Add README

README.md

@@ -1,26 +1,165 @@
 ---
 dataset_info:
   features:
-  - name: id
-    dtype: string
-  - name: url
-    dtype: string
-  - name: title
-    dtype: string
-  - name: chunks
-    sequence: string
-  - name: embeddings
-    sequence:
-      sequence: float32
+    - 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: 2580729273
-    num_examples: 534044
+    - name: train
+      num_bytes: 2580729273
+      num_examples: 534044
   download_size: 2307703671
   dataset_size: 2580729273
+
 configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+  - 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 [`Seznam/simcse-dist-mpnet-paracrawl-cs-en`](https://huggingface.co/Seznam/simcse-dist-mpnet-paracrawl-cs-en) model.
+
+## Usage
+
+Load the dataset:
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("karmiq/wikipedia-embeddings-cs-seznam-mpnet", 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 ) ...',
+    'Novověk Roku 1514 navrhl Mikuláš Koperník nový model, ve kterém bylo ...',
+    ...,
+  ],
+  'embeddings': [
+    [ 0.653917670249939, -0.879465639591217, 0.3993946313858032, ... ]
+    [ 0.0035442777443677187, -1.0201066732406616, -0.06573136150836945, ... ]
+  ]
+}
+```
+
+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']}: {info['index']['error']}"
+
+    progress.update(1)
+```
+</details>
+
+<details>
+<summary>Use <code>sentence_transformers.util.semantic_search</code></summary>
+
+```python
+import os
+import textwrap
+
+import sentence_transformers
+
+model = sentence_transformers.SentenceTransformer("Seznam/simcse-dist-mpnet-paracrawl-cs-en")
+
+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}]")
+
+# [0.58] Dynamika Fyzikální zákony [Newtonovy pohybové zákony]
+# [0.53] Teorie množin [Ordinální číslo]
+# ...
+```
+</details>
+
+The embeddings generation took about 25 minutes on an NVIDIA RTX 4090 24GB.
+
+## License
+
+See license of the original dataset: <https://huggingface.co/datasets/wikimedia/wikipedia>.