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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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1 external reviews
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Bharat P.
It’s Fast, Lightweight for NLP Task
Reviewed on Apr 10, 2025
Review provided by G2
What do you like best about the product?
The thing I like most about DistilBert is how fast and light weight it is use. It’s handle the Nlp tasks without the need of heavy resources like Bert . Its perfect for efficiency and doesn’t slow down the process.
What do you dislike about the product?
Distillbert does not have good or same level of accuracy if we compare it to Bert and Roberta.
If we need a deep context understanding we might face a drop in performance because of its fast and lightweight nature .
If we need a deep context understanding we might face a drop in performance because of its fast and lightweight nature .
What problems is the product solving and how is that benefiting you?
DistilBert is lightweight because of that it helped us to manage resources constraints without losing much performance, which is perfect perfect for large scale operations especially in environments which have limited computing resources, it great for processing large amount of context without slowing down because of computing power and also it good for maintaining smoothness in operations