Introducing ConTextual: How well can your Multimodal model jointly reason over text and image in text-rich scenes?
The Hugging Face blog post introduces ConTextual, a benchmark for evaluating multimodal models on jointly reasoning over text and images in text-rich scenes. The benchmark aims to assess how well models can understand and generate text and image content together. You can use ConTextual to compare the performance of different multimodal models. The benchmark provides a new way to evaluate and improve multimodal models.
Key takeaways
- ConTextual is a new benchmark for multimodal models.
- Evaluates joint reasoning over text and images in text-rich scenes.
- Assesses understanding and generation of text and image content.
Introducing ConTextual: How well can your Multimodal model jointly reason over text and image in text-rich scenes?
The Hugging Face blog post introduces ConTextual, a benchmark for evaluating multimodal models on jointly reasoning over text and images in text-rich scenes. The benchmark aims to assess how well models can understand and generate text and image content together. You can use ConTextual to compare the performance of different multimodal models. The benchmark provides a new way to evaluate and improve multimodal models.
Key takeaways
- ConTextual is a new benchmark for multimodal models.
- Evaluates joint reasoning over text and images in text-rich scenes.
- Assesses understanding and generation of text and image content.