update .gitattributes so git lfs will track .pkl files

.gitattributes

@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,45 @@
+from sentence_transformers import SentenceTransformer, CrossEncoder, util
+import torch
+import pickle
+import pandas as pd
+import gradio as gr
+
+bi_encoder = SentenceTransformer("multi-qa-MiniLM-L6-cos-v1")
+cross_encoder = CrossEncoder("cross-encoder/ms-marco-MiniLM-L-6-v2")
+corpus_embeddings=pd.read_pickle("corpus_embeddings_cpu.pkl")
+corpus=pd.read_pickle("corpus.pkl")
+
+def search(query,top_k=100):
+    print("Top 5 Answer by the NSE:")
+    print()
+    ans=[]
+    ##### Sematic Search #####
+    # Encode the query using the bi-encoder and find potentially relevant passages
+    question_embedding = bi_encoder.encode(query, convert_to_tensor=True)
+    hits = util.semantic_search(question_embedding, corpus_embeddings, top_k=top_k)
+    hits = hits[0]  # Get the hits for the first query
+
+    ##### Re-Ranking #####
+    # Now, score all retrieved passages with the cross_encoder
+    cross_inp = [[query, corpus[hit['corpus_id']]] for hit in hits]
+    cross_scores = cross_encoder.predict(cross_inp)
+
+    # Sort results by the cross-encoder scores
+    for idx in range(len(cross_scores)):
+        hits[idx]['cross-score'] = cross_scores[idx]
+
+    hits = sorted(hits, key=lambda x: x['cross-score'], reverse=True)
+    
+    for idx, hit in enumerate(hits[0:5]):
+        ans.append(corpus[hit['corpus_id']])
+    return ans[0],ans[1],ans[2],ans[3],ans[4]
+
+exp=["Who is steve jobs?","What is coldplay?","What is a turing test?","What is the most interesting thing about our universe?","What are the most beautiful places on earth?"]
+
+desc="This is a semantic search engine powered by SentenceTransformers (Nils_Reimers) with a retrieval and reranking system on Wikipedia corpus. This will return the top 5 results. So Quest on with Transformers."
+
+inp=gr.inputs.Textbox(lines=1, placeholder=None, default="", label="search you query here")
+out=gr.outputs.Textbox(type="auto",label="search results")
+
+iface = gr.Interface(fn=search, inputs=inp, outputs=[out,out,out,out,out],examples=exp,article=desc,title="Neural Search Engine",theme="huggingface",layout='vertical')
+iface.launch(share=True)

requirements.txt

@@ -0,0 +1,3 @@
+sentence-transformers==2.1.0
+torch==1.10.0
+pandas==1.1.5