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This is a Sentence Pair Classification model built upon a Text Embedding model from PyTorch Hub
Overview
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 Text 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 then fine-tuning the entire model on QNLI dataset. PyTorch, the PyTorch logo and any related marks are trademarks of Facebook, Inc.
Highlights
- This is a Sentence Pair Classification from PyTorch Hub: https://pytorch.org/hub/huggingface_pytorch-transformers/
Details
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Latest version
Deployed on AWS
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Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g4dn.xlarge Inference (Real-Time) Recommended | Model inference on the ml.g4dn.xlarge instance type, real-time mode | $0.00 |
ml.p2.xlarge Inference (Batch) Recommended | Model inference on the ml.p2.xlarge instance type, batch mode | $0.00 |
ml.m5.large Inference (Real-Time) | Model inference on the ml.m5.large instance type, real-time mode | $0.00 |
ml.m5.xlarge Inference (Real-Time) | Model inference on the ml.m5.xlarge instance type, real-time 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.p2.xlarge Inference (Real-Time) | Model inference on the ml.p2.xlarge instance type, real-time mode | $0.00 |
ml.p3.2xlarge Inference (Real-Time) | Model inference on the ml.p3.2xlarge instance type, real-time mode | $0.00 |
ml.m5.large Inference (Batch) | Model inference on the ml.m5.large instance type, batch mode | $0.00 |
ml.m5.xlarge Inference (Batch) | Model inference on the ml.m5.xlarge 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.
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Version release notes
This GPU version supports model run on GPU instance types
Additional details
Inputs
- Summary
The input is a pair of sentences.
- Input MIME type
- application/list-text
https://github.com/aws-samples/aws-marketplace-machine-learning/blob/master/using_open_source_model_packages/pytorch_spc_model/input_example
https://github.com/aws-samples/aws-marketplace-machine-learning/blob/master/using_open_source_model_packages/pytorch_spc_model/input_example
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Customer reviews
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2 external reviews
Star ratings include only reviews from verified AWS customers. External reviews can also include a star rating, but star ratings from external reviews are not averaged in with the AWS customer star ratings.
Louis R.
Lacks text analysis tool as well
Reviewed on Mar 13, 2024
Review provided by G2
What do you like best about the product?
RobBerTA Basically is the one to recognize the complex aspects of language. This undoubtedly helps for tasks like sentiment analysis, in which it shows the emotions of the people by analyzing the text sentiment. It seemed to me as if I was getting professional support in reading comprehension as a near accomplice.
What do you dislike about the product?
For use of RoBERTa-Base there is a technical impediment. Of course it shall use up some space in memory and has to be programed for each specific task. This contributes to inexperienced NLP beginners' psychological strain.
What problems is the product solving and how is that benefiting you?
The Breath of Life in my text analyses have been made more precise and deep by RoBERTa Base model. The idea of researching the every tiny nooks of language has led me into gaining ideas and ideas which I would have never thought any other way.
Imran A.
A Resilent Basis for NLP Projects
Reviewed on Mar 02, 2024
Review provided by G2
What do you like best about the product?
Roberta Base outstands as a pre trained language model . Showing me robust performance at natural language processing tasks. This flexibility makes me capable of coping with tasks from text classification to sentiment analysis. It rolls the resource consuming stuff for me.
What do you dislike about the product?
Roberta Base is an ineffective device, in more complex cases. That require exceptional precision and unusual and distinctive languages. Roberta Base can only be utilized in a limited manner. Considering this scenario, researching on other models that are pre-trained with particular domain training could be the most efficient.
What problems is the product solving and how is that benefiting you?
Our NLP kit is called Roberta Base. It is an accurate backbone for multiple NLP endeavors. It gives me the opportunity to come up with a double-quick . Its open source architecture permits me to customize