scb10x/typhoon-vision-preview-data

--- task_categories: - visual-question-answering - image-to-text language: - th - en pretty_name: Typhoon Vision Preview Data size_categories: - 100K<n<1M --- # Typhoon Vision Preview Data ## Dataset Overview This dataset is designed for visual question-answering and image-to-text tasks, supporting both Thai (th) and English (en) languages. ## Data Source The dataset is based on the Bunny image dataset. You can download the original images from [here](https://huggingface.co/datasets/BoyaWu10/Bunny-v1_0-data). ## Dataset Splits The dataset is organized into multiple splits, available as Hugging Face datasets: 1. **Pretrain**: Used for pretraining the adapter in LLaVA format. 2. **Finetune**: Used for finetuning in LLaVA format. 3. **Finetune_translated_stats**: Contains original texts, their Thai translations, and COMET scores (translation quality estimation). ### Pretraining Set - Comprises the original Bunny data. - Includes an additional 10% of translated data appended to the original set. - COMET QE scores were not computed for this set. ### Finetuning Set - Based on the same structure as the pretraining set. - The appended 10% data consists of top-performing translations, as determined by COMET scores. ## File Descriptions - `pretrain.json`: Dataset for pretraining the adapter in LLaVA format. - `finetune.json`: Dataset for finetuning in LLaVA format. - `finetune_translated_stats.json`: Contains original texts, Thai translations, and COMET scores. ## Usage Notes - The dataset is designed for use with the LLaVA (Large Language and Vision Assistant) format. - When using the finetuning set, be aware that it includes high-quality translations based on COMET scores. - The pretraining set can be used for initial model adaptation, while the finetuning set is optimized for final model tuning. ## Language Support This dataset supports bilingual tasks: - Thai (th) - English (en) Researchers and developers can use this dataset for tasks involving both languages, especially for cross-lingual visual question-answering and image-to-text generation.