xcodemind/webcode2m_purified

---
license: cc-by-4.0
size_categories:
- 100B<n<1T
task_categories:
- image-to-text
pretty_name: WebCode2M
configs:
- config_name: default
data_files:
- split: train
path: data/*.parquet
tags:
- code
---
WebCode2M: A Real-World Dataset for Code Generation from Webpage Designs

Features:
- `image`: the screenshot of the webpage.
- `bbox`: the layout information, i.e., the bounding boxes (Bbox) of all the elements in the webpage, which contains the size, position, and hierarchy information.
- `text`: the webpage code text including HTML/CSS code.
- `scale`: the scale of the screenshot, in the format [width, height].
- `lang`: the main language of the text content displayed on the rendered page (excluding HTML/CSS code). It is generated by a widely-applied [model](https://huggingface.co/papluca/xlm-roberta-base-language-detection) on HuggingFace, which achieved very high accuracy on its evaluation set. Currently, it supports the following 20 languages: arabic (ar), bulgarian (bg), german (de), modern greek (el), english (en), spanish (es), french (fr), hindi (hi), italian (it), japanese (ja), dutch (nl), polish (pl), portuguese (pt), russian (ru), swahili (sw), thai (th), turkish (tr), urdu (ur), vietnamese (vi), and chinese (zh).
- `tokens`: the count of tokens of HTML and CSS code, in the format of [CSS length, HTML length]. The tokens are generated by [GPT-2 tokenizer](https://huggingface.co/openai-community/gpt2).
- `score`: the score is obtained by the neural scorer proposed in the paper.
- `hash`: the hash code of the image object.

**Warning**: This dataset is sourced from the internet and, despite filtering efforts, may still contain a small amount of inappropriate content, such as explicit material or violence. Users should exercise caution.