ECLAIR

ECLAIR is a general purpose text-extraction model, specifically designed to handle documents. Given an image, ECLAIR is able to extract formatted-text, with bounding-boxes and the corresponding semantic class. This has downstream benefits for several tasks such as increasing the availability of training-data for Large Language Models (LLMs), improving the accuracy of retriever systems, and enhancing document understanding pipelines.

This model is for demonstration purposes and it is not for production usage.

License

GOVERNING TERMS: Access to this Eclair early access (EA) model is governed by the [NVIDIA Software and Model Evaluation License Agreement]

References

[1] https://huggingface.co/docs/transformers/en/model_doc/mbart

Model Architecture

Architecture Type: Transformer-based vision-encoder-decoder model

Network Architecture:

• Vision Encoder: ViT-H model (https://huggingface.co/nvidia/C-RADIO)
• Adapter Layer: 1D convolutions & norms to compress dimensionality and sequence length of the latent space (1280 tokens to 320 tokens)
• Decoder: mBart [1] 10 blocks
• Tokenizer: Galactica (https://arxiv.org/abs/2211.09085); same as Nougat tokenizer

Input

Input Type: Image, Text

Input Type(s): Red, Green, Blue (RGB) + Prompt (String)

Input Parameters: 2D, 1D

Other Properties Related to Input:

• Max Input Resolution (Width, Height): 1648, 2048

• Min Input Resolution (Width, Height): 1024, 1280

• Channel Count: 3

Output

Output Type: Text

Output Format: String

Output Parameters: 1D

Other Properties Related to Output: ECLAIR output format is a string which encodes text content (formatted or not) as well as bounding boxes and class attributes.

Software Integration

Runtime Engine(s): PyTorch

Supported Hardware Platform(s): NVIDIA Hopper/NVIDIA Ampere/NVIDIA Turing

Supported Operating System(s): Linux

Model Versions

Eclair-v1.1-beta: As part of this first release, we share the set of weights named overjoyed-adder.

Training Dataset

ECLAIR is first pre-trained on our internal datasets: human, synthetic and automated

Inference

Runtime Engine(s): PyTorch

Test Hardware: NVIDIA H100# Synchronization

Ethical Considerations

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their supporting model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

Please report security vulnerabilities or NVIDIA AI Concerns here.