Add pipeline tag and usage example

README.md

@@ -1,20 +1,20 @@
 ---
-library_name: transformers
+base_model:
+- google-bert/bert-base-uncased
 datasets:
 - microsoft/ms_marco
 language:
 - en
-base_model:
-- google-bert/bert-base-uncased
+library_name: transformers
+pipeline_tag: feature-extraction
+license: mit
 ---
 
 # Model Card
 This is the official model from the paper [Hypencoder: Hypernetworks for Information Retrieval](https://arxiv.org/abs/2502.05364).
 
-
 ## Model Details
-This is a Hypencoder Dual Enocder. It contains two trunks the text encoder and Hypencoder. The text encoder converts items into 768 dimension vectors while the Hypencoder converts text into a small neural network which takes the 768 dimension vector from the text encoder as input. This small network is then used to output a relevance score. To use this model please take a look at the [Github](https://github.com/jfkback/hypencoder-paper) page which contains the required code and details on how to run the model.
-
+This is a Hypencoder Dual Encoder. It contains two trunks the text encoder and Hypencoder. The text encoder converts items into 768 dimension vectors while the Hypencoder converts text into a small neural network which takes the 768 dimension vector from the text encoder as input. This small network is then used to output a relevance score. To use this model please take a look at the [Github](https://github.com/jfkback/hypencoder-paper) page which contains the required code and details on how to run the model.
 
 ### Model Variants
 We released the four models used in the paper. Each model is identical except the small neural networks, which we refer to as q-nets, have different numbers of hidden layers.
@@ -26,6 +26,57 @@ We released the four models used in the paper. Each model is identical except th
 | [jfkback/hypencoder.6_layer](https://huggingface.co/jfkback/hypencoder.6_layer) |          6        |
 | [jfkback/hypencoder.8_layer](https://huggingface.co/jfkback/hypencoder.8_layer) |          8        |
 
+## Usage
+
+```python
+from hypencoder_cb.modeling.hypencoder import Hypencoder, HypencoderDualEncoder, TextEncoder
+from transformers import AutoTokenizer
+
+dual_encoder = HypencoderDualEncoder.from_pretrained("jfkback/hypencoder.6_layer")
+tokenizer = AutoTokenizer.from_pretrained("jfkback/hypencoder.6_layer")
+
+query_encoder: Hypencoder = dual_encoder.query_encoder
+passage_encoder: TextEncoder = dual_encoder.passage_encoder
+
+queries = [
+    "how many states are there in india",
+    "when do concussion symptoms appear",
+]
+
+passages = [
+    "India has 28 states and 8 union territories.",
+    "Concussion symptoms can appear immediately or up to 72 hours after the injury.",
+]
+
+query_inputs = tokenizer(queries, return_tensors="pt", padding=True, truncation=True)
+passage_inputs = tokenizer(passages, return_tensors="pt", padding=True, truncation=True)
+
+q_nets = query_encoder(input_ids=query_inputs["input_ids"], attention_mask=query_inputs["attention_mask"]).representation
+passage_embeddings = passage_encoder(input_ids=passage_inputs["input_ids"], attention_mask=passage_inputs["attention_mask"]).representation
+
+# The passage_embeddings has shape (2, 768), but the q_nets expect the shape
+# (num_queries, num_items_per_query, input_hidden_size) so we need to reshape
+# the passage_embeddings.
+
+# In the simple case where each q_net only takes one passage, we can just
+# reshape the passage_embeddings to (num_queries, 1, input_hidden_size).
+passage_embeddings_single = passage_embeddings.unsqueeze(1)
+scores = q_nets(passage_embeddings_single)  # Shape (2, 1, 1)
+# [
+#    [[-12.1192]],
+#    [[-13.5832]]
+# ]
+
+# In the case where each q_net takes both passages we can reshape the
+# passage_embeddings to (num_queries, 2, input_hidden_size).
+passage_embeddings_double = passage_embeddings.repeat(2, 1).reshape(2, 2, -1)
+scores = q_nets(passage_embeddings_double)  # Shape (2, 2, 1)
+# [
+#    [[-12.1192], [-32.7046]],
+#    [[-34.0934], [-13.5832]]
+# ]
+```
+
 ## Citation
 **BibTeX:**
 ```