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.gitattributes

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README.md

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-# Amazon Counterfactual Statements
-
-This dataset is the *en-ext* split from [SetFit/amazon_counterfactual](https://huggingface.co/datasets/SetFit/amazon_counterfactual). As the original test set is rather small (1333 examples), a different split was created with 50-50 for training & testing.
-
-The dataset is described in [amazon-multilingual-counterfactual-dataset](https://github.com/amazon-research/amazon-multilingual-counterfactual-dataset) / [Paper](https://arxiv.org/pdf/2104.06893.pdf)
-
-It contains statements from Amazon reviews about events that did not or cannot take place.

train.jsonl → SetFit--amazon_counterfactual_en/json-test.parquet

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-size 814811
+oid sha256:75f77d780f492d6d06aeedaf5412ecb1abc407da19e972ca41e3ba0f341d3261
+size 364053

test.jsonl → SetFit--amazon_counterfactual_en/json-train.parquet

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-size 815592
+oid sha256:e7cc0a5a98e0b839ca6d00ae076c39e4e56eec603c2841b67f4bf8f345293dd5
+size 364235

prepare_data.py

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-from datasets import load_dataset
-import json
-import random
-
-dataset = load_dataset("SetFit/amazon_counterfactual", "en-ext")
-
-examples = list(dataset['train'])+list(dataset['validation'])+list(dataset['test'])
-random.seed(42)
-random.shuffle(examples)
-
-
-num_test = 5000
-data_splits = {'test': examples[0:num_test], 'train': examples[num_test:]}
-
-
-for split in data_splits.keys():
-    with open(f'{split}.jsonl', 'w') as fOut:
-        for row in data_splits[split]:
-            fOut.write(json.dumps(row)+"\n")