Update parquet files

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.
-
-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.

test.jsonl → SetFit--toxic_conversations/json-test.parquet

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+size 10475980

train.jsonl → SetFit--toxic_conversations/json-train-00000-of-00002.parquet

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+size 331694934

original.csv → SetFit--toxic_conversations/json-train-00001-of-00002.parquet

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-size 816211476
+oid sha256:e3f6ebbb682e0e51bd28e11d31a66af5fe5169d5b2a5e32bd1e164eab83e223d
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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")