Add benchmarking results

README.md

@@ -187,6 +187,15 @@ results = process(text, prompt)
 print(results)
 ```
 
+### Benchmarking
+Below is a table that highlights the performance of UTC models on the [CrossNER](https://huggingface.co/datasets/DFKI-SLT/cross_ner) dataset. The values represent the Micro F1 scores, with the estimation done at the word level.
+
+| Model                | AI     | Literature | Music  | Politics | Science |
+|----------------------|--------|------------|--------|----------|---------|
+| UTC-DeBERTa-small    | 0.8492 | 0.8792     | 0.864  | 0.9008   | 0.85    |
+| UTC-DeBERTa-base     | 0.8452 | 0.8587     | 0.8711 | 0.9147   | 0.8631  |
+| UTC-DeBERTa-large    | 0.8971 | 0.8978     | 0.9204 | 0.9247   | 0.8779  |
+
 ### Future reading
 Check our blogpost - ["As GPT4 but for token classification"](https://medium.com/p/9b5a081fbf27), where we highlighted possible use-cases of the model and why next-token prediction is not the only way to achive amazing zero-shot capabilites.
 While most of the AI industry is focused on generative AI and decoder-based models, we are committed to developing encoder-based models.