added dataset links and reproducibility steps

src/about.py

@@ -55,7 +55,9 @@ The evaluation metrics used in this leaderboard focus primarily on the F1-score,
 # Which evaluations are you running? how can people reproduce what you have?
 LLM_BENCHMARKS_TEXT = f"""
 
-Note: It is important to note that the purpose of this evaluation is purely academic and exploratory. The models assessed here have not been approved for clinical use, and their results should not be interpreted as clinically validated. The leaderboard serves as a platform for researchers to compare models, understand their strengths and limitations, and drive further advancements in the field of clinical NLP.
+#### Disclaimer & Advisory
+
+It is important to note that the purpose of this evaluation is purely academic and exploratory. The models assessed here have not been approved for clinical use, and their results should not be interpreted as clinically validated. The leaderboard serves as a platform for researchers to compare models, understand their strengths and limitations, and drive further advancements in the field of clinical NLP.
 
 ## About
 The Named Clinical Entity Recognition Leaderboard is aimed at advancing the field of natural language processing in healthcare. It provides a standardized platform for evaluating and comparing the performance of various language models in recognizing named clinical entities, a critical task for applications such as clinical documentation, decision support, and information extraction. By fostering transparency and facilitating benchmarking, the leaderboard's goal is to drive innovation and improvement in NLP models. It also helps researchers identify the strengths and weaknesses of different approaches, ultimately contributing to the development of more accurate and reliable tools for clinical use. Despite its exploratory nature, the leaderboard aims to play a role in guiding research and ensuring that advancements are grounded in rigorous and comprehensive evaluations. 
@@ -64,22 +66,22 @@ The Named Clinical Entity Recognition Leaderboard is aimed at advancing the fiel
 
 ### Datasets
 📈 We evaluate the models on 4 datasets, encompassing 6 entity types
-- NCBI 
-- CHIA
-- BIORED
-- BC5CD
+- [NCBI](https://huggingface.co/datasets/m42-health/m2_ncbi) 
+- [CHIA](https://huggingface.co/datasets/m42-health/m2_chia)
+- [BIORED](https://huggingface.co/datasets/m42-health/m2_biored)
+- [BC5CD](https://huggingface.co/datasets/m42-health/m2_bc5cdr)
 
 ### Evaluation Metrics
 We perceive NER objects as span(with character offsets) instead of token level artifacts. This enables us to expand to nested NER scenarios easily.
 
 
 ## Reproducibility
-To reproduce our results, here is the commands you can run:
+To reproduce our results, follow the steps detailed [here](https://github.com/WadoodAbdul/medics_ner/blob/master/docs/reproducing_results.md)
 
 """
 
 EVALUATION_QUEUE_TEXT = """
-Follow the steps detailed in the [medics_ner](https://github.com/WadoodAbdul/medics_ner/blob/3b415e9c4c9561ce5168374813072bde36658ff4/docs/submit_to_leaderboard.md) repo to upload you model to the leaderoard.
+Follow the steps detailed in the [medics_ner](https://github.com/WadoodAbdul/medics_ner/blob/master/docs/submit_to_leaderboard.md) repo to upload you model to the leaderoard.
 """
 
 CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"