clone from SetFit/toxic_conversations_50k

.gitattributes

@@ -35,3 +35,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.mp3 filter=lfs diff=lfs merge=lfs -text
 *.ogg filter=lfs diff=lfs merge=lfs -text
 *.wav filter=lfs diff=lfs merge=lfs -text
+*.jsonl filter=lfs diff=lfs merge=lfs -text

README.md

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+# Toxic Conversation
+This is a version of the [Jigsaw Unintended Bias in Toxicity Classification dataset](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/overview). It contains comments from the  Civil Comments platform together with annotations if the comment is toxic or not.
+
+This dataset just contains the first 50k training examples.
+
+10 annotators annotated each example and, as recommended in the task page, set a comment as toxic when target >= 0.5
+
+The dataset is inbalanced, with only about 8% of the comments marked as toxic.

prepare.py

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+import pandas as pd
+from collections import Counter
+import json
+import random
+
+
+df = pd.read_csv("original.csv")
+
+print(df)
+"""
+for field in ["target", "severe_toxicity", "obscene", "identity_attack", "insult", "threat"]:
+    print("\n\n", field)
+    num_greater = 0
+    for val in df[field]:
+        if val >= 0.5:
+            num_greater += 1
+
+    print(num_greater, len(df[field]), f"{num_greater/len(df[field])*100:.2f}%")
+"""
+
+
+rows = [{'text': row['comment_text'].strip(),
+        'label': 1 if row['target'] >= 0.5 else 0,
+        'label_text': "toxic" if row['target'] >= 0.5 else "not toxic",
+         } for idx, row in df.iterrows()]
+
+random.seed(42)
+random.shuffle(rows)
+
+num_test = 50000
+splits = {'test': rows[0:num_test], 'train': rows[num_test:]}
+
+print("Train:", len(splits['train']))
+print("Test:", len(splits['test']))
+
+num_labels = Counter()
+
+for row in splits['test']:
+    num_labels[row['label']] += 1
+print(num_labels)
+
+for split in ['train', 'test']:
+    with open(f'{split}.jsonl', 'w') as fOut:
+        for row in splits[split]:
+            fOut.write(json.dumps(row)+"\n")