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.gitattributes

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README.md

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-This is a version of the [20 newsgroups dataset](https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html#the-20-newsgroups-text-dataset) that is provided in Scikit-learn. From the Scikit-learn docs:
-
-> The 20 newsgroups dataset comprises around 18000 newsgroups posts on 20 topics split in two subsets: one for training (or development) and the other one for testing (or for performance evaluation). The split between the train and test set is based upon a messages posted before and after a specific date.
-
-We followed the [recommended practice](https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html#filtering-text-for-more-realistic-training) to remove headers, signature blocks, and quotations from each news article.

test.jsonl → SetFit--20_newsgroups/json-test.parquet

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+size 5473122

train.jsonl → SetFit--20_newsgroups/json-train.parquet

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create_dataset.py

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-from datasets import Dataset
-from sklearn.datasets import fetch_20newsgroups
-
-
-def main():
-    for split in ["train", "test"]:
-        # Follow recommendation to strip newsgroup metadata
-        # https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html#filtering-text-for-more-realistic-training
-        data = fetch_20newsgroups(subset=split, remove=("headers", "footers", "quotes"))
-        id2label = {idx: label for idx, label in enumerate(data["target_names"])}
-        d = {"text": data["data"], "label": data["target"]}
-        dset = Dataset.from_dict(d)
-        dset = dset.map(lambda x: {"label_text": id2label[x["label"]]})
-        dset.to_json(f"{split}.jsonl")
-
-
-if __name__ == "__main__":
-    main()