fix dataset

README.md

@@ -62,14 +62,12 @@ The data fields are the same among all splits.
 #### tweet_qa
 - `text`: a `string` feature.
 - `gold_label_str`: a `string` feature.
-- `paragraph`: a `string` feature.
-- `question`: a `string` feature.
+- `context`: a `string` feature.
 
 #### tweet_qg
 - `text`: a `string` feature.
 - `gold_label_str`: a `string` feature.
-- `paragraph`: a `string` feature.
-- `question`: a `string` feature.
+- `context`: a `string` feature.
 
 #### tweet_intimacy
 - `text`: a `string` feature.

process/tweet_ner.py

@@ -1,7 +1,66 @@
 import os
 import json
+from typing import List
+from pprint import pprint
 from datasets import load_dataset
 
+label2id = {
+    "B-corporation": 0,
+    "B-creative_work": 1,
+    "B-event": 2,
+    "B-group": 3,
+    "B-location": 4,
+    "B-person": 5,
+    "B-product": 6,
+    "I-corporation": 7,
+    "I-creative_work": 8,
+    "I-event": 9,
+    "I-group": 10,
+    "I-location": 11,
+    "I-person": 12,
+    "I-product": 13,
+    "O": 14
+}
+
+
+def decode_ner_tags(tag_sequence: List, input_sequence: List):
+    """ decode ner tag sequence """
+    def update_collection(_tmp_entity, _tmp_entity_type, _tmp_pos, _out):
+        if len(_tmp_entity) != 0 and _tmp_entity_type is not None:
+            _out.append({'type': _tmp_entity_type, 'entity': _tmp_entity, 'position': _tmp_pos})
+            _tmp_entity = []
+            _tmp_entity_type = None
+        return _tmp_entity, _tmp_entity_type, _tmp_pos, _out
+
+    assert len(tag_sequence) == len(input_sequence), str([len(tag_sequence), len(input_sequence)])
+    out = []
+    tmp_entity = []
+    tmp_pos = []
+    tmp_entity_type = None
+    for n, (_l, _i) in enumerate(zip(tag_sequence, input_sequence)):
+        if _l.startswith('B-'):
+            _, _, _, out = update_collection(tmp_entity, tmp_entity_type, tmp_pos, out)
+            tmp_entity_type = '-'.join(_l.split('-')[1:])
+            tmp_entity = [_i]
+            tmp_pos = [n]
+        elif _l.startswith('I-'):
+            tmp_tmp_entity_type = '-'.join(_l.split('-')[1:])
+            if len(tmp_entity) == 0:
+                # if 'I' not start with 'B', skip it
+                tmp_entity, tmp_entity_type, tmp_pos, out = update_collection(tmp_entity, tmp_entity_type, tmp_pos, out)
+            elif tmp_tmp_entity_type != tmp_entity_type:
+                # if the type does not match with the B, skip
+                tmp_entity, tmp_entity_type, tmp_pos, out = update_collection(tmp_entity, tmp_entity_type, tmp_pos, out)
+            else:
+                tmp_entity.append(_i)
+                tmp_pos.append(n)
+        elif _l == 'O':
+            tmp_entity, tmp_entity_type, tmp_pos, out = update_collection(tmp_entity, tmp_entity_type, tmp_pos, out)
+        else:
+            raise ValueError('unknown tag: {}'.format(_l))
+    _, _, _, out = update_collection(tmp_entity, tmp_entity_type, tmp_pos, out)
+    return out
+
 os.makedirs("data/tweet_ner7", exist_ok=True)
 data = load_dataset("tner/tweetner7")
 
@@ -10,6 +69,9 @@ def process(tmp):
     tmp = [i.to_dict() for _, i in tmp.iterrows()]
     for i in tmp:
         i.pop("id")
+        entities = decode_ner_tags(i['tags'].tolist(), i['tokens'].tolist())
+        pprint(entities)
+        input()
         i['gold_label_sequence'] = i.pop('tags').tolist()
         i['text_tokenized'] = i.pop('tokens').tolist()
         i['text'] = ' '.join(i['text_tokenized'])