| --- |
| dataset_info: |
| - config_name: 42B |
| features: |
| - name: word |
| dtype: string |
| - name: vector |
| list: float32 |
| splits: |
| - name: 300d |
| num_bytes: 2332012426 |
| num_examples: 1917494 |
| download_size: 2332049667 |
| dataset_size: 2332012426 |
| - config_name: 6B |
| features: |
| - name: word |
| dtype: string |
| - name: vector |
| list: float32 |
| splits: |
| - name: 50d |
| num_bytes: 86156474 |
| num_examples: 400000 |
| - name: 100d |
| num_bytes: 166156474 |
| num_examples: 400000 |
| - name: 200d |
| num_bytes: 326156474 |
| num_examples: 400000 |
| - name: 300d |
| num_bytes: 486156474 |
| num_examples: 400000 |
| download_size: 1063792425 |
| dataset_size: 1064625896 |
| - config_name: 840B |
| features: |
| - name: word |
| dtype: string |
| - name: vector |
| list: float32 |
| splits: |
| - name: 300d |
| num_bytes: 2670329198 |
| num_examples: 2196017 |
| download_size: 2672066809 |
| dataset_size: 2670329198 |
| - config_name: twitter27B |
| features: |
| - name: word |
| dtype: string |
| - name: vector |
| list: float32 |
| splits: |
| - name: 25d |
| num_bytes: 141330891 |
| num_examples: 1193514 |
| - name: 50d |
| num_bytes: 260682291 |
| num_examples: 1193514 |
| - name: 100d |
| num_bytes: 499385091 |
| num_examples: 1193514 |
| - name: 200d |
| num_bytes: 976790691 |
| num_examples: 1193514 |
| download_size: 1866400394 |
| dataset_size: 1878188964 |
| configs: |
| - config_name: 42B |
| data_files: |
| - split: 300d |
| path: 42B/300d-* |
| - config_name: 6B |
| data_files: |
| - split: 50d |
| path: 6B/50d-* |
| - split: 100d |
| path: 6B/100d-* |
| - split: 200d |
| path: 6B/200d-* |
| - split: 300d |
| path: 6B/300d-* |
| - config_name: 840B |
| data_files: |
| - split: 300d |
| path: 840B/300d-* |
| - config_name: twitter27B |
| data_files: |
| - split: 25d |
| path: twitter27B/25d-* |
| - split: 50d |
| path: twitter27B/50d-* |
| - split: 100d |
| path: twitter27B/100d-* |
| - split: 200d |
| path: twitter27B/200d-* |
| --- |
| |
|
|
| # GloVe Pre-trained Word Vectors |
|
|
| 该仓库将 GloVe(Global Vectors for Word Representation)预训练词向量整理为 Hugging Face Dataset。每个配置对应一套官方发布语料,每个 split 名称表示词向量维度。 |
|
|
| ## 配置 |
|
|
| | Config | 语料 | Split / 维度 | 词表大小 | |
| | --- | --- | --- | ---: | |
| | `6B` | Wikipedia 2014 + Gigaword 5 | `50d`, `100d`, `200d`, `300d` | 400,000 | |
| | `42B` | Common Crawl 42B tokens | `300d` | 1,917,494 | |
| | `840B` | Common Crawl 840B tokens | `300d` | 2,196,017 | |
| | `twitter27B` | Twitter 27B tokens | `25d`, `50d`, `100d`, `200d` | 1,193,514 | |
|
|
| 全部配置的下载大小合计约为 7.9 GB。只需加载所需配置和维度,无需下载其他向量。 |
|
|
| ## 字段说明 |
|
|
| 每行表示一个 token 及其词向量: |
|
|
| - `word`:原始词表中的 token。 |
| - `vector`:对应的 `float32` 向量;长度由 split 名称决定。 |
|
|
| 例如,`6B` 配置的 `100d` split 中,每个 `vector` 包含 100 个浮点数。 |
|
|
| ## 加载数据 |
|
|
| 加载 `6B` 的全部维度: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| |
| dataset = load_dataset("wliafe/glove", "6B") |
| print(dataset) |
| print(dataset["50d"][0]) |
| ``` |
|
|
| 只加载一个维度: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| |
| vectors = load_dataset( |
| "wliafe/glove", |
| "twitter27B", |
| split="100d", |
| ) |
| |
| print(vectors.features) |
| print(vectors[0]["word"]) |
| print(len(vectors[0]["vector"])) |
| ``` |
|
|
| 其他配置示例: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| |
| glove_42b = load_dataset("wliafe/glove", "42B", split="300d") |
| glove_840b = load_dataset("wliafe/glove", "840B", split="300d") |
| ``` |
|
|
| ## 查询词向量 |
|
|
| `Dataset.filter()` 可以直接查找少量 token,但它会扫描整个 split: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| |
| vectors = load_dataset("wliafe/glove", "6B", split="50d") |
| matches = vectors.filter(lambda row: row["word"] == "king") |
| |
| if len(matches) == 0: |
| raise KeyError("king 不在词表中") |
| |
| king_vector = matches[0]["vector"] |
| print(len(king_vector)) |
| ``` |
|
|
| 频繁查询时,建议一次性建立 token 到行号或向量的索引,并根据内存容量选择所需配置。大型配置不适合无条件转换为完整的 Python 字典。 |
|
|
| ## 使用说明 |
|
|
| - token 的大小写、标点和分词形式沿用原始 GloVe 文件。 |
| - 不同配置的词表互不保证一致。 |
| - split 名称是向量维度,不是训练集或测试集划分。 |
| - 向量以 `float32` 保存。 |
|
|
| ## 引用 |
|
|
| 如果该数据集对你的研究有帮助,请引用 GloVe: |
|
|
| ```bibtex |
| @inproceedings{pennington2014glove, |
| title={GloVe: Global Vectors for Word Representation}, |
| author={Pennington, Jeffrey and Socher, Richard and Manning, Christopher D.}, |
| booktitle={Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, |
| pages={1532--1543}, |
| year={2014} |
| } |
| ``` |
|
|
| 原始向量、语料说明和使用条款请以 [GloVe 官方项目页面](https://nlp.stanford.edu/projects/glove/) 为准。 |
|
|