx = np.random.choice(len(words), p=sampling_probs) if words[idx] != positive_word: negatives.append(words[idx]) return negatives # Example usage word_counts = Counter({ 'the': 1000000, 'a': 800000, 'is': 600000, 'cat': 5000, 'dog': 4500, 'elephant': 100, 'algorithm': 2000, 'neural': 1500, 'network': 3000 }) words, probs = build_negative_sampling_distribution(word_counts) print("Sampling distribution comparison:") print(f"{'Word':<12} {'Count':<10} {'Raw P(w)':<12} {'Adjusted P(w)':<12}") print("-" * 46) for w in ['the', 'cat', 'elephant']: raw_prob = word_counts[w] / sum(word_counts.values()) adj_prob = probs[words.index(w)] print(f"{w:<12} {word_counts[w]:<10} {raw_prob:<12.6f} {adj_prob:<12.6f}") # Output shows how rare words get boosted probability74:T5d2,import numpy as np def compute_keep_probability(word_freq, threshold=1e-5): """ Compute probability of keeping a word during training. Frequent words are heavily downsampled. """ if word_freq < threshold: return 1.0 # Always keep rare words # Original Word2Vec formula prob = np.sqrt(threshold / word_freq) + threshold / word_freq return min(prob, 1.0) # Example: Effect on common English words word_frequencies = { 'the': 0.07, # ~7% of words 'is': 0.01, # ~1% of words 'algorithm': 0.0001, # 0.01% 'eigenvalue': 0.000001 # 0.0001% } print("Subsampling effect (threshold = 1e-5):") print(f"{'Word':<15} {'Frequency':<12} {'Keep Prob':<12} {'Effect'}") print("-" * 55) for word, freq in word_frequencies.items(): prob = compute_keep_probability(freq) if prob < 0.05: effect = "Heavily downsampled" elif prob < 0.5: effect = "Moderately downsampled" elif prob < 1.0: effect = "Slightly downsampled" else: effect = "Always kept" print(f"{word:<15} {freq:<12.6f} {prob:<12.4f} {effect}") # Output: # Word Frequency Keep Prob Effect # ------------------------------------------------------- # the 0.070000 0.0139 Heavily downsampled # is 0.010000 0.0041 Heavily downsampled # algorithm 0.000100 0.4162 Moderately downsampled # eigenvalue 0.000001 1.0000 Always kept75:T754,from gensim.models import Word2Vec from gensim.models.callbacks import CallbackAny2Vec class LossCallback(CallbackAny2Vec): """Track training progress.""" def __init__(self): self.epoch = 0 def on_epoch_end(self, model): self.epoch += 1 print(f"Epoch {self.epoch} completed") # Assume 'sentences' is an iterable of tokenized sentences # sentences = [["the", "quick", "brown", "fox"], ["jumped", "over", "lazy", "dog"], ...] # Skip-gram with negative sampling (recommended defaults) sg_model = Word2Vec( sentences=sentences, vector_size=200, # Embedding dimension window=5, # Context window size min_count=5, # Ignore words with < 5 occurrences sg=1, # 1 for Skip-gram, 0 for CBOW negative=10, # Number of negative samples sample=1e-5, # Subsampling threshold epochs=5, # Training epochs workers=4, # Parallel threads callbacks=[LossCallback()] ) # CBOW with hierarchical softmax (alternative) cbow_model = Word2Vec( sentences=sentences, vector_size=200, window=5, min_count=5, sg=0, # CBOW hs=1, # Hierarchical softmax negative=0, # Disable negative sampling epochs=5, workers=4 ) # Access trained embeddings king_vector = sg_model.wv['king'] # 200-dimensional numpy array # Semantic similarity similarity = sg_model.wv.similarity('king', 'queen') print(f"Similarity(king, queen) = {similarity:.4f}") # Analogy: king - man + woman ≈ ? result = sg_model.wv.most_similar( positive=['king', 'woman'], negative=['man'], topn=5 ) print("king - man + woman ≈", result[0][0]) # Should be 'queen' # Save and load models sg_model.save("word2vec_skipgram.model") loaded_model = Word2Vec.load("word2vec_skipgram.model")76:T5cb,import numpy as np def analogy(model, a, b, c, topn=5): """ Solve analogy: a is to b as c is to ? Returns the word whose vector is closest to (b - a + c) """ try: # Compute the target vector # We want: a:b :: c:? # So: ? = b - a + c result = model.wv.most_similar( positive=[b, c], negative=[a], topn=topn ) return result except KeyError as e: return f"Word not in vocabulary: {e}" # Semantic analogies print("Semantic Analogies:") print(f"king:queen :: man:? → {analogy(model, 'king', 'queen', 'man')[0][0]}") print(f"France:Paris :: Italy:? → {analogy(model, 'France', 'Paris', 'Italy')[0][0]}") print(f"big:bigger :: small:? → {analogy(model, 'big', 'bigger', 'small')[0][0]}") # Syntactic analogies print(" Syntactic Analogies:") print(f"walk:walked :: run:? → {analogy(model, 'walk', 'walked', 'run')[0][0]}") print(f"slow:slowly :: quick:? → {analogy(model, 'slow', 'slowly', 'quick')[0][0]}") # Explore the neighborhood of a word def explore_neighborhood(model, word, topn=10): """Show the nearest neighbors of a word in embedding space.""" neighbors = model.wv.most_similar(word, topn=topn) 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This knowledge forms the foundation for all modern word embedding techniques.","variant":"info"},"type":"callout"}]},{"id":"traditional-representations-problem","title":"The Problem with Traditional Representations","content":[{"data":{"content":"Before Word2Vec, the dominant paradigm for representing words in computational systems was **one-hot encoding** and its extension, the **bag-of-words** model. While these approaches enabled significant progress in information retrieval and text classification, they suffer from fundamental limitations that Word2Vec elegantly addresses."},"type":"text"},{"data":{"rows":[["Dimensionality","V (vocabulary size: 10K-1M+)","d (typically 100-300)"],["Vector Density","Sparse (one 1, rest 0s)","Dense (all dimensions meaningful)"],["Similarity Computation","All word pairs equally distant","Similar words → similar vectors"],["Semantic Relationships","Not captured","Captured through linear relationships"],["Memory Requirements","O(V) per word","O(d) per word, d << V"],["Generalization","No generalization between words","Transfers knowledge between similar words"]],"caption":"Traditional vs. Distributed Representations","headers":["Property","One-Hot / Bag-of-Words","Word2Vec Embeddings"]},"type":"table"},{"data":{"content":"**The curse of dimensionality in word spaces:**\n\nConsider a vocabulary of 100,000 words represented as one-hot vectors. Each word occupies a unique axis in a 100,000-dimensional space. In this representation:\n\n- The words 'happy' and 'joyful' are **orthogonal**—their dot product is zero\n- The words 'happy' and 'refrigerator' are also **orthogonal**—equally distant\n- No amount of training data about 'happy' helps the model understand 'joyful'\n\nThis orthogonality destroys any hope of generalization. A model trained to classify 'happy' as positive sentiment must relearn this from scratch for 'joyful,' 'elated,' 'content,' and every other synonym."},"type":"text"},{"data":{"icon":"Lightbulb","title":"The Distributional Hypothesis","content":"Word2Vec succeeds because it operationalizes the **distributional hypothesis** from linguistics: 'You shall know a word by the company it keeps' (J.R. Firth, 1957). Words appearing in similar contexts tend to have similar meanings. 'Cat' and 'dog' both appear near 'pet,' 'feed,' 'veterinarian,' while 'algorithm' and 'theory' appear near 'computer,' 'mathematical,' 'prove.'","variant":"tip"},"type":"callout"},{"data":{"content":"**The key insight of distributed representations:**\n\nInstead of dedicating one dimension per word (one-hot), Word2Vec distributes each word's meaning across *many* dimensions, and each dimension participates in representing *many* words. This sharing creates:\n\n1. **Compression**: 300 dimensions can represent 1 million+ words\n2. **Similarity structure**: Related words cluster together in the space\n3. **Compositionality**: Vector arithmetic captures semantic relationships\n4. **Generalization**: What the model learns about one word transfers to similar words"},"type":"text"}],"titleType":"number","titleNumber":1},{"id":"skip-gram-architecture","title":"The Skip-gram Architecture","content":[{"data":{"content":"The **Skip-gram** model represents the more powerful of Word2Vec's two architectures. Its fundamental task is deceptively simple: given a center word, predict the surrounding context words. This seemingly trivial objective forces the model to learn rich semantic representations.