
Overview
This is a Text Classification model built upon a Text Embedding model from TensorFlow Hub . 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.
Highlights
- This is an Text Classification model from TensorFlow Hub: https://tfhub.dev/google/experts/bert/pubmed/1
Details
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Pricing
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge instance type, real-time mode | $0.00 |
ml.m5.xlarge Inference (Batch) Recommended | Model inference on the ml.m5.xlarge instance type, batch mode | $0.00 |
ml.c5.xlarge Inference (Real-Time) | Model inference on the ml.c5.xlarge instance type, real-time mode | $0.00 |
ml.c5.2xlarge Inference (Real-Time) | Model inference on the ml.c5.2xlarge instance type, real-time mode | $0.00 |
ml.c5.xlarge Inference (Batch) | Model inference on the ml.c5.xlarge instance type, batch mode | $0.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $0.00 |
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Version release notes
This CPU version supports model run on CPU instance types
Additional details
Inputs
- Summary
The input is a pair of paragraphs
- Input MIME type
- application/x-text
Resources
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