Update app.py

app.py

@@ -13,19 +13,19 @@ st.markdown('''
 
 Hi! This is the demo for the [flax sentence embeddings](https://huggingface.co/flax-sentence-embeddings) created for the **Flax/JAX community week 🤗**. 
 We trained three general-purpose flax-sentence-embeddings models: a **distilroberta base**, a **mpnet base** and a **minilm-l6**. 
-All were trained on all the datasets of the 1B+ train corpus with the v3 setup.
+All were trained on all the datasets of the [1B+ train corpus](https://huggingface.co/flax-sentence-embeddings/all_datasets_v4_MiniLM-L6#training-data) with the v3 setup.
 
-In addition, we trained 20 models focused on general-purpose, QuestionAnswering and Code search.
-View our models here: https://huggingface.co/flax-sentence-embeddings
+In addition, we trained [20 models](https://huggingface.co/flax-sentence-embeddings) focused on general-purpose, QuestionAnswering and Code search.
+View our models [here](https://huggingface.co/flax-sentence-embeddings)
 
-To evaluate the [flax-sentence-embeddings](https://huggingface.co/flax-sentence-embeddings) **Sentence Similarity models** for **Gender Bias based on stereotypical occupations**, we created an **evaluation set** which can be found [here](https://huggingface.co/datasets/manandey/Gender_Bias_Evaluation)
+To evaluate the [flax-sentence-embeddings](https://huggingface.co/flax-sentence-embeddings) *Sentence Similarity models* for *Gender Bias based on stereotypical occupations*, we created an *evaluation set* which can be found [here](https://huggingface.co/datasets/manandey/Gender_Bias_Evaluation)
 
 ''')
 
 if menu == "Sentence Similarity":
     st.header('Sentence Similarity')
     st.markdown('''
-**Instructions**: You can compare the similarity of a main text with other texts of your choice. In the background, we'll create an embedding for each text, and then we'll use the cosine similarity function to calculate a similarity metric between our main sentence and the others.
+**Instructions**: You can compare the similarity of the main text with other texts of your choice. In the background, we'll create an embedding for each text, and then we'll use the cosine similarity function to calculate a similarity metric between our main sentence and the others.
 
 For more cool information on sentence embeddings, see the [sBert project](https://www.sbert.net/examples/applications/computing-embeddings/README.html).
 ''')