| from datasets import concatenate_datasets, load_dataset |
| from sentence_transformers import SentenceTransformer |
| from tqdm import tqdm |
| from vicinity import Backend, Metric, Vicinity |
|
|
| print("Collecting Scandinavian wikipedia") |
| subsets = { |
| "danish": "20231101.da", |
| "norwegian_bokmål": "20231101.no", |
| "norwegian_nynorsk": "20231101.nn", |
| "swedish": "20231101.sv", |
| } |
| datasets = [] |
| for lang in tqdm(subsets, desc="Going through languages"): |
| ds = load_dataset("wikimedia/wikipedia", subsets[lang], split="train") |
| ds = ds.map(lambda example: {**example, "language": lang}) |
| datasets.append(ds) |
| dataset = concatenate_datasets(datasets) |
|
|
| print("Encoding texts") |
| encoder = SentenceTransformer("static-similarity-mrl-multilingual-v1") |
| text = [ |
| f"{title}\n\n {content}" |
| for title, content in zip(dataset["title"], dataset["text"]) |
| ] |
| embeddings = encoder.encode(text, show_progress_bar=True, batch_size=256) |
|
|
| print("Building vector store") |
| store = Vicinity.from_vectors_and_items( |
| vectors=embeddings, |
| items=list(dataset), |
| metric=Metric.COSINE, |
| index_type="hnsw", |
| backend_type=Backend.FAISS, |
| ) |
|
|
| print("Saving vector store") |
| store.save("scandi_wiki_vector_store") |
|
|
| print("DONE") |
|
|
