Update README.md

README.md

@@ -43,14 +43,16 @@ task_ids:
   - [Contributions](#contributions)
 
 ## Dataset Description
-
-- **Homepage:** [Github](https://github.com/ZihanWangKi/CrossWeigh)
-- **Repository:** [Github](https://github.com/ZihanWangKi/CrossWeigh)
+- **Repository:** [Github](https://github.com/ncats/epi4GARD/tree/master/EpiExtract4GARD#epiextract4gard)
 - **Paper:** Pending
 
 ### Dataset Summary
 
-EpiSet4NER is a dataset
+EpiSet4NER is a bronze-standard dataset for epidemiological entity recognition of location, epidemiologic types, and  created using weakly-supervised teaching methods
+
+locations, epidemiological identifiers (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%")
+
+These are the V3 training (456 abstracts), validation (114 abstracts), and programmatically generated test (50 abstracts) set. The training set was copied to ```datasets/EpiCustomV3``` and renamed *train.tsv*. The validation set was copied to ```datasets/EpiCustomV3``` and ```datasets/Large_DatasetV3``` and renamed *val.tsv*. The V3 test set (uncorrected) is important as it is used by *Find efficacy of test predictions.ipynb* to find the efficacy of the programmatic labeling, but was otherwise not used with the model.
 [NIH NCATS GARD](https://rarediseases.info.nih.gov/)
 
 An example of 'train' looks as follows.
@@ -61,31 +63,24 @@ An example of 'train' looks as follows.
     "ner_tags":  [0, 0, 0, 3, 0, 0, 0, 0, 0, 1, 0, 5, 6, 0],
 }
 ```
-
 ### Data Fields
 
 The data fields are the same among all splits.
-
-#### conllpp
 - `id`: a `string` feature.
 - `tokens`: a `list` of `string` features.
-- `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-PER` (1), `I-PER` (2), `B-ORG` (3), `I-ORG` (4).
- "O", #(0)
-                                "B-LOC", #(1)
-                                "I-LOC", #(2)
-                                "B-EPI", #(3)
-                                "I-EPI", #(4)
-                                "B-STAT", #(5)
-                                "I-STAT", #(6)
+- `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-LOC` (1), `I-LOC` (2), `B-EPI` (3), `I-EPI` (4),`B-STAT` (5),`I-STAT` (6).
 
-### Data Splits
+### Data Splits by number of tokens
 
-|  name   |train|validation|test|
-|---------|----:|---------:|---:|
-|EpiSet   |14041|      3250|3453|
+|name |train |validation|test|
+|---------|-----:|----:|----:|
+|EpiSet   |117888|31262|13910|
 
 ## Dataset Creation
 
+This bronze-standard dataset was created from 620 rare disease abstracts
+Programmatic Labeling using statistical and rule-based methods (Weakly Supervised Teaching)
+![Text Labeling](https://raw.githubusercontent.com/ncats/epi4GARD/master/EpiExtract4GARD/datasets/EpiCustomV3/Text%20Labeling4.png)
 ### Curation Rationale
 
 [More Information Needed]
@@ -118,7 +113,8 @@ See here and then here
 
 ### Discussion of Biases
 
-[More Information Needed]
+Generates *whole_abstract_set.csv* and *positive_abstract_set.csv*. *whole_abstract_set.csv* is a dataset created by sampling 500 rare disease names and their synonyms from *GARD.csv* until ≥50 abstracts had been returned or the search results were exhausted. Although ~25,000 abstracts were expected, 7699 unique abstracts were returned due to the limited research on rare diseases. After running each of these through the LSTM RNN classifier, the *positive_abstract_set.csv* was created from the abstracts which had an epidemiological probability >50%. *positive_abstract_set.csv* will be passed to *create_labeled_dataset_V2.ipynb*
+
 
 ### Other Known Limitations