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---
dataset_info:
features:
- name: id
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: chunks
sequence: string
- name: embeddings
sequence:
sequence: float32
splits:
- name: train
num_bytes: 3302394852
num_examples: 534044
download_size: 3029933751
dataset_size: 3302394852
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
language:
- cs
size_categories:
- 100K<n<1M
task_categories:
- text-generation
- fill-mask
license:
- cc-by-sa-3.0
- gfdl
---
This dataset contains the Czech subset of the [`wikimedia/wikipedia`](https://huggingface.co/datasets/wikimedia/wikipedia) dataset. Each page is divided into paragraphs, stored as a list in the `chunks` column. For every paragraph, embeddings are created using the [`intfloat/multilingual-e5-small`](https://huggingface.co/intfloat/multilingual-e5-small) model.
## Usage
Load the dataset:
```python
from datasets import load_dataset
ds = load_dataset("karmiq/wikipedia-embeddings-cs-e5-small", split="train")
ds[1]
```
```
{
'id': '1',
'url': 'https://cs.wikipedia.org/wiki/Astronomie',
'title': 'Astronomie',
'chunks': [
'Astronomie, řecky αστρονομία z άστρον ( astron ) hvězda a νόμος ( nomos )...',
'Myšlenky Aristotelovy rozvinul ve 2. století našeho letopočtu Klaudios Ptolemaios...',
...,
],
'embeddings': [
[0.09006806463003159, -0.009814552962779999, ...],
[0.10767366737127304, ...],
...
]
}
```
The structure makes it easy to use the dataset for implementing semantic search.
<details>
<summary>Load the data in Elasticsearch</summary>
```python
def doc_generator(data, batch_size=1000):
for batch in data.with_format("numpy").iter(batch_size):
for i, id in enumerate(batch["id"]):
output = {"id": id}
output["title"] = batch["title"][i]
output["url"] = batch["url"][i]
output["parts"] = [
{ "chunk": chunk, "embedding": embedding }
for chunk, embedding in zip(batch["chunks"][i], batch["embeddings"][i])
]
yield output
num_indexed, num_failed = 0, 0,
progress = tqdm(total=ds.num_rows, unit="doc", desc="Indexing")
for ok, info in parallel_bulk(
es,
index="wikipedia-search",
actions=doc_generator(ds),
raise_on_error=False,
):
if not ok:
print(f"ERROR {info['index']['status']}: "
f"{info['index']['error']['type']}: {info['index']['error']['caused_by']['type']}: "
f"{info['index']['error']['caused_by']['reason'][:250]}")
progress.update(1)
```
</details>
<details>
<summary>Use <code>sentence_transformers.util.semantic_search</code></summary>
```python
import sentence_transformers
model = sentence_transformers.SentenceTransformer("intfloat/multilingual-e5-small")
ds.set_format(type="torch", columns=["embeddings"], output_all_columns=True)
# Flatten the dataset
def explode_sequence(batch):
output = { "id": [], "url": [], "title": [], "chunk": [], "embedding": [] }
for id, url, title, chunks, embeddings in zip(
batch["id"], batch["url"], batch["title"], batch["chunks"], batch["embeddings"]
):
output["id"].extend([id for _ in range(len(chunks))])
output["url"].extend([url for _ in range(len(chunks))])
output["title"].extend([title for _ in range(len(chunks))])
output["chunk"].extend(chunks)
output["embedding"].extend(embeddings)
return output
ds_flat = ds.map(
explode_sequence,
batched=True,
remove_columns=ds.column_names,
num_proc=min(os.cpu_count(), 32),
desc="Flatten")
ds_flat
query = "Čím se zabývá fyzika?"
hits = sentence_transformers.util.semantic_search(
query_embeddings=model.encode(query),
corpus_embeddings=ds_flat["embedding"],
top_k=10)
for hit in hits[0]:
title = ds_flat[hit['corpus_id']]['title']
chunk = ds_flat[hit['corpus_id']]['chunk']
print(f"[{hit['score']:0.2f}] {textwrap.shorten(chunk, width=100, placeholder='…')} [{title}]")
# [0.90] Fyzika částic ( též částicová fyzika ) je oblast fyziky, která se zabývá částicemi. V širším smyslu… [Fyzika částic]
# [0.89] Fyzika ( z řeckého φυσικός ( fysikos ): přírodní, ze základu φύσις ( fysis ): příroda, archaicky… [Fyzika]
# ...
```
</details>
The embeddings generation took about 1 hour on an NVIDIA A100 80GB GPU.
## License
See license of the original dataset: <https://huggingface.co/datasets/wikimedia/wikipedia>.
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