Update README.md

README.md

@@ -11,63 +11,13 @@ datasets:
 
 It is a state-of-the-art language model for MPNet for Covid-19 dataset with focus on post-covid.
 ## How to use for Deepset Haystack
-###
+
 ```
-%cd /content/drive/MyDrive
 !sudo apt-get install git-lfs
-
 !git lfs install
-
 !git clone https://huggingface.co/shaina/CoQUAD_MPNet
 GIT_LFS_SKIP_SMUDGE=1
 
-from haystack.utils import clean_wiki_text, convert_files_to_dicts, fetch_archive_from_http, print_answers
-from haystack.nodes import FARMReader, TransformersReader
-from haystack.utils import launch_es
-
-launch_es()
-! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.2-linux-x86_64.tar.gz -q
-! tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz
-! chown -R daemon:daemon elasticsearch-7.9.2
-
-import os
-from subprocess import Popen, PIPE, STDOUT
-es_server = Popen(['elasticsearch-7.9.2/bin/elasticsearch'],
-                   stdout=PIPE, stderr=STDOUT,
-                   preexec_fn=lambda: os.setuid(1)  # as daemon
-                  )
-! sleep 30
-
-from haystack.document_stores import ElasticsearchDocumentStore
-document_store = ElasticsearchDocumentStore(host="localhost", username="", password="", index="document")
-
-import pandas as pd
-df=pd.read_excel('/content/covid.xlsx')
-df.fillna(value="", inplace=True)
-
-print(df.head())
-
-from typing import List
-import requests
-import pandas as pd
-from haystack import Document
-from haystack.document_stores import FAISSDocumentStore
-from haystack.nodes import RAGenerator, DensePassageRetriever
-
-titles = list(df["document_identifier"].values)
-texts = list(df["document_text"].values)
-documents: List[Document] = []
-for title, text in zip(titles, texts):
-    documents.append(
-        Document(
-            content=text,
-            meta={
-                "name": title or ""
-            }
-        )
-    )
-document_store.write_documents(documents)
-
 from haystack.nodes import ElasticsearchRetriever
 retriever = ElasticsearchRetriever(document_store=document_store)
 reader = FARMReader(model_name_or_path="/content/drive/MyDrive/CoQUAD_MPNet", use_gpu=True)
@@ -78,7 +28,6 @@ prediction = pipe.run(
     query="What is post-COVID?", params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}}
 )
 from pprint import pprint
-
 pprint(prediction)
 ```