Adding usage example with pipelines.

README.md

@@ -10,6 +10,17 @@ We utilize automatically generated samples from Wikipedia for training, where pa
 We use the same articles as ([Koshorek et al., 2018](https://arxiv.org/abs/1803.09337)), 
 albeit from a 2021 dump of Wikpeida, and split at paragraph boundaries instead of the sentence level.
 
+## Usage
+Preferred usage is through `transformers.pipeline`:
+
+```python
+from transformers import pipeline
+pipe = pipeline("text-classification", model="dennlinger/bert-wiki-paragraphs")
+
+pipe("{First paragraph} [SEP] {Second paragraph}")
+```
+A predicted "1" means that paragraphs belong to the same topic, a "0" indicates a disconnect.
+
 ## Training Setup
 The model was trained for 3 epochs from `bert-base-uncased` on paragraph pairs (limited to 512 subwork with the `longest_first` truncation strategy).
 We use a batch size of 24 wit 2 iterations gradient accumulation (effective batch size of 48), and a learning rate of 1e-4, with gradient clipping at 5.