VOCAB_MIN_COUNT=20)-lowerCase flag)glove_vectors.bin — raw np.float32, C-order, shape (1291147, 300). Row N is the vector for the word on line N of glove_vocab.txt.glove_vocab.txt — one word per line, UTF-8.config.json — metadata used by downstream notebooks.1import numpy as np
2vecs = np.memmap("glove_vectors.bin", dtype=np.float32, mode="r", shape=(1291147, 300))
3with open("glove_vocab.txt", encoding="utf-8") as f:
4 vocab = [line.rstrip() for line in f]
5word2idx = {w: i for i, w in enumerate(vocab)}jsanzolac/drifting-glove-distilled-r300 so the same training notebooks load it without modification.