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Rapidata

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https://rapidata.ai
RapidataAI
RapidataAI
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AI & ML interests

RLHF, Model Evaluation, Benchmarks, Data Labeling, Human Feedback, Computer Vision, Image Generation, Video Generation, LLMs, Translations

Recent Activity

Kchanger  updated a collection 13 days ago
Rapidata Benchmark Data
Kchanger  updated a collection 13 days ago
Rapidata Benchmark Data
Kchanger  updated a collection 13 days ago
Rapidata Benchmark Data
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Rapidata's activity

jasoncorkill 
posted an update 7 days ago
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3757
At Rapidata, we compared DeepL with LLMs like DeepSeek-R1, Llama, and Mixtral for translation quality using feedback from over 51,000 native speakers. Despite the costs, the performance makes it a valuable investment, especially in critical applications where translation quality is paramount. Now we can say that Europe is more than imposing regulations.

Our dataset, based on these comparisons, is now available on Hugging Face. This might be useful for anyone working on AI translation or language model evaluation.

Rapidata/Translation-deepseek-llama-mixtral-v-deepl
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jasoncorkill 
posted an update 13 days ago
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2170
Benchmarking Google's Veo2: How Does It Compare?

The results did not meet expectations. Veo2 struggled with style consistency and temporal coherence, falling behind competitors like Runway, Pika, Tencent, and even Alibaba. While the model shows promise, its alignment and quality are not yet there.

Google recently launched Veo2, its latest text-to-video model, through select partners like fal.ai. As part of our ongoing evaluation of state-of-the-art generative video models, we rigorously benchmarked Veo2 against industry leaders.

We generated a large set of Veo2 videos spending hundreds of dollars in the process and systematically evaluated them using our Python-based API for human and automated labeling.

Check out the ranking here: https://www.rapidata.ai/leaderboard/video-models

Rapidata/text-2-video-human-preferences-veo2
Kchanger 
updated a collection 13 days ago
Kchanger 
updated 2 datasets 14 days ago
Kchanger 
published a dataset 14 days ago
LinoGiger 
updated a dataset 14 days ago
Kchanger 
published a dataset 14 days ago
Kchanger 
updated a dataset 15 days ago
Sneccello 
updated a dataset 18 days ago
LinoGiger 
updated a dataset 18 days ago
jasoncorkill 
updated a Space 19 days ago
jasoncorkill 
posted an update 25 days ago
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3833
Has OpenGVLab Lumina Outperformed OpenAI’s Model?

We’ve just released the results from a large-scale human evaluation (400k annotations) of OpenGVLab’s newest text-to-image model, Lumina. Surprisingly, Lumina outperforms OpenAI’s DALL-E 3 in terms of alignment, although it ranks #6 in our overall human preference benchmark.

To support further development in text-to-image models, we’re making our entire human-annotated dataset publicly available. If you’re working on model improvements and need high-quality data, feel free to explore.

We welcome your feedback and look forward to any insights you might share!

Rapidata/OpenGVLab_Lumina_t2i_human_preference
jasoncorkill 
posted an update 28 days ago
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2473
The Sora Video Generation Aligned Words dataset contains a collection of word segments for text-to-video or other multimodal research. It is intended to help researchers and engineers explore fine-grained prompts, including those where certain words are not aligned with the video.

We hope this dataset will support your work in prompt understanding and advance progress in multimodal projects.

If you have specific questions, feel free to reach out.
Rapidata/sora-video-generation-aligned-words
jasoncorkill 
posted an update about 1 month ago
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2851
Integrating human feedback is vital for evolving AI models. Boost quality, scalability, and cost-effectiveness with our crowdsourcing tool!

..Or run A/B tests and gather thousands of responses in minutes. Upload two images, ask a question, and watch the insights roll in!

Check it out here and let us know your feedback: https://app.rapidata.ai/compare
jasoncorkill 
posted an update about 1 month ago
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2511
This dataset was collected in roughly 4 hours using the Rapidata Python API, showcasing how quickly large-scale annotations can be performed with the right tooling!

All that at less than the cost of a single hour of a typical ML engineer in Zurich!

The new dataset of ~22,000 human annotations evaluating AI-generated videos based on different dimensions, such as Prompt-Video Alignment, Word for Word Prompt Alignment, Style, Speed of Time flow and Quality of Physics.

Rapidata/text-2-video-Rich-Human-Feedback