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mwitiderrick
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Update app.py
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app.py
CHANGED
@@ -17,6 +17,14 @@ pipeline = Pipeline.create(task="zero_shot_text_classification", model_path="zoo
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inference = pipeline(sequences= text,labels=['politics', 'public health', 'Europe'],)
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print(inference)
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```
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## Resources
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[Classify Even Longer Customer Reviews Using Sparsity with DeepSparse](https://neuralmagic.com/blog/accelerate-customer-review-classification-with-sparse-transformers/)
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'''
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inference = pipeline(sequences= text,labels=['politics', 'public health', 'Europe'],)
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print(inference)
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```
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## Use case example
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Customer review classification is a great example of text classification in action.
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The ability to quickly classify sentiment from customers is an added advantage for any business.
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Therefore, whichever solution you deploy for classifying the customer reviews should deliver results in the shortest time possible.
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By being fast the solution will process more volume, hence cheaper computational resources are utilized.
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Deploying a deep learning model to tackle this problem is one solution.
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For this solution, decreasing the model’s latency and increasing its throughput is critical. This is why DeepSparse Pipelines have sparse text classification models.
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## Resources
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[Classify Even Longer Customer Reviews Using Sparsity with DeepSparse](https://neuralmagic.com/blog/accelerate-customer-review-classification-with-sparse-transformers/)
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'''
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