updated readme

README.md

@@ -30,11 +30,11 @@ Here on HuggingFace, we provide 3 pre-trained ULTRA checkpoints (all ~169k param
 
 ## ⚡️ Your Superpowers 
 
-ULTRA performs **link prediction** (KG completion): given a query `(head, relation, ?)`, it ranks all nodes in the graph as potential `tails`. 
+ULTRA performs **link prediction** (KG completion aka reasoning): given a query `(head, relation, ?)`, it ranks all nodes in the graph as potential `tails`. 
 
 
 1. Install the dependencies as listed in the Installation instructions on the [GitHub repo](https://github.com/DeepGraphLearning/ULTRA#installation).
-2. Clone this model repo to find the `UltraLinkPrediction` class in `modeling.py` and load the checkpoint (all the necessary model code is in this model repo as well).
+2. Clone this model repo to find the `UltraForKnowledgeGraphReasoning` class in `modeling.py` and load the checkpoint (all the necessary model code is in this model repo as well).
 
 * Run **zero-shot inference** on any graph:
 
@@ -50,6 +50,20 @@ test(model, mode="test", dataset=dataset, gpus=None)
 # hits@10:  0.666
 ```
 
+Or with `AutoModel`:
+
+```python
+from transformers import AutoModel
+from ultra.datasets import CoDExSmall
+from ultra.eval import test
+model = AutoModel.from_pretrained("mgalkin/ultra_4g", trust_remote_code=True)
+dataset = CoDExSmall(root="./datasets/")
+test(model, mode="test", dataset=dataset, gpus=None)
+# Expected results for ULTRA 4g
+# mrr:      0.464
+# hits@10:  0.666
+```
+
 * You can also **fine-tune** ULTRA on each graph, please refer to the [github repo](https://github.com/DeepGraphLearning/ULTRA#run-inference-and-fine-tuning) for more details on training / fine-tuning   
 * The model code contains 57 different KGs, please refer to the [github repo](https://github.com/DeepGraphLearning/ULTRA#datasets) for more details on what's available.