Use Markdown interspersed with HTML rather than pure HTML

README.md

@@ -6,54 +6,28 @@ colorTo: red
 sdk: static
 pinned: false
 ---
-<div class="lg:col-span-3">
-   <p class="mb-4">
-      SentenceTransformers 🤗 is a Python framework for state-of-the-art sentence, text and image embeddings. 
-   </p>
-   <p class="mb-4">
-      Install the <a
-         href="https://www.sbert.net/"
-         >Sentence Transformers</a
-         >
-      library.
-   </p>
-   <div
-      class="p-4 bg-gradient-to-b from-gray-50-to-white border border-gray-100 rounded-lg relative mb-4"
-      >
-      <pre class="break-words leading-1 whitespace-pre-line text-xs md:text-sm text-gray-800">
-	pip install -U sentence-transformers
-		</pre>
-   </div>
-   </p>
-   <div class="lg:col-span-3">
-      <p class="mb-4">
-         The usage is as simple as:
-      </p>
-      <div
-         class="p-4 bg-gradient-to-b from-gray-50-to-white border border-gray-100 rounded-lg relative mb-4"
-         >
-         <pre class="break-words leading-1 whitespace-pre-line text-xs md:text-sm text-gray-800">           
+SentenceTransformers 🤗 is a Python framework for state-of-the-art sentence, text and image embeddings. 
+
+Install the [Sentence Transformers](https://www.sbert.net/) library.
+```
+pip install -U sentence-transformers
+```
+
+The usage is as simple as:
+```python
 from sentence_transformers import SentenceTransformer
 model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
 
-#Sentences we want to encode. Example:
+# Sentences we want to encode. Example:
 sentence = ['This framework generates embeddings for each input sentence']
 
-#Sentences are encoded by calling model.encode()
+# Sentences are encoded by calling model.encode()
 embedding = model.encode(sentence)
-</pre>
-      </div>
-   </div>
-</div>
+```
+
+Hugging Face makes it easy to collaboratively build and showcase your [Sentence Transformers](https://www.sbert.net/) models! You can collaborate with your organization, upload and showcase your own models in your profile ❤️
+
 <div class="grid lg:grid-cols-3 gap-x-4 gap-y-7">
-<div class="lg:col-span-3">
-   <p class="mb-4">
-      Hugging Face makes it easy to collaboratively build and showcase your <a
-         href="https://www.sbert.net/">Sentence Transformers</a
-         >
-      models! You can collaborate with your organization, upload and showcase your own models in your profile ❤️
-   </p>
-</div>
 <a href="https://www.sbert.net/" class="block overflow-hidden group">
    <div
       class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center bg-[#FA8072]"
@@ -63,7 +37,7 @@ embedding = model.encode(sentence)
    <div class="underline">Documentation</div>
 </a>
 <a
-   href="https://github.com/UKPLab/sentence-transformers/blob/master/sentence_transformers/SentenceTransformer.py#L417"
+   href="https://sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer.save_to_hub"
    class="block overflow-hidden group"
    >
    <div
@@ -92,25 +66,14 @@ embedding = model.encode(sentence)
    </div>
    <div class="underline">Find all Sentence Transformers models on the 🤗 Hub</div>
 </a>
-<div class="lg:col-span-3">
-   <p class="mb-4">
-      To upload your Sentence Transformers models to the Hugging Face Hub log in with <code class="language-python">huggingface-cli login</code> and then use the <a
-         href="https://github.com/UKPLab/sentence-transformers/blob/master/sentence_transformers/SentenceTransformer.py#L417"
-         >save_to_hub</a
-         >
-      function within the Sentence Transformers library.
-   </p>
-   <div
- class="p-4 bg-gradient-to-b from-gray-50-to-white border border-gray-100 rounded-lg relative mb-4"
-      >
-      <pre
-         class="break-words leading-1 whitespace-pre-line text-xs md:text-sm text-gray-800">
-	from sentence_transformers import SentenceTransformer
-	# Load or train a model
-	model = ...
-	# Push to Hub
-	model.save_to_hub("my_new_model")
-		</pre>
-   </div>
-   </p>
-</div>
+</div>
+
+To upload your Sentence Transformers models to the Hugging Face Hub, log in with `huggingface-cli login` and use the [`save_to_hub`](https://sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer.save_to_hub) method within the Sentence Transformers library.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Load or train a model
+model = SentenceTransformer(...)
+# Push to Hub
+model.save_to_hub("my_new_model")
+```