Update README.md

README.md

@@ -48,7 +48,7 @@ task_ids:
 
 ### Dataset Summary
 
-EpiSet4NER is a bronze-standard dataset for epidemiological entity recognition of location, epidemiologic types (e.g. "prevalence", "annual incidence", "estimated occurrence"), and epidemiological rates (e.g. "1.7 per 1,000,000 live births", "2.1:1.000.000", "one in five million", "0.03%") created by the [Genetic and Rare Diseases Information Center (GARD)](https://rarediseases.info.nih.gov/), a program in [the National Center for Advancing Translational Sciences](https://ncats.nih.gov/), one of the 27 [National Institutes of Health](https://www.nih.gov/). It was labeled programmatically using spaCy NER and rule-based methods. This weakly-supervised teaching method allowed us to construct this imprecise dataset with minimal manual effort and achieve satisfactory performance on a multi-type token classification problem. The test set was manually corrected by 3 NCATS researchers and a GARD curator (genetic and rare disease expert). 
+EpiSet4NER is a bronze-standard dataset for epidemiological entity recognition of location, epidemiologic types (e.g. "prevalence", "annual incidence", "estimated occurrence"), and epidemiological rates (e.g. "1.7 per 1,000,000 live births", "2.1:1.000.000", "one in five million", "0.03%") created by the [Genetic and Rare Diseases Information Center (GARD)](https://rarediseases.info.nih.gov/), a program in [the National Center for Advancing Translational Sciences](https://ncats.nih.gov/), one of the 27 [National Institutes of Health](https://www.nih.gov/). It was labeled programmatically using spaCy NER and rule-based methods. This weakly-supervised teaching method allowed us to construct this imprecise dataset with minimal manual effort and achieve satisfactory performance on a multi-type token classification problem. The test set was manually corrected by 3 NCATS researchers and a GARD curator (genetic and rare disease expert). It was used to train [EpiExtract4GARD](https://huggingface.co/ncats/EpiExtract4GARD), a BioBERT-based model fine-tuned for NER.
 
 An example of 'train' looks as follows.
 ```
@@ -69,16 +69,19 @@ The data fields are the same among all splits.
 
 |name |train |validation|test|
 |---------|-----:|----:|----:|
-|EpiSet abstracts|456|114|50|
-|EpiSet tokens  |117888|31262|13910|
+|EpiSet \# of abstracts|456|114|50|
+|EpiSet \# tokens  |117888|31262|13910|
 
 
 ## Dataset Creation
-![EpiSet Creation Flowchart](https://raw.githubusercontent.com/ncats/epi4GARD/master/EpiExtract4GARD/datasets/EpiCustomV3/EpiSet%20Flowchart%20FINAL.png | width=200)
+![EpiSet Creation Flowchart](https://raw.githubusercontent.com/ncats/epi4GARD/master/EpiExtract4GARD/datasets/EpiCustomV3/EpiSet%20Flowchart%20FINAL.png)
+Other type
+<img src="https://raw.githubusercontent.com/ncats/epi4GARD/master/EpiExtract4GARD/datasets/EpiCustomV3/EpiSet%20Flowchart%20FINAL.png" height="200">
 *Figure 1:* Creation of EpiSet4NER by NIH/NCATS
 Comparing the programmatically labeled test set to the manually corrected test set allowed us to measure the precision, recall, and F1 of the programmatic labeling. 
 
-*Table 1:* Programmatic labeling of EpiSet4NER
+*Table 1:* Programmatic labeling of EpiSet4NER  
+  
 | Evaluation Level |          Entity          | Precision | Recall |   F1  |
 |:----------------:|:------------------------:|:---------:|:------:|:-----:|
 |   Entity-Level   |          Overall         |   0.559   |  0.662 | 0.606 |
@@ -92,7 +95,7 @@ Comparing the programmatically labeled test set to the manually corrected test s
 
 An example of the text labeling:
 ![Text Labeling](https://raw.githubusercontent.com/ncats/epi4GARD/master/EpiExtract4GARD/datasets/EpiCustomV3/Text%20Labeling4.png)
-*Figure 2:* Text Labeling using spaCy and rule-based labeling. Ideal labeling is bolded on the left. Actual programmatic output is on the right. [Abstract citation](https://pubmed.ncbi.nlm.nih.gov/33649778/)
+*Figure 2:* Text Labeling using spaCy and rule-based labeling. Ideal labeling is bolded on the left. Actual programmatic output is on the right. [\[Figure citation\]](https://pubmed.ncbi.nlm.nih.gov/33649778/)
 
 ### Curation Rationale
 
@@ -139,7 +142,7 @@ Assisting 25-30 millions Americans with rare diseases. Additionally can be usefu
 
 ### Dataset Curators
 
-NIH GARD
+[NIH GARD](https://rarediseases.info.nih.gov/about-gard/pages/23/about-gard)
 
 ### Licensing Information