Update README.md

README.md

@@ -25,4 +25,49 @@ pipeline_tag: sentence-similarity
 
 # Application:
 * This model is useful for the semantic search,sentence similarity,recommendation system.
-* You can fine-tune this model for your particular use cases.
+* You can fine-tune this model for your particular use cases.
+
+# Model Implementation
+
+# pip install -U sentence-transformers
+
+from sentence_transformers import SentenceTransformer, InputExample, losses
+import pandas as pd
+from sentence_transformers import SentenceTransformer, InputExample
+from torch.utils.data import DataLoader
+from sentence_transformers import SentenceTransformer, util
+
+model_name="Sakil/sentence_similarity_semantic_search"
+
+sentences = ['A man is eating food.',
+          'A man is eating a piece of bread.',
+          'The girl is carrying a baby.',
+          'A man is riding a horse.',
+          'A woman is playing violin.',
+          'Two men pushed carts through the woods.',
+          'A man is riding a white horse on an enclosed ground.',
+          'A monkey is playing drums.',
+          'Someone in a gorilla costume is playing a set of drums.'
+          ]
+
+#Encode all sentences
+embeddings = model.encode(sentences)
+
+#Compute cosine similarity between all pairs
+cos_sim = util.cos_sim(embeddings, embeddings)
+
+#Add all pairs to a list with their cosine similarity score
+all_sentence_combinations = []
+for i in range(len(cos_sim)-1):
+    for j in range(i+1, len(cos_sim)):
+        all_sentence_combinations.append([cos_sim[i][j], i, j])
+
+#Sort list by the highest cosine similarity score
+all_sentence_combinations = sorted(all_sentence_combinations, key=lambda x: x[0], reverse=True)
+
+print("Top-5 most similar pairs:")
+for score, i, j in all_sentence_combinations[0:5]:
+    print("{} \t {} \t {:.4f}".format(sentences[i], sentences[j], cos_sim[i][j]))
+
+
+# Github: [Sakil Ansari](https://github.com/Sakil786/hate_speech_detection_pretrained_model)