Amid an ongoing discussion about how education and learning will have to be reimagined in the wake of the AI boom, Indian Institute of Technology (IIT), Mandi Director Laxmidhar Behera said that the engineering college curriculum need not be revamped with the advent of AI because the new technologies cannot do research. …
Microsoft Corp. has announced that its AI assistant for Windows will commence its rollout on September 26, while the Office AI app will become widely accessible on November 1, reported Bloomberg. This development reflects Microsoft's ongoing integration of generative artificial intelligence into its product offerings.
During an event in New York, Microsoft CEO Satya Nadella stated…
Snapchat just added another generative AI feature, which allows the Plus subscribers to generative images using text prompts. Right now, a Snapchat+ subscription costs Rs 49 a month or Rs 499 a year, which enables exclusive features such as custom app icons, peek-a-peek, chat wallpaper, custom app themes, story rewatch, and more.
Snapchat has added quite…
Model Card for deberta-v3-base-prompt-injection
This model is a fine-tuned version of microsoft/deberta-v3-base on multiple combined datasets of prompt injections and normal prompts.
It aims to identify prompt injections, classifying inputs into two categories: 0 for no injection and 1 for injection detected.
It achieves the following results on the evaluation set:
Loss: 0.0010
Accuracy:…
UDOP model
The UDOP model was proposed in Unifying Vision, Text, and Layout for Universal Document Processing by Zineng Tang, Ziyi Yang, Guoxin Wang, Yuwei Fang, Yang Liu, Chenguang Zhu, Michael Zeng, Cha Zhang, Mohit Bansal.
Model description
UDOP adopts an encoder-decoder Transformer architecture based on T5 for document AI tasks like…
hf-tiny-model-private/tiny-random-LayoutLMForQuestionAnswering
Document Question Answering
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Updated
Mar 29, 2023
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5
Source link
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glpn-nyu-finetuned-diode-230530-193901
This model is a fine-tuned version of vinvino02/glpn-nyu on the diode-subset dataset.
It achieves the following results on the evaluation set:
Loss: 1.5356
Mae: 3.1497
Rmse: 3.6237
Abs Rel: 6.0096
Log Mae: 0.6926
Log Rmse: 0.8186
Delta1: 0.3020
Delta2: 0.3077
Delta3: 0.3094
Model description
More information needed
Intended uses & limitations
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