Merge branch 'master' into task_doc

lm_eval/datasets/gsm8k/dataset_infos.json

+{"gsm8k": {"description": "State-of-the-art language models can match human performance on many tasks, but \nthey still struggle to robustly perform multi-step mathematical reasoning. To \ndiagnose the failures of current models and support research, we introduce GSM8K,\na dataset of 8.5K high quality linguistically diverse grade school math word problems.\nWe find that even the largest transformer models fail to achieve high test performance, \ndespite the conceptual simplicity of this problem distribution.\n", "citation": "@misc{cobbe2021training,\n      title={Training Verifiers to Solve Math Word Problems},\n      author={Karl Cobbe and Vineet Kosaraju and Mohammad Bavarian and Jacob Hilton and Reiichiro Nakano and Christopher Hesse and John Schulman},\n      year={2021},\n      eprint={2110.14168},\n      archivePrefix={arXiv},\n      primaryClass={cs.LG}\n}\n", "homepage": "https://github.com/openai/grade-school-math", "license": "", "features": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "gsm8_k", "config_name": "gsm8k", "version": {"version_str": "0.0.1", "description": null, "major": 0, "minor": 0, "patch": 1}, "splits": {"train": {"name": "train", "num_bytes": 3963202, "num_examples": 7473, "dataset_name": "gsm8_k"}, "test": {"name": "test", "num_bytes": 713732, "num_examples": 1319, "dataset_name": "gsm8_k"}}, "download_checksums": {"https://raw.githubusercontent.com/openai/grade-school-math/master/grade_school_math/data/train.jsonl": {"num_bytes": 4166206, "checksum": "17f347dc51477c50d4efb83959dbb7c56297aba886e5544ee2aaed3024813465"}, "https://raw.githubusercontent.com/openai/grade-school-math/master/grade_school_math/data/test.jsonl": {"num_bytes": 749738, "checksum": "3730d312f6e3440559ace48831e51066acaca737f6eabec99bccb9e4b3c39d14"}}, "download_size": 4915944, "post_processing_size": null, "dataset_size": 4676934, "size_in_bytes": 9592878}}
\ No newline at end of file

lm_eval/datasets/gsm8k/gsm8k.py

+# 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.
+"""Grade School Math 8k dataset."""
+
+
+import json
+
+import datasets
+
+
+_CITATION = """\
+@misc{cobbe2021training,
+      title={Training Verifiers to Solve Math Word Problems},
+      author={Karl Cobbe and Vineet Kosaraju and Mohammad Bavarian and Jacob Hilton and Reiichiro Nakano and Christopher Hesse and John Schulman},
+      year={2021},
+      eprint={2110.14168},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG}
+}
+"""
+
+_DESCRIPTION = """\
+State-of-the-art language models can match human performance on many tasks, but 
+they still struggle to robustly perform multi-step mathematical reasoning. To 
+diagnose the failures of current models and support research, we introduce GSM8K,
+a dataset of 8.5K high quality linguistically diverse grade school math word problems.
+We find that even the largest transformer models fail to achieve high test performance, 
+despite the conceptual simplicity of this problem distribution.
+"""
+
+_HOMEPAGE = "https://github.com/openai/grade-school-math"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+_URLS = {
+    "train": "https://raw.githubusercontent.com/openai/grade-school-math/master/grade_school_math/data/train.jsonl",
+    "test": "https://raw.githubusercontent.com/openai/grade-school-math/master/grade_school_math/data/test.jsonl",
+}
+
+
+class GSM8K(datasets.GeneratorBasedBuilder):
+    """Grade School Math 8k"""
+
+    VERSION = datasets.Version("0.0.1")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="gsm8k", version=VERSION,
+                               description="The Grade School Math 8k dataset."),
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "question": datasets.Value("string"),
+                "answer": datasets.Value("string"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        urls = {"train": _URLS["train"], "test": _URLS["test"]}
+        data_dir = dl_manager.download_and_extract(urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir["train"],
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir["test"],
+                    "split": "test"
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    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, {
+                    "question": data["question"],
+                    "answer": data["answer"],
+                }

lm_eval/datasets/headqa/dataset_infos.json

+{"es": {"description": "HEAD-QA is a multi-choice HEAlthcare Dataset. The questions come from exams to access a specialized position in the\nSpanish healthcare system, and are challenging even for highly specialized humans. They are designed by the Ministerio\nde Sanidad, Consumo y Bienestar Social.\nThe dataset contains questions about the following topics: medicine, nursing, psychology, chemistry, pharmacology and biology.\n", "citation": "@inproceedings{vilares-gomez-rodriguez-2019-head,\n    title = \"{HEAD}-{QA}: A Healthcare Dataset for Complex Reasoning\",\n    author = \"Vilares, David  and\n      G{'o}mez-Rodr{'i}guez, Carlos\",\n    booktitle = \"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics\",\n    month = jul,\n    year = \"2019\",\n    address = \"Florence, Italy\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/P19-1092\",\n    doi = \"10.18653/v1/P19-1092\",\n    pages = \"960--966\",\n    abstract = \"We present HEAD-QA, a multi-choice question answering testbed to encourage research on complex reasoning. The questions come from exams to access a specialized position in the Spanish healthcare system, and are challenging even for highly specialized humans. We then consider monolingual (Spanish) and cross-lingual (to English) experiments with information retrieval and neural techniques. We show that: (i) HEAD-QA challenges current methods, and (ii) the results lag well behind human performance, demonstrating its usefulness as a benchmark for future work.\",\n}\n", "homepage": "https://aghie.github.io/head-qa/", "license": "MIT License", "features": {"name": {"dtype": "string", "id": null, "_type": "Value"}, "year": {"dtype": "string", "id": null, "_type": "Value"}, "category": {"dtype": "string", "id": null, "_type": "Value"}, "qid": {"dtype": "int32", "id": null, "_type": "Value"}, "qtext": {"dtype": "string", "id": null, "_type": "Value"}, "ra": {"dtype": "int32", "id": null, "_type": "Value"}, "answers": [{"aid": {"dtype": "int32", "id": null, "_type": "Value"}, "atext": {"dtype": "string", "id": null, "_type": "Value"}}]}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "head_qa", "config_name": "es", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1196021, "num_examples": 2657, "dataset_name": "head_qa"}, "test": {"name": "test", "num_bytes": 1169819, "num_examples": 2742, "dataset_name": "head_qa"}, "validation": {"name": "validation", "num_bytes": 556924, "num_examples": 1366, "dataset_name": "head_qa"}}, "download_checksums": {"https://drive.google.com/uc?export=download&confirm=t&id=1a_95N5zQQoUCq8IBNVZgziHbeM-QxG2t": {"num_bytes": 79365502, "checksum": "6ec29a3f55153d167f0bdf05395558919ba0b1df9c63e79ffceda2a09884ad8b"}}, "download_size": 79365502, "post_processing_size": null, "dataset_size": 2922764, "size_in_bytes": 82288266}, "en": {"description": "HEAD-QA is a multi-choice HEAlthcare Dataset. The questions come from exams to access a specialized position in the\nSpanish healthcare system, and are challenging even for highly specialized humans. They are designed by the Ministerio\nde Sanidad, Consumo y Bienestar Social.\nThe dataset contains questions about the following topics: medicine, nursing, psychology, chemistry, pharmacology and biology.\n", "citation": "@inproceedings{vilares-gomez-rodriguez-2019-head,\n    title = \"{HEAD}-{QA}: A Healthcare Dataset for Complex Reasoning\",\n    author = \"Vilares, David  and\n      G{'o}mez-Rodr{'i}guez, Carlos\",\n    booktitle = \"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics\",\n    month = jul,\n    year = \"2019\",\n    address = \"Florence, Italy\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/P19-1092\",\n    doi = \"10.18653/v1/P19-1092\",\n    pages = \"960--966\",\n    abstract = \"We present HEAD-QA, a multi-choice question answering testbed to encourage research on complex reasoning. The questions come from exams to access a specialized position in the Spanish healthcare system, and are challenging even for highly specialized humans. We then consider monolingual (Spanish) and cross-lingual (to English) experiments with information retrieval and neural techniques. We show that: (i) HEAD-QA challenges current methods, and (ii) the results lag well behind human performance, demonstrating its usefulness as a benchmark for future work.\",\n}\n", "homepage": "https://aghie.github.io/head-qa/", "license": "MIT License", "features": {"name": {"dtype": "string", "id": null, "_type": "Value"}, "year": {"dtype": "string", "id": null, "_type": "Value"}, "category": {"dtype": "string", "id": null, "_type": "Value"}, "qid": {"dtype": "int32", "id": null, "_type": "Value"}, "qtext": {"dtype": "string", "id": null, "_type": "Value"}, "ra": {"dtype": "int32", "id": null, "_type": "Value"}, "answers": [{"aid": {"dtype": "int32", "id": null, "_type": "Value"}, "atext": {"dtype": "string", "id": null, "_type": "Value"}}]}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "head_qa", "config_name": "en", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1123151, "num_examples": 2657, "dataset_name": "head_qa"}, "test": {"name": "test", "num_bytes": 1097349, "num_examples": 2742, "dataset_name": "head_qa"}, "validation": {"name": "validation", "num_bytes": 523462, "num_examples": 1366, "dataset_name": "head_qa"}}, "download_checksums": {"https://drive.google.com/uc?export=download&confirm=t&id=1a_95N5zQQoUCq8IBNVZgziHbeM-QxG2t": {"num_bytes": 79365502, "checksum": "6ec29a3f55153d167f0bdf05395558919ba0b1df9c63e79ffceda2a09884ad8b"}}, "download_size": 79365502, "post_processing_size": null, "dataset_size": 2743962, "size_in_bytes": 82109464}}
\ No newline at end of file

