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Merge pull request #317 from EleutherAI/Mistobaan/add-pre-commit 

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lm_eval/datasets/truthfulqa/dataset_infos.json
View file @ a2cada5d
{"multiple_choice": {"description": "TruthfulQA is a benchmark to measure whether a language model is truthful in\ngenerating answers to questions. The benchmark comprises 817 questions that\nspan 38 categories, including health, law, finance and politics. Questions are\ncrafted so that some humans would answer falsely due to a false belief or\nmisconception. To perform well, models must avoid generating false answers\nlearned from imitating human texts.\n\nThe multiple choice TruthfulQA task", "citation": "@misc{lin2021truthfulqa,\n title={TruthfulQA: Measuring How Models Mimic Human Falsehoods},\n author={Stephanie Lin and Jacob Hilton and Owain Evans},\n year={2021},\n eprint={2109.07958},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n", "homepage": "https://github.com/sylinrl/TruthfulQA", "license": "", "features": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "mc1_targets": {"choices": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "labels": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "mc2_targets": {"choices": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "labels": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "truthfulqa", "config_name": "multiple_choice", "version": {"version_str": "0.0.1", "description": null, "major": 0, "minor": 0, "patch": 1}, "splits": {"validation": {"name": "validation", "num_bytes": 610333, "num_examples": 817, "dataset_name": "truthfulqa"}}, "download_checksums": {"https://raw.githubusercontent.com/sylinrl/TruthfulQA/013686a06be7a7bde5bf8223943e106c7250123c/data/mc_task.json": {"num_bytes": 710607, "checksum": "6eb4125d25750c0145c4be2dce00440736684ab6f74ce6bff2139571cc758954"}}, "download_size": 710607, "post_processing_size": null, "dataset_size": 610333, "size_in_bytes": 1320940}, "generation": {"description": "TruthfulQA is a benchmark to measure whether a language model is truthful in\ngenerating answers to questions. The benchmark comprises 817 questions that\nspan 38 categories, including health, law, finance and politics. Questions are\ncrafted so that some humans would answer falsely due to a false belief or\nmisconception. To perform well, models must avoid generating false answers\nlearned from imitating human texts.\n\nThe generative TruthfulQA task", "citation": "@misc{lin2021truthfulqa,\n title={TruthfulQA: Measuring How Models Mimic Human Falsehoods},\n author={Stephanie Lin and Jacob Hilton and Owain Evans},\n year={2021},\n eprint={2109.07958},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n", "homepage": "https://github.com/sylinrl/TruthfulQA", "license": "", "features": {"category": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "best_answer": {"dtype": "string", "id": null, "_type": "Value"}, "correct_answers": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "incorrect_answers": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "source": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "truthfulqa", "config_name": "generation", "version": {"version_str": "0.0.1", "description": null, "major": 0, "minor": 0, "patch": 1}, "splits": {"validation": {"name": "validation", "num_bytes": 463860, "num_examples": 817, "dataset_name": "truthfulqa"}}, "download_checksums": {"https://raw.githubusercontent.com/sylinrl/TruthfulQA/013686a06be7a7bde5bf8223943e106c7250123c/TruthfulQA.csv": {"num_bytes": 443723, "checksum": "8d7dd15f033196140f032d97d30f037da7a7b1192c3f36f9937c1850925335a2"}}, "download_size": 443723, "post_processing_size": null, "dataset_size": 463860, "size_in_bytes": 907583}}
\ No newline at end of file
{"multiple_choice": {"description": "TruthfulQA is a benchmark to measure whether a language model is truthful in\ngenerating answers to questions. The benchmark comprises 817 questions that\nspan 38 categories, including health, law, finance and politics. Questions are\ncrafted so that some humans would answer falsely due to a false belief or\nmisconception. To perform well, models must avoid generating false answers\nlearned from imitating human texts.\n\nThe multiple choice TruthfulQA task", "citation": "@misc{lin2021truthfulqa,\n title={TruthfulQA: Measuring How Models Mimic Human Falsehoods},\n author={Stephanie Lin and Jacob Hilton and Owain Evans},\n year={2021},\n eprint={2109.07958},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n", "homepage": "https://github.com/sylinrl/TruthfulQA", "license": "", "features": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "mc1_targets": {"choices": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "labels": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "mc2_targets": {"choices": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "labels": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "truthfulqa", "config_name": "multiple_choice", "version": {"version_str": "0.0.1", "description": null, "major": 0, "minor": 0, "patch": 1}, "splits": {"validation": {"name": "validation", "num_bytes": 610333, "num_examples": 817, "dataset_name": "truthfulqa"}}, "download_checksums": {"https://raw.githubusercontent.com/sylinrl/TruthfulQA/013686a06be7a7bde5bf8223943e106c7250123c/data/mc_task.json": {"num_bytes": 710607, "checksum": "6eb4125d25750c0145c4be2dce00440736684ab6f74ce6bff2139571cc758954"}}, "download_size": 710607, "post_processing_size": null, "dataset_size": 610333, "size_in_bytes": 1320940}, "generation": {"description": "TruthfulQA is a benchmark to measure whether a language model is truthful in\ngenerating answers to questions. The benchmark comprises 817 questions that\nspan 38 categories, including health, law, finance and politics. Questions are\ncrafted so that some humans would answer falsely due to a false belief or\nmisconception. To perform well, models must avoid generating false answers\nlearned from imitating human texts.\n\nThe generative TruthfulQA task", "citation": "@misc{lin2021truthfulqa,\n title={TruthfulQA: Measuring How Models Mimic Human Falsehoods},\n author={Stephanie Lin and Jacob Hilton and Owain Evans},\n year={2021},\n eprint={2109.07958},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n", "homepage": "https://github.com/sylinrl/TruthfulQA", "license": "", "features": {"category": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "best_answer": {"dtype": "string", "id": null, "_type": "Value"}, "correct_answers": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "incorrect_answers": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "source": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "truthfulqa", "config_name": "generation", "version": {"version_str": "0.0.1", "description": null, "major": 0, "minor": 0, "patch": 1}, "splits": {"validation": {"name": "validation", "num_bytes": 463860, "num_examples": 817, "dataset_name": "truthfulqa"}}, "download_checksums": {"https://raw.githubusercontent.com/sylinrl/TruthfulQA/013686a06be7a7bde5bf8223943e106c7250123c/TruthfulQA.csv": {"num_bytes": 443723, "checksum": "8d7dd15f033196140f032d97d30f037da7a7b1192c3f36f9937c1850925335a2"}}, "download_size": 443723, "post_processing_size": null, "dataset_size": 463860, "size_in_bytes": 907583}}
lm_eval/datasets/truthfulqa/truthfulqa.py
View file @ a2cada5d
......@@ -65,38 +65,46 @@ class TruthfulqaConfig(datasets.BuilderConfig):
class Truthfulqa(datasets.GeneratorBasedBuilder):
"""TruthfulQA is a benchmark to measure whether a language model is truthful in
generating answers to questions."""
generating answers to questions."""
BUILDER_CONFIGS = [
TruthfulqaConfig(
name="multiple_choice",
url="https://raw.githubusercontent.com/sylinrl/TruthfulQA/013686a06be7a7bde5bf8223943e106c7250123c/data/mc_task.json",
features=datasets.Features({
"question": datasets.Value("string"),
"mc1_targets": {
"choices": datasets.features.Sequence(datasets.Value("string")),
"labels": datasets.features.Sequence(datasets.Value("int32")),
},
"mc2_targets": {
"choices": datasets.features.Sequence(datasets.Value("string")),
"labels": datasets.features.Sequence(datasets.Value("int32")),
features=datasets.Features(
{
"question": datasets.Value("string"),
"mc1_targets": {
"choices": datasets.features.Sequence(datasets.Value("string")),
"labels": datasets.features.Sequence(datasets.Value("int32")),
},
"mc2_targets": {
"choices": datasets.features.Sequence(datasets.Value("string")),
"labels": datasets.features.Sequence(datasets.Value("int32")),
},
}
}),
description="The multiple choice TruthfulQA task"
),
description="The multiple choice TruthfulQA task",
),
TruthfulqaConfig(
name="generation",
url="https://raw.githubusercontent.com/sylinrl/TruthfulQA/013686a06be7a7bde5bf8223943e106c7250123c/TruthfulQA.csv",
features=datasets.Features({
"category": datasets.Value("string"),
"question": datasets.Value("string"),
"best_answer": datasets.Value("string"),
"correct_answers": datasets.features.Sequence(datasets.Value("string")),
"incorrect_answers": datasets.features.Sequence(datasets.Value("string")),
"source": datasets.Value("string"),
}),
description="The generative TruthfulQA task"
)
features=datasets.Features(
{
"category": datasets.Value("string"),
"question": datasets.Value("string"),
"best_answer": datasets.Value("string"),
"correct_answers": datasets.features.Sequence(
datasets.Value("string")
),
"incorrect_answers": datasets.features.Sequence(
datasets.Value("string")
),
"source": datasets.Value("string"),
}
),
description="The generative TruthfulQA task",
),
]
def _info(self):
......@@ -138,15 +146,15 @@ generating answers to questions."""
"mc2_targets": {
"choices": row["mc2_targets"].keys(),
"labels": row["mc2_targets"].values(),
}
},
}
else:
# Generation data is in a `CSV` file.
with open(filepath, newline='') as f:
with open(filepath, newline="") as f:
contents = csv.DictReader(f)
for key, row in enumerate(contents):
# Ensure that references exist.
if not row['Correct Answers'] or not row['Incorrect Answers']:
if not row["Correct Answers"] or not row["Incorrect Answers"]:
continue
yield key, {
"category": row["Category"],
......@@ -154,6 +162,8 @@ generating answers to questions."""