\n\n**The training objective:**\n\nGiven a sequence of training words w₁, w₂, ..., wₜ, the Skip-gram model maximizes the average log probability:\n\n$$\\frac{1}{T} \\sum_{t=1}^{T} \\sum_{-c \\leq j \\leq c, j \neq 0} \\log P(w_{t+j} | w_t)$$\n\nwhere:\n- T is the total number of words in the corpus\n- c is the context window size (typically 5-10)\n- P(w_{t+j} | wₜ) is the probability of seeing context word w_{t+j} given center word wₜ"},"type":"text"},{"data":{"code":"$71","fileName":"skip_gram_intuition.py","showLineNumbers":true},"type":"code"},{"data":{"content":"**The neural network structure:**\n\nSkip-gram uses a simple two-layer neural network:\n\n1. **Input layer**: One-hot vector of the center word (dimension V)\n2. **Hidden layer**: Dense embedding (dimension d, typically 100-300)\n3. **Output layer**: Probability distribution over vocabulary (dimension V)\n\nCrucially, there are **two weight matrices**:\n- **W** (V × d): Input word embeddings — row wᵢ is the embedding when word i is the center\n- **W'** (d × V): Output word embeddings — column w'ⱼ is the embedding when word j is in context\n\nAfter training, we typically use only W as our word embeddings, though averaging W and W' sometimes yields better results."},"type":"text"},{"data":{"height":"250px","mermaidSyntax":"graph LR\n subgraph Input[\"Input Layer (V dims)\"]\n I[\"One-hot: w_t\"]\n end\n \n subgraph Hidden[\"Hidden Layer (d dims)\"]\n H[\"Embedding: v_wt\"]\n end\n \n subgraph Output[\"Output Layer (V dims)\"]\n O[\"Softmax: P(w|w_t)\"]\n end\n \n I -->|\"W (V×d)\"| H\n H -->|\"W' (d×V)\"| O\n \n style Input fill:#e1f5fe\n style Hidden fill:#fff3e0\n style Output fill:#e8f5e9"},"type":"mermaid-excalidraw"},{"data":{"content":"**The softmax probability:**\n\nThe basic Skip-gram formulation uses a softmax to define the probability of observing a context word given a center word:\n\n$$P(w_O | w_I) = \\frac{\\exp(v'_{w_O}^\\top v_{w_I})}{\\sum_{w=1}^{V} \\exp(v'_w^\\top v_{w_I})}$$\n\nwhere:\n- v_{wI} is the input embedding of the center word (from W)\n- v'_{wO} is the output embedding of the context word (from W')\n- The dot product v'_{wO}ᵀ v_{wI} measures compatibility between center and context\n\n**The computational problem:**\n\nThis softmax requires summing over the *entire vocabulary* (often 100K+ words) for *every training example*. With billions of training examples, this is computationally prohibitive. This motivates the efficiency techniques we'll cover shortly."},"type":"text"},{"data":{"icon":"Info","title":"Why Two Embedding Matrices?","content":"Having separate input (W) and output (W') matrices prevents a degenerate solution where the model simply memorizes word co-occurrences. With a single matrix, the optimization could collapse to a trivial solution. The separation forces the model to learn generalizable representations that predict context words through a compressed bottleneck.","variant":"note"},"type":"callout"}],"titleType":"number","titleNumber":2},{"id":"cbow-architecture","title":"The Continuous Bag-of-Words (CBOW) Architecture","content":[{"data":{"content":"**CBOW** (Continuous Bag-of-Words) inverts the Skip-gram objective: instead of predicting context words from a center word, CBOW predicts the center word from its surrounding context. This architectural flip has significant implications for training efficiency and the nature of learned representations.\n\n**The training objective:**\n\nGiven a sequence of training words, CBOW maximizes:\n\n$$\\frac{1}{T} \\sum_{t=1}^{T} \\log P(w_t | w_{t-c}, ..., w_{t-1}, w_{t+1}, ..., w_{t+c})$$\n\nThe model takes all context words within window size c and predicts the center word."