---
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: url
    dtype: string
  - name: text
    dtype: string
  - name: date
    dtype: string
  - name: metadata
    dtype: string
  splits:
  - name: train
    num_bytes: 4467051029
    num_examples: 1820241
  download_size: 1772035124
  dataset_size: 4467051029
---
# Dataset Card for "open-web-math-minhash"


## making of

On a high-RAM colab TPU (40 cores)

```python
from pathlib import Path
from tqdm.auto import tqdm

data_split = 'train'
text_column = 'text'

out_dir = Path(f"output/minhash/{ds_short_name}/{data_split}")
!mkdir -p $out_dir


ds_name = "open-web-math/open-web-math"
dataset_config = str(dataset_config)

!python -m text_dedup.minhash \
  --path $ds_name \
  --name $dataset_config \
  --split $data_split \
  --cache_dir "./cache" \
  --output $out_dir \
  --column $text_column \
  --ngram 5 --threshold 0.5 \
  --hash_func xxh3 --hash_bits 16 --num_perm 64 \
  --batch_size 10000

print(f"output dir is:\n\t{out_dir}")
!ls $out_dir
```

Console:

```sh
Resolving data files: 100% 114/114 [00:11<00:00,  9.79it/s]
Fingerprinting... (num_proc=40): 100% 6315233/6315233 [15:27<00:00, 6806.11 examples/s]
Iterating MinHashes...: 100% 632/632 [05:37<00:00,  1.87it/s]
Clustering...: 100% 14/14 [01:13<00:00,  5.22s/it]
Finding clusters... (num_proc=40): 100% 6315233/6315233 [10:57<00:00, 9602.90 examples/s]
Filtering clusters... (num_proc=40): 100% 6315233/6315233 [03:53<00:00, 27069.61 examples/s]
Saving the dataset (33/33 shards): 100% 1820241/1820241 [07:07<00:00, 4260.38 examples/s]
[10/11/23 23:41:46] INFO     Loading                         :
```