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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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<!-- Provide a quick summary of what the model is/does. --> |
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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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See the ACL 2024 paper for more details: [Multimodal Table Understanding](https://arxiv.org/abs/2406.08100) |
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## Model Details |
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<!-- Provide a longer summary of what this model is. --> |
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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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It was trained with a two-stage pipeline as LLaVA: |
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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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**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. |
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**Model Date:** Table-LLaVA 7B was trained in January 2024. |
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**Where to send questions or comments about the model:** https://github.com/SpursGoZmy/Table-LLaVA/issues |
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## Training dataset |
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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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| 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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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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## Evaluation dataset |
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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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## License |
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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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## Intended use |
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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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**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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## Limitations |
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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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