},"type":"text"},{"data":{"code":"$72","fileName":"cbow_intuition.py","showLineNumbers":true},"type":"code"},{"data":{"content":"**The neural network structure:**\n\nCBOW also uses a two-layer network, but with a key difference in how context is processed:\n\n1. **Input layer**: Multiple one-hot vectors (one per context word)\n2. **Projection layer**: Average (or sum) of context word embeddings\n3. **Output layer**: Probability distribution predicting the center word\n\n**The averaging step:**\n\nGiven context words {w_{t-c}, ..., w_{t+c}} (excluding wₜ), CBOW computes:\n\n$$\\hat{v} = \\frac{1}{2c} \\sum_{-c \\leq j \\leq c, j \neq 0} v_{w_{t+j}}$$\n\nThis averaged vector then predicts the center word via softmax:\n\n$$P(w_t | \\text{context}) = \\frac{\\exp(v'_{w_t}^\\top \\hat{v})}{\\sum_{w=1}^{V} \\exp(v'_w^\\top \\hat{v})}$$"},"type":"text"},{"data":{"height":"280px","mermaidSyntax":"graph LR\n subgraph Context[\"Context Words\"]\n C1[\"w(t-2)\"]\n C2[\"w(t-1)\"]\n C3[\"w(t+1)\"]\n C4[\"w(t+2)\"]\n end\n \n subgraph Projection[\"Projection Layer\"]\n P[\"Average: v̂\"]\n end\n \n subgraph Output[\"Output Layer\"]\n O[\"Softmax: P(w_t)\"]\n end\n \n C1 --> P\n C2 --> P\n C3 --> P\n C4 --> P\n P --> O\n \n style Context fill:#e1f5fe\n style Projection fill:#fff3e0\n style Output fill:#e8f5e9"},"type":"mermaid-excalidraw"},{"data":{"icon":"AlertCircle","title":"The 'Bag' in Bag-of-Words","content":"The averaging operation means CBOW treats context as an **unordered bag**—the words 'dog bites man' and 'man bites dog' produce identical context representations. This is a deliberate simplification that trades positional information for training efficiency. The model learns from word co-occurrence patterns, not sequential structure.","variant":"warning"},"type":"callout"}],"titleType":"number","titleNumber":3},{"id":"skipgram-vs-cbow","title":"Skip-gram vs. CBOW: When to Use Each","content":[{"data":{"content":"The choice between Skip-gram and CBOW depends on your data characteristics, computational constraints, and downstream task requirements. Each architecture makes different tradeoffs."},"type":"text"},{"data":{"left":[{"data":{"items":["**Better for rare words** — Each word gets equal update opportunity regardless of frequency","**Better for small datasets** — More training examples per word occurrence (one center generates multiple pairs)","**Captures subtle relationships** — Individual word-word predictions preserve fine-grained patterns","**Best for semantic tasks** — Excels at analogies, similarity judgments"],"title":{"icon":"Target","text":"Skip-gram Strengths","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"}],"right":[{"data":{"items":["**Faster training** — Fewer gradient updates per window position (one prediction vs. 2c predictions)","**Better for frequent words** — Averaging context smooths noise, benefits high-frequency terms","**Better for large datasets** — Speed advantage compounds with more data","**Good for syntactic tasks** — Averaging captures typical syntactic patterns"],"title":{"icon":"Zap","text":"CBOW Strengths","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"}]},"type":"two-column"},{"data":{"rows":[["Training time (relative)","Slower (10x more updates)","Faster (1 update per window)"],["Rare word quality","Excellent","Poor to moderate"],["Frequent word quality","Good","Excellent"],["Semantic accuracy","Higher (analogies, similarities)","Moderate"],["Syntactic accuracy","Moderate","Higher (part-of-speech, morphology)"],["Recommended corpus size","Small to medium (< 1B words)","Large (> 1B words)"],["Context utilization","Each context word separately","All context words averaged together"]],"caption":"Empirical Comparison: Skip-gram vs. CBOW","headers":["Property","Skip-gram","CBOW"]},"type":"table"},{"data":{"content":"**The gradient update perspective:**\n\nThe key difference lies in how training updates flow:\n\n- **Skip-gram**: For each center word occurrence, generates 2c separate predictions. Each context word gets a direct gradient signal. A rare word appearing in context still receives meaningful updates.\n\n- **CBOW**: For each center word occurrence, generates 1 prediction from averaged context. Rare context words get diluted updates (their contribution is averaged with frequent words).\n\nThis explains why Skip-gram better represents rare words—they receive undiluted gradient updates when they appear in either center or context position."