Update README.md

README.md

@@ -28,6 +28,8 @@ Such architecture brings several advantages over uni-encoder GLiNER:
 
 Utilization of ModernBERT uncovers up to 3 times better efficiency in comparison to DeBERTa-based models and context length up to 8,192 tokens while demonstrating comparable results.
 
+![inference time comparison](modernbert_inference_time.png "Inference time comparison")
+
 However, bi-encoder architecture has some drawbacks such as a lack of inter-label interactions that make it hard for the model to disambiguate semantically similar but contextually different entities.
 
 ### Installation & Usage
@@ -98,6 +100,9 @@ outputs = model.batch_predict_with_embeds(texts, entity_embeddings, labels)
 ```
 
 ### Benchmarks
+
+![results on different datasets](modernbert_benchmarking.png "Results on different datasets")
+
 Below you can see the table with benchmarking results on various named entity recognition datasets:
 
 | Dataset                | Score  |