Update README.md

README.md

@@ -23,7 +23,7 @@ base_model:
 
 GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoders (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios.
 
-This particular version utilize bi-encoder architecture, where textual encoder is [ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) and entity label encoder is sentence transformer - [BGE-base-en](https://huggingface.co/BAAI/bge-base-en-v1.5).
+This particular version utilizes bi-encoder architecture, where the textual encoder is [ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) and entity label encoder is sentence transformer - [BGE-base-en](https://huggingface.co/BAAI/bge-base-en-v1.5).
 
 Such architecture brings several advantages over uni-encoder GLiNER:
 * An unlimited amount of entities can be recognized at a single time;
@@ -81,7 +81,7 @@ European Championship => competitions
 
 If you want to use **flash attention** or increase sequence length, please, check the following code:
 
-Firstly, install flash attention and triton packages:
+Firstly, install Flash Attention and Triton packages:
 ```bash
 pip install flash-attn triton
 ```