Add summary/usage/loading code

README.md

@@ -8,12 +8,45 @@ tags:
 - gpt2
 ---
 
-This dataset contains the most common 1-6 contiguous token subsequences (n-grams) in an [open-source replication of the OpenWebText (OWT) dataset](https://huggingface.co/datasets/Skylion007/openwebtext). The OWT replication was compiled by Aaron Gokaslan and Vanya Cohen of Brown University.
+# Dataset Card for OpenWebText n-grams
 
-Below, we list the number of n-grams included. Alongside each, we show the minimum number of times each sequence occurs in the ~9B-token dataset (its frequency). 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.
+## Dataset Summary
 
-| dataset           | total  | n=1   | n=2    | n=3    | n=4    | n=5    | n=6    |
-| ---               | -----  |------ | ------ | ------ | ------ | ------ | ------ |
-| owt_1-6grams_246k | 245831 | 50257 (freq >= 0) | 58302 (freq >= 10000)  | 44560 (freq >= 10000)  | 32831 (freq > 5000) | 13566 (freq > 5000) | 12495 (freq > 2000) |
+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).
 
-This dataset was used by the authors to show that gpt2-small sparse autoencoders memorize the most commonly presented n-grams more exactly.
+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 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)