update the README.md

README.md

@@ -8,3 +8,60 @@ The original data from http://sbert.net/datasets/simplewiki-2020-11-01.jsonl.gz.
 
 We use `nq_distilbert-base-v1` model encode all the data to the PyTorch Tensors. And `normalize` the embeddings by using `sentence_transformers.util.normalize_embeddings`.
 
+
+```python
+!pip install sentence-transformers==2.3.1
+```
+
+
+```python
+
+import os
+import json
+import gzip
+from sentence_tranformers.util import http_get
+from sentence_transformers import SentenceTransformer
+from sentence_transformers.util import normalize_embeddings
+
+
+os.environ['DATASET_NAME']='simplewiki-2020-11-01.jsonl.gz'
+os.environ['DATASET_URL']='http://sbert.net/datasets/simplewiki-2020-11-01.jsonl.gz'
+
+os.environ['MODEL_NAME']='multi-qa-MiniLM-L6-cos-v1'
+os.environ['CROSS_CODE_NAME']='cross-encoder/ms-marco-MiniLM-L-6-v2'
+
+
+http_get(os.getenv('DATASET_URL'), os.getenv('DATASET_NAME'))
+
+passages=[]
+with gzip.open(os.getenv('DATASET_NAME'), 'rt', encoding='utf-8') as fIn:
+    for line in fIn:
+        data=json.loads(line.strip())
+        # add all paragraphs
+#         passages.extend(data['paragraphs'])
+
+        # only add the first paragraph
+#         passages.append(data['paragraph'][0])
+
+        for paragraph in data['paragraphs']:
+            # We encode the passages as [title, text]
+            passages.append([data['title'], paragraph])
+
+print('Passages:', len(passages))      
+
+
+bi_encoder=SentenceTransformer('nq-distilbert-base-v1')
+bi_encoder.max_seq_length=256
+bi_encoder.to('cuda')
+
+corpus_embeddings=bi_encoder.encode(passages, convert_to_tensor=True, show_progress_bar=True).to('cuda')
+corpus_embeddings=normalize_embeddings(corpus_embeddings)
+len(corpus_embeddings)
+
+
+import pandas as pd
+
+embedding_data=pd.DataFrame(corpus_embeddings.cpu())
+embedding_data.to_csv('simple_english_wikipedia_2020_11_01.csv', index=False)
+```
+