Update usage.

README.md

@@ -39,7 +39,9 @@ This model is trained for fine-grained document similarity tasks in **biomedical
 
 ### How to use
 
-The `aspire-contextualsentence-singlem-biomed` 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. Here we report performance on RELISH (biomedical/English), and TRECCOVID (biomedical/English). These are detailed on [github](https://github.com/allenai/aspire) and in our [paper](https://arxiv.org/abs/2111.08366). These datasets represent a abstract level retrieval task, where given a query scientific abstract the task requires the retrieval of relevant candidate abstracts. In using this sentence level model for abstract level retrieval we rank documents by the minimal L2 distance between the sentences in the query and candidate abstract.