| --- |
| 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 |
