Release v1.0.1

Major Updates

• 🚀 Supports automatically arranging operators from fastest to slowest based on their execution speed, and also supports automating the operator batch size according to the execution speed. #464
• 🚀 [UnitTest] Performance benchmark for efficiency tests of 4 modalities. Reports will be uploaded to internal wandb server. #483
• 💥 Added some useful OPs, including the construction of DPO training data and a lightweight user-customizable OP interface. See more details below~ #491 #492 #493

OPs

Text OPs

• pair_preference_mapper: Mapper to construct preference answers for QA pairs. #491

Script OPs

• python_lambda_mapper: Mapper for executing customized Python lambda functions on data samples. #492
• python_file_mapper: Mapper for executing customized Python functions on data samples. #493

Bugs Fixed

• Add an argument to control whether to open Monitor for data processing. It's True by default. #483
• For the mp start method of monitor, set it to "spawn" for Windows systems and "fork" for others. #483
• Update transformers version to >=4.47.0 to avoid "shape mismatch" bug from older version 4.46.3. #483
• Fix the logic errors in Turbo acceleration and batch processing, and ensure that map and filter are consistent in this part of the logic. #504

Others

• Pin the PyAV version to prevent inconsistent updates. #504
• Skip some unit test for audio OPs to avoid lazy_loader failure during multiprocessing. #503
• Remove unnecessary UNFORKABLE marks for some OPs. #491
• Refine the docker image building. Add a new self-hosted runner for docker image building, optimize the building logic for auto docker image building on release, change the default full image to a GPU-version image. #494 #501

Acknowledgment

Here we thank public contributors for their PRs and issues to make Data-Juicer better!

Release v1.0.0: Refactor DJ-Dataset & DJ-Operator, Sandbox, and more exciting features!

Major Updates

• 🚀 Refactor Data-Juicer Operator & Dataset for better usability! We combine our two backends, HuggingFace Dataset and Ray Dataset, into a unified DJ-Dataset, and unify and introduce new invoking interfaces. Based on this, we add a fault-tolerant strategy during the data processing, helping users to know the actual reasons for processing failure. #359 #366
• 🧪 [Experimental] Data-Juicer Sandbox toolkit is now available! Users are allowed to develop datasets and models in a co-development way with the highly customizable Sandbox to obtain better performance. For more details, please refer to the docs. #273 #291 #312 #332 #364
• 🚀 Basic API server based on FastAPI is now available in Data-Juicer! Now users can make use of the capabilities of OPs with API service. #468
• 🚀 Support adaptive resource management:
  • Adaptive number of processors for model-based OPs according to the GPU memory and other types of resource utilization. #270 #329 #354
  • Adaptive batch size for batched OPs according to their resource utilization to maximize the OP speed. #429
• 💥 We presented a tutorial of Multi-modal Data Processing for Foundation Models: Practical Guidance and Use Cases on KDD'24. #310
• 💥 A lot of additions and improvements were made to OPs, DJ-Engine, and CI/CD. See more details below~
• 🛝 A playground for Data-Juicer is opened for user trial. #277 #368

OPs

Text

• ray_document_deduplicator: supports Ray-based distributed exact-match deduplication for text-only datasets. #263
• Support sentencepiece tokenizer for MinHash deduplicators. #269
• generate_qa_from_text_mapper: generates question and answer pairs from input texts. #333 #454
• generate_qa_from_examples_mapper: generates question and answer pairs based on examples. #338 #454
• optimize_qa_mapper: optimizes the question-answer pairs in question-answering samples. #338 #454
• optimize_query_mapper: optimizes the query in question-answering samples. #338 #454
• optimize_response_mapper: optimizes the response in question-answering samples. #454
• calibrate_qa_mapper: calibrates question-answer pairs based on reference text. #463
• calibrate_query_mapper: calibrates query in question-answer pairs based on reference text. #463
• calibrate_response_mapper: calibrates response in question-answer pairs based on reference text. #463
• text_chunk_mapper: splits input text to chunks. #481
• extract_entity_attribute_mapper: extracts attributes for given entities from the text. #481
• extract_entity_relation_mapper: extracts entities and relations in the text for knowledge graph. #481
• extract_event_mapper: extracts events and relevant characters in the text. #481
• extract_keyword_mapper: generates keywords for the text. #481
• extract_nickname_mapper: extracts nickname relationship in the text.. #481

