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This is a Sentence Pair Classification model built upon a Text Embedding model from PyTorch Hub

This is a Object Detection Answering model from TensorFlow Hub

This is a Sentence Pair Classification model built upon a Text Embedding model from PyTorch Hub

This is a Image Classification model from TensorFlow Hub

This is a Object Detection Answering model from TensorFlow Hub

Scale your rendering workloads to tens of thousands of cores in minutes. Utilizing the render farm deployment kit (RFDK) and AWS Thinkbox Deadline for render orchestration, Nimble Studio provides an integrated render farm along with this Deadline Linux Farm Worker AMI that leverages EC2 Spot pricing.

It takes a text string as input and classifies the input text as either a positive or negative movie review. The Text Embedding model which is pre-trained on MEDLINE/PubMed returns an embedding of the input text. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.

This is a Sentence Pair Classification model built upon a Text Embedding model from PyTorch Hub

It takes a pair of sentences as input and classifies the input pair to 'entailment' or 'no-entailment'. The class label entailment implies the second sentence entails the first sentence, and the no-entailment implies it does not. The Text Embedding model which is pre-trained on English and German Wikipedia returns an embedding of the input pair of sentences. The model available for deployment is created by attaching a binary classification layer to the output of the Text Embedding model, and the

This is a Sentence Pair Classification model built upon a Text Embedding model from PyTorch Hub

Nimble Studio's StudioBuilder helps you create a virtual studio from scratch. Walk through and set up your studio by creating networking, render farm, and storage resources. The StudioBuilder process creates and deploys new resour

It takes a text string as input and classifies the input text as either a positive or negative movie review. The Text Embedding model which is pre-trained on WikiPedia and BookCorpus returns an embedding of the input text. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.

Use the Nimble Studio Windows Stream AMI to bring and install additional software to fit your studio's needs.

It takes an image as input and classifies the image to one of the multiple classes. The model available for deployment is pre-trained on ImageNet which comprises images of different classes. The model predicts classes including the additional class for background. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.

This is a Extractive Question Answering model from PyTorch Hub

This is a Extractive Question Answering model built upon a Text Embedding model from [PyTorch Hub](https://pytorch.org/hub/huggingface_pytorch-transformers/ ). It takes as input a pair of question-context strings, and returns a sub-string from the context as a answer to the question. The Text Embedding model which is pre-trained on English Text returns an embedding of the input pair of question-context strings.

This is a Extractive Question Answering model built upon a Text Embedding model from [PyTorch Hub](https://pytorch.org/hub/huggingface_pytorch-transformers/ ). It takes as input a pair of question-context strings, and returns a sub-string from the context as a answer to the question. The Text Embedding model which is pre-trained on Multilingual Wikipedia returns an embedding of the input pair of question-context strings.

It takes an image as input and classifies the image to one of the multiple classes. The model available for deployment is pre-trained on ImageNet which comprises images of different classes. The model predicts classes including the additional class for background. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc.

This is a Extractive Question Answering model from PyTorch Hub

This is a Object Detection Answering model from TensorFlow Hub