"best_answer": row["Best Answer"],
# split on ";"
"correct_answers": row["Correct Answers"].strip().split(";"),
"incorrect_answers": row["Incorrect Answers"].strip().split(";"),
"incorrect_answers": row["Incorrect Answers"]
.strip()
.split(";"),
"source": row["Source"],
}
lm_eval/datasets/unscramble/unscramble.py
View file @ a2cada5d
......@@ -64,8 +64,9 @@ class Unscramble(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("0.0.1")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name=name, version=version,
description=_DESCRIPTIONS[name])
datasets.BuilderConfig(
name=name, version=version, description=_DESCRIPTIONS[name]
)
for name, version in zip(_NAMES, [VERSION] * len(_NAMES))
]
......
lm_eval/datasets/wikitext/dataset_infos.json
View file @ a2cada5d
{"wikitext-103-v1": {"description": " The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified\n Good and Featured articles on Wikipedia. The dataset is available under the Creative Commons Attribution-ShareAlike\n License.\n", "citation": "@misc{merity2016pointer,\n title={Pointer Sentinel Mixture Models},\n author={Stephen Merity and Caiming Xiong and James Bradbury and Richard Socher},\n year={2016},\n eprint={1609.07843},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n", "homepage": "https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/", "license": "Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)", "features": {"page": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "wikitext", "config_name": "wikitext-103-v1", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 1281262, "num_examples": 62, "dataset_name": "wikitext"}, "train": {"name": "train", "num_bytes": 539297488, "num_examples": 29444, "dataset_name": "wikitext"}, "validation": {"name": "validation", "num_bytes": 1142488, "num_examples": 60, "dataset_name": "wikitext"}}, "download_checksums": {"https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-v1.zip": {"num_bytes": 190229076, "checksum": "242ba0f20b329cfdf1ccc61e9e9e5b59becf189db7f7a81cd2a0e2fc31539590"}}, "download_size": 190229076, "post_processing_size": null, "dataset_size": 541721238, "size_in_bytes": 731950314}, "wikitext-2-v1": {"description": " The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified\n Good and Featured articles on Wikipedia. The dataset is available under the Creative Commons Attribution-ShareAlike\n License.\n", "citation": "@misc{merity2016pointer,\n title={Pointer Sentinel Mixture Models},\n author={Stephen Merity and Caiming Xiong and James Bradbury and Richard Socher},\n year={2016},\n eprint={1609.07843},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n", "homepage": "https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/", "license": "Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)", "features": {"page": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "wikitext", "config_name": "wikitext-2-v1", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 1256634, "num_examples": 62, "dataset_name": "wikitext"}, "train": {"name": "train", "num_bytes": 10799034, "num_examples": 629, "dataset_name": "wikitext"}, "validation": {"name": "validation", "num_bytes": 1121860, "num_examples": 60, "dataset_name": "wikitext"}}, "download_checksums": {"https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-v1.zip": {"num_bytes": 4475746, "checksum": "92675f1d63015c1c8b51f1656a52d5bdbc33aafa60cc47a218a66e7ee817488c"}}, "download_size": 4475746, "post_processing_size": null, "dataset_size": 13177528, "size_in_bytes": 17653274}, "wikitext-103-raw-v1": {"description": " The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified\n Good and Featured articles on Wikipedia. The dataset is available under the Creative Commons Attribution-ShareAlike\n License.\n", "citation": "@misc{merity2016pointer,\n title={Pointer Sentinel Mixture Models},\n author={Stephen Merity and Caiming Xiong and James Bradbury and Richard Socher},\n year={2016},\n eprint={1609.07843},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n", "homepage": "https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/", "license": "Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)", "features": {"page": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "wikitext", "config_name": "wikitext-103-raw-v1", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 1290775, "num_examples": 62, "dataset_name": "wikitext"}, "train": {"name": "train", "num_bytes": 540656522, "num_examples": 29444, "dataset_name": "wikitext"}, "validation": {"name": "validation", "num_bytes": 1147025, "num_examples": 60, "dataset_name": "wikitext"}}, "download_checksums": {"https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-raw-v1.zip": {"num_bytes": 191984949, "checksum": "91c00ae287f0d699e18605c84afc9e45c192bc6b7797ff8837e5474655a33794"}}, "download_size": 191984949, "post_processing_size": null, "dataset_size": 543094322, "size_in_bytes": 735079271}, "wikitext-2-raw-v1": {"description": " The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified\n Good and Featured articles on Wikipedia. The dataset is available under the Creative Commons Attribution-ShareAlike\n License.\n", "citation": "@misc{merity2016pointer,\n title={Pointer Sentinel Mixture Models},\n author={Stephen Merity and Caiming Xiong and James Bradbury and Richard Socher},\n year={2016},\n eprint={1609.07843},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n", "homepage": "https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/", "license": "Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)", "features": {"page": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "wikitext", "config_name": "wikitext-2-raw-v1", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 1290775, "num_examples": 62, "dataset_name": "wikitext"}, "train": {"name": "train", "num_bytes": 10942633, "num_examples": 629, "dataset_name": "wikitext"}, "validation": {"name": "validation", "num_bytes": 1147025, "num_examples": 60, "dataset_name": "wikitext"}}, "download_checksums": {"https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip": {"num_bytes": 4721645, "checksum": "ef7edb566e3e2b2d31b29c1fdb0c89a4cc683597484c3dc2517919c615435a11"}}, "download_size": 4721645, "post_processing_size": null, "dataset_size": 13380433, "size_in_bytes": 18102078}}
\ No newline at end of file
{"wikitext-103-v1": {"description": " The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified\n Good and Featured articles on Wikipedia. The dataset is available under the Creative Commons Attribution-ShareAlike\n License.\n", "citation": "@misc{merity2016pointer,\n title={Pointer Sentinel Mixture Models},\n author={Stephen Merity and Caiming Xiong and James Bradbury and Richard Socher},\n year={2016},\n eprint={1609.07843},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n", "homepage": "https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/", "license": "Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)", "features": {"page": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "wikitext", "config_name": "wikitext-103-v1", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 1281262, "num_examples": 62, "dataset_name": "wikitext"}, "train": {"name": "train", "num_bytes": 539297488, "num_examples": 29444, "dataset_name": "wikitext"}, "validation": {"name": "validation", "num_bytes": 1142488, "num_examples": 60, "dataset_name": "wikitext"}}, "download_checksums": {"https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-v1.zip": {"num_bytes": 190229076, "checksum": "242ba0f20b329cfdf1ccc61e9e9e5b59becf189db7f7a81cd2a0e2fc31539590"}}, "download_size": 190229076, "post_processing_size": null, "dataset_size": 541721238, "size_in_bytes": 731950314}, "wikitext-2-v1": {"description": " The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified\n Good and Featured articles on Wikipedia. The dataset is available under the Creative Commons Attribution-ShareAlike\n License.\n", "citation": "@misc{merity2016pointer,\n title={Pointer Sentinel Mixture Models},\n author={Stephen Merity and Caiming Xiong and James Bradbury and Richard Socher},\n year={2016},\n eprint={1609.07843},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n", "homepage": "https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/", "license": "Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)", "features": {"page": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "wikitext", "config_name": "wikitext-2-v1", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 1256634, "num_examples": 62, "dataset_name": "wikitext"}, "train": {"name": "train", "num_bytes": 10799034, "num_examples": 629, "dataset_name": "wikitext"}, "validation": {"name": "validation", "num_bytes": 1121860, "num_examples": 60, "dataset_name": "wikitext"}}, "download_checksums": {"https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-v1.zip": {"num_bytes": 4475746, "checksum": "92675f1d63015c1c8b51f1656a52d5bdbc33aafa60cc47a218a66e7ee817488c"}}, "download_size": 4475746, "post_processing_size": null, "dataset_size": 13177528, "size_in_bytes": 17653274}, "wikitext-103-raw-v1": {"description": " The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified\n Good and Featured articles on Wikipedia. The dataset is available under the Creative Commons Attribution-ShareAlike\n License.\n", "citation": "@misc{merity2016pointer,\n title={Pointer Sentinel Mixture Models},\n author={Stephen Merity and Caiming Xiong and James Bradbury and Richard Socher},\n year={2016},\n eprint={1609.07843},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n", "homepage": "https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/", "license": "Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)", "features": {"page": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "wikitext", "config_name": "wikitext-103-raw-v1", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 1290775, "num_examples": 62, "dataset_name": "wikitext"}, "train": {"name": "train", "num_bytes": 540656522, "num_examples": 29444, "dataset_name": "wikitext"}, "validation": {"name": "validation", "num_bytes": 1147025, "num_examples": 60, "dataset_name": "wikitext"}}, "download_checksums": {"https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-raw-v1.zip": {"num_bytes": 191984949, "checksum": "91c00ae287f0d699e18605c84afc9e45c192bc6b7797ff8837e5474655a33794"}}, "download_size": 191984949, "post_processing_size": null, "dataset_size": 543094322, "size_in_bytes": 735079271}, "wikitext-2-raw-v1": {"description": " The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified\n Good and Featured articles on Wikipedia. The dataset is available under the Creative Commons Attribution-ShareAlike\n License.