barandinho/wikipedia_tr_embedded

This is the embedded version of barandinho/wikipedia_tr the dataset was chunked (chunk_size=2048, chunk_overlap=256) and then put into an embedding model.
The embedding model used for this dataset is sentence-transformers/distiluse-base-multilingual-cased-v1 so you have to use it if you wanna do similarity search
It is one of the best embedding model for Turkish according to our tests.
Embedding dimension is 64 and values are int8

You can do similarity search with usearch below is an example for similarity search given a query.

#!pip install sentence-transformers datasets usearch
import numpy as np
from datasets import load_dataset
from usearch.index import Index
from sentence_transformers import SentenceTransformer

# Load dataset and corresponding embedding model
ds = load_dataset('barandinho/wikipedia_tr_embedded', split="train")
embd = SentenceTransformer("sentence-transformers/distiluse-base-multilingual-cased-v1", trust_remote_code=True)

# Get embeddings as a list to create usearch Index
dtype = np.int8
embeddings = [embedding for embedding in ds['embed_int8']]
embeddings = np.asarray(embeddings, dtype=dtype)

num_dim = 64
index = Index(ndim=num_dim, metric='cos')
index.add(np.arange(len(embeddings)), embeddings)

q = 'Fatih Sultan Mehmet' # quality of the query is very important for wanted results
q_embd = embd.encode(q, precision='binary')
q_embd = np.asarray(q_embd, dtype=dtype)

# Get top 3 results
matches = index.search(q_embd, 3)

for match in matches:
    idx = int(match.key)
    print(ds[idx]['title'])
    print(ds[idx]['text'])
    print("--"*10)

---

dataset_info: features: - name: url dtype: string - name: title dtype: string - name: text dtype: string - name: embed_int8 sequence: int64 splits: - name: train num_bytes: 1252396519 num_examples: 762059 download_size: 501996398 dataset_size: 1252396519 configs: - config_name: default data_files: - split: train path: data/train-*