},"type":"text"},{"data":{"icon":"Lightbulb","title":"Practical Recommendation","content":"For most NLP applications, **Skip-gram with negative sampling** (covered next) is the default choice. It produces high-quality embeddings for both frequent and rare words, and modern implementations are fast enough for large corpora. Use CBOW only when training speed is critical and your vocabulary has few rare words of importance.","variant":"tip"},"type":"callout"}],"titleType":"number","titleNumber":4},{"id":"negative-sampling","title":"Training Efficiency: Negative Sampling","content":[{"data":{"content":"The naive softmax formulation requires computing a sum over the entire vocabulary for every training example—clearly impractical for real-world vocabularies. **Negative Sampling** is the key optimization that made Word2Vec practical at scale.\n\n**The core idea:**\n\nInstead of predicting the probability of a context word among all vocabulary words, negative sampling reframes the task as **binary classification**: distinguish real (center, context) pairs from fake ones.\n\nFor each real pair (wI, wO), we generate k 'negative' examples by sampling random words that *didn't* appear in the context. The model learns to assign high probability to real pairs and low probability to fake pairs."},"type":"text"},{"data":{"content":"**The negative sampling objective:**\n\nFor a (center word, context word) pair, maximize:\n\n$$\\log \\sigma(v'_{w_O}^\\top v_{w_I}) + \\sum_{i=1}^{k} \\mathbb{E}_{w_i \\sim P_n(w)} [\\log \\sigma(-v'_{w_i}^\\top v_{w_I})]$$\n\nwhere:\n- σ(x) = 1/(1 + e^{-x}) is the sigmoid function\n- k is the number of negative samples (typically 5-20)\n- Pₙ(w) is the noise distribution for sampling negative words\n\n**The noise distribution:**\n\nThe original Word2Vec paper uses a modified unigram distribution:\n\n$$P_n(w) = \\frac{f(w)^{0.75}}{\\sum_{w'} f(w')^{0.75}}$$\n\nThe 3/4 power (0.75) flattens the distribution, giving rare words more chance of being sampled as negatives. This prevents the model from simply learning to predict against very frequent words."},"type":"text"},{"data":{"code":"$73","fileName":"negative_sampling.py","showLineNumbers":true},"type":"code"},{"data":{"rows":[["'the'","1,000,000","41.7%","28.2%","↓ 32%"],["'cat'","5,000","0.21%","0.45%","↑ 114%"],["'elephant'","100","0.004%","0.028%","↑ 600%"]],"caption":"Effect of the 0.75 Power on Sampling Distribution","headers":["Word","Frequency","Unigram P(w)","Adjusted P(w)^0.75","Relative Change"]},"type":"table"},{"data":{"icon":"Info","title":"Why Negative Sampling Works","content":"Negative sampling can be understood as an approximation to the gradient of the original softmax objective. More specifically, it approximates the partition function using importance sampling. The method is mathematically motivated by Noise Contrastive Estimation (NCE), though Word2Vec's formulation is a simplified variant optimized for embedding quality rather than density estimation.","variant":"note"},"type":"callout"}],"titleType":"number","titleNumber":5},{"id":"hierarchical-softmax","title":"Training Efficiency: Hierarchical Softmax","content":[{"data":{"content":"**Hierarchical Softmax** is an alternative approximation to the full softmax that reduces the computational complexity from O(V) to O(log V). It organizes the vocabulary into a binary tree structure, where each leaf node represents a word.