datasets:
- SpursgoZmy/MMTab
- liuhaotian/LLaVA-Instruct-150K
- liuhaotian/LLaVA-Pretrain
language:
- en
metrics:
- accuracy
- bleu
- f1
pipeline_tag: image-text-to-text
Table LLaVA Model Card
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.
See the ACL 2024 paper for more details: Multimodal Table Understanding
Model Details
Model Type: Table LLaVA 7B strictly follows the LLaVA-v1.5 model architecture and training pipeline, with CLIP-ViT-L-336px as visual encoder (336*336 image resolution), Vicuna-v1.5-7B as base LLM and a two-layer MLP as vision-language connector.
It was trained with a two-stage pipeline as LLaVA:
- Pre-training: train the vision-language connector with image-caption data and table recognition data.
- Instruction tuning: train the vision-language connector and the base LLM with multimodal instruction following data of tabular and non-tabular tasks.
Code Base: We use the official code of LLaVA-v1.5 for model training and inference, and the saved model checkpoint is uploaded to this repository.
Model Date: Table-LLaVA 7B was trained in January 2024.
Where to send questions or comments about the model: https://github.com/SpursGoZmy/Table-LLaVA/issues
Training dataset
The training data includes original LLaVA-1.5 data and specially constructed multimodal instruction-following data from the MMTab dataset, which is a large-scale dataset covering a wide range of table images and table-related tasks.
| Training Stage | Data Description | Data Size | Hugging Face Dataset |
|---|---|---|---|
| Pre-training | 558K original LLaVA-1.5 pre-training data | 558K | blip_laion_cc_sbu_558k.json |
| 150K table recognition data | 150K | MMTab-pre_pretrain_data_llava_format_150K.json | |
| Instruction Fine-tuning | 665K original LLaVA-1.5 fine-tuning data | 665K | llava_v1_5_mix665k.json |
| 232K multimodal instruction tuning data of 14 tabular tasks | 232K | MMTab-instruct_sft_data_llava_format_232K.json |
We also provide the merged pre-training and instruction fine-tuning data in the MMTab dataset, i.e., enhanced_llava_pretrain_data_708K.json and enhanced_llava_sft_data_898K.json, which was used to train Table LLaVA.
Evaluation dataset
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. We also evaluate Table LLaVA on two non-tabular benchmarks: TextVQA and llava-bench-in-the-wild.
License
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.
Intended use
Primary intended uses: The primary use of Table LLaVA is research on large multimodal models and chatbots, especially for multimodal table understanding.
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.
Limitations
Though the proposed Table-LLaVA demonstrates great performance on a wide range of table-based tasks, the resolution of input images (336*336) is relatively low and may limit the upper bound of its capacity. Luckily, with the emergence of MLLMs which possess higher input image resolution (e.g., Monkey (Li et al., 2023d), LLaVA-Next (Liu et al., 2024)), researchers can use MMTab to develop more powerful tabular MLLM in the future research.