Extended explanation in README.

Added information on training specifics, as well as note that htis is topically specific (legal texts). Further clarified on the prediction targets.

README.md

@@ -8,8 +8,14 @@ tokenizer = AutoTokenizer.from_pretrained('dennlinger/roberta-cls-consec')
 model = AutoModel.from_pretrained('dennlinger/roberta-cls-consec')
 ```
 
+# Input Format
+The model expects two segments that are separated with the `[SEP]` token. In our training setup, we had entire paragraphs as samples (or up to 512 tokens across two paragraphs), specifically trained on a Terms of Service data set. Note that this might lead to poor performance on "general" topics, such as news articles or Wikipedia.
+
 # 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.
+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 "coherence" of two segments.  
+If you are experimenting via the Huggingface Model API, the following are interpretations of the `LABEL`s:
+* `LABEL_0`: Two input segments separated by `[SEP]` do *not* belong to the same topic.
+* `LABEL_1`: Two input segments separated by `[SEP]` do belong to the same topic.
 
 # Performance
 The results of this model can be found in the paper. We average over models from five different random seeds, which is why the specific results for this model might be different from the exact values in the paper.