\n", "citation": "@misc{merity2016pointer,\n title={Pointer Sentinel Mixture Models},\n author={Stephen Merity and Caiming Xiong and James Bradbury and Richard Socher},\n year={2016},\n eprint={1609.07843},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n", "homepage": "https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/", "license": "Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)", "features": {"page": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "wikitext", "config_name": "wikitext-2-raw-v1", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 1290775, "num_examples": 62, "dataset_name": "wikitext"}, "train": {"name": "train", "num_bytes": 10942633, "num_examples": 629, "dataset_name": "wikitext"}, "validation": {"name": "validation", "num_bytes": 1147025, "num_examples": 60, "dataset_name": "wikitext"}}, "download_checksums": {"https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip": {"num_bytes": 4721645, "checksum": "ef7edb566e3e2b2d31b29c1fdb0c89a4cc683597484c3dc2517919c615435a11"}}, "download_size": 4721645, "post_processing_size": null, "dataset_size": 13380433, "size_in_bytes": 18102078}}
lm_eval/datasets/wikitext/wikitext.py
View file @ a2cada5d
......@@ -123,86 +123,111 @@ class Wikitext(datasets.GeneratorBasedBuilder):
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"data_file": os.path.join(
data_dir, "wiki.test.tokens"), "split": "test"},
gen_kwargs={
"data_file": os.path.join(data_dir, "wiki.test.tokens"),
"split": "test",
},
),
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"data_file": os.path.join(
data_dir, "wiki.train.tokens"), "split": "train"},
gen_kwargs={
"data_file": os.path.join(data_dir, "wiki.train.tokens"),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"data_file": os.path.join(
data_dir, "wiki.valid.tokens"), "split": "valid"},
gen_kwargs={
"data_file": os.path.join(data_dir, "wiki.valid.tokens"),
"split": "valid",
},
),
]
else:
if self.config.name == "wikitext-103-raw-v1":
data_file = dl_manager.download_and_extract(
self.config.data_url)
data_file = dl_manager.download_and_extract(self.config.data_url)
data_dir = os.path.join(data_file, "wikitext-103-raw")
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"data_file": os.path.join(
data_dir, "wiki.test.raw"), "split": "test"},
gen_kwargs={
"data_file": os.path.join(data_dir, "wiki.test.raw"),
"split": "test",
},
),
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"data_file": os.path.join(
data_dir, "wiki.train.raw"), "split": "train"},
gen_kwargs={
"data_file": os.path.join(data_dir, "wiki.train.raw"),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"data_file": os.path.join(
data_dir, "wiki.valid.raw"), "split": "valid"},
gen_kwargs={
"data_file": os.path.join(data_dir, "wiki.valid.raw"),
"split": "valid",
},
),
]
else:
if self.config.name == "wikitext-2-raw-v1":
data_file = dl_manager.download_and_extract(
self.config.data_url)
data_file = dl_manager.download_and_extract(self.config.data_url)
data_dir = os.path.join(data_file, "wikitext-2-raw")
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"data_file": os.path.join(
data_dir, "wiki.test.raw"), "split": "test"},
gen_kwargs={
"data_file": os.path.join(data_dir, "wiki.test.raw"),
"split": "test",
},
),
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"data_file": os.path.join(
data_dir, "wiki.train.raw"), "split": "train"},
gen_kwargs={
"data_file": os.path.join(data_dir, "wiki.train.raw"),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"data_file": os.path.join(
data_dir, "wiki.valid.raw"), "split": "valid"},
gen_kwargs={
"data_file": os.path.join(data_dir, "wiki.valid.raw"),
"split": "valid",
},
),
]
else:
if self.config.name == "wikitext-2-v1":
data_file = dl_manager.download_and_extract(
self.config.data_url)
self.config.data_url
)
data_dir = os.path.join(data_file, "wikitext-2")
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"data_file": os.path.join(
data_dir, "wiki.test.tokens"), "split": "test"},
gen_kwargs={
"data_file": os.path.join(
data_dir, "wiki.test.tokens"
),
"split": "test",
},
),
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_file": os.path.join(data_dir, "wiki.train.tokens"),
"data_file": os.path.join(
data_dir, "wiki.train.tokens"
),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"data_file": os.path.join(data_dir, "wiki.valid.tokens"),
"data_file": os.path.join(
data_dir, "wiki.valid.tokens"
),
"split": "valid",
},
),
......@@ -216,12 +241,12 @@ class Wikitext(datasets.GeneratorBasedBuilder):
data = f.read().split("\n")
for line in data:
rline = line.replace("= = =", "===").replace("= =", "==").strip()
if rline.startswith('= ') and rline.strip().endswith(' ='):
page = '\n'.join(ret)
if rline.startswith("= ") and rline.strip().endswith(" ="):
page = "\n".join(ret)
if page.strip():
yield key, {"page": page}
key += 1
ret = []
ret.append(line)
page = '\n'.join(ret)
page = "\n".join(ret)
yield key, {"page": page}
lm_eval/decontamination/archiver.py
View file @ a2cada5d
......@@ -8,12 +8,14 @@ import mmap
import tqdm
from pathlib import Path
def json_serial(obj):
"""JSON serializer for objects not serializable by default json code"""
if isinstance(obj, (datetime.datetime,)):
return obj.isoformat()
raise TypeError ("Type %s not serializable" % type(obj))
raise TypeError("Type %s not serializable" % type(obj))
# Modified version of lm_dataformat Archive for single file.
class Archive:
......@@ -21,26 +23,32 @@ class Archive:
self.file_path = file_path
dir_name = os.path.dirname(file_path)
if dir_name:
os.makedirs(dir_name, exist_ok=True)
self.fh = open(self.file_path, 'wb')
os.makedirs(dir_name, exist_ok=True)
self.fh = open(self.file_path, "wb")
self.cctx = zstandard.ZstdCompressor(level=compression_level)
self.compressor = self.cctx.stream_writer(self.fh)
self.compressor = self.cctx.stream_writer(self.fh)
def add_data(self, data, meta={}):
self.compressor.write(json.dumps({'text': data, 'meta': meta}, default=json_serial).encode('UTF-8') + b'\n')
self.compressor.write(
json.dumps({"text": data, "meta": meta}, default=json_serial).encode(
"UTF-8"
)
+ b"\n"
)
def commit(self):
self.compressor.flush(zstandard.FLUSH_FRAME)
self.compressor.flush(zstandard.FLUSH_FRAME)
self.fh.flush()
self.fh.close()
# Modified version of lm_dataformat Reader with self.fh set, allowing peeking for tqdm.
class Reader:
def __init__(self):
pass
def read(self, file, get_meta=False, autojoin_paragraphs=True, para_joiner='\n\n'):
with open(file, 'rb') as fh:
def read(self, file, get_meta=False, autojoin_paragraphs=True, para_joiner="\n\n"):
with open(file, "rb") as fh:
self.fh = fh
cctx = zstandard.ZstdDecompressor()
reader = io.BufferedReader(cctx.stream_reader(fh))
......@@ -52,53 +60,58 @@ class Reader:
yield ob
continue
text = ob['text']
text = ob["text"]
if autojoin_paragraphs and isinstance(text, list):
text = para_joiner.join(text)
if get_meta:
yield text, (ob['meta'] if 'meta' in ob else {})
yield text, (ob["meta"] if "meta" in ob else {})
else:
yield text
class TextArchive:
def __init__(self, file_path, mode="rb+"):
self.file_path = file_path
dir_name = os.path.dirname(file_path)
if dir_name:
os.makedirs(dir_name, exist_ok=True)
os.makedirs(dir_name, exist_ok=True)
if not os.path.exists(file_path):
Path(file_path).touch()
self.fh = open(self.file_path, mode)
self.fh = open(self.file_path, mode)
def add_data(self, data):
self.fh.write(data.encode('UTF-8') + b'\n')
self.fh.write(data.encode("UTF-8") + b"\n")
def commit(self):
self.fh.flush()
self.fh.close()
class TextReader:
def __init__(self, file_path):
self.file_path = file_path
# Optimized mmap read with infrequent tqdm updates to maintain speed
# Tested up to 250MB/s.
# Tested up to 250MB/s.
def read_tqdm(self, update_frequency=10000):
current_file_position = 0
line_counter = 0
with open(self.file_path, 'r') as fh, \
tqdm.tqdm(total=os.path.getsize(self.file_path), dynamic_ncols=True,
unit="byte", unit_scale=1) as progress:
with open(self.file_path, "r") as fh, tqdm.tqdm(
total=os.path.getsize(self.file_path),
dynamic_ncols=True,
unit="byte",
unit_scale=1,
) as progress:
with mmap.mmap(fh.fileno(), length=0, access=mmap.ACCESS_READ) as mmap_obj:
for line in iter(mmap_obj.readline, b""):
line = line.decode("utf-8")
line_counter += 1
if line_counter == update_frequency:
new_file_pos = mmap_obj.tell()
new_file_pos = mmap_obj.tell()
bytes_read = new_file_pos - current_file_position
current_file_position = new_file_pos
progress.update(bytes_read)
......@@ -107,24 +120,24 @@ class TextReader:
def read_and_tell(self):
current_file_position = 0
with open(self.file_path, 'r', encoding="utf8") as fh:
with open(self.file_path, "r", encoding="utf8") as fh:
with mmap.mmap(fh.fileno(), length=0, access=mmap.ACCESS_READ) as mmap_obj:
for line in iter(mmap_obj.readline, b""):
line = line.decode("utf-8")
new_file_pos = mmap_obj.tell()
line = line.decode("utf-8")
new_file_pos = mmap_obj.tell()
raw_bytes_read = new_file_pos - current_file_position
current_file_position = new_file_pos
yield line[:-1], raw_bytes_read
def read(self):
with open(self.file_path, 'r', encoding="utf8") as fh:
with open(self.file_path, "r", encoding="utf8") as fh:
with mmap.mmap(fh.fileno(), length=0, access=mmap.ACCESS_READ) as mmap_obj:
for line in iter(mmap_obj.readline, b""):
line = line.decode("utf-8")
line = line.decode("utf-8")
yield line[:-1]
def read_slow(self):
with open(self.file_path, 'r', encoding="utf8") as fh:
with open(self.file_path, "r", encoding="utf8") as fh:
while True:
line = fh.readline()
if line == -1 or line == "":
......@@ -132,16 +145,17 @@ class TextReader:
else:
yield line[:-1]
# Optimized for speed. Decompresses the archive in shell before
# using the mmap'd TextReader.
class ZStdTextReader:
def __init__(self, file):
self.file = file
self.file = file
def read_tqdm(self):
def read_tqdm(self):
decompressed_file = self.file[:-4]
print("Decompressing file, please wait...")
