---
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
- config_name: text-only
  data_files:
  - split: train
    path: text-only/train-*
  - split: validation
    path: text-only/validation-*
  - split: test
    path: text-only/test-*
dataset_info:
- config_name: default
  features:
  - name: id
    dtype: string
  - name: url
    dtype: string
  - name: title
    dtype: string
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 15368746858.779654
    num_examples: 5673373
  - name: validation
    num_bytes: 404439922.64724064
    num_examples: 149299
  - name: test
    num_bytes: 404442631.57310516
    num_examples: 149300
  download_size: 9703633440
  dataset_size: 16177629413
- config_name: text-only
  features:
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 14834731398.280304
    num_examples: 5673373
  - name: validation
    num_bytes: 390386911.46022856
    num_examples: 149299
  - name: test
    num_bytes: 390389526.2594667
    num_examples: 149300
  download_size: 9374463601
  dataset_size: 15615507835.999998
license: cc-by-sa-3.0
task_categories:
- text-generation
- fill-mask
- feature-extraction
language:
- en
tags:
- wiki
- wikipedia
- pretrain
size_categories:
- 1M<n<10M
source_datasets: graelo/wikipedia
---

# wikipedia - 20230901.en - deduped

> purpose: train with less data while maintaining (most) of the quality

This is really more of a "high quality diverse sample" rather than _"we are trying to remove literal duplicate documents"_. Source dataset: [graelo/wikipedia](https://huggingface.co/datasets/graelo/wikipedia).

## configs

### `default`

command:

```sh
python -m text_dedup.minhash \
  --path $ds_name \
  --name $dataset_config \
  --split $data_split \
  --cache_dir "./cache" \
  --output $out_dir \
  --column $text_column \
  --ngram 4 --threshold 0.6 \
  --hash_func xxh3 --hash_bits 16 --num_perm 64 \
  --batch_size 10000
```

dedup:

```sh
Fingerprinting... (num_proc=40): 100% 6705754/6705754 [06:57<00:00, 16063.27 examples/s]
Iterating MinHashes...: 100% 671/671 [04:13<00:00,  2.65it/s]
Clustering...: 100% 10/10 [00:21<00:00,  2.18s/it]
Finding clusters... (num_proc=40): 100% 6705754/6705754 [06:38<00:00, 16839.42 examples/s]
Filtering clusters... (num_proc=40): 100% 6705754/6705754 [02:25<00:00, 46058.39 examples/s]
Saving the dataset (39/39 shards): 100% 5971972/5971972 [03:47<00:00, 26266.10 examples/s]
[10/23/23 02:29:41] INFO     Loading                         : 78.82s
```

result:

```python
DatasetDict({
    train: Dataset({
        features: ['id', 'url', 'title', 'text'],
        num_rows: 5673373
    })
    validation: Dataset({
        features: ['id', 'url', 'title', 'text'],
        num_rows: 149299
    })
    test: Dataset({
        features: ['id', 'url', 'title', 'text'],
        num_rows: 149300
    })
})
```

### text-only

This is the same thing but with all columns except for 'text' removed.

```python
from datasets import load_dataset

# If the dataset is gated/private, make sure you have run huggingface-cli login
config_name = "text-only"
dataset = load_dataset("BEE-spoke-data/wikipedia-deduped", config_name)
```

## token counts 

### train

Using `tiktoken` GPT-4 tokenizer, `train` split, and `text` column:

|       |      num_tokens |
|:------|----------------:|
| count |     5.67337e+06 |
| mean  |   612.413       |
| std   |   739.331       |
| min   |     3           |
| 25%   |   163           |
| 50%   |   359           |
| 75%   |   761           |
| max   | 34298           |

total: 3,474,446,396

---