lm_eval/datasets/headqa/headqa.py

+# coding=utf-8
+# 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.
+#
+# NOTE: This is an exact copy of
+# https://github.com/huggingface/datasets/blob/3804442bb7cfcb9d52044d92688115cfdc69c2da/datasets/head_qa/head_qa.py 
+# with the exception of the `image` feature. This is to avoid adding `Pillow`
+# as a dependency.
+"""HEAD-QA: A Healthcare Dataset for Complex Reasoning."""
+
+
+import json
+import os
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{vilares-gomez-rodriguez-2019-head,
+    title = "{HEAD}-{QA}: A Healthcare Dataset for Complex Reasoning",
+    author = "Vilares, David  and
+      G{\'o}mez-Rodr{\'i}guez, Carlos",
+    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
+    month = jul,
+    year = "2019",
+    address = "Florence, Italy",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/P19-1092",
+    doi = "10.18653/v1/P19-1092",
+    pages = "960--966",
+    abstract = "We present HEAD-QA, a multi-choice question answering testbed to encourage research on complex reasoning. The questions come from exams to access a specialized position in the Spanish healthcare system, and are challenging even for highly specialized humans. We then consider monolingual (Spanish) and cross-lingual (to English) experiments with information retrieval and neural techniques. We show that: (i) HEAD-QA challenges current methods, and (ii) the results lag well behind human performance, demonstrating its usefulness as a benchmark for future work.",
+}
+"""
+
+_DESCRIPTION = """\
+HEAD-QA is a multi-choice HEAlthcare Dataset. The questions come from exams to access a specialized position in the
+Spanish healthcare system, and are challenging even for highly specialized humans. They are designed by the Ministerio
+de Sanidad, Consumo y Bienestar Social.
+The dataset contains questions about the following topics: medicine, nursing, psychology, chemistry, pharmacology and biology.
+"""
+
+_HOMEPAGE = "https://aghie.github.io/head-qa/"
+
+_LICENSE = "MIT License"
+
+_URL = "https://drive.google.com/uc?export=download&confirm=t&id=1a_95N5zQQoUCq8IBNVZgziHbeM-QxG2t"
+
+_DIRS = {"es": "HEAD", "en": "HEAD_EN"}
+
+
+class HeadQA(datasets.GeneratorBasedBuilder):
+    """HEAD-QA: A Healthcare Dataset for Complex Reasoning"""
+
+    VERSION = datasets.Version("1.1.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="es", version=VERSION, description="Spanish HEAD dataset"),
+        datasets.BuilderConfig(name="en", version=VERSION, description="English HEAD dataset"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "es"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "name": datasets.Value("string"),
+                    "year": datasets.Value("string"),
+                    "category": datasets.Value("string"),
+                    "qid": datasets.Value("int32"),
+                    "qtext": datasets.Value("string"),
+                    "ra": datasets.Value("int32"),
+                    "answers": [
+                        {
+                            "aid": datasets.Value("int32"),
+                            "atext": datasets.Value("string"),
+                        }
+                    ],
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        data_dir = dl_manager.download_and_extract(_URL)
+
+        dir = _DIRS[self.config.name]
+        data_lang_dir = os.path.join(data_dir, dir)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"data_dir": data_dir, "filepath": os.path.join(data_lang_dir, f"train_{dir}.json")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"data_dir": data_dir, "filepath": os.path.join(data_lang_dir, f"test_{dir}.json")},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"data_dir": data_dir, "filepath": os.path.join(data_lang_dir, f"dev_{dir}.json")},
+            ),
+        ]
+
+    def _generate_examples(self, data_dir, filepath):
+        """Yields examples."""
+        with open(filepath, encoding="utf-8") as f:
+            head_qa = json.load(f)
+            for exam_id, exam in enumerate(head_qa["exams"]):
+                content = head_qa["exams"][exam]
+                name = content["name"].strip()
+                year = content["year"].strip()
+                category = content["category"].strip()
+                for question in content["data"]:
+                    qid = int(question["qid"].strip())
+                    qtext = question["qtext"].strip()
+                    ra = int(question["ra"].strip())
+
+                    aids = [answer["aid"] for answer in question["answers"]]
+                    atexts = [answer["atext"].strip() for answer in question["answers"]]
+                    answers = [{"aid": aid, "atext": atext} for aid, atext in zip(aids, atexts)]
+
+                    id_ = f"{exam_id}_{qid}"
+                    yield id_, {
+                        "name": name,
+                        "year": year,
+                        "category": category,
+                        "qid": qid,
+                        "qtext": qtext,
+                        "ra": ra,
+                        "answers": answers,
+                    }