os.system(f"zstd -d {self.file}") # linux decompress is faster
os.system(f"zstd -d {self.file}") # linux decompress is faster
reader = TextReader(decompressed_file)
yield from reader.read_tqdm()
os.remove(decompressed_file)
\ No newline at end of file
os.remove(decompressed_file)
lm_eval/decontamination/decontaminate.py
View file @ a2cada5d
......@@ -9,6 +9,7 @@ import collections
from .janitor import Janitor, word_ngrams
from .archiver import ZStdTextReader
# Was used for testing the evaluator decoupled from the full logic below
def get_train_overlap_stub(docs, ngrams_path, ngrams_n_size):
simulated_overlap = 0.1
......@@ -49,7 +50,7 @@ def get_train_overlap(docs_by_task_set, ngrams_path, limit):
return f"data/{task_name}/{task_set}_{ngrams_n_size}grams_limit{limit}.overlaps"
lookups = {}
duplicates = {} # (task_name, task_set): set(doc_ids)}
duplicates = {} # (task_name, task_set): set(doc_ids)}
sets_to_decontaminate = len(docs_by_task_set.keys())
for (task_name, task_set), docs in docs_by_task_set.items():
......@@ -57,19 +58,27 @@ def get_train_overlap(docs_by_task_set, ngrams_path, limit):
os.mkdir(f"data/{task_name}")
# Check if we've decontaminated this combination before
overlaps_dump_path = get_overlaps_dump_path(task_name, task_set, ngrams_n_size, limit)
overlaps_dump_path = get_overlaps_dump_path(
task_name, task_set, ngrams_n_size, limit
)
if os.path.exists(overlaps_dump_path):
duplicates[(task_name, task_set)] = pickle.load(open(overlaps_dump_path, "rb"))
duplicates[(task_name, task_set)] = pickle.load(
open(overlaps_dump_path, "rb")
)
sets_to_decontaminate -= 1
continue
else:
duplicates[(task_name, task_set)] = set()
# Build/load the task lookup {ngram: set(documents)}.
task_set_lookup_path = f"data/{task_name}/{task_set}_{ngrams_n_size}grams_limit{limit}.lookup"
task_set_lookup_path = (
f"data/{task_name}/{task_set}_{ngrams_n_size}grams_limit{limit}.lookup"
)
if os.path.exists(task_set_lookup_path):
print(f"{task_set_lookup_path} available, loading...")
lookups[(task_name, task_set)] = pickle.load(open(task_set_lookup_path, "rb"))
print(f"{task_set_lookup_path} available, loading...")
lookups[(task_name, task_set)] = pickle.load(
open(task_set_lookup_path, "rb")
)
else:
print(f"{task_set_lookup_path} not available, building...")
lookup = collections.defaultdict(set)
......@@ -79,7 +88,7 @@ def get_train_overlap(docs_by_task_set, ngrams_path, limit):
for ngram in ngrams:
lookup[ngram].add(doc_id)
pickle.dump(lookup, open(task_set_lookup_path,"wb"))
pickle.dump(lookup, open(task_set_lookup_path, "wb"))
lookups[(task_name, task_set)] = lookup
elapsed = time.perf_counter() - start
......@@ -89,7 +98,7 @@ def get_train_overlap(docs_by_task_set, ngrams_path, limit):
if sets_to_decontaminate > 0:
print("Merging lookups...")
start = time.perf_counter()
start = time.perf_counter()
merged_lookup = collections.defaultdict(list)
for (task_name, task_set), lookup in lookups.items():
for ngram, doc_ids in lookup.items():
......@@ -112,20 +121,26 @@ def get_train_overlap(docs_by_task_set, ngrams_path, limit):
non_matching_unique = 0
current_ngram = ""
for line in reader.read_tqdm(): # Scan training set ngrams file
for line in reader.read_tqdm(): # Scan training set ngrams file
total_ngrams += 1
[ngram, document_id] = line.rsplit(" ", 1)
if ngram != current_ngram: # Only need to match the ngram once in training set
if (
ngram != current_ngram
): # Only need to match the ngram once in training set
unique_ngrams += 1
current_ngram = ngram
if ngram in merged_lookup:
matched_ngrams.append(ngram) # For logging
matched_ngrams.append(ngram) # For logging
matching_unique += 1
for task_name, task_set, doc_ids in merged_lookup[ngram]:
task_doc_set = duplicates[(task_name, task_set)]
for doc_id in doc_ids: # Record contamination across all relevant task/set combos
for (
doc_id
) in (
doc_ids
): # Record contamination across all relevant task/set combos
task_doc_set.add(doc_id)
del merged_lookup[ngram] # No point matching again
del merged_lookup[ngram] # No point matching again
else:
non_matching_unique += 1
......@@ -143,11 +158,12 @@ def get_train_overlap(docs_by_task_set, ngrams_path, limit):
print(duplicates)
# Dump overlaps separately
# Dump overlaps separately
for (task_name, task_set), doc_ids in duplicates.items():
overlaps_dump_path = get_overlaps_dump_path(task_name, task_set, ngrams_n_size, limit)
pickle.dump(doc_ids, open(overlaps_dump_path,"wb"))
overlaps_dump_path = get_overlaps_dump_path(
task_name, task_set, ngrams_n_size, limit
)
pickle.dump(doc_ids, open(overlaps_dump_path, "wb"))
# Strip task set and return
return {task_name: doc_ids for (task_name, task_set), doc_ids in duplicates.items()}
lm_eval/decontamination/janitor.py
View file @ a2cada5d
......@@ -9,8 +9,9 @@ from pprint import pprint
# c++ -O3 -Wall -shared -std=c++11 -fPIC $(python3 -m pybind11 --includes) janitor_util.cpp -o janitor_util$(python3-config --extension-suffix) -undefined dynamic_lookup
try:
import janitor_util
JANITOR_CPP = True
except Exception as e:
except Exception:
print("WARNING: C++ module could not be loaded. Janitor running in python mode")
traceback.print_exc()
JANITOR_CPP = False
......@@ -41,6 +42,7 @@ def word_ngrams(s, n):
ngram_seqs = form_ngrams(iter(tokens), n)
return (" ".join(ngram) for ngram in ngram_seqs)
# Does character sequences only - combined faster function to play around with later
# def word_ngrams_indices_combined(sequence, n):
# current_word = ""
......@@ -70,7 +72,7 @@ def split_indices(s):
"""Splits a string on whitespaces and records the indices of each in the original string.
@:return generator((word, (start_idx, end_idx)), ...)
"""
return ((m.group(0), (m.start(), m.end() - 1)) for m in re.finditer(r'\S+', s))
return ((m.group(0), (m.start(), m.end() - 1)) for m in re.finditer(r"\S+", s))
def word_ngrams_indices(s, n):
......@@ -90,22 +92,27 @@ def word_ngrams_indices(s, n):
# ([word, word, ...], [(start,end), (start,end), ...]),
# ...
# )
ngram_indices_pairs = (zip(*ngram_with_indices) for ngram_with_indices in ngram_seqs_with_indices)
ngram_indices_pairs = (
zip(*ngram_with_indices) for ngram_with_indices in ngram_seqs_with_indices
)
# Generator of ( (word_ngram, (start, end)), (word_ngram, start, end)), ...)
return ((" ".join(ngram_seq), (indices[0][0], indices[-1][1])) for ngram_seq, indices in ngram_indices_pairs)
return (
(" ".join(ngram_seq), (indices[0][0], indices[-1][1]))
for ngram_seq, indices in ngram_indices_pairs
)
class Janitor:
