Add dataset file

Signed-off-by: Aisuko <urakiny@gmail.com>

.gitattributes

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 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+*.csv filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,68 @@
 ---
 license: apache-2.0
 ---
+
+# Overview
+
+Original from the sentences-transformers library.
+
+Only for researching purposes.
+
+Adapter by Aisuko
+
+# Installation
+
+```python
+!pip install sentence-transformers==2.3.1
+```
+
+# Computing Embeddings for a large set of sentences
+
+```python
+import os
+import csv
+import time
+
+from sentence_transformers import SentenceTransformer
+from sentence_transformers.util import http_get
+
+if __name__=='__main__':
+    url='http://qim.fs.quoracdn.net/quora_duplicate_questions.tsv'
+    dataset_path='quora_duplicate_questions.tsv'
+#     max_corpus_size=50000  # max number of sentences to deal with
+    
+    if not os.path.exists(dataset_path):
+        http_get(url, dataset_path)
+    
+    # get all unique sentences from the file
+    corpus_sentences=set()
+    with open(dataset_path, encoding='utf8') as fIn:
+        reader=csv.DictReader(fIn, delimiter='\t', quoting=csv.QUOTE_MINIMAL)
+        for row in reader:
+            corpus_sentences.add(row['question1'])
+            corpus_sentences.add(row['question2'])
+#             if len(corpus_sentences)>=max_corpus_size:
+#                 break
+                
+    corpus_sentences=list(corpus_sentences)
+    model=SentenceTransformer('all-MiniLM-L6-v2').to('cuda')
+    model.max_seq_length=256
+    
+    pool=model.start_multi_process_pool()
+    
+    # computing the embeddings using the multi-process pool
+    emb=model.encode_multi_process(corpus_sentences, pool,batch_size=128,chunk_size=1024,normalize_embeddings=True)
+    print('Embeddings computed. Shape:', emb.shape)
+    
+    # optional : stop the processes in the pool
+    model.stop_multi_process_pool(pool)
+```
+
+# Save the csv file
+
+```python
+import pandas as pd
+
+corpus_embedding=pd.DataFrame(emb)
+corpus_embedding.to_csv('quora_questions.csv',index=False)
+```

quora_questions.csv

@@ -0,0 +1,3 @@
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+oid sha256:12013b9a3438ac3bd362508f8109965dfa319ab53a1fabbbfeb70ae4e7fd09af
+size 2524694588