Update README.md

README.md

@@ -8,7 +8,7 @@ datasets:
 
 # Adapter `allenai/specter2_adhoc_query` for allenai/specter2
 
-An [adapter](https://adapterhub.ml) for the `allenai/specter2` model that was trained on the [allenai/scirepeval](https://huggingface.co/datasets/allenai/scirepeval/) dataset.
+An [adapter](https://adapterhub.ml) for the [`allenai/specter2`](https://huggingface.co/allenai/specter2) model that was trained on the [allenai/scirepeval](https://huggingface.co/datasets/allenai/scirepeval/) dataset.
 
 This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
 
@@ -26,7 +26,7 @@ Now, the adapter can be loaded and activated like this:
 ```python
 from transformers import AutoAdapterModel
 
-model = AutoAdapterModel.from_pretrained("allenai/specter2")
+model = AutoAdapterModel.from_pretrained("allenai/specter2_base")
 adapter_name = model.load_adapter("allenai/specter2_adhoc_query", source="hf", set_active=True)
 ```
 
@@ -50,7 +50,7 @@ Task Formats trained on:
 - Proximity
 - Adhoc Search
 
-This is the adhoc search query specific adapter. For tasks where papers have to retrieved for a short textual query, use this adapter to encode the query and [allenai/specter2_proximity](https://huggingface.co/allenai/specter2_proximity) to encode the candidates.
+**This is the adhoc search query specific adapter. For tasks where papers have to retrieved for a short textual query, use this adapter to encode the query and [allenai/specter2_proximity](https://huggingface.co/allenai/specter2_proximity) to encode the candidates.**
 
   
 It builds on the work done in [SciRepEval: A Multi-Format Benchmark for Scientific Document Representations](https://api.semanticscholar.org/CorpusID:254018137) and we evaluate the trained model on this benchmark as well.
@@ -79,7 +79,7 @@ It builds on the work done in [SciRepEval: A Multi-Format Benchmark for Scientif
 
 |Model|Name and HF link|Description|
 |--|--|--|
-|Retrieval*|[allenai/specter2_proximity](https://huggingface.co/allenai/specter2_proximity)|Encode papers as queries and candidates eg. Link Prediction, Nearest Neighbor Search|
+|Retrieval*|[allenai/specter2_proximity](https://huggingface.co/allenai/specter2)|Encode papers as queries and candidates eg. Link Prediction, Nearest Neighbor Search|
 |Adhoc Query|[allenai/specter2_adhoc_query](https://huggingface.co/allenai/specter2_adhoc_query)|Encode short raw text queries for search tasks. (Candidate papers can be encoded with proximity)|
 |Classification|[allenai/specter2_classification](https://huggingface.co/allenai/specter2_classification)|Encode papers to feed into linear classifiers as features|
 |Regression|[allenai/specter2_regression](https://huggingface.co/allenai/specter2_regression)|Encode papers to feed into linear regressors as features|
@@ -90,10 +90,10 @@ It builds on the work done in [SciRepEval: A Multi-Format Benchmark for Scientif
 from transformers import AutoTokenizer, AutoModel
 
 # load model and tokenizer
-tokenizer = AutoTokenizer.from_pretrained('allenai/specter2')
+tokenizer = AutoTokenizer.from_pretrained('allenai/specter2_base')
 
 #load base model
-model = AutoModel.from_pretrained('allenai/specter2')
+model = AutoModel.from_pretrained('allenai/specter2_base')
 
 #load the adapter(s) as per the required task, provide an identifier for the adapter in load_as argument and activate it
 model.load_adapter("allenai/specter2_adhoc_query", source="hf", load_as="specter2_adhoc_query", set_active=True)