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tablellava

Image-Text-to-Text
Transformers
PyTorch
English
llava
text-generation
Inference Endpoints
Model card Files Community
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  1. README.md +81 -0
  2. config.json +40 -0
  3. generation_config.json +9 -0
  4. preprocessor_config.json +46 -0
  5. pytorch_model-00002-of-00002.bin +3 -0
  6. pytorch_model.bin.index.json +725 -0
  7. special_tokens_map.json +24 -0
  8. tokenizer.model +3 -0
  9. tokenizer_config.json +35 -0
README.md ADDED
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+ ---
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+ datasets:
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+ - SpursgoZmy/MMTab
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+ - liuhaotian/LLaVA-Instruct-150K
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+ - liuhaotian/LLaVA-Pretrain
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+ language:
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+ - en
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+ metrics:
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+ - accuracy
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+ - bleu
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+ - f1
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+ pipeline_tag: image-text-to-text
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+ ---
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+ # Table LLaVA Model Card
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+
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+ <!-- Provide a quick summary of what the model is/does. -->
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+
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+ Table LLaVA 7B is an open-source multimodal chatbot for understanding different table images and fulfilling diverse table-related requests, e.g., question answering, table cell description and structure understanding.
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+
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+ See the ACL 2024 paper for more details: [Multimodal Table Understanding](https://arxiv.org/abs/2406.08100)
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+
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+ ## Model Details
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+
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+ <!-- Provide a longer summary of what this model is. -->
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+
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+ **Model Type:** Table LLaVA 7B strictly follows the [LLaVA-v1.5](https://arxiv.org/abs/2310.03744) model architecture and training pipeline,
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+ with [CLIP-ViT-L-336px](https://huggingface.co/openai/clip-vit-large-patch14-336) as visual encoder (336*336 image resolution),
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+ [Vicuna-v1.5-7B](https://huggingface.co/lmsys/vicuna-7b-v1.5) as base LLM and a two-layer MLP as vision-language connector.
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+
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+ It was trained with a two-stage pipeline as LLaVA:
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+
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+ 1. Pre-training: train the vision-language connector with image-caption data and table recognition data.
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+ 2. Instruction tuning: train the vision-language connector and the base LLM with multimodal instruction following data of tabular and non-tabular tasks.
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+
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+ **Code Base:** We use the official code of [LLaVA-v1.5](https://github.com/haotian-liu/LLaVA) for model training and inference,
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+ and the saved model checkpoint is uploaded to this repository. Thus, Table LLaVA can be used in the same way as the normal LLaVA v1.5 model with its original code.
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+
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+ **Model Date:** Table-LLaVA 7B was trained in January 2024.
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+
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+ **Where to send questions or comments about the model:** https://github.com/SpursGoZmy/Table-LLaVA/issues
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+
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+ ## Training dataset
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+
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+ The training data includes original LLaVA-1.5 data and specially constructed
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+ multimodal instruction-following data from the [MMTab dataset](https://huggingface.co/datasets/SpursgoZmy/MMTab),
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+ which is a large-scale dataset covering a wide range of table images and table-related tasks.
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+
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+ | Training Stage | Data Description | Data Size | Hugging Face Dataset |
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+ | :---: | :---: | :---: | :---: |
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+ | Pre-training | 558K original LLaVA-1.5 pre-training data | 558K | [blip_laion_cc_sbu_558k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain) |
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+ | | 150K table recognition data | 150K | [MMTab-pre_pretrain_data_llava_format_150K.json](https://huggingface.co/datasets/SpursgoZmy/MMTab) |
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+ | Instruction Fine-tuning | 665K original LLaVA-1.5 fine-tuning data | 665K | [llava_v1_5_mix665k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K) |
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+ | | 232K multimodal instruction tuning data of 14 tabular tasks | 232K | [MMTab-instruct_sft_data_llava_format_232K.json](https://huggingface.co/datasets/SpursgoZmy/MMTab) |
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+
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+ We also provide the merged pre-training and instruction fine-tuning data in the MMTab dataset,
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+ i.e., enhanced_llava_pretrain_data_708K.json and enhanced_llava_sft_data_898K.json, which was used to train Table LLaVA.
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+
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+ ## Evaluation dataset
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+
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+ A collection of 17 held-in and 7 held-out tabular benchmarks, including 15 table-related tasks, e.g., table question answering and table2text generation.
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+ We also evaluate Table LLaVA on two non-tabular benchmarks:
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+ [TextVQA](https://textvqa.org/) and [llava-bench-in-the-wild](https://huggingface.co/datasets/liuhaotian/llava-bench-in-the-wild).
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+
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+ ## License
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+
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+ Table LLaVA is based on LLaVA-1.5 and thus follows its license. Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved.
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+
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+ ## Intended use
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+
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+ **Primary intended uses:** The primary use of Table LLaVA is research on large multimodal models and chatbots, especially for multimodal table understanding.
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+
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+ **Primary intended users:** The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
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+
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+ ## Limitations
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+
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+ Table LLaVA takes one table image as the model input. Digesting multiple table images would be valuable to support more application scenarios. Though the proposed Table-LLaVA demonstrates
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+ great performance on a wide range of table-based
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+ tasks, the resolution of input images (336*336) is relatively
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+ low and may limit the upper bound of its capacity. Luckily, with the emergence of MLLMs which
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+ possess higher input image resolution (e.g., Monkey (Li et al., 2023d), LLaVA-Next (Liu et al.,
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+ 2024)), researchers can use MMTab to develop more powerful tabular MLLM in the future research.
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