# FIXME delete_chars: Should anything else go here? Special chars?
def __init__(
self,
ngram_n=13,
window_to_remove=200,
too_dirty_cutoff=10,
minimum_slice_length=200,
delete_chars=string.punctuation
self,
ngram_n=13,
window_to_remove=200,
too_dirty_cutoff=10,
minimum_slice_length=200,
delete_chars=string.punctuation,
):
self.ngram_n = ngram_n
self.window_to_remove = window_to_remove
......@@ -121,7 +128,7 @@ class Janitor:
self.translation_table = str.maketrans(
string.ascii_lowercase + string.ascii_uppercase, # These characters
string.ascii_lowercase * 2, # Become these characters
self.delete_chars # These are deleted
self.delete_chars, # These are deleted
)
##############
......@@ -129,14 +136,13 @@ class Janitor:
##############
def save_contamination_ngrams(self, filename):
with open(filename, 'wb') as fp:
with open(filename, "wb") as fp:
pickle.dump(filename, fp)
def load_contamination_ngrams(self, filename):
with open(filename, 'rb') as fp:
with open(filename, "rb") as fp:
self.dirt_ngrams = pickle.load(fp)
##############
# Call these :)
##############
......@@ -152,7 +158,7 @@ class Janitor:
def clean(self, dirty_string):
"""Clean a string (e.g. a training set) by removing all ngrams previously
reigstered as contaminants. Returns a list of clean chunks, or empty if
registered as contaminants. Returns a list of clean chunks, or empty if
the string was too dirty"""
if JANITOR_CPP:
return self.clean_cpp(dirty_string)
......@@ -171,11 +177,11 @@ class Janitor:
end = min(len(dirty_string), end + self.window_to_remove)
if start - splice_idx > self.minimum_slice_length:
clean_chunks.append(dirty_string[splice_idx: start])
clean_chunks.append(dirty_string[splice_idx:start])
splice_idx = end
if end < len(dirty_string) - self.minimum_slice_length:
clean_chunks.append(dirty_string[end+1:])
clean_chunks.append(dirty_string[end + 1 :])
return clean_chunks
......@@ -184,10 +190,14 @@ class Janitor:
##############
def register_contaminant_cpp(self, dirt_string):
self.dirt_ngrams.update(janitor_util.clean_ngram(dirt_string, self.delete_chars, self.ngram_n))
self.dirt_ngrams.update(
janitor_util.clean_ngram(dirt_string, self.delete_chars, self.ngram_n)
)
def clean_cpp(self, dirty_string):
contamination_indices = janitor_util.clean_ngram_with_indices(dirty_string, self.delete_chars, self.ngram_n)
contamination_indices = janitor_util.clean_ngram_with_indices(
dirty_string, self.delete_chars, self.ngram_n
)
return self._split_chunks(dirty_string, contamination_indices)
##############
......@@ -198,7 +208,9 @@ class Janitor:
return s.translate(self.translation_table)
def register_contaminant_python(self, dirt_string):
self.dirt_ngrams.update(word_ngrams(self.normalize_string(dirt_string), self.ngram_n))
self.dirt_ngrams.update(
word_ngrams(self.normalize_string(dirt_string), self.ngram_n)
)
def clean_python(self, dirty_string):
contamination_indices = (
......@@ -249,29 +261,29 @@ class Janitor:
# data = f.read()
# jan = Janitor(too_dirty_cutoff=1000)
# jan.register_contaminant('''
# theories is that there is a connection between &quot;geekdom&quot; and autism.
# theories is that there is a connection between &quot;geekdom&quot; and autism.
# This is hinted, for instance, by a ''Wired Magazine'' article in 2001 entitled &quot;
# The [[Geek]] Syndrome&quot;, which is a point argued by many in the autism rights
# movement{{ref|Wired}}. This article, many professionals assert, is just one example of
# The [[Geek]] Syndrome&quot;, which is a point argued by many in the autism rights
# movement{{ref|Wired}}. This article, many professionals assert, is just one example of
# the media's application of mental disease labels to what is actually variant normal behavior
# &amp;mdash;they argue that shyness, lack of athletic ability or social skills, and intellectual
# interests, even when they seem unusual to others, are not in themselves signs of autism or
# interests, even when they seem unusual to others, are not in themselves signs of autism or
# Asperger's syndrome. Others assert that it is actually the medical profession which is applying
# mental disease labels to children who in the past would have simply been accepted as a little
# different or even labeled 'gifted'. See [[clinomorphism]] for further discussion of this issue.
# Due to the recent publicity surrounding autism and autis
# ultan Al Nahyan]] granted [[Petroleum]] concessions, and oil was first found in 1958. At first,
# oil money had a marginal impact. A few lowrise concete buildings were erected, and the first
# paved road was completed in 1961, but Sheikh Shakbut, uncertain whether the new oil royalties
# would last, took a cautious approach, prefering to save the revenue rather than investing it in
# development. His brother, [[Zayed bin Sultan Al Nahayan]], saw that oil wealth had the potential
# to transform Abu Dhabi. The ruling Al Nahayan family decided that Sheikh Zayed should replace his
# brother as Ruler and carry out his vision of developing the country. On [[August 6]], [[1966]],
# with the assistance of the British, Sheikh Zayed became the new ruler. See generally, Al-Fahim, M,
# ''From Rags to Riches: A Story of Abu Dhabi'', Chapter Six (London Centre of Arab Studies, 1995),
# ISBN 1 900404 00 1. With the announcement by Britain in 1968 that it would withdraw from the
# Gulf area by 1971, Sheikh Zayed became the main driving force behind the formation of the
# [[United Arab Emirates]]. After the Emirates gained independence in 1971,
# oil money had a marginal impact. A few lowrise concete buildings were erected, and the first
# paved road was completed in 1961, but Sheikh Shakbut, uncertain whether the new oil royalties
# would last, took a cautious approach, preferring to save the revenue rather than investing it in
# development. His brother, [[Zayed bin Sultan Al Nahayan]], saw that oil wealth had the potential
# to transform Abu Dhabi. The ruling Al Nahayan family decided that Sheikh Zayed should replace his
# brother as Ruler and carry out his vision of developing the country. On [[August 6]], [[1966]],
# with the assistance of the British, Sheikh Zayed became the new ruler. See generally, Al-Fahim, M,
# ''From Rags to Riches: A Story of Abu Dhabi'', Chapter Six (London Centre of Arab Studies, 1995),
# ISBN 1 900404 00 1. With the announcement by Britain in 1968 that it would withdraw from the
# Gulf area by 1971, Sheikh Zayed became the main driving force behind the formation of the
# [[United Arab Emirates]]. After the Emirates gained independence in 1971,
# ''')
# """
......
lm_eval/evaluator.py
View file @ a2cada5d
......@@ -11,19 +11,29 @@ import numpy as np
from lm_eval.utils import positional_deprecated, run_task_tests
from lm_eval.decontamination.decontaminate import get_train_overlap
@positional_deprecated
def simple_evaluate(model, model_args=None, tasks=[],
num_fewshot=0, batch_size=None, device=None,
no_cache=False, limit=None, bootstrap_iters=100000,
description_dict=None, check_integrity=False,
decontamination_ngrams_path=None):
def simple_evaluate(
model,
model_args=None,
tasks=[],
num_fewshot=0,
batch_size=None,
device=None,
no_cache=False,
limit=None,
bootstrap_iters=100000,
description_dict=None,
check_integrity=False,
decontamination_ngrams_path=None,
):
"""Instantiate and evaluate a model on a list of tasks.
:param model: Union[str, LM]
Name of model or LM object, see lm_eval.models.get_model
:param model_args: Optional[str]
String arguments for each model class, see LM.create_from_arg_string.
String arguments for each model class, see LM.create_from_arg_string.
Ignored if `model` argument is a LM object.
:param tasks: list[Union[str, Task]]
List of task names or Task objects. Task objects will be taken to have name task.EVAL_HARNESS_NAME if defined and type(task).__name__ otherwise.
......@@ -40,7 +50,7 @@ def simple_evaluate(model, model_args=None, tasks=[],
:param bootstrap_iters:
Number of iterations for bootstrap statistics
:param description_dict: dict[str, str]
Dictionary of custom task descriptions of the form: `task_name: description`
Dictionary of custom task descriptions of the form: `task_name: description`
:param check_integrity: bool
Whether to run the relevant part of the test suite for the tasks
:return
......@@ -52,19 +62,25 @@ def simple_evaluate(model, model_args=None, tasks=[],
assert tasks != [], "No tasks specified"
if isinstance(model, str):
if model_args is None: model_args = ""
lm = lm_eval.models.get_model(model).create_from_arg_string(model_args, {
'batch_size': batch_size, 'device': device
})
if model_args is None:
model_args = ""
lm = lm_eval.models.get_model(model).create_from_arg_string(
model_args, {"batch_size": batch_size, "device": device}
)
else:
assert isinstance(model, lm_eval.base.LM)
lm = model
if not no_cache:
lm = lm_eval.base.CachingLM(
lm, 'lm_cache/' + model + '_' + model_args.replace('=', '-').replace(',', '_').replace('/', '-') + '.db'
lm,
"lm_cache/"
+ model
+ "_"
+ model_args.replace("=", "-").replace(",", "_").replace("/", "-")
+ ".db",
)
task_dict = lm_eval.tasks.get_task_dict(tasks)
if check_integrity:
......@@ -76,7 +92,7 @@ def simple_evaluate(model, model_args=None, tasks=[],
num_fewshot=num_fewshot,
limit=limit,
description_dict=description_dict,
decontamination_ngrams_path=decontamination_ngrams_path,
decontamination_ngrams_path=decontamination_ngrams_path,
)
# add info about the model and few shot config
......@@ -89,16 +105,26 @@ def simple_evaluate(model, model_args=None, tasks=[],
"no_cache": no_cache,
"limit": limit,
"bootstrap_iters": bootstrap_iters,
"description_dict": description_dict
"description_dict": description_dict,
}
return results
decontaminate_suffix = "_decontaminate"
@positional_deprecated
def evaluate(lm, task_dict, provide_description=None, num_fewshot=0, limit=None, bootstrap_iters=100000, description_dict=None,
decontamination_ngrams_path=None):
def evaluate(
lm,
task_dict,
provide_description=None,
num_fewshot=0,
limit=None,
bootstrap_iters=100000,
description_dict=None,
decontamination_ngrams_path=None,
):
"""Instantiate and evaluate a model on a list of tasks.
