Word Embeddings — Semantic Representations

import torch
import torch.nn as nn
import numpy as np

# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
# STATIC EMBEDDINGS: nn.Embedding — same vector regardless of context
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

vocab_size = 30_000
embed_dim   = 300

embedding_layer = nn.Embedding(vocab_size, embed_dim, padding_idx=0)
# Shape of weight matrix: [30000, 300] — each row is a word vector

# Lookup: token ID → embedding vector
token_ids = torch.tensor([42, 101, 999])   # 3 tokens
vecs = embedding_layer(token_ids)
print(f"Embedding output: {vecs.shape}")   # [3, 300]

# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
# PRETRAINED WORD2VEC / GLOVE — load as embedding weights
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

# pip install gensim
import gensim.downloader as api

# Load GloVe (Global Vectors for Word Representation)
# Trained on 840B tokens from Common Crawl
# glove_model = api.load("glove-wiki-gigaword-300")  # 300-dim, 400K words

# The famous analogies (demonstrates semantic geometry of embedding space):
# glove_model.most_similar(positive=["king", "woman"], negative=["man"], topn=1)
# → [('queen', 0.85)]  → king - man + woman ≈ queen ✓

# France - Paris + Berlin ≈ Germany (country - capital + capital ≈ country)
# doctor - man + woman ≈ nurse  (reveals gender bias in training data!)

# BIAS IN WORD EMBEDDINGS — critical concern
embedding_biases = {
    "Gender": "doctor→man, nurse→woman learned from biased corpus",
    "Race":   "certain names associated with positive/negative words",
    "Geography": "country embeddings reflect geopolitical biases",
}
print("\nEmbedding biases to be aware of:")
for bias_type, example in embedding_biases.items():
    print(f"  {bias_type}: {example}")

# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
# CONTEXTUAL EMBEDDINGS — different vector per context
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
# Static: "bank" always → same vector
# Contextual: "bank" in:
#   "I put money in the bank" → financial institution embedding
#   "We sat on the river bank" → riverbank embedding

from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = AutoModel.from_pretrained("bert-base-uncased")

def get_contextual_embedding(text: str, word: str):
    """Get BERT contextual embedding for a specific word in context."""
    tokens = tokenizer(text, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**tokens)
    # outputs.last_hidden_state: [1, seq_len, 768] — one embedding per token
    embeddings = outputs.last_hidden_state.squeeze(0)  # [seq_len, 768]
    print(f"Contextual embedding shape: {embeddings.shape}")

    # Find the word's position in tokens
    token_ids_list = tokens["input_ids"][0].tolist()
    word_id = tokenizer.convert_tokens_to_ids(word)
    if word_id in token_ids_list:
        pos = token_ids_list.index(word_id)
        return embeddings[pos]  # word-specific embedding
    return embeddings.mean(0)  # fallback: mean pooling

# "bank" gets different vectors in different contexts
emb1 = get_contextual_embedding("I put money in the bank", "bank")
emb2 = get_contextual_embedding("We sat on the river bank", "bank")

# Cosine similarity between the two "bank" embeddings
cos_sim = torch.nn.functional.cosine_similarity(emb1.unsqueeze(0), emb2.unsqueeze(0))
print(f"\nSimilarity of 'bank' in financial vs river context: {cos_sim.item():.3f}")
# Should be < 1.0 — different contexts → different embeddings