\n\n**The core idea:**\n\nInstead of computing probabilities for all V words, hierarchical softmax represents each word as a path from the root to a leaf in a binary tree. The probability of a word is the product of probabilities at each branch point along its path:\n\n$$P(w | w_I) = \\prod_{j=1}^{L(w)} \\sigma\\left([\\![ n(w,j+1) = \\text{left-child}(n(w,j)) ]\\!] \\cdot v'_{n(w,j)}^\\top v_{w_I}\\right)$$\n\nwhere:\n- L(w) is the path length from root to word w\n- n(w,j) is the j-th node on the path to w\n- [[·]] is 1 if the condition is true, -1 otherwise"},"type":"text"},{"data":{"content":"**Building the Huffman tree:**\n\nWord2Vec uses a **Huffman tree** where frequent words have shorter paths. This is optimal because:\n\n1. Frequent words (trained more often) require fewer sigmoid computations\n2. Rare words (trained less often) have longer paths but this occurs infrequently\n3. Expected path length is minimized: O(H(p)) ≈ O(log V) for typical word distributions\n\n**Comparison with negative sampling:**"},"type":"text"},{"data":{"rows":[["Complexity per example","O(k·d), k ≈ 5-20","O(log V · d)"],["Best for","Skip-gram, large corpora","CBOW, smaller vocabularies"],["Representation","Direct word vectors","Internal node vectors + word positions"],["Quality on rare words","Better (direct updates)","Slightly worse (path-based)"],["Implementation","Simple","More complex"]],"headers":["Property","Negative Sampling","Hierarchical Softmax"]},"type":"table"},{"data":{"icon":"Lightbulb","title":"Practical Choice","content":"**Negative sampling is almost always preferred** in practice. It's simpler to implement, typically produces slightly better embeddings, and the constant factor (k ≈ 5-20) is often smaller than log V for reasonable vocabularies. Use hierarchical softmax only when you need a proper probability distribution over words (negative sampling doesn't normalize).","variant":"tip"},"type":"callout"}],"titleType":"number","titleNumber":6},{"id":"subsampling-frequent-words","title":"Subsampling Frequent Words","content":[{"data":{"content":"Extremely common words like 'the,' 'a,' 'is' present two problems for Word2Vec training:\n\n1. **Uninformative context**: 'the' appears near virtually every word, providing little discriminative signal\n2. **Wasted computation**: These words dominate training despite contributing little to embedding quality\n\n**The subsampling technique:**\n\nWord2Vec probabilistically discards frequent words during training. Each word w is kept with probability:\n\n$$P(\\text{keep } w) = \\sqrt{\\frac{t}{f(w)}} + \\frac{t}{f(w)}$$\n\nwhere:\n- f(w) is the frequency of word w in the corpus\n- t is a threshold (typically 10^{-5})\n\nWords with frequency < t are always kept. Words with frequency >> t are heavily downsampled."},"type":"text"},{"data":{"code":"$74","fileName":"subsampling.py","showLineNumbers":true},"type":"code"},{"data":{"icon":"Info","title":"Why This Improves Quality","content":"Subsampling has a dual benefit: (1) it speeds up training by processing fewer tokens, and (2) it **widens effective context windows**. When 'the' is removed from 'the quick brown fox,' the effective window now connects 'quick' more directly to words that were previously too far away. This strengthens meaningful word associations.","variant":"note"},"type":"callout"}],"titleType":"number","titleNumber":7},{"id":"implementation-hyperparameters","title":"Implementation Details and Hyperparameters","content":[{"data":{"content":"Training high-quality Word2Vec embeddings requires careful attention to hyperparameters. Each parameter involves tradeoffs between quality, speed, and memory."},"type":"text"},{"data":{"rows":[["Embedding dimension (d)","50-300","Higher = more expressive but slower; diminishing returns past ~300","100-200 for most tasks; 300 for demanding semantic tasks"],["Window size (c)","2-10","Larger = more syntactic; smaller = more semantic","5 for balanced; 2-3 for semantic; 8-10 for syntactic"],["Negative samples (k)","5-20","More = stabler gradients but slower; diminishing returns past ~15","5-10 for large data; 15-20 for small data"],["Subsampling threshold (t)","10^-5 to 10^-3","Smaller = more aggressive downsampling of frequent words","10^-5 for general use"],["Min word count","5-100","Words below threshold are excluded from vocabulary","5-10 for large corpora; higher for small"],["Learning rate (α)","0.01-0.05","Higher = faster but less stable; typically decays during training","0.025 initial, decaying to 0.0001"],["Training epochs","1-20","More = better but diminishing returns; large corpora need fewer","5 for large data; 10-20 for small"]],"caption":"Word2Vec Hyperparameters and Guidelines","headers":["Parameter","Typical Range","Effect","Recommendation"]},"type":"table"},{"data":{"code":"$75","fileName":"word2vec_training.py","showLineNumbers":true},"type":"code"},{"data":{"icon":"AlertCircle","title":"Common Pitfalls","content":"**Vocabulary issues**: Words not in the training vocabulary get no embedding. For out-of-vocabulary words, consider FastText (covered later) which handles subwords.