With millions of possible tokens or IDs, storing a vocabulary is expensive. The hashing trick maps each feature straight to one of n_features columns with a hash, with no vocabulary needed. A second hash picks a sign (+1 or −1), so collisions cancel out on average instead of piling up.
Use h = int(hashlib.md5(token.encode()).hexdigest(), 16):
h % n_features+1 if (h >> 64) % 2 == 0 else -1Implement hash_vectorize(docs, n_features=2**10): tokenise each doc by lowercasing and splitting on whitespace, and add sign to X[i, column] for every token occurrence. Return a float array. Then implement collision_rate(tokens, n_features): the fraction of distinct tokens that share a column with at least one other distinct token.