:param lm: obj
......@@ -114,7 +140,7 @@ def evaluate(lm, task_dict, provide_description=None, num_fewshot=0, limit=None,
:param bootstrap_iters:
Number of iterations for bootstrap statistics
:param description_dict: dict[str, str]
Dictionary of custom task descriptions of the form: `task_name: description`
Dictionary of custom task descriptions of the form: `task_name: description`
:return
Dictionary of results
"""
......@@ -124,14 +150,16 @@ def evaluate(lm, task_dict, provide_description=None, num_fewshot=0, limit=None,
assert not provide_description # not implemented.
if provide_description is not None:
# nudge people to not specify it at all
print("WARNING: provide_description is deprecated and will be removed in a future version in favor of description_dict")
print(
"WARNING: provide_description is deprecated and will be removed in a future version in favor of description_dict"
)
decontaminate = decontamination_ngrams_path is not None
task_dict_items = [
(name, task)
for name, task in task_dict.items()
if(task.has_validation_docs() or task.has_test_docs())
if (task.has_validation_docs() or task.has_test_docs())
]
results = collections.defaultdict(dict)
......@@ -140,7 +168,7 @@ def evaluate(lm, task_dict, provide_description=None, num_fewshot=0, limit=None,
requests = collections.defaultdict(list)
requests_origin = collections.defaultdict(list)
overlaps = collections.defaultdict(list) # {task_name: contaminated_docs}
overlaps = collections.defaultdict(list) # {task_name: contaminated_docs}
# If we ever run into issues where the eval tasks don't fit in memory and we can't afford a machine with bigger
# memory, we can always modify this plumbing to support that, but I didn't want to include it just yet because
......@@ -159,9 +187,9 @@ def evaluate(lm, task_dict, provide_description=None, num_fewshot=0, limit=None,
# TODO: the test-fallback-to-val system isn't final, we should revisit it at some point
if task.has_test_docs():
task_doc_func = task.test_docs
task_set = "test" # Required for caching in the decontamination
task_set = "test" # Required for caching in the decontamination
elif task.has_validation_docs():
task_set = "val" # Required for caching in the decontamination
task_set = "val" # Required for caching in the decontamination
task_doc_func = task.validation_docs
else:
raise RuntimeError("Task has neither test_docs nor validation_docs")
......@@ -172,19 +200,22 @@ def evaluate(lm, task_dict, provide_description=None, num_fewshot=0, limit=None,
rnd.seed(42)
rnd.shuffle(task_docs)
description = description_dict[task_name] if description_dict and task_name in description_dict else ""
description = (
description_dict[task_name]
if description_dict and task_name in description_dict
else ""
)
for doc_id, doc in enumerate(itertools.islice(task_docs, 0, limit)):
if decontaminate and task.should_decontaminate():
docs_for_decontamination[(task_name, task_set)].append(task.doc_to_decontamination_query(doc))
docs_for_decontamination[(task_name, task_set)].append(
task.doc_to_decontamination_query(doc)
)
docs[(task_name, doc_id)] = doc
ctx = task.fewshot_context(
doc=doc,
num_fewshot=num_fewshot,
rnd=rnd,
description=description
doc=doc, num_fewshot=num_fewshot, rnd=rnd, description=description
)
reqs = task.construct_requests(doc, ctx)
if not isinstance(reqs, (list, tuple)):
......@@ -198,7 +229,9 @@ def evaluate(lm, task_dict, provide_description=None, num_fewshot=0, limit=None,
# Compare all tasks/sets at once to ensure a single training set scan
if decontaminate:
print("Finding train/test overlap, please wait...")
overlaps = get_train_overlap(docs_for_decontamination, decontamination_ngrams_path, limit)
overlaps = get_train_overlap(
docs_for_decontamination, decontamination_ngrams_path, limit
)
# all responses for each (task, doc)
process_res_queue = collections.defaultdict(list)
......@@ -212,11 +245,13 @@ def evaluate(lm, task_dict, provide_description=None, num_fewshot=0, limit=None,
print("Running", reqtype, "requests")
resps = getattr(lm, reqtype)([req.args for req in reqs])
resps = [x if req.index is None else x[req.index] for x, req in zip(resps, reqs)]
resps = [
x if req.index is None else x[req.index] for x, req in zip(resps, reqs)
]
for resp, (i, task_name, doc, doc_id) in zip(resps, requests_origin[reqtype]):
process_res_queue[(task_name, doc_id)].append((i, resp))
vals = collections.defaultdict(list)
# unpack results and sort back in order and return control to Task
......@@ -235,13 +270,15 @@ def evaluate(lm, task_dict, provide_description=None, num_fewshot=0, limit=None,
if decontaminate and task_name in overlaps:
if doc_id not in overlaps[task_name]:
vals[(task_name, metric + decontaminate_suffix)].append(value)
# aggregate results
for (task_name, metric), items in vals.items():
task = task_dict[task_name]
real_metric = metric # key when looking up the metric with task.aggregation
real_metric = metric # key when looking up the metric with task.aggregation
if metric.endswith(decontaminate_suffix):
real_metric = metric.replace(decontaminate_suffix, "") # decontaminated still uses the same metric
real_metric = metric.replace(
decontaminate_suffix, ""
) # decontaminated still uses the same metric
results[task_name][metric] = task.aggregation()[real_metric](items)
# hotfix: bleu, chrf, ter seem to be really expensive to bootstrap
......@@ -249,16 +286,15 @@ def evaluate(lm, task_dict, provide_description=None, num_fewshot=0, limit=None,
stderr = lm_eval.metrics.stderr_for_metric(
metric=task.aggregation()[real_metric],
bootstrap_iters=min(bootstrap_iters, 1000) if metric in ["bleu", "chrf", "ter"] else bootstrap_iters,
bootstrap_iters=min(bootstrap_iters, 1000)
if metric in ["bleu", "chrf", "ter"]
else bootstrap_iters,
)
if stderr is not None:
results[task_name][metric + "_stderr"] = stderr(items)
return {
"results": dict(results),
"versions": dict(versions)
}
return {"results": dict(results), "versions": dict(versions)}
def make_table(result_dict):
......@@ -280,9 +316,9 @@ def make_table(result_dict):
if m + "_stderr" in dic:
se = dic[m + "_stderr"]
values.append([k, version, m, '%.4f' % v, '±', '%.4f' % se])
values.append([k, version, m, "%.4f" % v, "±", "%.4f" % se])
else:
values.append([k, version, m, '%.4f' % v, '', ''])
values.append([k, version, m, "%.4f" % v, "", ""])
k = ""
version = ""
md_writer.value_matrix = values
......
lm_eval/metrics.py
View file @ a2cada5d
......@@ -103,6 +103,7 @@ def weighted_mean(items):
def weighted_perplexity(items):
return math.exp(-weighted_mean(items))
def bits_per_byte(items):
return -weighted_mean(items) / math.log(2)
......@@ -184,8 +185,10 @@ def _sacreformat(refs, preds):
return refs, preds
# stderr stuff
class _bootstrap_internal:
def __init__(self, f, n):
self.f = f
......@@ -203,9 +206,10 @@ class _bootstrap_internal:
def bootstrap_stderr(f, xs, iters):
import multiprocessing as mp
pool = mp.Pool(mp.cpu_count())
# this gives a biased estimate of the stderr (i.e w/ the mean, it gives something
# equivalent to stderr calculated without Bessel's correction in the stddev.
# equivalent to stderr calculated without Bessel's correction in the stddev.
# Unfortunately, I haven't been able to figure out what the right correction is
# to make the bootstrap unbiased - i considered multiplying by sqrt(n/(n-1)) but
# that would be ad-hoc and I can't prove that that would actually be an unbiased estimator)
......@@ -213,10 +217,15 @@ def bootstrap_stderr(f, xs, iters):
res = []
chunk_size = min(1000, iters)
from tqdm import tqdm
print("bootstrapping for stddev:", f.__name__)
for bootstrap in tqdm(pool.imap(
for bootstrap in tqdm(
pool.imap(
_bootstrap_internal(f, chunk_size),
[(i, xs) for i in range(iters // chunk_size)]), total=iters // chunk_size):
[(i, xs) for i in range(iters // chunk_size)],
),
total=iters // chunk_size,
):
# sample w replacement
res.extend(bootstrap)
......@@ -238,17 +247,13 @@ def stderr_for_metric(metric, bootstrap_iters):
if metric in bootstrappable:
return lambda x: bootstrap_stderr(metric, x, iters=bootstrap_iters)
stderr = {
mean: mean_stderr,
acc_all: acc_all_stderr
}
stderr = {mean: mean_stderr, acc_all: acc_all_stderr}
return stderr.get(metric, None)
def yesno(x):
if x:
return 'yes'
return "yes"
else:
return 'no'
return "no"
lm_eval/models/gpt2.py
View file @ a2cada5d
......@@ -4,44 +4,70 @@ from lm_eval.base import BaseLM
class HFLM(BaseLM):
def __init__(self, device='cuda', pretrained='gpt2', revision='main', subfolder=None, tokenizer=None, batch_size=1):
def __init__(
self,
device="cuda",
pretrained="gpt2",
revision="main",
subfolder=None,
tokenizer=None,
batch_size=1,
):
super().__init__()
assert isinstance(device, str)
assert isinstance(pretrained, str)
assert isinstance(batch_size, int)
if device:
if device:
if device not in ["cuda", "cpu"]:
device = int(device)
self._device = torch.device(device)
print(f"Using device '{device}'")
else:
print("Device not specificed")
print("Device not specified")
print(f"Cuda Available? {torch.cuda.is_available()}")
self._device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
self._device = (
torch.device("cuda")
if torch.cuda.is_available()
else torch.device("cpu")
)
# TODO: update this to be less of a hack once subfolder is fixed in HF
self.gpt2 = transformers.AutoModelForCausalLM.from_pretrained(
pretrained, revision=revision + ("/" + subfolder if subfolder is not None else "")
pretrained,
revision=revision + ("/" + subfolder if subfolder is not None else ""),
).to(self.device)
self.gpt2.eval()
# pretrained tokenizer for neo is broken for now so just hard-coding this to gpt2
self.tokenizer = transformers.AutoTokenizer.from_pretrained(
pretrained if tokenizer is None else tokenizer, revision=revision, subfolder=subfolder)
pretrained if tokenizer is None else tokenizer,
revision=revision,
subfolder=subfolder,
)
assert isinstance(self.tokenizer, (
transformers.GPT2Tokenizer, transformers.GPT2TokenizerFast,
transformers.T5Tokenizer, transformers.T5TokenizerFast,
)), "this tokenizer has not been checked for compatibility yet!"