\n\n**Overfitting on small data**: With fewer than ~1 million tokens, consider using pre-trained embeddings and fine-tuning rather than training from scratch.\n\n**Non-determinism**: Word2Vec uses random initialization and random negative sampling. Set seeds for reproducibility if needed.","variant":"warning"},"type":"callout"}],"titleType":"number","titleNumber":8},{"id":"learned-representations","title":"Understanding the Learned Representations","content":[{"data":{"content":"What exactly does Word2Vec learn? The embedding space exhibits remarkable structure that emerges purely from co-occurrence statistics:\n\n**Linear substructures:**\n\nWord2Vec embeddings capture semantic and syntactic relationships as approximately linear transformations. The classic examples:\n\n- **Gender**: king - man + woman ≈ queen\n- **Tense**: walking - walk + swim ≈ swimming \n- **Country-capital**: Paris - France + Italy ≈ Rome\n- **Comparative**: bigger - big + small ≈ smaller"},"type":"text"},{"data":{"code":"$76","fileName":"analogy_evaluation.py","showLineNumbers":true},"type":"code"},{"data":{"content":"**Why linear relationships emerge:**\n\nThe linear structure isn't explicitly encoded—it emerges from the training objective. When Skip-gram maximizes log P(context | center) using dot products, it implicitly learns that:\n\n- Words in similar contexts should have similar vectors (cluster together)\n- Consistent context *differences* should produce consistent vector *differences*\n\nIf 'king' and 'queen' differ primarily in the gender of associated contexts (he/she, his/her), while 'man' and 'woman' share this same pattern, then the vectors will naturally organize such that (king - queen) ≈ (man - woman)."},"type":"text"},{"data":{"icon":"Info","title":"Limitations of Vector Arithmetic","content":"While compelling, the analogy evaluations have caveats: (1) They work for some relationships better than others, (2) They're sensitive to how the evaluation set is constructed, (3) They measure a narrow aspect of semantic knowledge. Don't over-interpret analogy accuracy as a complete measure of embedding quality.","variant":"note"},"type":"callout"}],"titleType":"number","titleNumber":9},{"id":"summary","title":"Summary: Word2Vec Foundations","content":[{"data":{"content":"Word2Vec fundamentally changed how we represent words in machine learning. Let's consolidate the key concepts:"},"type":"text"},{"data":{"type":"ordered","items":["**Distributed representations** compress vocabulary into dense vectors where similar words are nearby and relationships are captured as linear offsets.","**Skip-gram** predicts context from center words—better for rare words and semantic tasks, slower to train.","**CBOW** predicts center from averaged context—faster to train, better for frequent words and syntactic patterns.","**Negative sampling** makes training tractable by reformulating as binary classification against randomly sampled negative examples.","**Subsampling** frequent words speeds training and improves quality by extending effective context windows.","**The resulting embeddings** exhibit remarkable properties: similar words cluster together, and semantic relationships emerge as vector arithmetic."],"title":{"icon":"CheckCircle","text":"Key Takeaways","type":"icon","color":"primary","variant":"outline-circle"},"variant":"accent"},"type":"list"},{"data":{"content":"**What's next:**\n\nNow that we understand how Word2Vec produces individual word embeddings, the next pages explore how to use these embeddings for representing documents and sentences. We'll cover **Average Word2Vec** (simple averaging) and **TF-IDF weighted Word2Vec** (weighted averaging that respects word importance)."