|
--- |
|
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 |
|
|
|
--- |
|
|
