README.md

---
license: apache-2.0
language:
- th
- en
tags:
- meme
pretty_name: token_awareness
size_categories:
- 1K<n<10K
---
# Token Awareness Dataset
Built from "strawberry" meme 🍓, most LLM can't count the character of the word. Since this dataset is very easy to generate, we create a dataset for this benchmark just for fun.

For this dataset, we support only Thai (th) and English (en) language.

## Dataset Creation
We sample words for each language from these sources:
- Thai: [pythainlp's Thai words](https://github.com/PyThaiNLP/pythainlp/blob/dev/pythainlp/corpus/words_th.txt)
- English: [dwyl's English words](https://github.com/dwyl/english-words/blob/master/words_alpha.txt)

We then sample 500 word each weigted by the word score, which was computed using this simple heuristic:
$$
S(w) = \frac{\sqrt{|w|}}{\frac{1}{|w|} \sum_{i=1}^{|w|-1} \text{freq}(u_i) + \sum_{j=1}^{|w|-2} \text{freq}(b_j)}
$$

where:
- $|w|$ is the length of the word.
- $u_i$ represents the unigram (single character) in the word.
- $b_j$ represents the bigram (pair of consecutive characters) in the word.
- $\text{freq}(u_i)$ is the frequency of unigram $u_i$ in the overall corpus.
- $\text{freq}(b_j)$ is the frequency of bigram $b_j$ in the overall corpus.

we use a random seed of 42.

## Author
Chompakorn Chaksangchaichot