Update README.md

README.md

@@ -13,7 +13,7 @@ An [adapter](https://adapterhub.ml) for the [`allenai/specter2_base`](https://hu
 This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
 
 **Aug 2023 Update:**
-1. **The SPECTER 2.0 Base and proximity adapter models have been renamed in Hugging Face based upon usage patterns as follows:**
+1. **The SPECTER2 Base and proximity adapter models have been renamed in Hugging Face based upon usage patterns as follows:**
 
 |Old Name|New Name|
 |--|--|
@@ -23,11 +23,11 @@ This adapter was created for usage with the **[adapter-transformers](https://git
 2. **We have a parallel version (termed [aug2023refresh](https://huggingface.co/allenai/specter2_aug2023refresh)) where the base transformer encoder version is pre-trained on a collection of newer papers (published after 2018).
    However, for benchmarking purposes, please continue using the current version.**
 
-## SPECTER 2.0
+## SPECTER2
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-SPECTER 2.0 is the successor to [SPECTER](https://huggingface.co/allenai/specter) and is capable of generating task specific embeddings for scientific tasks when paired with [adapters](https://huggingface.co/models?search=allenai/specter-2_).
+SPECTER2 is the successor to [SPECTER](https://huggingface.co/allenai/specter) and is capable of generating task specific embeddings for scientific tasks when paired with [adapters](https://huggingface.co/models?search=allenai/specter-2_).
 This is the base model to be used along with the adapters.
 Given the combination of title and abstract of a scientific paper or a short texual query, the model can be used to generate effective embeddings to be used in downstream applications.
 
@@ -58,7 +58,7 @@ adapter_name = model.load_adapter("allenai/specter2_regression", source="hf", se
 
 ## Model Description
 
-SPECTER 2.0 has been trained on over 6M triplets of scientific paper citations, which are available [here](https://huggingface.co/datasets/allenai/scirepeval/viewer/cite_prediction_new/evaluation).
+SPECTER2 has been trained on over 6M triplets of scientific paper citations, which are available [here](https://huggingface.co/datasets/allenai/scirepeval/viewer/cite_prediction_new/evaluation).
 Post that it is trained with additionally attached task format specific adapter modules on all the [SciRepEval](https://huggingface.co/datasets/allenai/scirepeval) training tasks.
 
 Task Formats trained on:
@@ -84,9 +84,9 @@ It builds on the work done in [SciRepEval: A Multi-Format Benchmark for Scientif
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [https://github.com/allenai/SPECTER2_0](https://github.com/allenai/SPECTER2_0)
+- **Repository:** [https://github.com/allenai/SPECTER2](https://github.com/allenai/SPECTER2)
 - **Paper:** [https://api.semanticscholar.org/CorpusID:254018137](https://api.semanticscholar.org/CorpusID:254018137)
-- **Demo:** [Usage](https://github.com/allenai/SPECTER2_0/blob/main/README.md)
+- **Demo:** [Usage](https://github.com/allenai/SPECTER2/blob/main/README.md)
 
 # Uses
 
@@ -177,19 +177,11 @@ We also evaluate and establish a new SoTA on [MDCR](https://github.com/zoranmedi
 |[SPECTER](https://huggingface.co/allenai/specter)|54.7|57.4|68.0|(30.6, 25.5)|
 |[SciNCL](https://huggingface.co/malteos/scincl)|55.6|57.8|69.0|(32.6, 27.3)|
 |[SciRepEval-Adapters](https://huggingface.co/models?search=scirepeval)|61.9|59.0|70.9|(35.3, 29.6)|
-|[SPECTER 2.0-Adapters](https://huggingface.co/models?search=allenai/specter-2)|**62.3**|**59.2**|**71.2**|**(38.4, 33.0)**|
+|[SPECTER2 Base](allenai/specter2_base)|56.3|73.6|69.1|(38.0, 32.4)|
+|[SPECTER2-Adapters](https://huggingface.co/models?search=allenai/specter-2)|**62.3**|**59.2**|**71.2**|**(38.4, 33.0)**|
 
-Please cite the following works if you end up using SPECTER 2.0:
+Please cite the following works if you end up using SPECTER2:
 
-[SPECTER paper](https://api.semanticscholar.org/CorpusID:215768677):  
-
-```bibtex
-@inproceedings{specter2020cohan,
-  title={{SPECTER: Document-level Representation Learning using Citation-informed Transformers}},
-  author={Arman Cohan and Sergey Feldman and Iz Beltagy and Doug Downey and Daniel S. Weld},
-  booktitle={ACL},
-  year={2020}
-}
 ```
 [SciRepEval paper](https://api.semanticscholar.org/CorpusID:254018137)
 ```bibtex