Update README.md

README.md

@@ -1,3 +1,90 @@
 ---
 license: apache-2.0
+task_categories:
+- image-to-text
+- visual-question-answering
+language:
+- en
+tags:
+- data-juicer
+- pretraining
+- multimodal
+size_categories:
+- 100K<n<1M
 ---
+
+# LLaVA pretrain -- LCS-558k (refined by Data-Juicer)
+
+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.
+
+This dataset is usually used to pretrain a Multimodal Large Language Model.
+
+**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).
+
+## Dataset Information
+
+- Number of samples: 500,380 (Keep ~89.65% from the original dataset)
+
+## Refining Recipe
+```yaml
+project_name: 'llava-1.5-pretrain-dataset-refine-recipe'
+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
+export_path: 'blip_laion_cc_sbu_558k_dj_fmt_only_caption_refined.jsonl'
+
+np: 42                                                            # number of subprocess to process your dataset
+text_keys: 'text'                                                 # the key name of field where the sample texts to be processed, e.g., `text`, `instruction`, `output`, ...
+
+# for multimodal data processing
+image_key: 'images'                                               # Key name of field to store the list of sample image paths.
+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.
+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.
+
+open_tracer: true
+
+# process schedule: a list of several process operators with their arguments
+process:
+  - fix_unicode_mapper:                                           # fix unicode errors in text.
+  - punctuation_normalization_mapper:                             # normalize unicode punctuations to English punctuations.
+
+  # 558128
+  # Filter ops
+  - alphanumeric_filter:           #558087                        # filter text with alphabet/numeric ratio out of specific range.
+      tokenization: false                                           # Whether to count the ratio of alphanumeric to the total number of tokens.
+      min_ratio: 0.60                                               # the min ratio of filter range
+  - character_repetition_filter:    #546105                       # filter text with the character repetition ratio out of specific range
+      rep_len: 10                                                   # repetition length for char-level n-gram
+      max_ratio: 0.09373663                                         # the max ratio of filter range
+  - flagged_words_filter:          #543960                        # filter text with the flagged-word ratio larger than a specific max value
+      lang: en                                                      # consider flagged words in what language
+      tokenization: false                                           # whether to use model to tokenize documents
+      max_ratio: 0.0                                                # the max ratio to filter text
+  - perplexity_filter:         #532029                            # filter text with perplexity score out of specific range
+      lang: en                                                      # compute perplexity in what language
+      max_ppl: 14435.5806                                           # the max perplexity score to filter text
+  - special_characters_filter:    #531968                         # filter text with special-char ratio out of specific range
+      min_ratio: 0.16534802                                          # the min ratio of filter range
+      max_ratio: 0.42023757                                          # the max ratio of filter range
+  - word_repetition_filter:   # 530773                            # filter text with the word repetition ratio out of specific range
+      lang: en                                                       # sample in which language
+      tokenization: false                                            # whether to use model to tokenize documents
+      rep_len: 10                                                    # repetition length for word-level n-gram
+      max_ratio: 0.03085751                                          # the max ratio of filter range
+
+  - 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
+      min_ratio: 0.333                                              # the min aspect ratio of filter range
+      max_ratio: 3.0                                                # the max aspect ratio of filter range
+      any_or_all: any                                               # keep this sample when any/all images meet the filter condition
+  - image_shape_filter:             #533966                       # filter samples according to the widths and heights of images in them
+      max_width: 727.8798422276                                     # the max width of width filter range
+      max_height: 606.2421072264                                    # the max height of height filter range
+      any_or_all: any                                               # keep this sample when any/all images meet the filter condition
+  - image_size_filter:          # 533966                          # filter samples according to the size of images (in bytes) within them
+      max_size: "124KB"                                             # the max size of filter range
+      any_or_all: any                                               # keep this sample when any/all images meet the filter condition
+  - image_text_similarity_filter:     #544202                     # filter samples according to the similarity between text and images.
+      hf_clip: openai/clip-vit-base-patch32                         # name of used Hugging Face clip
+      min_score: 0.20315419                                         # the min similarity of filter range
+  - image_text_matching_filter:                                   # filter samples according to the matching score between image and text.
+      hf_blip: Salesforce/blip-itm-base-coco                        # name of used Hugging Face blip
+      min_score: 0.44930778                                         # the min matching score of filter range
+```