Update usage.

README.md

@@ -39,7 +39,9 @@ This model is trained for fine-grained document similarity tasks in **computer s
 
 ### How to use
 
-The `aspire-contextualsentence-singlem-compsci` model can be used via the `transformers` library combined with additional code to obtain contextual sentence embeddings from a transformer model. Use it per this example usage script: [`aspire/examples/ex_aspire_consent.py`](https://github.com/allenai/aspire/blob/main/examples/ex_aspire_consent.py)
+This model can be used via the `transformers` library and some additional code to compute contextual sentence vectors.
+
+View example usage in the model github repo: https://github.com/allenai/aspire#tsaspire
 
 ### Variable and metrics
 This model is evaluated on information retrieval datasets with document level queries. Performance here is reported on CSFCube (computer science/English). This is detailed on [github](https://github.com/allenai/aspire) and in our [paper](https://arxiv.org/abs/2111.08366). CSFCube presents a finer-grained query via selected sentences in a query abstract based on which a finer-grained retrieval must be made from candidate abstracts.