Update app.py

app.py

@@ -7,9 +7,11 @@ markdownn = '''
 Text Classification involves assigning a label to a given text. For example, sentiment analysis is an example of a text classification use case. 
 ![Text Classification Pipeline with DeepSparse](https://huggingface.co/spaces/neuralmagic/nlp-text-classification/resolve/main/text-classification.png)
 ## What is DeepSparse
-DeepSparse is sparsity-aware inference runtime offering GPU-class performance on CPUs and APIs to integrate ML into your application. DeepSparse provides sparsified pipelines for computer vision and NLP. 
+DeepSparse is an inference runtime offering GPU-class performance on CPUs and APIs to integrate ML into your application. Sparsification is a powerful technique for optimizing models for inference, reducing the compute needed with a limited accuracy tradeoff. DeepSparse is designed to take advantage of model sparsity, enabling you to deploy models with the flexibility and scalability of software on commodity CPUs with the best-in-class performance of hardware accelerators, enabling you to standardize operations and reduce infrastructure costs.
+Similar to Hugging Face, DeepSparse provides off-the-shelf pipelines for computer vision and NLP that wrap the model with proper pre- and post-processing to run performantly on CPUs by using sparse models.
+
 The text classification Pipeline, for example, wraps an NLP model with the proper preprocessing and postprocessing pipelines, such as tokenization.
-### Inference
+### Inference API Example
 Here is sample code for a text classification pipeline: 
 ```
 from deepsparse import Pipeline
@@ -17,7 +19,7 @@ pipeline = Pipeline.create(task="zero_shot_text_classification", model_path="zoo
 inference = pipeline(sequences= text,labels=['politics', 'public health', 'Europe'],)
 print(inference)
 ```
-## Use case example
+## Use Case Description
 Customer review classification is a great example of text classification in action. 
 
 The ability to quickly classify sentiment from customers is an added advantage for any business. 
@@ -25,8 +27,9 @@ Therefore, whichever solution you deploy for classifying the customer reviews sh
 By being fast the solution will process more volume, hence cheaper computational resources are utilized.  
 
 When deploying a text classification model, decreasing the model’s latency and increasing its throughput is critical. This is why DeepSparse Pipelines have sparse text classification models. 
-## Resources 
-[Classify Even Longer Customer Reviews Using Sparsity with DeepSparse](https://neuralmagic.com/blog/accelerate-customer-review-classification-with-sparse-transformers/)
+
+[Want to train a sparse model on your data? Checkout the documentation on sparse transfer learning](https://docs.neuralmagic.com/use-cases/natural-language-processing/question-answering)
+
 '''
 task = "zero_shot_text_classification"
 dense_classification_pipeline = Pipeline.create(