},"type":"text"},{"data":{"icon":"CheckCircle","title":"Page Complete","content":"You now have a comprehensive understanding of Word2Vec's architecture, training procedures, and the remarkable properties of learned embeddings. These concepts form the foundation for all modern word embedding techniques and remain central to practical NLP applications.","variant":"success"},"type":"callout"}],"titleType":"number","titleNumber":10}],"isLocked":false},{"id":"seed_b786f03991793dd229cb933d7a88cd32","title":"Average Word2Vec: Document Representation through Vector Averaging","order":1,"content":null,"isLocked":true},{"id":"seed_ca3e82787e5cbde8ee221cba1109b267","title":"TF-IDF Weighted Word2Vec: Principled Word Importance Weighting","order":2,"content":null,"isLocked":true},{"id":"seed_373581ffe928200559170c7911220d40","title":"GloVe: Global Vectors for Word Representation","order":3,"content":null,"isLocked":true},{"id":"seed_af8fa64c1a2408b2174a796a5acbbbe4","title":"FastText: Subword Embeddings for Morphological Richness and OOV Handling","order":4,"content":null,"isLocked":true},{"id":"seed_94485eed4514fa4e4a8dd85c63971ece","title":"Pre-trained Embeddings: Practical Strategies for Real-World NLP","order":5,"content":null,"isLocked":true}]}},"dataUpdateCount":1,"dataUpdatedAt":1792317742898,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["learningContent","getModule"],{"input":{"slug":"word-embeddings"},"type":"query"}],"queryHash":"[[\"learningContent\",\"getModule\"],{\"input\":{\"slug\":\"word-embeddings\"},\"type\":\"query\"}]"},{"dehydratedAt":1792317743643,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792317743342,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["learningContent","getProgress"],{"input":{"moduleId":"seed_3d273e329985fcfe73057d89830f5a82"},"type":"query"}],"queryHash":"[[\"learningContent\",\"getProgress\"],{\"input\":{\"moduleId\":\"seed_3d273e329985fcfe73057d89830f5a82\"},\"type\":\"query\"}]"},{"dehydratedAt":1792317743643,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792317743343,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["feedback","get"],{"input":{"contentType":"LEARNING_PAGE","contentId":"seed_b786f03991793dd229cb933d7a88cd32"},"type":"query"}],"queryHash":"[[\"feedback\",\"get\"],{\"input\":{\"contentId\":\"seed_b786f03991793dd229cb933d7a88cd32\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792317743643,"state":{"data":{"json":null},"dataUpdateCount":1,"dataUpdatedAt":1792317743343,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["feedback","getUserLike"],{"input":{"contentType":"LEARNING_PAGE","contentId":"seed_b786f03991793dd229cb933d7a88cd32"},"type":"query"}],"queryHash":"[[\"feedback\",\"getUserLike\"],{\"input\":{\"contentId\":\"seed_b786f03991793dd229cb933d7a88cd32\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792317743643,"state":{"data":{"json":[]},"dataUpdateCount":1,"dataUpdatedAt":1792317743343,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"success","fetchStatus":"idle"},"queryKey":[["highlights","getHighlights"],{"input":{"contentType":"LEARNING_PAGE","contentId":"seed_b786f03991793dd229cb933d7a88cd32"},"type":"query"}],"queryHash":"[[\"highlights\",\"getHighlights\"],{\"input\":{\"contentId\":\"seed_b786f03991793dd229cb933d7a88cd32\",\"contentType\":\"LEARNING_PAGE\"},\"type\":\"query\"}]"},{"dehydratedAt":1792317743643,"state":{"data":"$undefined","dataUpdateCount":0,"dataUpdatedAt":0,"error":null,"errorUpdateCount":0,"errorUpdatedAt":0,"fetchFailureCount":0,"fetchFailureReason":null,"fetchMeta":null,"isInvalidated":false,"status":"pending","fetchStatus":"fetching"},"queryKey":[["highlights","getPopularHighlights"],{"input":{"contentType":"LEARNING_PAGE","contentId":"seed_b786f03991793dd229cb933d7a88cd32","minCount":2},"type":"query"}],"queryHash":"[[\"highlights\",\"getPopularHighlights\"],{\"input\":{\"contentId\":\"seed_b786f03991793dd229cb933d7a88cd32\",\"contentType\":\"LEARNING_PAGE\",\"minCount\":2},\"type\":\"query\"}]","promise":"$@77"}]},"children":"$L78"}]