Translation using the T5 (Text-to-Text Transfer Transformer) small model is an NLP task where the model converts text from one language to another. T5 frames translation as a text-to-text generation problem.

Text summarization using the T5 (Text-to-Text Transfer Transformer) small model is a natural language processing (NLP) task where the model generates concise summaries of input text. T5 is a transformer-based model developed by Google that treats every NLP problem as a text-to-text task.

Objection detection is one of the key use cases for CV. The job requires to detect the objects and coordinates in a given image. In this image, we. are showing the examples for single object detection. The same can be expanded to multiple objects.

Objection detection is one of the key use cases for CV. The job requires to detect the objects and coordinates in a given image. In this image, we. are showing the examples for single object detection. The same can be expanded to multiple objects.

For object segmentation, we are using U-Net model trained on ClincDB data. For this example, we are showing benchmarking of DL Backtrace (Default Mode), DL Backtrace (Contrastive Positive & Negative modes) & GradCam

For object segmentation, we are using U-Net model trained on CamVid data. For this example, we are showing benchmarking of DL Backtrace (Default Mode), DL Backtrace (Contrastive Positive & Negative modes) & GradCam

In CV, image classification is a classic problem statement. In this example, we will benchmark multiple explainability techniques. ResNet model is trained on CIFAR-10 data.

Code interpreter has emerged as one of the successful use cases for LLMs. In this example, we can learn how the models use the instructions to generate the code.

Code interpreter has emerged as one of the successful use cases for LLMs. In this example, we can learn how the models use the instructions to generate the code.

In this example, we gave a chan of thought input to see how the model would use the instructions in providing the answer

In this example, we gave a chan of thought input to see how the model would use the instructions in providing the answer

In this example, we gave a chan of thought input to see how the model would use the instructions in providing the answer

In this example, we are running 'Sentiment analysis' using Llama 3.2 3B instruct. The prompt is provided with the statement to classify 'Negative' or 'Positive' sentiment

In this example, we are running 'Sentiment analysis' using Llama 3.2 1B instruct. The prompt is provided with the statement to classify 'Negative' or 'Positive' sentiment