lm_eval/datasets/hendrycks_ethics/dataset_infos.json

+{"commonsense": {"description": "The ETHICS dataset is a benchmark that spans concepts in justice, well-being,\nduties, virtues, and commonsense morality. Models predict widespread moral\njudgments about diverse text scenarios. This requires connecting physical and\nsocial world knowledge to value judgements, a capability that may enable us\nto steer chatbot outputs or eventually regularize open-ended reinforcement\nlearning agents.\n\nThe Commonsense subset contains examples focusing on moral standards and principles that most people intuitively accept.", "citation": "@article{hendrycks2021ethics\n    title={Aligning AI With Shared Human Values},\n    author={Dan Hendrycks and Collin Burns and Steven Basart and Andrew Critch and Jerry Li and Dawn Song and Jacob Steinhardt},\n    journal={Proceedings of the International Conference on Learning Representations (ICLR)},\n    year={2021}\n}\n", "homepage": "https://github.com/hendrycks/ethics", "license": "", "features": {"label": {"dtype": "int32", "id": null, "_type": "Value"}, "input": {"dtype": "string", "id": null, "_type": "Value"}, "is_short": {"dtype": "bool", "id": null, "_type": "Value"}, "edited": {"dtype": "bool", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "hendrycks_ethics", "config_name": "commonsense", "version": {"version_str": "0.0.1", "description": null, "major": 0, "minor": 0, "patch": 1}, "splits": {"train": {"name": "train", "num_bytes": 14435215, "num_examples": 13910, "dataset_name": "hendrycks_ethics"}, "test": {"name": "test", "num_bytes": 3150094, "num_examples": 3885, "dataset_name": "hendrycks_ethics"}}, "download_checksums": {"https://people.eecs.berkeley.edu/~hendrycks/ethics.tar": {"num_bytes": 35585024, "checksum": "40acbf1ac0da79a2aabef394d58889136b8d38b05be09482006de2453fb06333"}}, "download_size": 35585024, "post_processing_size": null, "dataset_size": 17585309, "size_in_bytes": 53170333}, "deontology": {"description": "The ETHICS dataset is a benchmark that spans concepts in justice, well-being,\nduties, virtues, and commonsense morality. Models predict widespread moral\njudgments about diverse text scenarios. This requires connecting physical and\nsocial world knowledge to value judgements, a capability that may enable us\nto steer chatbot outputs or eventually regularize open-ended reinforcement\nlearning agents.\n\nThe Deontology subset contains examples focusing on whether an act is required, permitted, or forbidden according to a set of rules or constraints", "citation": "@article{hendrycks2021ethics\n    title={Aligning AI With Shared Human Values},\n    author={Dan Hendrycks and Collin Burns and Steven Basart and Andrew Critch and Jerry Li and Dawn Song and Jacob Steinhardt},\n    journal={Proceedings of the International Conference on Learning Representations (ICLR)},\n    year={2021}\n}\n", "homepage": "https://github.com/hendrycks/ethics", "license": "", "features": {"group_id": {"dtype": "int32", "id": null, "_type": "Value"}, "label": {"dtype": "int32", "id": null, "_type": "Value"}, "scenario": {"dtype": "string", "id": null, "_type": "Value"}, "excuse": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "hendrycks_ethics", "config_name": "deontology", "version": {"version_str": "0.0.1", "description": null, "major": 0, "minor": 0, "patch": 1}, "splits": {"train": {"name": "train", "num_bytes": 1931475, "num_examples": 18164, "dataset_name": "hendrycks_ethics"}, "test": {"name": "test", "num_bytes": 384602, "num_examples": 3596, "dataset_name": "hendrycks_ethics"}}, "download_checksums": {"https://people.eecs.berkeley.edu/~hendrycks/ethics.tar": {"num_bytes": 35585024, "checksum": "40acbf1ac0da79a2aabef394d58889136b8d38b05be09482006de2453fb06333"}}, "download_size": 35585024, "post_processing_size": null, "dataset_size": 2316077, "size_in_bytes": 37901101}, "justice": {"description": "The ETHICS dataset is a benchmark that spans concepts in justice, well-being,\nduties, virtues, and commonsense morality. Models predict widespread moral\njudgments about diverse text scenarios. This requires connecting physical and\nsocial world knowledge to value judgements, a capability that may enable us\nto steer chatbot outputs or eventually regularize open-ended reinforcement\nlearning agents.\n\nThe Justice subset contains examples focusing on how a character treats another person", "citation": "@article{hendrycks2021ethics\n    title={Aligning AI With Shared Human Values},\n    author={Dan Hendrycks and Collin Burns and Steven Basart and Andrew Critch and Jerry Li and Dawn Song and Jacob Steinhardt},\n    journal={Proceedings of the International Conference on Learning Representations (ICLR)},\n    year={2021}\n}\n", "homepage": "https://github.com/hendrycks/ethics", "license": "", "features": {"group_id": {"dtype": "int32", "id": null, "_type": "Value"}, "label": {"dtype": "int32", "id": null, "_type": "Value"}, "scenario": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "hendrycks_ethics", "config_name": "justice", "version": {"version_str": "0.0.1", "description": null, "major": 0, "minor": 0, "patch": 1}, "splits": {"train": {"name": "train", "num_bytes": 2516501, "num_examples": 21791, "dataset_name": "hendrycks_ethics"}, "test": {"name": "test", "num_bytes": 309427, "num_examples": 2704, "dataset_name": "hendrycks_ethics"}}, "download_checksums": {"https://people.eecs.berkeley.edu/~hendrycks/ethics.tar": {"num_bytes": 35585024, "checksum": "40acbf1ac0da79a2aabef394d58889136b8d38b05be09482006de2453fb06333"}}, "download_size": 35585024, "post_processing_size": null, "dataset_size": 2825928, "size_in_bytes": 38410952}, "utilitarianism": {"description": "The ETHICS dataset is a benchmark that spans concepts in justice, well-being,\nduties, virtues, and commonsense morality. Models predict widespread moral\njudgments about diverse text scenarios. This requires connecting physical and\nsocial world knowledge to value judgements, a capability that may enable us\nto steer chatbot outputs or eventually regularize open-ended reinforcement\nlearning agents.\n\nThe Utilitarianism subset contains scenarios that should be ranked from most pleasant to least pleasant for the person in the scenario", "citation": "@article{hendrycks2021ethics\n    title={Aligning AI With Shared Human Values},\n    author={Dan Hendrycks and Collin Burns and Steven Basart and Andrew Critch and Jerry Li and Dawn Song and Jacob Steinhardt},\n    journal={Proceedings of the International Conference on Learning Representations (ICLR)},\n    year={2021}\n}\n", "homepage": "https://github.com/hendrycks/ethics", "license": "", "features": {"activity": {"dtype": "string", "id": null, "_type": "Value"}, "baseline": {"dtype": "string", "id": null, "_type": "Value"}, "rating": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "hendrycks_ethics", "config_name": "utilitarianism", "version": {"version_str": "0.0.1", "description": null, "major": 0, "minor": 0, "patch": 1}, "splits": {"train": {"name": "train", "num_bytes": 2241770, "num_examples": 13738, "dataset_name": "hendrycks_ethics"}, "test": {"name": "test", "num_bytes": 749768, "num_examples": 4808, "dataset_name": "hendrycks_ethics"}}, "download_checksums": {"https://people.eecs.berkeley.edu/~hendrycks/ethics.tar": {"num_bytes": 35585024, "checksum": "40acbf1ac0da79a2aabef394d58889136b8d38b05be09482006de2453fb06333"}}, "download_size": 35585024, "post_processing_size": null, "dataset_size": 2991538, "size_in_bytes": 38576562}, "virtue": {"description": "The ETHICS dataset is a benchmark that spans concepts in justice, well-being,\nduties, virtues, and commonsense morality. Models predict widespread moral\njudgments about diverse text scenarios. 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\ No newline at end of file

