dataset_info:
features:
- name: sentence
dtype: string
- name: cluster
dtype: int64
- name: embedding_512
sequence: float32
splits:
- name: train
num_bytes: 2091790625
num_examples: 990526
download_size: 2669870589
dataset_size: 2091790625
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
task_categories:
- sentence-similarity
language:
- en
size_categories:
- 100K<n<1M
Dataset Card for "wikianswers_embeddings_512"
Dataset Summary
nikhilchigali/wikianswers_embeddings_512
is a subset of the embedding-data/WikiAnswers
(Link)
As opposed to the original dataset with 3,386,256 rows, this dataset contains only .13% of the total rows(sets). The sets of sentences have been unraveled into individual items with corresponding cluster IDs to identify sentences from the same set. Each Sentence has its associated cluster ID and embeddings of dimension 512.
Languages
English.
Dataset Structure
Each example in the dataset contains a sentence and its cluster id of other equivalent sentences. The sentences in the same cluster are paraphrases of each other. The embeddings for the dataset are created using the distiluse-base-multilingual-cased-v1
model.
{"sentence": [sentence], "cluster": [cluster_id], "embedding_512": [embeddings]}
{"sentence": [sentence], "cluster": [cluster_id], "embedding_512": [embeddings]}
{"sentence": [sentence], "cluster": [cluster_id], "embedding_512": [embeddings]}
...
{"sentence": [sentence], "cluster": [cluster_id], "embedding_512": [embeddings]}
Usage Example
Install the 🤗 Datasets library with pip install datasets
and load the dataset from the Hub with:
from datasets import load_dataset
dataset = load_dataset("nikhilchigali/wikianswers_embeddings_512")
The dataset is loaded as a DatasetDict and has the format for N examples:
DatasetDict({
train: Dataset({
features: ['sentence', "cluster", "embedding_512"],
num_rows: N
})
})
Review an example i with:
dataset["train"][i]
Source Data
embedding-data/WikiAnswers
on HuggingFace (Link)