Usage instructions update.

README.md

@@ -39,21 +39,7 @@ This model is trained for document similarity tasks in **computer science** scie
 
 ### How to use
 
-**`aspire-biencoder-compsci-spec`** model can be used via the `transformers` library:
-
-```
-from transformers import AutoModel, AutoTokenizer
-aspire_bienc = AutoModel.from_pretrained('allenai/aspire-biencoder-compsci-spec')
-aspire_tok = AutoTokenizer.from_pretrained('allenai/aspire-biencoder-compsci-spec')
-title = "Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity"
-abstract = "We present a new scientific document similarity model based on matching fine-grained aspects of texts."
-d=[title+aspire_tok.sep_token+abstract]
-inputs = aspire_tok(d, padding=True, truncation=True, return_tensors="pt", max_length=512)
-result = aspire_bienc(**inputs)
-clsrep = result.last_hidden_state[:,0,:]
-```
-
-**`aspire-biencoder-compsci-spec-full`**, can be used as follows: 1) Download the [`aspire-biencoder-compsci-spec-full.zip`](https://drive.google.com/file/d/1AHtzyEpyn7DeFYOdt86ik4n0tGaG5kMC/view?usp=sharing), and 2) Use it per this example usage script: [`aspire/examples/ex_aspire_bienc.py`](https://github.com/allenai/aspire/blob/main/examples/ex_aspire_bienc.py)
+Follow instructions for use detailed on the model github repo: https://github.com/allenai/aspire#specter-cocite
 
 ### 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. The bi-encoder above ignores the finer grained query sentences and uses the whole abstract - this presents a baseline in the paper.