lm_eval/datasets/hendrycks_ethics/hendrycks_ethics.py

+# 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.
+"""ETHICS dataset."""
+# TODO: Add the `hard` dataset splits.
+
+
+import csv
+import os
+
+import datasets
+
+
+_CITATION = """\
+@article{hendrycks2021ethics
+    title={Aligning AI With Shared Human Values},
+    author={Dan Hendrycks and Collin Burns and Steven Basart and Andrew Critch and Jerry Li and Dawn Song and Jacob Steinhardt},
+    journal={Proceedings of the International Conference on Learning Representations (ICLR)},
+    year={2021}
+}
+"""
+
+_DESCRIPTION = """\
+The ETHICS dataset is a benchmark that spans concepts in justice, well-being,
+duties, virtues, and commonsense morality. Models predict widespread moral
+judgments about diverse text scenarios. This requires connecting physical and
+social world knowledge to value judgements, a capability that may enable us
+to steer chatbot outputs or eventually regularize open-ended reinforcement
+learning agents.
+"""
+
+_HOMEPAGE = "https://github.com/hendrycks/ethics"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+_URLS = "https://people.eecs.berkeley.edu/~hendrycks/ethics.tar"
+
+
+class EthicsConfig(datasets.BuilderConfig):
+    """BuilderConfig for Hendrycks ETHICS."""
+
+    def __init__(self, prefix, features, **kwargs):
+        """BuilderConfig for Hendrycks ETHICS.
+
+        Args:
+        prefix: *string*, prefix to add to the dataset name for path location.
+        features: *list[string]*, list of the features that will appear in the
+            feature dict.
+        """
+        # Version history:
+        super().__init__(version=datasets.Version("0.0.1"), **kwargs)
+        self.prefix = prefix
+        self.features = features
+
+
+class HendrycksEthics(datasets.GeneratorBasedBuilder):
+    """The ETHICS dataset is a benchmark that spans concepts in justice, well-being, duties, virtues, and commonsense morality."""
+
+    BUILDER_CONFIGS = [
+        EthicsConfig(
+            name="commonsense",
+            prefix="cm",
+            features=datasets.Features({
+                "label": datasets.Value("int32"),
+                "input": datasets.Value("string"),
+                "is_short": datasets.Value("bool"),
+                "edited": datasets.Value("bool"),
+            }),
+            description="The Commonsense subset contains examples focusing on moral standards and principles that most people intuitively accept."
+        ),
+        EthicsConfig(
+            name="deontology",
+            prefix="deontology",
+            features=datasets.Features({
+                "group_id": datasets.Value("int32"),
+                "label": datasets.Value("int32"),
+                "scenario": datasets.Value("string"),
+                "excuse": datasets.Value("string"),
+            }),
+            description="The Deontology subset contains examples focusing on whether an act is required, permitted, or forbidden according to a set of rules or constraints",
+        ),
+        EthicsConfig(
+            name="justice",
+            prefix="justice",
+            features=datasets.Features({
+                "group_id": datasets.Value("int32"),
+                "label": datasets.Value("int32"),
+                "scenario": datasets.Value("string"),
+            }),
+            description="The Justice subset contains examples focusing on how a character treats another person",
+        ),
+        EthicsConfig(
+            name="utilitarianism",
+            prefix="util",
+            features=datasets.Features({
+                "activity": datasets.Value("string"),
+                "baseline": datasets.Value("string"),
+                "rating": datasets.Value("string"),  # Empty rating.
+            }),
+            description="The Utilitarianism subset contains scenarios that should be ranked from most pleasant to least pleasant for the person in the scenario",
+        ),
+        EthicsConfig(
+            name="virtue",
+            prefix="virtue",
+            features=datasets.Features({
+                "group_id": datasets.Value("int32"),
+                "label": datasets.Value("int32"),
+                "scenario": datasets.Value("string"),
+                "trait": datasets.Value("string"),
+            }),
+            description="The Virtue subset contains scenarios focusing on whether virtues or vices are being exemplified",
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=f"{_DESCRIPTION}\n{self.config.description}",
+            features=self.config.features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        urls = _URLS
+        data_dir = dl_manager.download_and_extract(urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "ethics", self.config.name, f"{self.config.prefix}_train.csv"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "ethics", self.config.name, f"{self.config.prefix}_test.csv"),
+                    "split": "test"
+                },
+            )
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+        with open(filepath, newline='') as f:
+            if self.config.name == "utilitarianism":
+                contents = csv.DictReader(
+                    f, fieldnames=['activity', "baseline"])
+            else:
+                contents = csv.DictReader(f)
+            # For subsets with grouped scenarios, tag them with an id.
+            group_id = 0
+            for key, row in enumerate(contents):
+                if self.config.name == "deontology":
+                    # Scenarios come in groups of 4.
+                    if key % 4 == 0 and key != 0:
+                        group_id += 1
+                    yield key, {
+                        "group_id": group_id,
+                        "label": row["label"],
+                        "scenario": row["scenario"],
+                        "excuse": row["excuse"],
+                    }
+                elif self.config.name == "justice":
+                    # Scenarios come in groups of 4.
+                    if key % 4 == 0 and key != 0:
+                        group_id += 1
+                    yield key, {
+                        "group_id": group_id,
+                        "label": row["label"],
+                        "scenario": row["scenario"],
+                    }
+                elif self.config.name == "commonsense":
+                    yield key, {
+                        "label": row["label"],
+                        "input": row["input"],
+                        "is_short": row["is_short"],
+                        "edited": row["edited"],
+                    }
+                elif self.config.name == "virtue":
+                    # Scenarios come in groups of 5.
+                    if key % 5 == 0 and key != 0:
+                        group_id += 1
+                    scenario, trait = row["scenario"].split(" [SEP] ")
+                    yield key, {
+                        "group_id": group_id,
+                        "label": row["label"],
+                        "scenario": scenario,
+                        "trait": trait,
+                    }
+                elif self.config.name == "utilitarianism":
+                    yield key, {
+                        "activity": row["activity"],
+                        "baseline": row["baseline"],
+                        "rating": "",
+                    }

