init

data/tempo_wic/test.jsonl

@@ -1,3 +1,3 @@
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-oid sha256:c1ccb104f713ad9d7852640d15b6d0c286327f571970b3edce9c26509651afdd
-size 1416017
+oid sha256:975f67ba599362ef3bea5d749932abe572f2443e73b1d8e4e8a753cd5acd6162
+size 1284151

data/tempo_wic/train.jsonl

@@ -1,3 +1,3 @@
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-oid sha256:ff7b7b23ee1c8b3b7a87b80d460b145beaed7a7c8f1d2722cd56b2072aa141e7
-size 1574854
+oid sha256:3972efcca73ec01c81feceec04e8fa2efa9e00d33f827decb8e8a7033c48507d
+size 1439969

data/tempo_wic/validation.jsonl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:42052a317896a2722fabf71b797673cd91e5d98b5330623abdf414c3c60d6f1a
-size 383998
+oid sha256:b44124d18d379d1b8330a2c3872cd5ea0eec0b6fdac2d3eb8d268f652a274b6c
+size 347362

data/tweet_intimacy/test.jsonl

@@ -1,3 +1,3 @@
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-oid sha256:bb6d7c7be0302e3bc65a3d81458ca3c9fb86b0c9917c6660ace797a139893d9d
-size 40914
+oid sha256:38e723fe91f2f18ec346c82aff75ac7323fc4098b789d9b266d581945c1be37a
+size 38638

data/tweet_intimacy/train.jsonl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:86b44b6a7877310f0964dd47dbd6c83427d795177583b17bacd4aadb31fd3568
-size 127091
+oid sha256:1af59bd18d4443e1276a7462e8fd15ddf62f62236a16b54770203e7ac840879b
+size 119898

data/tweet_intimacy/validation.jsonl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:471770bd25edb1b968492e2ae15b89c820d2854f7785d13ac10c6575f86cecc8
-size 42661
+oid sha256:56f3b4d36f87ca43a7eb3033775194169ef3e48bf11ac6c4b943ae194f035357
+size 40087

process/tempo_wic.py

@@ -22,12 +22,17 @@ for s in ['train', 'validation', 'test']:
     data_jl = []
     for _, i in data.iterrows():
         i = i.to_dict()
-        tmp = {"id": i['id'], "word": i["word"], "label_binary": i["label"]}
+        tmp = {"word": i["word"], "gold_label_binary": i["label"]}
         tmp.update({f"{k}_1": v for k, v in i['tweet1'].items()})
-        tmp.update({f"{k}_2": v for k, v in i['tweet2'].items()})
         tmp['text_1_tokenized'] = tmp.pop('tokens_1')
+        tmp.update({f"{k}_2": v for k, v in i['tweet2'].items()})
         tmp['text_2_tokenized'] = tmp.pop('tokens_2')
+        tmp.pop("id")
+        tmp.pop("text_start_1")
+        tmp.pop("text_end_1")
+        tmp.pop("text_start_2")
+        tmp.pop("text_end_2")
         data_jl.append(tmp)
+
     with open(f"data/tempo_wic/{s}.jsonl", "w") as f:
         f.write("\n".join([json.dumps(i) for i in data_jl]))
-

process/tweet_intimacy.py

@@ -9,14 +9,14 @@ df_test = df_test[df_test['language'] == 'English']
 df_test.pop("language")
 test = [i.to_dict() for _, i in df_test.iterrows()]
 for i in test:
-    i['label_float'] = i.pop("label")
+    i['gold_score'] = i.pop("label")
 
 df_train = pd.read_csv("misc/multilingual_tweet_intimacy/train.csv")
 df_train = df_train[df_train['language'] == 'English']
 df_train.pop("language")
 train = [i.to_dict() for _, i in df_train.iterrows()]
 for i in train:
-    i['label_float'] = i.pop("label")
+    i['gold_score'] = i.pop("label")
 seed(42)
 shuffle(train)
 val = train[:len(test)]

process/tweet_ner.py

@@ -10,10 +10,13 @@ def process(tmp):
     tmp.pop("label_name")
     tmp = [i.to_dict() for _, i in tmp.iterrows()]
     for i in tmp:
-        i['label_sequence'] = i.pop('tags').tolist()
+        i.pop("id")
+        i['gold_label_sequence'] = i.pop('tags').tolist()
         i['text_tokenized'] = i.pop('token').tolist()
+        i['text'] = ' '.join(i['text_tokenized'])
     return tmp
 
+
 train = process(data["train_2020"].to_pandas())
 val = process(data["validation_2020"].to_pandas())
 test = process(data["test_2021"].to_pandas())

process/unify_sp_symbol_2.py

@@ -0,0 +1,44 @@
+import json
+import re
+
+from glob import glob
+
+for i in glob("data/tweet_intimacy/*.jsonl"):
+    with open(i) as f:
+        data = [json.loads(j) for j in f.readlines()]
+    for d in data:
+        for c in ['text']:
+            tmp = d[c].replace("{{URL}}", "@url")
+            # tmp = re.sub(r"\bhttp\b", "{{URL}}", tmp)
+            tmp = re.sub(r"@user", "{{USERNAME}}", tmp)
+            tmp = re.sub(r"(@[\S]+)", r"{\1@}", tmp)
+            d[c] = tmp
+
+    with open(i, "w") as f:
+        f.write("\n".join([json.dumps(j) for j in data]))
+
+# for i in glob("data/tweet_qa/*.jsonl"):
+#     with open(i) as f:
+#         data = [json.loads(j) for j in f.readlines()]
+#     for d in data:
+#         for c in ['text', "paragraph", "question", "label_str"]:
+#             tmp = d[c]
+#             tmp = re.sub(r"(@[\S]+)", r"{\1@}", tmp)
+#             tmp = tmp.replace(")@}", '@})')
+#             d[c] = tmp
+#
+#     with open(i, "w") as f:
+#         f.write("\n".join([json.dumps(j) for j in data]))
+
+# for i in glob("data/tweet_similarity/*.jsonl"):
+#     with open(i) as f:
+#         data = [json.loads(j) for j in f.readlines()]
+#     for d in data:
+#         for c in ['text_1', "text_2"]:
+#             tmp = d[c]
+#             # tmp = re.sub(r"(@[\S]+)\b", r"{\1@}", tmp)
+#             tmp = tmp.replace("{@user@}", "{{USERNAME}}")
+#             d[c] = tmp
+#
+#     with open(i, "w") as f:
+#         f.write("\n".join([json.dumps(j) for j in data]))