changed notation to IOB

README.md

@@ -3,7 +3,7 @@ annotations_creators:
 - other
 language_creators:
 - found
-languages:
+language:
 - bg, cs, da, de, el, en, es, et, fi, fr, ga, hu, it, lt, lv, mt, nl, pt, ro, sk, sv
 license:
 - cc-by-4.0
@@ -148,6 +148,62 @@ The tagset used for the global and the fine-grained named entities is the follow
     - Model
     - Type
 
+The final coarse grained tagset (in IOB notation) is the following: 
+
+`['O', 'B-ORGANISATION', 'I-ORGANISATION', 'B-ADDRESS', 'I-ADDRESS', 'B-DATE', 'I-DATE', 'B-PERSON', 'I-PERSON', 'B-AMOUNT', 'I-AMOUNT', 'B-TIME', 'I-TIME']`
+
+
+The final fine grained tagset (in IOB notation) is the following: 
+
+`[
+        'o',
+        'b-day',
+        'i-day',
+        'b-month',
+        'i-month',
+        'b-year',
+        'i-year',
+        'b-title',
+        'i-title',
+        'b-family name',
+        'i-family name',
+        'b-initial name',
+        'i-initial name',
+        'b-age',
+        'i-age',
+        'b-value',
+        'i-value',
+        'b-unit',
+        'i-unit',
+        'b-country',
+        'i-country',
+        'b-city',
+        'i-city',
+        'b-place',
+        'i-place',
+        'b-territory',
+        'i-territory',
+        'b-role',
+        'i-role',
+        'b-profession',
+        'i-profession',
+        'b-marital status',
+        'i-marital status',
+        'b-url',
+        'i-url',
+        'b-ethnic category',
+        'i-ethnic category',
+        'b-standard abbreviation',
+        'i-standard abbreviation'
+        'b-type',
+        'i-type',
+        'b-building',
+        'i-building',
+        'b-nationality',
+        'i-nationality',
+]`
+
+
 ### Data Splits
 
 Splits created by Joel Niklaus.

convert_to_hf_dataset.py

@@ -26,6 +26,9 @@ annotation_labels = {'ADDRESS': ['building', 'city', 'country', 'place', 'postco
 # make all coarse_grained upper case and all fine_grained lower case
 annotation_labels = {key.upper(): [label.lower() for label in labels] for key, labels in annotation_labels.items()}
 print(annotation_labels)
+print("coarse_grained:", list(annotation_labels.keys()))
+print("fine_grained:",
+      [finegrained for finegrained in [finegrained_list for finegrained_list in annotation_labels.values()]])
 
 base_path = Path("extracted")
 
@@ -92,15 +95,31 @@ def get_token_annotations(token, annotations):
         if token.start >= annotation.start and token.stop <= annotation.stop:  # course_grained annotation
             # we don't support multilabel annotations for each token for simplicity.
             # So when a token already has an annotation for either coarse or fine grained, we don't assign new ones.
-            if coarse_grained != "O" and is_coarse_grained(label):
+            if coarse_grained == "O" and is_coarse_grained(label):
                 coarse_grained = label
-            elif fine_grained != "o" and is_fine_grained(label):
+            elif fine_grained == "o" and is_fine_grained(label):
                 # some DATE are mislabeled as day but it is hard to correct this. So we ignore it
                 fine_grained = label
 
     return coarse_grained.upper(), fine_grained.lower()
 
 
+def generate_IOB_labelset(series, casing_function):
+    last_ent = ""
+    new_series = []
+    for ent in series:
+        if ent in ["o", "O"]:
+            ent_to_add = ent
+        else:
+            if ent != last_ent:  # we are the first one
+                ent_to_add = "B-" + ent
+            else:
+                ent_to_add = "I-" + ent
+        new_series.append(casing_function(ent_to_add))
+        last_ent = ent
+    return new_series
+
+
 def get_annotated_sentence(result_sentence, sentence):
     result_sentence["tokens"] = []
     result_sentence["coarse_grained"] = []
@@ -112,6 +131,8 @@ def get_annotated_sentence(result_sentence, sentence):
             result_sentence["tokens"].append(token)
             result_sentence["coarse_grained"].append(coarse_grained)
             result_sentence["fine_grained"].append(fine_grained)
+    result_sentence["coarse_grained"] = generate_IOB_labelset(result_sentence["coarse_grained"], str.upper)
+    result_sentence["fine_grained"] = generate_IOB_labelset(result_sentence["fine_grained"], str.lower)
     return result_sentence
 
 
@@ -147,6 +168,8 @@ for language in languages:
     df, not_parsable_files = parse_files(language)
     file_names = df.file_name.unique()
 
+    # df.coarse_grained.apply(lambda x: print(set(x)))
+
     # split by file_name
     num_fn = len(file_names)
     train_fn, validation_fn, test_fn = np.split(np.array(file_names), [int(.8 * num_fn), int(.9 * num_fn)])
@@ -178,6 +201,13 @@ train = pd.concat(train_dfs)
 validation = pd.concat(validation_dfs)
 test = pd.concat(test_dfs)
 
+df = pd.concat([train, validation, test])
+print(f"The final coarse grained tagset (in IOB notation) is the following: "
+      f"`{list(df.coarse_grained.explode().unique())}`")
+print(f"The final fine grained tagset (in IOB notation) is the following: "
+      f"`{list(df.fine_grained.explode().unique())}`")
+
+
 # save splits
 def save_splits_to_jsonl(config_name):
     # save to jsonl files for huggingface

test.jsonl

@@ -1,3 +1,3 @@
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-oid sha256:53198e9702180bd89cbd40074f81b4ba979d78ec357994b80c69aa893b6ec2a5
-size 7674217
+oid sha256:9c04197907b6539cf2020f34f91fb643c91dcc8590fc3b2301d765e130eb4e06
+size 7717849

train.jsonl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:325546e5f3eebba6a84181d50dff46b29fc316f98d002742ffc57f3f4a9bb02c
-size 21943866
+oid sha256:c2f305e569d60293e6a808e176c8ad82e97f2019967bb424bac3d3696d61adee
+size 22116076

validation.jsonl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7ab0afb00adecf07f5af59b3a289bcb7fd05f729fb5e54368b00b124f7545e96
-size 2852596
+oid sha256:b3dea9d63c656d0db6cf306296252e1adceed09991abfb650d040b928c555684
+size 2874796