Update README.md

README.md

@@ -19,6 +19,8 @@ inference: false
 
  This model provides the best embedding for the Entity Recognition task in English.
 
+ This model is based on our [Paper](https://arxiv.org/abs/2402.15343).
+
 **Checkout other models by NuMind:**
 * SOTA Multilingual Entity Recognition Foundation Model: [link](https://huggingface.co/numind/entity-recognition-multilingual-general-sota-v1)
 * SOTA Sentiment Analysis Foundation Model: [English](https://huggingface.co/numind/generic-sentiment-v1), [Multilingual](https://huggingface.co/numind/generic-sentiment-multi-v1)
@@ -29,6 +31,8 @@ inference: false
 
 **Metrics:**
 
+Read more about evaluation protocol & datasets in our [paper](https://arxiv.org/abs/2402.15343).
+
 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.
@@ -46,8 +50,6 @@ NuNER v1.0 has similar performance to 7B LLMs (70 times bigger that NuNER v1.0)
 | UniversalNER (7B) | 57.89 ± 4.34 | 71.02 ± 1.53 |
 |   NuNER v1.0 (100M) | 58.75 ± 0.93 | 70.30 ± 0.35 |
 
-Read more about evaluation protocol & datasets in our [paper](https://arxiv.org/abs/2402.15343) and [blog post](https://www.numind.ai/blog/a-foundation-model-for-entity-recognition).
-
 ## Usage
 
 Embeddings can be used out of the box or fine-tuned on specific datasets.