lm_eval/datasets/hendrycks_math/dataset_infos.json

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Each\nproblem in Math has a full step-by-step solution which can be used to teach\nmodels to generate answer derivations and explanations.\n", "citation": "@article{hendrycksmath2021,\n  title={Measuring Mathematical Problem Solving With the Math Dataset},\n  author={Dan Hendrycks and Collin Burns and Saurav Kadavath and Akul Arora and Steven Basart and Eric Tang and Dawn Song and Jacob Steinhardt},\n  journal={NeurIPS},\n  year={2021}\n}\n", "homepage": "https://github.com/hendrycks/math", "license": "", "features": {"problem": {"dtype": "string", "id": null, "_type": "Value"}, "level": {"dtype": "string", "id": null, "_type": "Value"}, "type": {"dtype": "string", "id": null, "_type": "Value"}, "solution": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "hendrycks_math", "config_name": "algebra", "version": {"version_str": "0.0.1", "description": null, "major": 0, "minor": 0, "patch": 1}, "splits": {"train": {"name": "train", "num_bytes": 955021, "num_examples": 1744, "dataset_name": "hendrycks_math"}, "test": {"name": "test", "num_bytes": 648291, "num_examples": 1187, "dataset_name": "hendrycks_math"}}, "download_checksums": {"https://people.eecs.berkeley.edu/~hendrycks/MATH.tar": {"num_bytes": 20327936, "checksum": "0fbe4fad0df66942db6c221cdcc95b298cc7f4595a2f0f518360cce84e90d9ac"}}, "download_size": 20327936, "post_processing_size": null, "dataset_size": 1603312, "size_in_bytes": 21931248}, "counting_and_probability": {"description": "MATH is a dataset of 12,500 challenging competition mathematics problems. Each\nproblem in Math has a full step-by-step solution which can be used to teach\nmodels to generate answer derivations and explanations.\n", "citation": "@article{hendrycksmath2021,\n  title={Measuring Mathematical Problem Solving With the Math Dataset},\n  author={Dan Hendrycks and Collin Burns and Saurav Kadavath and Akul Arora and Steven Basart and Eric Tang and Dawn Song and Jacob Steinhardt},\n  journal={NeurIPS},\n  year={2021}\n}\n", "homepage": "https://github.com/hendrycks/math", "license": "", "features": {"problem": {"dtype": "string", "id": null, "_type": "Value"}, "level": {"dtype": "string", "id": null, "_type": "Value"}, "type": {"dtype": "string", "id": null, "_type": "Value"}, "solution": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "hendrycks_math", "config_name": "counting_and_probability", "version": {"version_str": "0.0.1", "description": null, "major": 0, "minor": 0, "patch": 1}, "splits": {"train": {"name": "train", "num_bytes": 667385, "num_examples": 771, "dataset_name": "hendrycks_math"}, "test": {"name": "test", "num_bytes": 353803, "num_examples": 474, "dataset_name": "hendrycks_math"}}, "download_checksums": {"https://people.eecs.berkeley.edu/~hendrycks/MATH.tar": {"num_bytes": 20327936, "checksum": "0fbe4fad0df66942db6c221cdcc95b298cc7f4595a2f0f518360cce84e90d9ac"}}, "download_size": 20327936, "post_processing_size": null, "dataset_size": 1021188, "size_in_bytes": 21349124}, "geometry": {"description": "MATH is a dataset of 12,500 challenging competition mathematics problems. Each\nproblem in Math has a full step-by-step solution which can be used to teach\nmodels to generate answer derivations and explanations.\n", "citation": "@article{hendrycksmath2021,\n  title={Measuring Mathematical Problem Solving With the Math Dataset},\n  author={Dan Hendrycks and Collin Burns and Saurav Kadavath and Akul Arora and Steven Basart and Eric Tang and Dawn Song and Jacob Steinhardt},\n  journal={NeurIPS},\n  year={2021}\n}\n", "homepage": "https://github.com/hendrycks/math", "license": "", "features": {"problem": {"dtype": "string", "id": null, "_type": "Value"}, "level": {"dtype": "string", "id": null, "_type": "Value"}, "type": {"dtype": "string", "id": null, "_type": "Value"}, "solution": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "hendrycks_math", "config_name": "geometry", "version": {"version_str": "0.0.1", "description": null, "major": 0, "minor": 0, "patch": 1}, "splits": {"train": {"name": "train", "num_bytes": 1077241, "num_examples": 870, "dataset_name": "hendrycks_math"}, "test": {"name": "test", "num_bytes": 523126, "num_examples": 479, "dataset_name": "hendrycks_math"}}, "download_checksums": {"https://people.eecs.berkeley.edu/~hendrycks/MATH.tar": {"num_bytes": 20327936, "checksum": "0fbe4fad0df66942db6c221cdcc95b298cc7f4595a2f0f518360cce84e90d9ac"}}, "download_size": 20327936, "post_processing_size": null, "dataset_size": 1600367, "size_in_bytes": 21928303}, "intermediate_algebra": {"description": "MATH is a dataset of 12,500 challenging competition mathematics problems. 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\ No newline at end of file

lm_eval/datasets/hendrycks_math/hendrycks_math.py

+# 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.
+"""MATH dataset."""
+
+
+import json
+import os
+import pathlib
+
+import datasets
+
+
+_CITATION = """\
+@article{hendrycksmath2021,
+  title={Measuring Mathematical Problem Solving With the Math Dataset},
+  author={Dan Hendrycks and Collin Burns and Saurav Kadavath and Akul Arora and Steven Basart and Eric Tang and Dawn Song and Jacob Steinhardt},
+  journal={NeurIPS},
+  year={2021}
+}
+"""
+
+_DESCRIPTION = """\
+MATH is a dataset of 12,500 challenging competition mathematics problems. Each
+problem in Math has a full step-by-step solution which can be used to teach
+models to generate answer derivations and explanations.
+"""
+
+_HOMEPAGE = "https://github.com/hendrycks/math"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+_URLS = "https://people.eecs.berkeley.edu/~hendrycks/MATH.tar"
+
+_NAMES = [
+    'algebra',
+    'counting_and_probability',
+    'geometry',
+    'intermediate_algebra',
+    'number_theory',
+    'prealgebra',
+    'precalculus',
+]
+
+
+class HendrycksMath(datasets.GeneratorBasedBuilder):
+    """MATH is a dataset of 12,500 challenging competition mathematics problems."""
+
+    VERSION = datasets.Version("0.0.1")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name=name, version=version, description=name)
+        for name, version in zip(_NAMES, [VERSION] * len(_NAMES))
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "problem": datasets.Value("string"),
+                "level": datasets.Value("string"),
+                "type": datasets.Value("string"),
+                "solution": datasets.Value("string"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        urls = _URLS
+        data_dir = dl_manager.download_and_extract(urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "basepath": os.path.join(data_dir, "MATH", "train", self.config.name),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "basepath": os.path.join(data_dir, "MATH", "test", self.config.name),
+                    "split": "test"
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, basepath, split):
+        key = 0
+        for file in sorted(pathlib.Path(basepath).iterdir()):
+            with open(file, "r", encoding='utf-8') as f:
+                data = json.load(f)
+                yield key, {
+                    "problem": data["problem"],
+                    "level": data["level"],
+                    "type": data["type"],
+                    "solution": data["solution"],
+                }
+                key += 1

