|
|
--- |
|
|
dataset_info: |
|
|
features: |
|
|
- name: text |
|
|
dtype: string |
|
|
- name: id |
|
|
dtype: string |
|
|
- name: dump |
|
|
dtype: string |
|
|
- name: url |
|
|
dtype: string |
|
|
- name: date |
|
|
dtype: string |
|
|
- name: file_path |
|
|
dtype: string |
|
|
- name: language |
|
|
dtype: string |
|
|
- name: language_score |
|
|
dtype: float64 |
|
|
- name: language_script |
|
|
dtype: string |
|
|
- name: minhash_cluster_size |
|
|
dtype: int64 |
|
|
- name: top_langs |
|
|
dtype: string |
|
|
splits: |
|
|
- name: train |
|
|
num_bytes: 13252144546 |
|
|
num_examples: 2293647 |
|
|
download_size: 6366393037 |
|
|
dataset_size: 13252144546 |
|
|
configs: |
|
|
- config_name: default |
|
|
data_files: |
|
|
- split: train |
|
|
path: data/train-* |
|
|
--- |
|
|
## Small Arabic FineWeb2 Sample Dataset |
|
|
A small extract (2.3 million rows compared to 58 million in the original: |
|
|
FineWeb2 [arb_Arab subset](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2/viewer/arb_Arab)). |
|
|
First, I filtered for items with 95% or more Arabic (language_score is not reliable), |
|
|
then I randomly sampled the 2.3M from the result. |
|
|
See [this post](https://www.linkedin.com/posts/akhooli_a-small-arabic-fineweb2-sample-dataset-activity-7283099806003060736-g5cq) |
|
|
Code: |
|
|
```python |
|
|
from datasets import load_dataset |
|
|
import pandas as pd |
|
|
from pprint import pprint |
|
|
ds = load_dataset("akhooli/fineweb2_ar_24_sample") |
|
|
import random |
|
|
max_n = len(ds['train']) |
|
|
index = random.randint(0,max_n) |
|
|
pprint(ds['train'][index]['text']) |
|
|
``` |