assert isinstance(
self.tokenizer,
(
transformers.GPT2Tokenizer,
transformers.GPT2TokenizerFast,
transformers.T5Tokenizer,
transformers.T5TokenizerFast,
),
), "this tokenizer has not been checked for compatibility yet!"
self.vocab_size = self.tokenizer.vocab_size
if isinstance(self.tokenizer, (transformers.GPT2Tokenizer, transformers.GPT2TokenizerFast)):
assert self.tokenizer.encode('hello\n\nhello') == [31373, 198, 198, 31373], \
self.tokenizer.encode('hello\n\nhello')
if isinstance(
self.tokenizer, (transformers.GPT2Tokenizer, transformers.GPT2TokenizerFast)
):
assert self.tokenizer.encode("hello\n\nhello") == [
31373,
198,
198,
31373,
], self.tokenizer.encode("hello\n\nhello")
# multithreading and batching
self.batch_size_per_gpu = batch_size # todo: adaptive batch size
......@@ -80,7 +106,7 @@ class HFLM(BaseLM):
def tok_encode(self, string: str):
return self.tokenizer.encode(string, add_special_tokens=False)
def tok_decode(self, tokens):
return self.tokenizer.decode(tokens)
......@@ -94,13 +120,10 @@ class HFLM(BaseLM):
"""
with torch.no_grad():
return self.gpt2(inps)[0][:, :, :50257]
def _model_generate(self, context, max_length, eos_token_id):
return self.gpt2.generate(
context,
max_length=max_length,
eos_token_id=eos_token_id,
do_sample=False
context, max_length=max_length, eos_token_id=eos_token_id, do_sample=False
)
......
lm_eval/models/gpt3.py
View file @ a2cada5d
......@@ -31,22 +31,24 @@ def get_result(response, ctxlen):
if top_token != token:
is_greedy = False
break
return continuation_logprobs, is_greedy
def oa_completion(**kwargs):
""" Query OpenAI API for completion.
"""Query OpenAI API for completion.
Retry with back-off until they respond
"""
import openai
backoff_time = 3
while True:
try:
return openai.Completion.create(**kwargs)
except openai.error.OpenAIError:
import traceback
traceback.print_exc()
time.sleep(backoff_time)
backoff_time *= 1.5
......@@ -66,16 +68,19 @@ class GPT3LM(BaseLM):
super().__init__()
import openai
self.engine = engine
self.tokenizer = transformers.GPT2TokenizerFast.from_pretrained('gpt2')
self.tokenizer = transformers.GPT2TokenizerFast.from_pretrained("gpt2")
self.vocab_size = self.tokenizer.vocab_size
# to make the annoying "Using pad_token, but it is not set yet." error go away
self.tokenizer.pad_token = "<|endoftext|>"
assert self.tokenizer.encode('hello\n\nhello') == [31373, 198, 198, 31373]
assert self.tokenizer.encode("hello\n\nhello") == [31373, 198, 198, 31373]
self.truncate = truncate
self.end_of_text_token_id = self.tokenizer.convert_tokens_to_ids(["<|endoftext|>"])[0]
self.end_of_text_token_id = self.tokenizer.convert_tokens_to_ids(
["<|endoftext|>"]
)[0]
# Read from environment variable OPENAI_API_SECRET_KEY
openai.api_key = os.environ["OPENAI_API_SECRET_KEY"]
......@@ -105,7 +110,7 @@ class GPT3LM(BaseLM):
def tok_encode(self, string: str):
return self.tokenizer.encode(string, add_special_tokens=False)
def tok_decode(self, tokens):
return self.tokenizer.decode(tokens)
......@@ -118,17 +123,22 @@ class GPT3LM(BaseLM):
# we care about and so we need some kind of backup for when it isn't
toks = x[1] + x[2]
return -len(toks), tuple(toks)
reord = utils.Reorderer(requests, _collate)
for chunk in tqdm(list(utils.chunks(reord.get_reordered(), self.REQ_CHUNK_SIZE)), disable=disable_tqdm):
re_ord = utils.Reorderer(requests, _collate)
for chunk in tqdm(
list(utils.chunks(re_ord.get_reordered(), self.REQ_CHUNK_SIZE)),
disable=disable_tqdm,
):
inps = []
ctxlens = []
for cache_key, context_enc, continuation_enc in chunk:
# max_length+1 because the API takes up to 2049 tokens, including the first context token
inp = (context_enc + continuation_enc)[-(self.max_length+1):]
inp = (context_enc + continuation_enc)[-(self.max_length + 1) :]
# TODO: the logic is much simpler if we just look at the length of continuation tokens
ctxlen = len(context_enc) - max(0, len(context_enc) + len(continuation_enc) - (self.max_length+1))
ctxlen = len(context_enc) - max(
0, len(context_enc) + len(continuation_enc) - (self.max_length + 1)
)
inps.append(inp)
ctxlens.append(ctxlen)
......@@ -137,11 +147,14 @@ class GPT3LM(BaseLM):
engine=self.engine,
prompt=inps,
echo=True,
max_tokens=0, temperature=0.,
max_tokens=0,
temperature=0.0,
logprobs=10,
)
for resp, ctxlen, (cache_key, context_enc, continuation_enc) in zip(response.choices, ctxlens, chunk):
for resp, ctxlen, (cache_key, context_enc, continuation_enc) in zip(
response.choices, ctxlens, chunk
):
answer = get_result(resp, ctxlen)
res.append(answer)
......@@ -150,7 +163,7 @@ class GPT3LM(BaseLM):
if cache_key is not None:
self.cache_hook.add_partial("loglikelihood", cache_key, answer)
return reord.get_original(res)
return re_ord.get_original(res)
def greedy_until(self, requests):
if not requests:
......@@ -160,8 +173,8 @@ class GPT3LM(BaseLM):
def _collate(x):
toks = self.tok_encode(x[0])
return len(toks), x[0]
reord = utils.Reorderer(requests, _collate)
re_ord = utils.Reorderer(requests, _collate)
def sameuntil_chunks(xs, size):
ret = []
......@@ -172,39 +185,41 @@ class GPT3LM(BaseLM):
ret = []
lastuntil = x[1]
ret.append(x)
if ret:
yield ret, lastuntil
# todo: more intelligent batching for heterogeneous `until`
for chunk, until in tqdm(list(sameuntil_chunks(reord.get_reordered(), self.REQ_CHUNK_SIZE))):
for chunk, until in tqdm(
list(sameuntil_chunks(re_ord.get_reordered(), self.REQ_CHUNK_SIZE))
):
inps = []
for context, _ in chunk:
context_enc = self.tok_encode(context)
inp = context_enc[-(self.max_length - self.max_gen_toks):]
inp = context_enc[-(self.max_length - self.max_gen_toks) :]
inps.append(inp)
response = oa_completion(
engine=self.engine,
prompt=inps,
max_tokens=self.max_gen_toks,
temperature=0.,
max_tokens=self.max_gen_toks,
temperature=0.0,
logprobs=10,
stop=until,
)
for resp, (context, until_) in zip(response.choices, chunk):
s = resp['text']
s = resp["text"]
for term in until_:
s = s.split(term)[0]
# partial caching
self.cache_hook.add_partial("greedy_until", (context, until_), s)
res.append(s)
return reord.get_original(res)
return re_ord.get_original(res)
def _model_call(self, inps):
# Isn't used because we override _loglikelihood_tokens
......
