Update app.py

app.py

@@ -34,7 +34,7 @@ def predict(context,question):
 
 md = """This prediction model is designed to answer a question about a given input text--reading comprehension. The model does not just answer questions in general -- it only works from the text that you provide. However, automated reading comprehension can be a valuable task.
 
-The model is based on the Zafrir et al. (2021) paper: [Prune Once for All: Sparse Pre-Trained Language Models](https://arxiv.org/abs/2111.05754). The model can be found [here](https://huggingface.co/Intel/bert-base-uncased-squadv1.1-sparse-80-1x4-block-pruneofa). It has had weight pruning and model distillation applied to create a sparse weight pattern that is maintained even after fine-tuning has been applied. According to Zafrir et al., their "results show the best compression-to-accuracy ratio for BERT-Base". This model is still in FP32, but can be quantized to INT8 with the [Intel® Neural Compressor](https://github.com/intel/neural-compressor). 
+The model is based on the Zafrir et al. (2021) paper: [Prune Once for All: Sparse Pre-Trained Language Models](https://arxiv.org/abs/2111.05754). The model can be found [here](https://huggingface.co/Intel/bert-base-uncased-squadv1.1-sparse-80-1x4-block-pruneofa). It has had weight pruning and model distillation applied to create a sparse weight pattern that is maintained even after fine-tuning has been applied. According to Zafrir et al. (2021), their "results show the best compression-to-accuracy ratio for BERT-Base". This model is still in FP32, but can be quantized to INT8 with the [Intel® Neural Compressor](https://github.com/intel/neural-compressor). 
 
 The training dataset used is the English Wikipedia dataset (2500M words), and then fine-tuned on the SQuADv1.1 dataset containing 89K training examples, compiled by Rajpurkar et al. (2016): [100, 000+ Questions for Machine Comprehension of Text](https://arxiv.org/abs/1606.05250).