Created model card (#2)

- Created model card (1e86dc338a1f39aaed5132d35a898c901b283aba)
- Update README.md (7db87e514444e4ed0b3f090ac1c95c00b25be5d5)
- Update README.md (d6c2911beaa2fb3c8b3318795976c08a87b21228)


Co-authored-by: Anush Shetty <[email protected]>

README.md

@@ -1,4 +1,34 @@
 ---
 license: apache-2.0
+pipeline_tag: sentence-similarity
 ---
-This is an ONNX Export of [prithivida/Splade_PP_en_v1](https://huggingface.co/prithivida/Splade_PP_en_v1)
+
+ONNX port of [prithivida/Splade_PP_en_v1](https://huggingface.co/prithivida/Splade_PP_en_v1) for text classification and similarity searches.
+
+### Usage
+
+Here's an example of performing inference using the model with [FastEmbed](https://github.com/qdrant/fastembed).
+
+```py
+from fastembed import SparseTextEmbedding
+
+documents = [
+    "You should stay, study and sprint.",
+    "History can only prepare us to be surprised yet again.",
+]
+
+model = SparseTextEmbedding(model_name="prithivida/Splade_PP_en_v1")
+embeddings = list(model.embed(documents))
+
+# [
+#     SparseEmbedding(values=array(
+#         [0.45940185, 0.64054322, 0.2425732, 0.1623179, 1.20566428,
+#          0.62039357...]),
+#                     indices=array([1012, 1998, 2000, 2005, 2017, 2022...])),
+#     SparseEmbedding(values=array([
+#         0.09767706, 0.4374367, 0.00468039, 1.01167965, 1.02318227, 1.30155718
+#     ...]),
+#                     indices=array([2017, 2022, 2025, 2057, 2064, 2069...]))
+# ]
+
+```