lm_eval/datasets/lambada/dataset_infos.json

+{"original": {"description": "LAMBADA is a dataset to evaluate the capabilities of computational models for text\nunderstanding by means of a word prediction task. LAMBADA is a collection of narrative\ntexts sharing the characteristic that human subjects are able to guess their last\nword if they are exposed to the whole text, but not if they only see the last\nsentence preceding the target word. To succeed on LAMBADA, computational models\ncannot simply rely on local context, but must be able to keep track of information\nin the broader discourse.\n\nThe LAMBADA dataset", "citation": "@misc{\n    author={Paperno, Denis and Kruszewski, Germ\u00e1n and Lazaridou, Angeliki and Pham, Quan Ngoc and Bernardi, Raffaella and Pezzelle, Sandro and Baroni, Marco and Boleda, Gemma and Fern\u00e1ndez, Raquel}, \n    title={The LAMBADA dataset},\n    DOI={10.5281/zenodo.2630551},\n    publisher={Zenodo},\n    year={2016},\n    month={Aug}\n}\n", "homepage": "https://zenodo.org/record/2630551#.X4Xzn5NKjUI", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "lambada", "config_name": "original", "version": {"version_str": "0.0.1", "description": null, "major": 0, "minor": 0, "patch": 1}, "splits": {"validation": {"name": "validation", "num_bytes": 1709449, "num_examples": 5153, "dataset_name": "lambada"}}, "download_checksums": {"http://eaidata.bmk.sh/data/lambada_test.jsonl": {"num_bytes": 1819752, "checksum": "4aa8d02cd17c719165fc8a7887fddd641f43fcafa4b1c806ca8abc31fabdb226"}}, "download_size": 1819752, "post_processing_size": null, "dataset_size": 1709449, "size_in_bytes": 3529201}, "en": {"description": "LAMBADA is a dataset to evaluate the capabilities of computational models for text\nunderstanding by means of a word prediction task. LAMBADA is a collection of narrative\ntexts sharing the characteristic that human subjects are able to guess their last\nword if they are exposed to the whole text, but not if they only see the last\nsentence preceding the target word. To succeed on LAMBADA, computational models\ncannot simply rely on local context, but must be able to keep track of information\nin the broader discourse.\n\nThe English translated LAMBADA dataset", "citation": "@misc{\n    author={Paperno, Denis and Kruszewski, Germ\u00e1n and Lazaridou, Angeliki and Pham, Quan Ngoc and Bernardi, Raffaella and Pezzelle, Sandro and Baroni, Marco and Boleda, Gemma and Fern\u00e1ndez, Raquel}, \n    title={The LAMBADA dataset},\n    DOI={10.5281/zenodo.2630551},\n    publisher={Zenodo},\n    year={2016},\n    month={Aug}\n}\n", "homepage": "https://zenodo.org/record/2630551#.X4Xzn5NKjUI", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "lambada", "config_name": "en", "version": {"version_str": "0.0.1", "description": null, "major": 0, "minor": 0, "patch": 1}, "splits": {"validation": {"name": "validation", "num_bytes": 1709449, "num_examples": 5153, "dataset_name": "lambada"}}, "download_checksums": {"http://eaidata.bmk.sh/data/lambada_test_en.jsonl": {"num_bytes": 1819752, "checksum": "4aa8d02cd17c719165fc8a7887fddd641f43fcafa4b1c806ca8abc31fabdb226"}}, "download_size": 1819752, "post_processing_size": null, "dataset_size": 1709449, "size_in_bytes": 3529201}, "fr": {"description": "LAMBADA is a dataset to evaluate the capabilities of computational models for text\nunderstanding by means of a word prediction task. LAMBADA is a collection of narrative\ntexts sharing the characteristic that human subjects are able to guess their last\nword if they are exposed to the whole text, but not if they only see the last\nsentence preceding the target word. To succeed on LAMBADA, computational models\ncannot simply rely on local context, but must be able to keep track of information\nin the broader discourse.\n\nThe French translated LAMBADA dataset", "citation": "@misc{\n    author={Paperno, Denis and Kruszewski, Germ\u00e1n and Lazaridou, Angeliki and Pham, Quan Ngoc and Bernardi, Raffaella and Pezzelle, Sandro and Baroni, Marco and Boleda, Gemma and Fern\u00e1ndez, Raquel}, \n    title={The LAMBADA dataset},\n    DOI={10.5281/zenodo.2630551},\n    publisher={Zenodo},\n    year={2016},\n    month={Aug}\n}\n", "homepage": "https://zenodo.org/record/2630551#.X4Xzn5NKjUI", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "lambada", "config_name": "fr", "version": {"version_str": "0.0.1", "description": null, "major": 0, "minor": 0, "patch": 1}, "splits": {"validation": {"name": "validation", "num_bytes": 1948795, "num_examples": 5153, "dataset_name": "lambada"}}, "download_checksums": {"http://eaidata.bmk.sh/data/lambada_test_fr.jsonl": {"num_bytes": 2028703, "checksum": "941ec6a73dba7dc91c860bf493eb66a527cd430148827a4753a4535a046bf362"}}, "download_size": 2028703, "post_processing_size": null, "dataset_size": 1948795, "size_in_bytes": 3977498}, "de": {"description": "LAMBADA is a dataset to evaluate the capabilities of computational models for text\nunderstanding by means of a word prediction task. LAMBADA is a collection of narrative\ntexts sharing the characteristic that human subjects are able to guess their last\nword if they are exposed to the whole text, but not if they only see the last\nsentence preceding the target word. To succeed on LAMBADA, computational models\ncannot simply rely on local context, but must be able to keep track of information\nin the broader discourse.\n\nThe German translated LAMBADA dataset", "citation": "@misc{\n    author={Paperno, Denis and Kruszewski, Germ\u00e1n and Lazaridou, Angeliki and Pham, Quan Ngoc and Bernardi, Raffaella and Pezzelle, Sandro and Baroni, Marco and Boleda, Gemma and Fern\u00e1ndez, Raquel}, \n    title={The LAMBADA dataset},\n    DOI={10.5281/zenodo.2630551},\n    publisher={Zenodo},\n    year={2016},\n    month={Aug}\n}\n", "homepage": "https://zenodo.org/record/2630551#.X4Xzn5NKjUI", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "lambada", "config_name": "de", "version": {"version_str": "0.0.1", "description": null, "major": 0, "minor": 0, "patch": 1}, "splits": {"validation": {"name": "validation", "num_bytes": 1904576, "num_examples": 5153, "dataset_name": "lambada"}}, "download_checksums": {"http://eaidata.bmk.sh/data/lambada_test_de.jsonl": {"num_bytes": 1985231, "checksum": "51c6c1795894c46e88e4c104b5667f488efe79081fb34d746b82b8caa663865e"}}, "download_size": 1985231, "post_processing_size": null, "dataset_size": 1904576, "size_in_bytes": 3889807}, "it": {"description": "LAMBADA is a dataset to evaluate the capabilities of computational models for text\nunderstanding by means of a word prediction task. LAMBADA is a collection of narrative\ntexts sharing the characteristic that human subjects are able to guess their last\nword if they are exposed to the whole text, but not if they only see the last\nsentence preceding the target word. To succeed on LAMBADA, computational models\ncannot simply rely on local context, but must be able to keep track of information\nin the broader discourse.\n\nThe Italian translated LAMBADA dataset", "citation": "@misc{\n    author={Paperno, Denis and Kruszewski, Germ\u00e1n and Lazaridou, Angeliki and Pham, Quan Ngoc and Bernardi, Raffaella and Pezzelle, Sandro and Baroni, Marco and Boleda, Gemma and Fern\u00e1ndez, Raquel}, \n    title={The LAMBADA dataset},\n    DOI={10.5281/zenodo.2630551},\n    publisher={Zenodo},\n    year={2016},\n    month={Aug}\n}\n", "homepage": "https://zenodo.org/record/2630551#.X4Xzn5NKjUI", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "lambada", "config_name": "it", "version": {"version_str": "0.0.1", "description": null, "major": 0, "minor": 0, "patch": 1}, "splits": {"validation": {"name": "validation", "num_bytes": 1813420, "num_examples": 5153, "dataset_name": "lambada"}}, "download_checksums": {"http://eaidata.bmk.sh/data/lambada_test_it.jsonl": {"num_bytes": 1894613, "checksum": "86654237716702ab74f42855ae5a78455c1b0e50054a4593fb9c6fcf7fad0850"}}, "download_size": 1894613, "post_processing_size": null, "dataset_size": 1813420, "size_in_bytes": 3708033}, "es": {"description": "LAMBADA is a dataset to evaluate the capabilities of computational models for text\nunderstanding by means of a word prediction task. LAMBADA is a collection of narrative\ntexts sharing the characteristic that human subjects are able to guess their last\nword if they are exposed to the whole text, but not if they only see the last\nsentence preceding the target word. To succeed on LAMBADA, computational models\ncannot simply rely on local context, but must be able to keep track of information\nin the broader discourse.\n\nThe Spanish translated LAMBADA dataset", "citation": "@misc{\n    author={Paperno, Denis and Kruszewski, Germ\u00e1n and Lazaridou, Angeliki and Pham, Quan Ngoc and Bernardi, Raffaella and Pezzelle, Sandro and Baroni, Marco and Boleda, Gemma and Fern\u00e1ndez, Raquel}, \n    title={The LAMBADA dataset},\n    DOI={10.5281/zenodo.2630551},\n    publisher={Zenodo},\n    year={2016},\n    month={Aug}\n}\n", "homepage": "https://zenodo.org/record/2630551#.X4Xzn5NKjUI", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "lambada", "config_name": "es", "version": {"version_str": "0.0.1", "description": null, "major": 0, "minor": 0, "patch": 1}, "splits": {"validation": {"name": "validation", "num_bytes": 1821735, "num_examples": 5153, "dataset_name": "lambada"}}, "download_checksums": {"http://eaidata.bmk.sh/data/lambada_test_es.jsonl": {"num_bytes": 1902349, "checksum": "ffd760026c647fb43c67ce1bc56fd527937304b348712dce33190ea6caba6f9c"}}, "download_size": 1902349, "post_processing_size": null, "dataset_size": 1821735, "size_in_bytes": 3724084}}
\ No newline at end of file

