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README.md

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 ---
 title: README
-emoji: 🏃
-colorFrom: indigo
-colorTo: pink
+emoji: ❤️
+colorFrom: red
+colorTo: red
 sdk: static
 pinned: false
 ---
+SentenceTransformers 🤗 is a Python framework for state-of-the-art sentence, text and image embeddings. 
 
-Edit this `README.md` markdown file to author your organization card.
+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 SparseEncoder
+
+# 1. Load a pretrained SparseEncoder model
+model = SparseEncoder("naver/splade-cocondenser-ensembledistil")
+
+# The sentences to encode
+sentences = [
+    "The weather is lovely today.",
+    "It's so sunny outside!",
+    "He drove to the stadium.",
+]
+
+# 2. Calculate sparse embeddings by calling model.encode()
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 30522] - sparse representation with vocabulary size dimensions
+
+# 3. Calculate the embedding similarities
+similarities = model.similarity(embeddings, embeddings)
+print(similarities)
+# tensor([[   35.629,     9.154,     0.098],
+#         [    9.154,    27.478,     0.019],
+#         [    0.098,     0.019,    29.553]])
+
+# 4. Check sparsity stats
+stats = SparseEncoder.sparsity(embeddings)
+print(f"Sparsity: {stats['sparsity_ratio']:.2%}")
+# Sparsity: 99.84%
+```
+
+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">
+<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]"
+      >
+      <img alt="" src="https://huggingface.co/spaces/sparse-encoder/README/resolve/main/sbertLogo.png" class="w-40" />
+   </div>
+   <div class="underline">Documentation</div>
+</a>
+<a
+   href="https://sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer.push_to_hub"
+   class="block overflow-hidden group"
+   >
+   <div
+      class="w-full h-40 mb-2 bg-gray-900 group-hover:bg-gray-850 rounded-lg flex items-start justify-start overflow-hidden"
+      >
+      <img
+         alt=""
+         src="https://huggingface.co/spaces/sparse-encoder/README/resolve/main/push-to-hub.png"
+         class="w-full h-40 object-cover overflow-hidden"
+         />
+   </div>
+   <div class="underline">Push your Sentence Transformers models to the Hub ❤️ </div>
+</a>
+<a
+   href="https://huggingface.co/models?library=sentence-transformers&other=sparse&sort=downloads"
+   class="block overflow-hidden group"
+   >
+   <div
+      class="w-full h-40 mb-2 bg-gray-900 group-hover:bg-gray-850 rounded-lg flex items-start justify-start overflow-hidden"
+      >
+      <img
+         alt=""
+         src="https://huggingface.co/spaces/sparse-encoder/README/resolve/main/sbert-hf.png"
+         class="w-full h-40 object-cover overflow-hidden"
+         />
+   </div>
+   <div class="underline">Find all SparseEncoder models on the 🤗 Hub</div>
+</a>
+</div>
+
+To upload your SparseEncoder models to the Hugging Face Hub, log in with `huggingface-cli login` and use the [`push_to_hub`](https://sbert.net/docs/package_reference/sparse_encoder/SparseEncoder.html#sentence_transformers.sparse_encoder.SparseEncoder.push_to_hub) method within the Sentence Transformers library.
+```python
+from sentence_transformers import SparseEncoder
+
+# Load or train a model
+model = SparseEncoder(...)
+# Push to Hub
+model.push_to_hub("my_new_model")
+```
+
+Note that this repository hosts for now only examples of sparse-encoder models from the SentenceTransformers package that can be easily reproduced with the different training script examples. 
+
+More details at [Sparse Encoder > Training Examples](https://sbert.net/docs/sparse_encoder/training/examples.html) for the examples scripts and [Sparse Encoder > Pretrained Models](https://sbert.net/docs/sparse_encoder/pretrained_models.html) for the community pre-trained models, that you can also found for some of them in the following collections.