Create README.md for query2query model (#2)

- Create README.md for query2query model (7852c4c1e57f231733f55468d764a93f835ac35d)


Co-authored-by: Rahil Bathwal <rahilbathwal@users.noreply.huggingface.co>

README.md

@@ -0,0 +1,44 @@
+---
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+---
+
+# {MODEL_NAME}
+
+This is a [sentence-transformers](https://www.SBERT.net) model: It maps queries to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search over queries.
+
+<!--- Describe your model here -->
+
+## Usage (Sentence-Transformers)
+
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+
+```python
+from sentence_transformers import SentenceTransformer
+queries = ["flight cost from nyc to la", "ticket prices from nyc to la"]
+
+model = SentenceTransformer('{MODEL_NAME}')
+embeddings = model.encode(queries)
+print(embeddings)
+```
+
+## Training
+The model was trained for 1M steps with a batch size of 1024 at a learning rate of 2e-5 using a cosine learning rate scheduler with 10000 warmup steps.
+
+## Full Model Architecture
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: DataParallel 
+  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
+  (2): Normalize()
+)
+```