Fixed markdown and removed misplaced metrics form readme

README.md

@@ -11,18 +11,22 @@ tags:
 - sustainable-development-goals
 - impact-tech
 - text-classification
-- BERT for Startup SDG Classification
 ---
+
+# BERT for Startup SDG Classification
 This is a bert-base-uncased model fine-tuned to classify startup company descriptions into one of the 17 UN Sustainable Development Goals (SDGs), plus a "no-impact" category.   
 This model was trained by Kfir Bar as part of the research paper: "Using Language Models for Classifying Startups Into the UN’s 17 Sustainable Development Goals" (2022).   
 This repository is hosted by Alon Mannor to make the original model weights accessible to the public.   
   
-Model Details  
-Base Model: bert-base-uncased  
-Task: Text Classification  
-Labels: 18 (0: No Impact, 1-17: corresponding SDG)  
-Label Mapping (id2label) The model outputs a logit for each of the 18 classes.  
+## Model Details  
+* Base Model: bert-base-uncased  
+* Task: Text Classification  
+* Labels: 18 (0: No Impact, 1-17: corresponding SDG)
+
+### Label Mapping (id2label)  
+The model outputs a logit for each of the 18 classes.  
 The mapping from the index (ID) to the label name is as follows:  
+````json
 {
   "0": "0: No Impact",  
   "1": "SDG 1: No Poverty",  
@@ -42,9 +46,10 @@ The mapping from the index (ID) to the label name is as follows:
   "15": "SDG 15: Life on Land",  
   "16": "SDG 16: Peace and Justice Strong Institutions",  
   "17": "SDG 17: Partnerships to achieve the Goal"  
-}  
+}
+````
 
-How to Use:  
+### How to Use:  
 
 You can use this model directly with the text-classification pipeline.
 
@@ -69,17 +74,23 @@ print(result_2)
 # [{'label': '0: No Impact', 'score': 0.95...}]
 ````
 
-Training Data:  
+### Training Data:  
 The model was trained on a dataset of 4,247 startup descriptions (from the Gidron et al. 2023 extension) aggregated from two main sources, which were manually annotated by experts:  
-Rainmaking (Compass): A global database of impact-focused startups.  
-Start-up Nation Central (SNC): A database of Israeli startups, including both impact and non-impact companies.  
+1. Rainmaking (Compass): A global database of impact-focused startups.  
+2. Start-up Nation Central (SNC): A database of Israeli startups, including both impact and non-impact companies.  
   
-Performance  
+### Performance  
 The model was evaluated on a test set of 866 startups from the original paper.  
-Task:  F1-Weighted  F1-Macro  F1-Micro  18-Label (Full  )0.7900.4730.7906-Label (5Ps)0.8360.6020.836  
+
+| Task            | F1-Weighted |
+| :-------------: | :---------: |
+| 18-Label (Full) | 0.79        |
+| 6-Label (5Ps)   | 0.836       |
+
+
 The performance for the 6-label task (People, Planet, Prosperity, Peace, Partnerships, No-Impact) was aggregated from the 18-label predictions.  
 
-Citation:  
+### Citation:  
 If you use this model or its underlying research, please cite the original paper:  
 @inproceedings{bar2022usinglm,  
   title={Using Language Models for Classifying Startups Into the UN’s 17 Sustainable Development Goals},