Update README.md

README.md

@@ -24,6 +24,9 @@ The model was trained on a random collection of **English** sentences from Wikip
 # Model Usage
 ### Example 1) - Sentence Similarity
 
+<details>
+<summary> Click to expand </summary>
+  
 ```python
 from transformers import AutoTokenizer, AutoModel
 import torch.nn as nn
@@ -69,8 +72,13 @@ cos_sim = sim(embeddings.unsqueeze(1),
 print(f"Distance: {cos_sim[0,1].detach().item()}")
 ```
 
+</details>
+
 ### Example 2) - Clustering
 
+<details>
+<summary> Click to expand </summary>
+
 ```python
 from transformers import AutoTokenizer, AutoModel
 import torch.nn as nn
@@ -146,9 +154,13 @@ umap_plot.points(umap_model, labels = np.array(classes),theme='fire')
 
 ![UMAP Cluster](https://raw.githubusercontent.com/TJKlein/tjklein.github.io/master/images/miCSE_UMAP_small2.png)
 
+</details>
 
 ### Example 3) - Using [SentenceTransformers](https://www.sbert.net/)
 
+<details>
+<summary> Click to expand </summary>
+
 ```python
 from sentence_transformers import SentenceTransformer, util
 from sentence_transformers import models
@@ -182,6 +194,7 @@ for i in range(len(sentences1)):
     print(f"Similarity {cosine_scores[i][i]:.2f}: {sentences1[i]} << vs. >> {sentences2[i]}")
 ```
 
+</details>
 
 # Benchmark