Image

• image_face_blur_mapper: blurs faces detected in images. #249
• image_nsfw_filter: keeps samples containing images with NSFW scores below the threshold. #252
• image_watermark_filter: keeps samples containing images with predicted watermark probabilities below the threshold. #256
• ray_image_deduplicator: supports Ray-based distributed exact-match deduplication for image or image-text datasets. #263
• image_pair_similarity_filter: keeps image pairs with image feature cosine similarity within the specified range based on a CLIP model. #393
• image_tagging_mapper: generates image tags from the input images. #423
• image_face_count_filter: keeps samples containing images with face counts within the specified range. #446

Video

• video_face_blur_mapper: blurs faces detected in videos. #253
• video_remove_watermark_mapper: removes the watermarks in given regions from the videos. #236
• video_nsfw_filter: keeps samples containing videos with NSFW scores below the threshold. #252
• video_watermark_filter: keeps samples containing videos with predicted watermark probabilities below the threshold. #256
• ray_video_deduplicator: supports Ray-based distributed exact-match deduplication for video or video-text datasets. #263
• video_tagging_from_frames_filter: keeps samples containing videos with given tags. #260
• video_captioning_from_frames_mapper: generates samples whose captions are generated based on an image-to-text model and sampled video frames. Captions from different frames will be concatenated into a single string. #257
• video_captioning_from_summarizer_mapper: generates video captions by summarizing several kinds of generated texts (captions from video/audio/frames, tags from audio/frames, ...). #250
• video_motion_score_raft_filter: keeps samples with video motion scores (based on RAFT model) within a specific range. #478
• Enhance the video_motion_score_filter to support float sampling FPS, frame resizing, optical flow magnitude normalization, and so on. #361

Misc.

• Switch face detection used in 3 OPs (image_face_ratio_filter, image_face_blur_mapper, video_face_blur_mapper) from dlib to OpenCV to avoid dependency problems. #320
• Deduplicators for multimodal datasets are allowed to consider text information as well. #313
• Support batched processing for some OPs. #406 #435

Others (Engine, Job Control and Tools)

• Support more multimodal (video) dataset conversion tools: MSR-VTT #248
• Support distributed processing script for Slurm. #242
• Support Minhash-LSH deduplication tools based on Spark. #290
• Enable GPU usage for Ray executor. #274
• Add debug mode for Data-Juicer. #303
• Add video generation tools for several metrics. #273 #312
• Deploy a self-hosted runner for unit tests and enable unit tests for Ray mode. #304
• Add sampled frames from videos for video OPs to support OP fusion. #271
• Allow to save stats for each OP respectively by specifying the exporting paths for them. #309
• Add a new field to record the source files of multimodal data when they are augmented or regenerated by some OPs, so it's convenient to trace back. #317
• Support turbo mode to disable some processing-unrelated functions to maximize the processing speed and save resource utilization. #402
• Update type annotations from jsonargparse to Pydantic. #422
• Add a Monitor module to monitor the resource utilization during data processing for each OP. #429
• Allow lazy importing for third-party libraries and installing dependencies if they are not installed. #414 #443
• Allow batched processing for all OPs based on the single-sample version of compute_stats/process methods to avoid modifying them to a batched version manually. #448
• Enable unit test coverage report. #460
• Support invoking API models for interaction with OpenAI-compatible APIs. #463 #479

Document Updates

• Refine documentation system based on Sphinx. #245
• Regular document updates. #234 #246
• Update the class importing and document building logics for better automation. #299
• Reorganize the operator documents for better reading. #472

Bugs Fixed

• Fix the bug of non-existent videos returned by the video splitting function given a short duration. #243
• Fix the bug that the produced multimodal data would be stored in nested dirs in different ops. #247
• Fix some problems in demos. #244
• Fix "Undefined punctuation_pattern" error in two OPs. #301
• Exceptions and errors can be reraised to the upper level and the status code can be returned to the system correctly. #287
• Fix the bug of out-of-work type hint checking for config files. #302
• Fix the bug of parameters in the base classes that can not be parsed in some OPs. #311
• Fix the memory leaking of video OPs. #374
• Fix the bug of two OPs (video_aesthetics_filter and image_diffusion_mapper) that can not make use of GPUs. #389
• Fix the bug of checkpoints not being restored correctly when the current process list has fewer OPs then the previous one. #391

Acknowledgment

Here we thank public contributors for their PRs to make Data-Juicer better!

