Include copy+paste code for use.

README.md

@@ -1,5 +1,12 @@
 # About this model: Topical Change Detection in Documents
-This model has been fine-tuned for the task described in the paper *Topical Change Detection in Documents via Embeddings of Long Sequences* and is our best-performing base-transformer model. You can find more detailed information in our GitHub page for the paper [here](https://github.com/dennlinger/TopicalChange), or read the [paper itself](https://arxiv.org/abs/2012.03619). The weights are based on RoBERTa-base.
+This network has been fine-tuned for the task described in the paper *Topical Change Detection in Documents via Embeddings of Long Sequences* and is our best-performing base-transformer model. You can find more detailed information in our GitHub page for the paper [here](https://github.com/dennlinger/TopicalChange), or read the [paper itself](https://arxiv.org/abs/2012.03619). The weights are based on RoBERTa-base.
+
+# Load the model
+```python
+from transformers import AutoModel, AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained('dennlinger/roberta-cls-consec')
+model = AutoModel.from_pretrained('dennlinger/roberta-cls-consec')
+```
 
 # Training objective
 The training task is to determine whether two text segments (paragraphs) belong to the same topical section or not. This can be utilized to create a topical segmentation of a document by consecutively predicting the "togetherness" of two models.