# glove100_angular

<br />

- **Description**:

Pre-trained Global Vectors for Word Representation (GloVe) embeddings for
approximate nearest neighbor search. This dataset consists of two splits:

1. 'database': consists of 1,183,514 data points, each has features: 'embedding' (100 floats), 'index' (int64), 'neighbors' (empty list).
2. 'test': consists of 10,000 data points, each has features: 'embedding' (100 floats), 'index' (int64), 'neighbors' (list of 'index' and 'distance' of the nearest neighbors in the database.)

- **Homepage** :
  <https://nlp.stanford.edu/projects/glove/>

- **Source code** :
  [`tfds.nearest_neighbors.glove_100_angular.Glove100Angular`](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/nearest_neighbors/glove_100_angular/glove_100_angular.py)

- **Versions**:

  - **`1.0.0`** (default): Initial release.
- **Download size** : `462.93 MiB`

- **Dataset size** : `567.90 MiB`

- **Auto-cached**
  ([documentation](https://www.tensorflow.org/datasets/performances#auto-caching)):
  No

- **Splits**:

| Split | Examples |
|---|---|
| `'database'` | 1,183,514 |
| `'test'` | 10,000 |

- **Feature structure**:

    FeaturesDict({
        'embedding': Tensor(shape=(100,), dtype=float32),
        'index': Scalar(shape=(), dtype=int64, description=Index within the split.),
        'neighbors': Sequence({
            'distance': Scalar(shape=(), dtype=float32, description=Neighbor distance.),
            'index': Scalar(shape=(), dtype=int64, description=Neighbor index.),
        }),
    })

- **Feature documentation**:

| Feature | Class | Shape | Dtype | Description |
|---|---|---|---|---|
|   | FeaturesDict |   |   |   |
| embedding | Tensor | (100,) | float32 |   |
| index | Scalar |   | int64 | Index within the split. |
| neighbors | Sequence |   |   | The computed neighbors, which is only available for the test split. |
| neighbors/distance | Scalar |   | float32 | Neighbor distance. |
| neighbors/index | Scalar |   | int64 | Neighbor index. |

- **Supervised keys** (See
  [`as_supervised` doc](https://www.tensorflow.org/datasets/api_docs/python/tfds/load#args)):
  `None`

- **Figure**
  ([tfds.show_examples](https://www.tensorflow.org/datasets/api_docs/python/tfds/visualization/show_examples)):
  Not supported.

- **Examples**
  ([tfds.as_dataframe](https://www.tensorflow.org/datasets/api_docs/python/tfds/as_dataframe)):

<button id="displaydataframe">Display examples...</button>

- **Citation**:

    @inproceedings{pennington2014glove,
      author = {Jeffrey Pennington and Richard Socher and Christopher D. Manning},
      booktitle = {Empirical Methods in Natural Language Processing (EMNLP)},
      title = {GloVe: Global Vectors for Word Representation},
      year = {2014},
      pages = {1532--1543},
      url = {http://www.aclweb.org/anthology/D14-1162},
    }