• @chg0901 helps to fix typos in documents. #237
• @lingzhq helps to update the paper list in Awesome Data-Model Co-Development of MLLMs. #289
• @shiweijiezero helps fix the bugs in updating the data keys. #300
• @seanzhang-zhichen helps to support multiple patterns for replace_content_mapper. #319
• @simplaj helps to fix a bug of a non-predefined attribute for video_captioning_from_summarizer_mapper. #343
• @zhenqincn helps to reorganize the paper list and add more papers from our survey in Awesome Data-Model Co-Development of MLLMs. #352 #381 #456 #461
• @2108038773 helps to add trust_remote_code argument for some public models on HuggingFace. #382 #385
• @TobyJasper helps to fix typos in documents and contribute a new OP image_face_count_filter. #392 #452
• @co63oc helps to fix some typos in documents and code. #427

Release v0.2.0: Multimodal Support & DJ-SORA

New Features

• 🚀 We introduce DJ-SORA to provide open large-scale, high-quality datasets for SORA-like models. #227
• 🚀 We introduce hundreds of dedicated video, image, audio, text, and other multi-modal data processing operators and tools.
• 💥 Our paper has been accepted by SIGMOD'24 industrial track! #211
• 💥 "BetterMixture" — Our second data-centric LLM competition has kicked off and is about to end soon. #174

New OPs

Multimodal

• video_frames_text_similarity_filter: keeps samples whose similarities between sampled video frame images and text within a specific range. #227
• video_tagging_from_frames_mapper: generates video tags from frames extracted from the video. #227
• video_tagging_from_audio_mapper: generates video tags from audio streams extracted from videos. #227
• video_captioning_from_video_mapper: generates captions from frame images extracted from video to augment datasets. #227
• video_captioning_from_audio_mapper: captions a video according to its audio streams. #227
• image_captioning_mapper: generates captions based on a language model and the image. This OP will increase the number of samples in the dataset. #131 #191 #227
• image_captioning_from_gpt4v_mapper: generates captions based on GPT-4-Vision and the image. This OP will increase the number of samples in the dataset. #214 #227
• image_diffusion_mapper: generates and augments the images based on the Stable Diffusion model and their original images and texts. This OP will increase the number of samples in the dataset. #200

Video

Filter

• video_duration_filter: keeps samples whose videos' durations are within a specified range. #227
• video_aspect_ratio_filter: filters samples according to the aspect ratios of videos (a fraction of width by height, r=w/h) in them. #227
• video_resolution_filter: filters samples according to the resolution of videos in them. #227
• video_ocr_area_ratio_filter: keeps samples whose detected text area ratios for specified frames in the video are within a specified range. #227
• video_aesthetics_filter: filters samples according to the aesthetics score of frame images extracted from videos. #227
• video_motion_score_filter: keeps samples with video motion scores within a specific range. #227

Mapper

• video_split_by_scene_mapper: splits videos into scene clips. #227
• video_split_by_duration_mapper: splits videos by specified duration interval. #227
• video_split_by_key_frame_mapper: splits videos by their keyframes. #227
• video_resize_aspect_ratio_mapper: resizes aspect ratios of videos (a fraction of width by height, r=w/h) to a specified range. #227
• video_resize_resolution_mapper: maps videos to ones with a given resolution range. #227
• video_ffmpeg_wrapped_mapper: a wrapper to apply ffmpeg to video data more conveniently. #227

Deduplicator

• video_deduplicator: deduplicates samples at document-level using exact matching of videos between documents. #227

Audio

• audio_duration_filter: keeps samples whose audios' durations are within a specified range. #177
• audio_size_filter: keeps samples whose audios' sizes are within a specified range. #184
• audio_nmf_snr_filter: keeps samples whose audios' Signal Noise Ratios (computed based on Non-Negative Matrix Factorization algorithm) are within a specified range. #189
• audio_ffmpeg_wrapped_mapper: a wrapper to apply ffmpeg to audio data more conveniently. #227

Image

• image_blur_mapper: adds random noises to images to blur them. #180
• image_aesthetics_filter: filter samples according to the aesthetics scores of images. #227

Document Updates

• "Bad" Data Exhibition EN ZH: shows how Data-Juicer finds those "bad" data and how they look like.
• Awesome LLM Data EN: a collection of awesome LLM datasets with fine-grained tags.
• Developer Guide enhancement EN ZH: adds guides on how to accelerate the models in your OP with GPUs and how to implement a batched OP for sample augmentation. #203 #220
• OP Insight Visualization Demo code: adds a demo to visualize how each OP works.

