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This is a Extractive Question Answering model from PyTorch Hub
4
Overview
This is a Extractive Question Answering model built upon a Text Embedding model from PyTorch Hub . 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. PyTorch, the PyTorch logo and any related marks are trademarks of Facebook, Inc.
Highlights
- This is an Extractive Question Answering model from PyTorch Hub: https://pytorch.org/hub/huggingface_pytorch-transformers/
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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 text paragraph and a question.
- Input MIME type
- application/list-text
https://github.com/aws-samples/aws-marketplace-machine-learning/blob/master/using_open_source_model_packages/pytorch_eqa_model/input_example
https://github.com/aws-samples/aws-marketplace-machine-learning/blob/master/using_open_source_model_packages/pytorch_eqa_model/input_example
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Customer reviews
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