init

.gitattributes

@@ -61,3 +61,6 @@ data/tweet_ner7/test.jsonl filter=lfs diff=lfs merge=lfs -text
 data/tweet_ner7/train.jsonl filter=lfs diff=lfs merge=lfs -text
 data/tweet_ner7/validation.jsonl filter=lfs diff=lfs merge=lfs -text
 data/tweet_qa/test.jsonl filter=lfs diff=lfs merge=lfs -text
+data/tweet_intimacy/validation.jsonl filter=lfs diff=lfs merge=lfs -text
+data/tweet_intimacy/test.jsonl filter=lfs diff=lfs merge=lfs -text
+data/tweet_intimacy/train.jsonl filter=lfs diff=lfs merge=lfs -text

data/tweet_intimacy/test.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:65d90ca4aa24f1ce66dee43de896a3cec2d66cc61ef99cfa5fca890843b999cd
+size 39034

data/tweet_intimacy/train.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d298076649d94845628a800b2fb9f801b009518d9cde6b45845d21b06de0f30d
+size 121089

data/tweet_intimacy/validation.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:40a4dc181f8cf3a8e259e510efc533762c7ac7baee7f8058e7b8d23e37f89e85
+size 40483

process/tweet_intimacy.py

@@ -0,0 +1,33 @@
+import os
+import json
+from random import shuffle, seed
+import pandas as pd
+
+os.makedirs("data/tweet_intimacy", exist_ok=True)
+df_test = pd.read_csv("misc/multilingual_tweet_intimacy/test.csv")
+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")
+
+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")
+seed(42)
+shuffle(train)
+val = train[:len(test)]
+train = train[len(test):]
+
+with open("data/tweet_intimacy/train.jsonl", "w") as f:
+    f.write("\n".join([json.dumps(i) for i in train]))
+with open("data/tweet_intimacy/validation.jsonl", "w") as f:
+    f.write("\n".join([json.dumps(i) for i in val]))
+with open("data/tweet_intimacy/test.jsonl", "w") as f:
+    f.write("\n".join([json.dumps(i) for i in test]))
+
+
+

super_tweet_eval.py

@@ -63,6 +63,20 @@ _TWEET_QA_CITATION = """\
   year={2019}
 }
 """
+_TWEET_INTIMACY_DESCRIPTION = """\
+See https://sites.google.com/umich.edu/semeval-2023-tweet-intimacy/home for more detail. We randomly take the same amount of 
+test set as the validation set.
+"""
+_TWEET_INTIMACY_CITATION = """\
+@misc{pei2023semeval,
+      title={SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis}, 
+      author={Jiaxin Pei and Vítor Silva and Maarten Bos and Yozon Liu and Leonardo Neves and David Jurgens and Francesco Barbieri},
+      year={2023},
+      eprint={2210.01108},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+"""
 
 
 class SuperTweetEvalConfig(datasets.BuilderConfig):
@@ -113,6 +127,13 @@ class SuperTweetEval(datasets.GeneratorBasedBuilder):
             citation=_TWEET_QA_CITATION,
             features=["text", "label_str", "paragraph", "question"],
             data_url="https://huggingface.co/datasets/cardiffnlp/super_tweet_eval/resolve/main/data/tweet_qa",
+        ),
+        SuperTweetEvalConfig(
+            name="tweet_intimacy",
+            description=_TWEET_INTIMACY_DESCRIPTION,
+            citation=_TWEET_INTIMACY_CITATION,
+            features=["text", "label_float"],
+            data_url="https://huggingface.co/datasets/cardiffnlp/super_tweet_eval/resolve/main/data/tweet_intimacy",
         )
     ]
 
@@ -130,6 +151,8 @@ class SuperTweetEval(datasets.GeneratorBasedBuilder):
                 'B-corporation', 'B-creative_work', 'B-event', 'B-group', 'B-location', 'B-person', 'B-product',
                 'I-corporation', 'I-creative_work', 'I-event', 'I-group', 'I-location', 'I-person', 'I-product', 'O']
             features["label_sequence"] = datasets.Sequence(datasets.features.ClassLabel(names=names))
+        if self.config.name == "tweet_intimacy":
+            features["label_float"] = datasets.Value("float32")
 
         return datasets.DatasetInfo(
             description=_SUPER_TWEET_EVAL_DESCRIPTION + "\n" + self.config.description,