lm_eval/datasets/lambada/lambada.py

+# 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.
+# TODO: Address all TODOs and remove all explanatory comments
+"""LAMBADA dataset."""
+
+
+import json
+
+import datasets
+
+
+_CITATION = """\
+@misc{
+    author={Paperno, Denis and Kruszewski, Germán and Lazaridou, Angeliki and Pham, Quan Ngoc and Bernardi, Raffaella and Pezzelle, Sandro and Baroni, Marco and Boleda, Gemma and Fernández, Raquel}, 
+    title={The LAMBADA dataset},
+    DOI={10.5281/zenodo.2630551},
+    publisher={Zenodo},
+    year={2016},
+    month={Aug}
+}
+"""
+
+_DESCRIPTION = """\
+LAMBADA is a dataset to evaluate the capabilities of computational models for text
+understanding by means of a word prediction task. LAMBADA is a collection of narrative
+texts sharing the characteristic that human subjects are able to guess their last
+word if they are exposed to the whole text, but not if they only see the last
+sentence preceding the target word. To succeed on LAMBADA, computational models
+cannot simply rely on local context, but must be able to keep track of information
+in the broader discourse.
+"""
+
+_HOMEPAGE = "https://zenodo.org/record/2630551#.X4Xzn5NKjUI"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+_URLS = {
+    "original": "http://eaidata.bmk.sh/data/lambada_test.jsonl",
+    "en": "http://eaidata.bmk.sh/data/lambada_test_en.jsonl",
+    "fr": "http://eaidata.bmk.sh/data/lambada_test_fr.jsonl",
+    "de": "http://eaidata.bmk.sh/data/lambada_test_de.jsonl",
+    "it": "http://eaidata.bmk.sh/data/lambada_test_it.jsonl",
+    "es": "http://eaidata.bmk.sh/data/lambada_test_es.jsonl",
+}
+
+
+class Lambada(datasets.GeneratorBasedBuilder):
+    """LAMBADA is a dataset to evaluate the capabilities of computational models for text understanding by means of a word prediction task."""
+
+    VERSION = datasets.Version("0.0.1")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="original", version=VERSION, description="The LAMBADA dataset"),
+        datasets.BuilderConfig(name="en", version=VERSION, description="The English translated LAMBADA dataset"),
+        datasets.BuilderConfig(name="fr", version=VERSION, description="The French translated LAMBADA dataset"),
+        datasets.BuilderConfig(name="de", version=VERSION, description="The German translated LAMBADA dataset"),
+        datasets.BuilderConfig(name="it", version=VERSION, description="The Italian translated LAMBADA dataset"),
+        datasets.BuilderConfig(name="es", version=VERSION, description="The Spanish translated LAMBADA dataset"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "original"
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "text": datasets.Value("string"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=f"{_DESCRIPTION}\n{self.config.description}",
+            features=features,
+            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.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir,
+                    "split": "validation",
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    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, {
+                    "text": data["text"]
+                }

lm_eval/datasets/logiqa/dataset_infos.json

+{"logiqa": {"description": "LogiQA is a dataset for testing human logical reasoning. It consists of 8,678 QA\ninstances, covering multiple types of deductive reasoning. Results show that state-\nof-the-art neural models perform by far worse than human ceiling. The dataset can\nalso serve as a benchmark for reinvestigating logical AI under the deep learning\nNLP setting.\n", "citation": "@misc{liu2020logiqa,\n    title={LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning}, \n    author={Jian Liu and Leyang Cui and Hanmeng Liu and Dandan Huang and Yile Wang and Yue Zhang},\n    year={2020},\n    eprint={2007.08124},\n    archivePrefix={arXiv},\n    primaryClass={cs.CL}\n}\n", "homepage": "https://github.com/lgw863/LogiQA-dataset", "license": "", "features": {"label": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "options": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "logiqa", "config_name": "logiqa", "version": {"version_str": "0.0.1", "description": null, "major": 0, "minor": 0, "patch": 1}, "splits": {"train": {"name": "train", "num_bytes": 6419852, "num_examples": 7376, "dataset_name": "logiqa"}, "test": {"name": "test", "num_bytes": 571705, "num_examples": 651, "dataset_name": "logiqa"}, "validation": {"name": "validation", "num_bytes": 562437, "num_examples": 651, "dataset_name": "logiqa"}}, "download_checksums": {"https://raw.githubusercontent.com/lgw863/LogiQA-dataset/master/Train.txt": {"num_bytes": 6281272, "checksum": "7d5bb1f58278e33b395744cd2ad8d7600faa0b3c4d615c659a44ec1181d759fa"}, "https://raw.githubusercontent.com/lgw863/LogiQA-dataset/master/Test.txt": {"num_bytes": 559060, "checksum": "359acb78c37802208f7fde9e2f6574b8526527c63d6a336f90a53f1932cb4701"}, "https://raw.githubusercontent.com/lgw863/LogiQA-dataset/master/Eval.txt": {"num_bytes": 550021, "checksum": "4c49e6753b7262c001506b9151135abf722247035ab075dad93acdea5789c01f"}}, "download_size": 7390353, "post_processing_size": null, "dataset_size": 7553994, "size_in_bytes": 14944347}}
\ No newline at end of file

