qwen3_glove_512

GloVe word embeddings trained on English Wikipedia where each "word" is a Qwen/Qwen3-Embedding-8B token id.

Training

• Corpus: jsanzolac/ga_wikipedia (English Wikipedia dump 2023-11-01)
• Tokenizer: Qwen/Qwen3-Embedding-8B (no special tokens)
• Implementation: stanfordnlp/GloVe
• Vector size: 512
• Min vocab count: 1
• Window size: 15
• Iterations: 15
• x_max: 10

Files

File	Purpose
vectors.txt	GloVe text format: <qwen3_id> v1 v2 ... v512
vectors.bin	Binary format (-binary 2)
vocab.txt	Qwen3 id and its corpus count
token_id_to_string.json	Mapping from Qwen3 id → decoded string

Quick start

import numpy as np
from transformers import AutoTokenizer
from huggingface_hub import hf_hub_download

vec_path = hf_hub_download("jsanzolac/qwen3_glove_512", "vectors.txt")
tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-Embedding-8B")

embeddings = {}
with open(vec_path) as f:
    for line in f:
        parts = line.rstrip().split(" ")
        embeddings[int(parts[0])] = np.asarray(parts[1:], dtype=np.float32)

def embed(text):
    ids = tok.encode(text, add_special_tokens=False)
    return np.mean([embeddings[i] for i in ids if i in embeddings], axis=0)