Bugs Fixed

• Fix stats computation error in the ray mode due to the inappropriate initialization method. #173
• Fix the bug that some images will be lost when converting their paths to absolute paths. #178
• Fix the dependency problems of OPs who depend on other OPs. #181
• Fix the bug that the predict.py tool gets stuck on the help page. #183
• Fix face_area_filter: constrains the detection coordinates within the image. #202
• Fix MMC4 conversion tools: resolves the situation where multiple images match the same sentence. #195
• Fix or update invalid links in Data-Juicer. #201 #219

Others

• Optimize the model management module. #196 #227
• Optimize the unit test actions. #195 #196 #216 #227
• Optimize the multiprocessing strategy and model inference efficiency could be increased due to GPU support. #203 #217 #222 #227
• Update the docker image with JDK. #208
• Support more multimodal (video) dataset conversion tools: #227
  • InternVid: 234M video-caption data
  • Youku-mPLUG: 36TB video-caption data
  • Video-ChatGPT: 100k video-instruction data
• Optimize the generated multimodal data storage. #227
• Support running data-juicer process jobs on Aliyun PAI-DLC. #227
• Better support for multi-machine distributed data processing in Ray mode. #227

Acknowledgment

Here we thank public contributors for their PRs to make Data-Juicer better!

• @liuyanyi helps to fix a bug in quality classifier tools. #183
• @co63oc helps to fix some typos. #215
• @liuyanyi helps to provide the solution to add JDK in the docker image. #182 #208
• @zhenqincn helps to add more papers to the Awesome LLM Data doc. #226

Release v0.1.3: support more Python versions; support multimodal data; more OPs; bugs fixed

New Features

• Data-Juicer now supports Python3.7-3.10!
  • We released a pybind version of simhash-py library named simhash-pybind to solve the Python version limitation problem.
  • We test several version-depend third-party libraries (e.g. dill, kenlm, ...) and validate their availability on different Python versions.
• Multimodal dataset analysis and processing are now supported. #64 #91 #95 #106
  • A novel intermediate multimodal sample format: using some special tokens to split text chunks and represent non-text information.
  • Several dataset format conversion tools for popular multimodal datasets: LLaVA, MMC4, WavCaps, ......
  • Lots of multimodal OPs are also released: see categories Image and Multimodal in the section New OPs below.
• Auto-HPO tools are now available, which can help users find better hyperparameters for OPs according to specified object functions or with simple 3-sigma rules only. #65 #140
• Some content cleaning mappers (e.g. email, IP, ...) now support replacing regex patterns with specified strings, not just with empty ones. Additionally, a general version OP is implemented as a new OP replace_content_mapper. #143
• Some collectors, metrics, and drawing functions are added to the analysis module to help users measure the token distribution of a single dataset or distribution difference between different datasets. #160

New OPs

Text

• chinese_convert_mapper: converts Chinese between Traditional Chinese, Simplified Chinese, and Japanese Kanji (by opencc) #51
• remove_non_chinese_character_mapper: removes non-Chinese characters in text samples. #51
• text_action_filter: keeps samples containing action verbs in their texts. #122
• text_entity_dependency_filter: keeps samples containing entity nouns related to other tokens in the dependency tree of the texts. #122
• replace_content_mapper: replaces all content in the text that matches a specific regular expression pattern with a designated replacement string. #143
• remove_repeat_sentences_mapper: Remove repeated sentences in the text. #149

Image

• image_shape_filter: keeps samples containing images with widths and heights within the specified ranges. #74
• image_aspect_ratio_filter: keeps samples containing images with aspect ratios (w/h) within the specified range. #64
• image_size_filter: keeps samples containing images whose sizes in bytes are within the specified range. #73
• face_area_filter: keeps samples containing images with face area ratios within the specified range. #110
• image_deduplicator: deduplicates samples at document-level using exact matching of images between documents. #72

Multimodal

• image_text_similarity_filter: keeps samples with image-text feature cosine similarity within the specified range based on a CLIP model. #69
• image_text_matching_filter: keeps samples with image-text classification matching scores within the specified range based on a BLIP model. #100
• phrase_grounding_recall_filter: keeps samples whose locating/grounding recalls of phrases extracted from text in the images are within a specified range. #139