lm_eval/datasets/logiqa/logiqa.py

+# 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.
+"""LogiQA dataset."""
+
+
+import datasets
+
+
+_CITATION = """\
+@misc{liu2020logiqa,
+    title={LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning}, 
+    author={Jian Liu and Leyang Cui and Hanmeng Liu and Dandan Huang and Yile Wang and Yue Zhang},
+    year={2020},
+    eprint={2007.08124},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+"""
+
+_DESCRIPTION = """\
+LogiQA is a dataset for testing human logical reasoning. It consists of 8,678 QA
+instances, covering multiple types of deductive reasoning. Results show that state-
+of-the-art neural models perform by far worse than human ceiling. The dataset can
+also serve as a benchmark for reinvestigating logical AI under the deep learning
+NLP setting.
+"""
+
+_HOMEPAGE = "https://github.com/lgw863/LogiQA-dataset"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+_URLS = {
+    "train": "https://raw.githubusercontent.com/lgw863/LogiQA-dataset/master/Train.txt",
+    "validation": "https://raw.githubusercontent.com/lgw863/LogiQA-dataset/master/Eval.txt",
+    "test": "https://raw.githubusercontent.com/lgw863/LogiQA-dataset/master/Test.txt",
+}
+
+
+class Logiqa(datasets.GeneratorBasedBuilder):
+    """LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning"""
+
+    VERSION = datasets.Version("0.0.1")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="logiqa", version=VERSION, description="The LogiQA dataset."),
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "label": datasets.Value("string"),
+                "context": datasets.Value("string"),
+                "question": datasets.Value("string"),
+                "options": datasets.features.Sequence(
+                    datasets.Value("string")
+                ),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        urls = {"train": _URLS["train"], "test": _URLS["test"], "validation": _URLS["validation"]}
+        data_dir = dl_manager.download_and_extract(urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir["train"],
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir["test"],
+                    "split": "test"
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir["validation"],
+                    "split": "validation",
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+        def normalize(text):
+            return text.replace(".", ". ").strip()
+        with open(filepath, encoding="utf-8") as f:
+            data = f.read().strip().split("\n\n")
+            for key, row in enumerate(data):
+                example = row.split("\n")
+                yield key, {
+                    "label": example[0].strip(),
+                    "context": normalize(example[1]),
+                    "question": normalize(example[2]),
+                    "options": [normalize(option[2:]) for option in example[3:]],
+                }

lm_eval/datasets/mutual/dataset_infos.json

+{"mutual": {"description": "MuTual is a retrieval-based dataset for multi-turn dialogue reasoning, which is\nmodified from Chinese high school English listening comprehension test data.\n\nThe MuTual dataset.", "citation": "@inproceedings{mutual,\n    title = \"MuTual: A Dataset for Multi-Turn Dialogue Reasoning\",\n    author = \"Cui, Leyang  and Wu, Yu and Liu, Shujie and Zhang, Yue and Zhou, Ming\" ,\n    booktitle = \"Proceedings of the 58th Conference of the Association for Computational Linguistics\",\n    year = \"2020\",\n    publisher = \"Association for Computational Linguistics\",\n}\n", "homepage": "https://github.com/Nealcly/MuTual", "license": "", "features": {"answers": {"dtype": "string", "id": null, "_type": "Value"}, "options": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "article": {"dtype": "string", "id": null, "_type": "Value"}, "id": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "mutual", "config_name": "mutual", "version": {"version_str": "0.0.1", "description": null, "major": 0, "minor": 0, "patch": 1}, "splits": {"train": {"name": "train", "num_bytes": 5141602, "num_examples": 7088, "dataset_name": "mutual"}, "test": {"name": "test", "num_bytes": 634396, "num_examples": 886, "dataset_name": "mutual"}, "validation": {"name": "validation", "num_bytes": 624271, "num_examples": 886, "dataset_name": "mutual"}}, "download_checksums": {"https://github.com/Nealcly/MuTual/archive/master.zip": {"num_bytes": 10997878, "checksum": "bb325cf6c672f0f02699993a37138b0fa0af6fcfc77ec81dfbe46add4d7b29f9"}}, "download_size": 10997878, "post_processing_size": null, "dataset_size": 6400269, "size_in_bytes": 17398147}, "mutual_plus": {"description": "MuTual is a retrieval-based dataset for multi-turn dialogue reasoning, which is\nmodified from Chinese high school English listening comprehension test data.\n\nMuTualPlus is a more difficult MuTual that replaces positive responses with a safe responses.", "citation": "@inproceedings{mutual,\n    title = \"MuTual: A Dataset for Multi-Turn Dialogue Reasoning\",\n    author = \"Cui, Leyang  and Wu, Yu and Liu, Shujie and Zhang, Yue and Zhou, Ming\" ,\n    booktitle = \"Proceedings of the 58th Conference of the Association for Computational Linguistics\",\n    year = \"2020\",\n    publisher = \"Association for Computational Linguistics\",\n}\n", "homepage": "https://github.com/Nealcly/MuTual", "license": "", "features": {"answers": {"dtype": "string", "id": null, "_type": "Value"}, "options": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "article": {"dtype": "string", "id": null, "_type": "Value"}, "id": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "mutual", "config_name": "mutual_plus", "version": {"version_str": "0.0.1", "description": null, "major": 0, "minor": 0, "patch": 1}, "splits": {"train": {"name": "train", "num_bytes": 4921179, "num_examples": 7088, "dataset_name": "mutual"}, "test": {"name": "test", "num_bytes": 606620, "num_examples": 886, "dataset_name": "mutual"}, "validation": {"name": "validation", "num_bytes": 597340, "num_examples": 886, "dataset_name": "mutual"}}, "download_checksums": {"https://github.com/Nealcly/MuTual/archive/master.zip": {"num_bytes": 10997878, "checksum": "bb325cf6c672f0f02699993a37138b0fa0af6fcfc77ec81dfbe46add4d7b29f9"}}, "download_size": 10997878, "post_processing_size": null, "dataset_size": 6125139, "size_in_bytes": 17123017}}
\ No newline at end of file