Update README.md

README.md

@@ -19,6 +19,8 @@ inference: false
 
  This model provides the best embedding for the Entity Recognition task in English.
 
+ We suggest using **newer version of this model: [NuNER v2.0](https://huggingface.co/numind/NuNER-v2.0)**
+
  This is the model from our [**Paper**](https://arxiv.org/abs/2402.15343): **NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data**
 
 
@@ -36,7 +38,7 @@ Read more about evaluation protocol & datasets in our [paper](https://arxiv.org/
 
 Here is the aggregated performance of the models over several datasets.
 
-k=X means that as a training data for this evaluation, we took only X examples for each class, trained the model, and evaluated it on the full test set.
+k=X means that as training data for this evaluation, we took only X examples for each class, trained the model, and evaluated it on the full test set.
 
 | Model | k=1 | k=4 | k=16 | k=64 |
 |----------|----------|----------|----------|----------|
@@ -44,7 +46,7 @@ k=X means that as a training data for this evaluation, we took only X examples f
 |   RoBERTa-base + NER-BERT pre-training | 32.3 | 50.9 | 61.9 | 67.6 |
 |   NuNER v1.0  |   **39.4** | **59.6** | **67.8** | **71.5** |
 
-NuNER v1.0 has similar performance to 7B LLMs (70 times bigger that NuNER v1.0) created specifically for NER task.
+NuNER v1.0 has similar performance to 7B LLMs (70 times bigger than NuNER v1.0) created specifically for the NER task.
 
 | Model | k=8~16| k=64~128 |
 |----------|----------|----------|