Update README.md

README.md

@@ -13,7 +13,12 @@ datasets:
 
 An [adapter](https://adapterhub.ml) for the [`allenai/specter2_base`](https://huggingface.co/allenai/specter2_base) 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.
+This adapter was created for usage with the **[adapters](https://github.com/adapter-hub/adapters)** library.
+
+**Dec 2023 Update:**
+
+Model usage updated to be compatible with latest versions of transformers and adapters (newly released update to adapter-transformers) libraries.
+
 
 **Aug 2023 Update:**
 1. **The SPECTER2 Base and proximity adapter models have been renamed in Hugging Face based upon usage patterns as follows:**
@@ -40,17 +45,17 @@ Given the combination of title and abstract of a scientific paper or a short tex
 
 ## Usage
 
-First, install `adapter-transformers`:
+First, install `adapters`:
 
 ```
-pip install -U adapter-transformers
+pip install -U adapters
 ```
-_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
+_Note: adapters is built as an add-on transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml)_
 
 Now, the adapter can be loaded and activated like this:
 
 ```python
-from transformers import AutoAdapterModel
+from adapters import AutoAdapterModel
 
 model = AutoAdapterModel.from_pretrained("allenai/specter2_base")
 adapter_name = model.load_adapter("allenai/specter2_classification", source="hf", set_active=True)
@@ -105,13 +110,14 @@ It builds on the work done in [SciRepEval: A Multi-Format Benchmark for Scientif
 *Proximity model should suffice for downstream task types not mentioned above
 
 ```python
-from transformers import AutoTokenizer, AutoModel
+from transformers import AutoTokenizer
+from adapters import AutoAdapterModel
 
 # load model and tokenizer
 tokenizer = AutoTokenizer.from_pretrained('allenai/specter2_base')
 
 #load base model
-model = AutoModel.from_pretrained('allenai/specter2_base')
+model = AutoAdapterModel.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_classification", source="hf", load_as="classification", set_active=True)