Create icl_consistency_test.py

icl_consistency_test.py

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+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+
+This 🤗 dataset provides data for the GenBench CBT task 'The ICL consistency test' (see https://github.com/GenBench/genbench_cbt/tree/main/src/genbench/tasks/icl_consistency_test). 
+The ICL consistency test measures the consistency of LLM predictions on the same data points across many different equivalent prompting setups. 
+The score in the associated metric (Cohen's kappa) can be understood as a measure of a model's prediction consistency in the face of task-irrelevant information.
+
+For an easy evaluation of any 🤗 models, we refer to the code provided in the GenBench task. For in-depth information on the task, we refer to the associated 
+publications (Weber et al., 2023,2023) and the respective GenBench doc.md (https://github.com/GenBench/genbench_cbt/blob/main/src/genbench/tasks/icl_consistency_test/doc.md).
+
+Evaluation on the relevant metrics can be done via the example_evaluation.py script in the GenBench repository.
+
+- Weber, L., Bruni, E., & Hupkes, D. (2023, December). Mind the instructions: a holistic evaluation of consistency and interactions in prompt-based learning. 
+        In Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL) (pp. 294-313).
+- Weber, L., Bruni, E., & Hupkes, D. (2023). The ICL Consistency Test. arXiv preprint arXiv:2312.04945.
+
+"""
+
+import json
+import os
+
+import datasets
+
+_CITATION = """\
+@inproceedings{weber2023mind,
+  title={Mind the instructions: a holistic evaluation of consistency and interactions in prompt-based learning},
+  author={Weber, Lucas and Bruni, Elia and Hupkes, Dieuwke},
+  booktitle={Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)},
+  pages={294--313},
+  year={2023}
+},
+@article{weber2023icl,
+  title={The ICL Consistency Test},
+  author={Weber, Lucas and Bruni, Elia and Hupkes, Dieuwke},
+  journal={arXiv preprint arXiv:2312.04945},
+  year={2023}
+}
+"""
+
+_DESCRIPTION = """\
+In prompting, models are sensitive to task-irrelevant information in their prompt. This test can be used to quantify this sensitivity of any 🤗 model. 
+The ICL consistency test does this by measuring a model's prediction consistency across many different semantically equivalent prompting setups.
+"""
+
+_HOMEPAGE = "https://github.com/GenBench/genbench_cbt/blob/main/src/genbench/tasks/icl_consistency_test/doc.md"
+
+_LICENSE = ""
+
+_URL = "https://huggingface.co/datasets/LucasWeber/icl_consistency_test/blob/main/"
+_URLS = {
+    "anli": _URL + "genbench_all_anli.jsonl", 
+    "mnli": _URL + "genbench_all_glue%2Bmnli.jsonl",
+}
+
+
+class ICLConsistencyTest(datasets.GeneratorBasedBuilder):
+    """
+    In prompting, models are sensitive to task-irrelevant information in their prompt. This test can be used to quantify this sensitivity of any 🤗 model. 
+    The ICL consistency test does this by measuring a model's prediction consistency across many different semantically equivalent prompting setups.
+    """
+
+    VERSION = datasets.Version("1.1.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="anli", version=VERSION, description="This part of the ICL consistency test covers data points from ANLI"),
+        datasets.BuilderConfig(name="mnli", version=VERSION, description="This part of the ICL consistency test covers data points from MNLI"),
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+                {
+                    "input": datasets.Value("string"),
+                    "target": datasets.Value("string"),
+                    "target_numeric": datasets.Value("int32"),
+                    "data_ID": datasets.Value("int32"),
+                    "setup_ID": datasets.Value("string")
+                }
+            )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features, 
+            supervised_keys=("input", "target"),
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        urls = _URLS[self.config.name]
+        data_dir = dl_manager.download_and_extract(urls)
+        
+        return [
+            datasets.SplitGenerator(
+                     name=datasets.Split.TEST, 
+                     gen_kwargs={"filepath": data_dir[self.config.name], 
+                                 "split": "test"},
+                     ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        with open(filepath, encoding="utf-8") as f:
+            for key, row in enumerate(f):
+                data = json.loads(row)
+                yield key, {
+                        "input": data["input"],
+                        "target": data["target"],
+                        "target_numeric": data["target_numeric"],
+                        "data_ID": data["data_ID"],
+                        "setup_ID": data["setup_ID"],
+                    }
+