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---
license: mit
pretty_name: OpenWebText n-grams
size_categories:
- 100K<n<1M
tags:
- openwebtext
- gpt2
---
# Dataset Card for OpenWebText n-grams
## Dataset Summary
This dataset contains 246K of the most common token-based (GPT-2/GPT-3) n-grams (n=1 to n=6), in the [OpenWebText (OWT) dataset](https://huggingface.co/datasets/Skylion007/openwebtext).
For convenient searching, it provides full tokens/strings, as well as per-position tokens/strings.
## Usage
Generally, this dataset allows identifying the most common n-grams in a text corpus.
When researching LLMs [tokenized similarly to GPT-2/GPT-3](https://platform.openai.com/tokenizer), it allows:
- Constructing intermediate vectors spanning the most common short phrases (n-grams), e.g. for similarity sampling.
- Fast searches for common phrases containing particular tokens or substrings (and in particular sequence positions).
- Showing the effects of training set n-gram frequency.
The authors (Thomas Dooms and Dan Wilhelm) used this dataset to show that sparse auto-encoders are biased toward reconstructing the most common n-grams.
## Loading the Dataset
We recommend you convert the dataset to a Pandas DataFrame for easy querying:
```python
from datasets import load_dataset
ngrams = load_dataset('danwil/owt-ngrams')['train'].to_pandas()
```
## Contents
Below, we list the number of n-grams and their count/frequency in the original ~9B-token OWT corpus.
- We include all individual tokens (1-grams).
- Note that if an n-gram occurs >N times, then every contiguous subsequence must also occur >N times.
| | total | n=1 | n=2 | n=3 | n=4 | n=5 | n=6 |
| --- | ----- |------ | ------ | ------ | ------ | ------ | ------ |
| owt_1-6grams_246k | 245831 | 50257 | 58302 | 44560 | 32831 | 13566 | 12495 |
| count in OWT | | >= 0 | >= 10000 | >= 10000 | > 5000 | > 5000 | > 2000 |
**Point of Contact:** [Dan Wilhelm](mailto:dan@danwil.com) |