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--- |
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license: apache-2.0 |
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task_categories: |
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- image-to-text |
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- visual-question-answering |
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language: |
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- en |
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tags: |
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- data-juicer |
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- pretraining |
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- multimodal |
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size_categories: |
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- 100K<n<1M |
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--- |
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# LLaVA pretrain -- LCS-558k (refined by Data-Juicer) |
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A refined version of LLaVA pretrain dataset (LCS-558k) by [Data-Juicer](https://github.com/alibaba/data-juicer). Removing some "bad" samples from the original dataset to make it higher-quality. |
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This dataset is usually used to pretrain a Multimodal Large Language Model. |
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**Notice**: Here is a small subset for previewing. The whole dataset is available [here](https://dail-wlcb.oss-cn-wulanchabu.aliyuncs.com/MM_data/our_refined_data/LLaVA-1.5/public/llava-pretrain-refine-result.json) (About 115MB). |
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## Dataset Information |
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- Number of samples: 500,380 (Keep ~89.65% from the original dataset) |
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## Refining Recipe |
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```yaml |
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project_name: 'llava-1.5-pretrain-dataset-refine-recipe' |
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dataset_path: 'blip_laion_cc_sbu_558k_dj_fmt_only_caption.jsonl' # converted LLaVA pretrain dataset in Data-Juicer format with only_keep_caption is True. See tools/multimodal/source_format_to_data_juicer_format/llava_to_dj.py |
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export_path: 'blip_laion_cc_sbu_558k_dj_fmt_only_caption_refined.jsonl' |
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np: 42 # number of subprocess to process your dataset |
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text_keys: 'text' # the key name of field where the sample texts to be processed, e.g., `text`, `instruction`, `output`, ... |
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# for multimodal data processing |
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image_key: 'images' # Key name of field to store the list of sample image paths. |
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image_special_token: '<image>' # The special token that represents an image in the text. For LLaVA, it's "<image>". Should be aligned with the args when running conversion tools. |
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eoc_special_token: '<|__dj__eoc|>' # The special token that represents the end of a chunk in the text. In default, it's "<|__dj__eoc|>". You can specify your own special token according to your input dataset. Should be aligned with the args when running conversion tools. |
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open_tracer: true |
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# process schedule: a list of several process operators with their arguments |
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process: |
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- fix_unicode_mapper: # fix unicode errors in text. |
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- punctuation_normalization_mapper: # normalize unicode punctuations to English punctuations. |
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# 558128 |
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# Filter ops |
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- alphanumeric_filter: #558087 # filter text with alphabet/numeric ratio out of specific range. |
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tokenization: false # Whether to count the ratio of alphanumeric to the total number of tokens. |
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min_ratio: 0.60 # the min ratio of filter range |
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- character_repetition_filter: #546105 # filter text with the character repetition ratio out of specific range |
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rep_len: 10 # repetition length for char-level n-gram |
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max_ratio: 0.09373663 # the max ratio of filter range |
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- flagged_words_filter: #543960 # filter text with the flagged-word ratio larger than a specific max value |
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lang: en # consider flagged words in what language |
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tokenization: false # whether to use model to tokenize documents |
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max_ratio: 0.0 # the max ratio to filter text |
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- perplexity_filter: #532029 # filter text with perplexity score out of specific range |
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lang: en # compute perplexity in what language |
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max_ppl: 14435.5806 # the max perplexity score to filter text |
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- special_characters_filter: #531968 # filter text with special-char ratio out of specific range |
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min_ratio: 0.16534802 # the min ratio of filter range |
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max_ratio: 0.42023757 # the max ratio of filter range |
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- word_repetition_filter: # 530773 # filter text with the word repetition ratio out of specific range |
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lang: en # sample in which language |
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tokenization: false # whether to use model to tokenize documents |
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rep_len: 10 # repetition length for word-level n-gram |
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max_ratio: 0.03085751 # the max ratio of filter range |
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- image_aspect_ratio_filter: #542389 # filter samples according to the aspect ratios of images (a fraction of width by height, r=w/h) in them |
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min_ratio: 0.333 # the min aspect ratio of filter range |
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max_ratio: 3.0 # the max aspect ratio of filter range |
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any_or_all: any # keep this sample when any/all images meet the filter condition |
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- image_shape_filter: #533966 # filter samples according to the widths and heights of images in them |
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max_width: 727.8798422276 # the max width of width filter range |
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max_height: 606.2421072264 # the max height of height filter range |
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any_or_all: any # keep this sample when any/all images meet the filter condition |
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- image_size_filter: # 533966 # filter samples according to the size of images (in bytes) within them |
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max_size: "124KB" # the max size of filter range |
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any_or_all: any # keep this sample when any/all images meet the filter condition |
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- image_text_similarity_filter: #544202 # filter samples according to the similarity between text and images. |
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hf_clip: openai/clip-vit-base-patch32 # name of used Hugging Face clip |
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min_score: 0.20315419 # the min similarity of filter range |
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- image_text_matching_filter: # filter samples according to the matching score between image and text. |
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hf_blip: Salesforce/blip-itm-base-coco # name of used Hugging Face blip |
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min_score: 0.44930778 # the min matching score of filter range |
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``` |
