# Dataset Card for "c4-code-10k"

10K elements of C4 and 10K elements of code parrot clean (Python code).

Note that these are the datasets used to train my interpretability-friendly models, but is *not* of the correct mixture. Those models were trained on 83% C4 and 17% Python Code (ish) by tokens. This dataset has 10K strings of each, and by tokens is about 22M of code and 5M of C4 (code is longer and harder to compress!)

[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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# Dataset Card for "c4-code-10k"

10K elements of C4 and 10K elements of code parrot clean (Python code).

Note that these are the datasets used to train my interpretability-friendly models, but is *not* of the correct mixture. Those models were trained on 83% C4 and 17% Python Code (ish) by tokens. This dataset has 10K strings of each, and by tokens is about 22M of code and 5M of C4 (code is longer and harder to compress!)

More Information needed
# Dataset Card for "c4-code-10k"

10K elements of C4 and 10K elements of code parrot clean (Python code).

Note that these are the datasets used to train my interpretability-friendly models, but is *not* of the correct mixture. Those models were trained on 83% C4 and 17% Python Code (ish) by tokens. This dataset has 10K strings of each, and by tokens is about 22M of code and 5M of C4 (code is longer and harder to compress!)

More Information needed
TAGS
#region-us 
 # Dataset Card for "c4-code-10k"

10K elements of C4 and 10K elements of code parrot clean (Python code).

Note that these are the datasets used to train my interpretability-friendly models, but is *not* of the correct mixture. Those models were trained on 83% C4 and 17% Python Code (ish) by tokens. This dataset has 10K strings of each, and by tokens is about 22M of code and 5M of C4 (code is longer and harder to compress!)

More Information needed
passage: TAGS
#region-us 
# Dataset Card for "c4-code-10k"

10K elements of C4 and 10K elements of code parrot clean (Python code).

Note that these are the datasets used to train my interpretability-friendly models, but is *not* of the correct mixture. Those models were trained on 83% C4 and 17% Python Code (ish) by tokens. This dataset has 10K strings of each, and by tokens is about 22M of code and 5M of C4 (code is longer and harder to compress!)

More Information needed