Bugs fixed

• Fix the pandas==2.0.0 fsspec==2023.3.0 to avoid unexpected errors from third-party dependencies. #38 #42
• Fix the bug when OPs nlpaug_en_mapper and nlpcda_zh_mapper generate indefinite numbers of augmented samples. #76
• Fix the bug of maximum_line_length_filter might generate unaligned types of stats (int v.s. float), which leads to an error when processing datasets. #147
• Fix the bug of missing attribute dataset_dir when the input dataset path is remote or a mixture of several datasets. #155 #157
• Fix the bug of commandline arguments parsing error in some cases. #108 #165
• Store simhash value as string type to avoid errors from PyArrow. #168 #170

Others

• Dependency importing optimization: only require and import some dependencies when using. #35 #82
• Release demos and datasets on HuggingFace, and release models trained with our refined datasets on both ModelScope and HuggingFace. #42 #54
• Optimize the cache directory selection logic. #43
• Support limiting the number of samples when mixing datasets. #86
• Avoid extra unnecessary model preparation when enabling tokenization in some OPs. #99
• OP language_id_score_filter supports keeping samples in multiple languages now. #125 #151

Acknowledgement

Here we thank public contributors for their PRs to make Data-Juicer better!

• @JONGSKY helps to remove some unnecessary code. #85
• @xuruidong helps to fix several broken links in the README doc. #142

Release v0.1.2: more core functions are available now.

New OPs

• nlpaug_en_mapper: simple data augmentation using nlpaug library for English corpus. #17
• nlpcda_zh_mapper: simple data augmentation using nlpcda library for Chinese corpus. #17
• token_num_filter: filter out samples by the number of tokens in them. HF tokenizers are supported. #24

New features

• OP Fusion #14
  • Now Filters that share the same contextual variables can be fused into one OP, saving at most 25% time when processing datasets.
• Cache management #19
  • Cache management works now for our Data-Juicer due to the new serialization method being applied.
  • Cache compression is supported: it will automatically compress caches when they are useless and decompress them if needed, which saves at most 50% disk space.
• Distributed data processing with Ray is supported now. #21
• Config sys optimization:
  • Only keep text_keys and remove previous misleading arg text_key(s)_to_process/load. #13
  • A new argument export_in_parallel is added to control whether export the result datasets in parallel. #17
  • Display the config table after config parsing is ready. #17

Others

• Replace original string constants with constant enums. #13
• Expand the checkpoint protection range to cover the exporting process. #14
• Remove extra intermediate variables storage in document_simhash_deduplicator to save more memory. #14
• Docs updates. #15 #16
• PyPi package is available. You can install data-juicer by pip install py-data-juicer now. #23
• Docker building is available now. The official docker image for Docker Hub is in progress. #23
• Deploy the unit tests for Data-Juicer. #29

Release v0.1.0, the first internal version for open-source

Summarization - Table of Contents

• Data-Juicer: A Data-Centric Text Processing System for Large Language Models
• Table of Contents
  • Features
  • Prerequisites
  • Installation
  • Quick Start
    • Data Processing
    • Data Analysis
    • Data Visualization
    • Build Up Config Files
    • Preprocess raw data (Optional)
  • Documentation | 文档
  • Data Recipes
  • Demos
  • License
  • Contributing
  • References

Features

• Broad Range of Operators: Equipped with 50+ core operators (OPs), including Formatters, Mappers, Filters, Deduplicators, and beyond.

• Specialized Toolkits: Feature-rich specialized toolkits such as Text Quality Classifier, Dataset Splitter, Analysers, Evaluators, and more that elevate your dataset handling capabilities.

• Systematic & Reusable: Empowering users with a systematic library of reusable config recipes and OPs, designed to function independently of specific datasets, models, or tasks.

• Data-in-the-loop: Allowing detailed data analyses with an automated report generation feature for a deeper understanding of your dataset. Coupled with real-time multi-dimension automatic evaluation capabilities, it supports a feedback loop at multiple stages in the LLM development process.

• Comprehensive Processing Recipes: Offering tens of pre-built data processing recipes for pre-training, SFT, en, zh, and more scenarios.

• User-Friendly Experience: Designed for simplicity, with comprehensive documentation, easy start guides and demo configs, and intuitive configuration with simple adding/removing OPs from existing configs.

• Flexible & Extensible: Accommodating most types of data formats (e.g., jsonl, parquet, csv, ...) and allowing flexible combinations of OPs. Feel free to implement your own OPs for customizable data processing.

• Enhanced Efficiency: Providing a speedy data processing pipeline requiring less memory, optimized for maximum productivity.