metadata
dataset_info:
features:
- name: id
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: text
dtype: string
- name: embedding
sequence: float32
splits:
- name: train
num_bytes: 73850973
num_examples: 3001
download_size: 49787145
dataset_size: 73850973
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: gfdl
task_categories:
- text-generation
- fill-mask
language:
- en
size_categories:
- 1K<n<10K
this is a subset of the wikimedia/wikipedia dataset code for creating this dataset :
from datasets import load_dataset, Dataset
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("mixedbread-ai/mxbai-embed-large-v1")
# load dataset in streaming mode (no download and it's fast)
dataset = load_dataset(
"wikimedia/wikipedia", "20231101.en", split="train", streaming=True
)
# select 3000 samples
from tqdm import tqdm
data = Dataset.from_dict({})
for i, entry in enumerate(dataset):
# each entry has the following columns
# ['id', 'url', 'title', 'text']
data = data.add_item(entry)
if i == 3000:
break
# free memory
del dataset
# embed the dataset
def embed(row):
return {"embedding" : model.encode(row["text"])}
data = data.map(embed)
# push to hub
data.push_to_hub("not-lain/wikipedia-small-3000-embedded")