Update app.py

app.py

@@ -8,9 +8,11 @@ Named Entity Recognition is the task of extracting and locating named entities i
 ![Named Entity Recognition Pipeline with DeepSparse](https://huggingface.co/spaces/neuralmagic/nlp-ner/resolve/main/named.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. 
-The pipelines are similar to Hugging Face pipelines but are faster because they have been pruned and quantized. SparseML Named Entity Recognition Pipelines integrate with Hugging Face’s Transformers library to enable the sparsification of a large set of transformers models. 
-### Inference
+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.
+
+SparseML Named Entity Recognition Pipelines integrate with Hugging Face’s Transformers library to enable the sparsification of a large set of transformers models. 
+### Inference API Example
 Here is sample code for a token classification pipeline: 
 ```python
 from deepsparse import Pipeline
@@ -18,10 +20,11 @@ pipeline = Pipeline.create(task="ner", model_path="zoo:nlp/token_classification/
 inference = pipeline(text)
 print(inference)
 ```
-## Use case example 
+## Use Case Description
 The Named Entity Recognition Pipeline can process text before storing the information in a database. 
 For example, you may want to process text and store the entities in different columns depending on the entity type. 
 
+[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 = "ner"
 dense_qa_pipeline = Pipeline.create(