distilbert model instead

app.py

@@ -2,9 +2,11 @@ import gradio as gr
 from transformers import pipeline
 import time
 
-sparse_qa_pipeline = pipeline(task="question-answering",model="Intel/bert-base-uncased-squadv1.1-sparse-80-1x4-block-pruneofa")
+# sparse_qa_pipeline = pipeline(task="question-answering",model="Intel/bert-base-uncased-squadv1.1-sparse-80-1x4-block-pruneofa")
+sparse_qa_pipeline = pipeline(task="question-answering",model="Intel/distilbert-base-uncased-squadv1.1-sparse-80-1x4-block-pruneofa-int8")
 
-dense_qa_pipeline = pipeline(task="question-answering",model="csarron/bert-base-uncased-squad-v1")
+# dense_qa_pipeline = pipeline(task="question-answering",model="csarron/bert-base-uncased-squad-v1")
+dense_qa_pipeline = pipeline(task="question-answering",model="distilbert-base-uncased-distilled-squad")
 
 def greet(name):
     return "Hello " + name + "!!"
@@ -34,11 +36,11 @@ def predict(context,question):
     return sparse_answer,sparse_duration,dense_answer,dense_duration
 
 md = """
-If you came looking for chatGPT, sorry to disappoint, but this is different. This prediction model is designed to answer a question about a text. It is designed to do reading comprehension. The model does not just answer questions in general -- it only works from the text that you provide. However, accomplishing accurate reading comprehension can be a valuable task.
+If you came looking for chatGPT, sorry to disappoint, but this is different. This prediction model is designed to answer a question about a given input text. It is designed to do 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. The main idea of this BERT-Base model is that it is much more fast and efficient in deployment than its dense counterpart: (https://huggingface.co/csarron/bert-base-uncased-squad-v1). 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. (2016), 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) for further compression. 
 
-The training dataset used is the English Wikipedia dataset (2500M words), and then fine-tuned on the SQuADv1.1 dataset containing 89K training examples by Rajpurkar et al. (2016): [100, 000+ Questions for Machine Comprehension of Text](https://arxiv.org/abs/1606.05250).
+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).
 
 Author of Hugging Face Space: Benjamin Consolvo, AI Solutions Engineer Manager at Intel | Date last updated: 01/05/2023
 """

requirements.txt

@@ -1,3 +1,4 @@
 transformers
 torch
-tensorflow
+tensorflow
+neural_compressor