add examples of parsing annotations

#2
by davanstrien HF staff - opened
Files changed (1) hide show
  1. README.md +43 -0
README.md CHANGED
@@ -168,6 +168,49 @@ Volunteers and Expert annotators
168
 
169
  ## Considerations for Using the Data
170
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
171
  ### Social Impact of Dataset
172
 
173
  This dataset can be used to see how words change in meaning over time
 
168
 
169
  ## Considerations for Using the Data
170
 
171
+ ## Accessing the annotations
172
+
173
+ Each example text has multiple annotations. These annotations may not always agree. There are various approaches one could take to calculate agreement, including a majority vote, rating some annotators more highly, or calculating a score based on the 'votes' of annotators. Since there are many ways of doing this, we have not implemented this as part of the dataset loading script.
174
+
175
+ An example of how one could generate an "OCR quality rating" based on the number of times an annotator labelled an example with `Illegible OCR`:
176
+
177
+ ```python
178
+ from collections import Counter
179
+
180
+
181
+ def calculate_ocr_score(example):
182
+ annotator_responses = [response['response'] for response in example['annotator_responses_english']]
183
+ counts = Counter(annotator_responses)
184
+ bad_ocr_ratings = counts.get("Illegible OCR")
185
+ if bad_ocr_ratings is None:
186
+ bad_ocr_ratings = 0
187
+ return round(1 - bad_ocr_ratings/len(annotator_responses),3)
188
+
189
+
190
+ dataset = dataset.map(lambda example: {"ocr_score":calculate_ocr_score(example)})
191
+ ```
192
+
193
+ To take the majority vote (or return a tie) based on whether a example is labelled contentious or not:
194
+
195
+ ```python
196
+
197
+ def most_common_vote(example):
198
+ annotator_responses = [response['response'] for response in example['annotator_responses_english']]
199
+ counts = Counter(annotator_responses)
200
+ contentious_count = counts.get("Contentious according to current standards")
201
+ if not contentious_count:
202
+ contentious_count = 0
203
+ not_contentious_count = counts.get("Not contentious")
204
+ if not not_contentious_count:
205
+ not_contentious_count = 0
206
+ if contentious_count > not_contentious_count:
207
+ return "contentious"
208
+ if contentious_count < not_contentious_count:
209
+ return "not_contentious"
210
+ if contentious_count == not_contentious_count:
211
+ return "tied"
212
+ ```
213
+
214
  ### Social Impact of Dataset
215
 
216
  This dataset can be used to see how words change in meaning over time