lm_eval/tasks/__init__.py
View file @ a2cada5d
......@@ -22,14 +22,12 @@ from . import naturalqs
from . import sat
from . import arithmetic
from . import lambada
from . import race
from . import piqa
from . import prost
from . import mc_taco
from . import triviaqa
from . import pubmedqa
from . import sciq
from . import webqs
from . import qasper
from . import qa4mre
from . import translation
......@@ -59,8 +57,8 @@ from . import storycloze
# 6 total
gpt3_translation_benchmarks = {
"wmt14": ['en-fr', 'fr-en'], # French
"wmt16": ['en-ro', 'ro-en', 'de-en', 'en-de'], # German, Romanian
"wmt14": ["en-fr", "fr-en"], # French
"wmt16": ["en-ro", "ro-en", "de-en", "en-de"], # German, Romanian
}
......@@ -68,7 +66,7 @@ gpt3_translation_benchmarks = {
selected_translation_benchmarks = {
**gpt3_translation_benchmarks,
"wmt20": sacrebleu.get_langpairs_for_testset("wmt20"),
"iwslt17": ['en-ar', 'ar-en'] # Arabic
"iwslt17": ["en-ar", "ar-en"], # Arabic
}
# 319 total
......@@ -92,7 +90,7 @@ TASK_REGISTRY = {
"rte": glue.RTE,
"qnli": glue.QNLI,
"qqp": glue.QQP,
#"stsb": glue.STSB, # not implemented yet
# "stsb": glue.STSB, # not implemented yet
"sst": glue.SST,
"wnli": glue.WNLI,
# SuperGLUE
......@@ -103,34 +101,26 @@ TASK_REGISTRY = {
"record": superglue.ReCoRD,
"wic": superglue.WordsInContext,
"wsc": superglue.SGWinogradSchemaChallenge,
# Order by benchmark/genre?
"coqa": coqa.CoQA,
"drop": drop.DROP,
"lambada": lambada.LAMBADA,
"lambada_cloze": lambada_cloze.LAMBADA_cloze,
# multilingual lambada
**lambada_multilingual.construct_tasks(),
"wikitext": wikitext.WikiText,
# "cbt-cn": cbt.CBTCN, # disabled pending context length fix
# "cbt-ne": cbt.CBTNE, # disabled pending context length fix
"piqa": piqa.PiQA,
"prost": prost.PROST,
"mc_taco": mc_taco.MCTACO,
# Science related
"pubmedqa" : pubmedqa.Pubmed_QA,
"sciq" : sciq.SciQ,
"pubmedqa": pubmedqa.Pubmed_QA,
"sciq": sciq.SciQ,
"qasper": qasper.QASPER,
"qa4mre_2011" : qa4mre.QA4MRE_2011,
"qa4mre_2012" : qa4mre.QA4MRE_2012,
"qa4mre_2013" : qa4mre.QA4MRE_2013,
"qa4mre_2011": qa4mre.QA4MRE_2011,
"qa4mre_2012": qa4mre.QA4MRE_2012,
"qa4mre_2013": qa4mre.QA4MRE_2013,
"triviaqa": triviaqa.TriviaQA,
"arc_easy": arc.ARCEasy,
"arc_challenge": arc.ARCChallenge,
......@@ -142,7 +132,7 @@ TASK_REGISTRY = {
"squad2": squad.SQuAD2,
"race": race.RACE,
# "naturalqs": naturalqs.NaturalQs, # not implemented yet
"headqa": headqa.HeadQAEsDeprecated, # for backwards compat - headqa used to default to es
"headqa": headqa.HeadQAEsDeprecated, # for backwards compat - headqa used to default to es
"headqa_es": headqa.HeadQAEs,
"headqa_en": headqa.HeadQAEn,
"mathqa": mathqa.MathQA,
......@@ -152,21 +142,17 @@ TASK_REGISTRY = {
"anli_r1": anli.ANLIRound1,
"anli_r2": anli.ANLIRound2,
"anli_r3": anli.ANLIRound3,
"ethics_cm": hendrycks_ethics.EthicsCM,
"ethics_deontology": hendrycks_ethics.EthicsDeontology,
"ethics_justice": hendrycks_ethics.EthicsJustice,
"ethics_utilitarianism_original": hendrycks_ethics.EthicsUtilitarianismOriginal,
"ethics_utilitarianism": hendrycks_ethics.EthicsUtilitarianism,
"ethics_virtue": hendrycks_ethics.EthicsVirtue,
"truthfulqa_mc": truthfulqa.TruthfulQAMultipleChoice,
"truthfulqa_gen": truthfulqa.TruthfulQAGeneration,
"truthfulqa_mc": truthfulqa.TruthfulQAMultipleChoice,
"truthfulqa_gen": truthfulqa.TruthfulQAGeneration,
# dialogue
"mutual": mutual.MuTual,
"mutual_plus": mutual.MuTualPlus,
# math
"math_algebra": hendrycks_math.MathAlgebra,
"math_counting_and_prob": hendrycks_math.MathCountingAndProbability,
......@@ -177,7 +163,6 @@ TASK_REGISTRY = {
"math_precalc": hendrycks_math.MathPrecalculus,
"math_asdiv": asdiv.Asdiv,
"gsm8k": gsm8k.GradeSchoolMath8K,
# arithmetic
"arithmetic_2da": arithmetic.Arithmetic2DPlus,
"arithmetic_2ds": arithmetic.Arithmetic2DMinus,
......@@ -191,22 +176,18 @@ TASK_REGISTRY = {
"arithmetic_1dc": arithmetic.Arithmetic1DComposite,
# TODO Perhaps make these groups of tasks
# e.g. anli, arithmetic, openai_translations, harness_translations
# hendrycksTest (57 tasks)
**hendrycks_test.create_all_tasks(),
# e.g. wmt14-fr-en
**translation.create_tasks_from_benchmarks(gpt3_translation_benchmarks),
# chef's selection, mostly wmt20
**translation.create_tasks_from_benchmarks(selected_translation_benchmarks),
# Word Scrambling and Manipulation Tasks
"anagrams1": unscramble.Anagrams1,
"anagrams2": unscramble.Anagrams2,
"cycle_letters": unscramble.CycleLetters,
"random_insertion": unscramble.RandomInsertion,
"reversed_words": unscramble.ReversedWords,
# Pile
"pile_arxiv": pile.PileArxiv,
"pile_books3": pile.PileBooks3,
......@@ -230,7 +211,6 @@ TASK_REGISTRY = {
"pile_ubuntu-irc": pile.PileUbuntuIrc,
"pile_wikipedia": pile.PileWikipedia,
"pile_youtubesubtitles": pile.PileYoutubeSubtitles,
# BLiMP
"blimp_adjunct_island": blimp.BlimpAdjunctIsland,
"blimp_anaphor_gender_agreement": blimp.BlimpAnaphorGenderAgreement,
......@@ -299,7 +279,6 @@ TASK_REGISTRY = {
"blimp_wh_vs_that_no_gap_long_distance": blimp.BlimpWhVsThatNoGapLongDistance,
"blimp_wh_vs_that_with_gap": blimp.BlimpWhVsThatWithGap,
"blimp_wh_vs_that_with_gap_long_distance": blimp.BlimpWhVsThatWithGapLongDistance,
# Requires manual download of data.
# "storycloze_2016": storycloze.StoryCloze2016,
# "storycloze_2018": storycloze.StoryCloze2018,
......@@ -313,7 +292,7 @@ ALL_TASKS = sorted(list(TASK_REGISTRY))
def get_task(task_name):
try:
return TASK_REGISTRY[task_name]
except KeyError as e:
except KeyError:
print("Available tasks:")
pprint(TASK_REGISTRY)
raise KeyError(f"Missing task {task_name}")
......@@ -323,19 +302,25 @@ def get_task_name_from_object(task_object):
for name, class_ in TASK_REGISTRY.items():
if class_ is task_object:
return name
# this gives a mechanism for non-registered tasks to have a custom name anyways when reporting
return task_object.EVAL_HARNESS_NAME if hasattr(task_object, "EVAL_HARNESS_NAME") else type(task_object).__name__
return (
task_object.EVAL_HARNESS_NAME
if hasattr(task_object, "EVAL_HARNESS_NAME")
else type(task_object).__name__
)
def get_task_dict(task_name_list: List[Union[str, lm_eval.base.Task]]):
task_name_dict = {
task_name: get_task(task_name)()
for task_name in task_name_list if isinstance(task_name, str)
for task_name in task_name_list
if isinstance(task_name, str)
}
task_name_from_object_dict = {
get_task_name_from_object(task_object): task_object
for task_object in task_name_list if not isinstance(task_object, str)
for task_object in task_name_list
if not isinstance(task_object, str)
}
assert set(task_name_dict.keys()).isdisjoint(set(task_name_from_object_dict.keys()))
return {**task_name_dict, **task_name_from_object_dict}
lm_eval/tasks/anli.py
View file @ a2cada5d
......@@ -61,10 +61,15 @@ class ANLIBase(Task):
def doc_to_text(self, doc):
# OA does this a bit weirdly: they prepend "anli 1: anli 1: " to the beginning
# of the prompt (yes, repeating it!). also, " True, False, or Neither?" is directly
# appended onto the question, with no "Answer:" or even a newline. Do we *really*
# of the prompt (yes, repeating it!). also, " True, False, or Neither?" is directly
# appended onto the question, with no "Answer:" or even a newline. Do we *really*
# want to do it exactly as OA did?
return doc['premise'] + '\nQuestion: ' + doc['hypothesis'] + ' True, False, or Neither?\nAnswer:'
return (
doc["premise"]
+ "\nQuestion: "
+ doc["hypothesis"]
+ " True, False, or Neither?\nAnswer:"
)
def should_decontaminate(self):
return True
......@@ -76,27 +81,27 @@ class ANLIBase(Task):
# True = entailment
# False = contradiction
# Neither = neutral
return " " + ["True", "Neither", "False"][doc['label']]
return " " + ["True", "Neither", "False"][doc["label"]]
def construct_requests(self, doc, ctx):
""" Uses RequestFactory to construct Requests and returns an iterable of
"""Uses RequestFactory to construct Requests and returns an iterable of
Requests which will be sent to the LM.
:param doc:
The document as returned from training_docs, validation_docs, or test_docs.
:param ctx: str
The context string, generated by fewshot_context. This includes the natural
The context string, generated by fewshot_context. This includes the natural
language description, as well as the few shot examples, and the question
part of the document for `doc`.
part of the document for `doc`.
"""
ll_true, _ = rf.loglikelihood(ctx, " True")
ll_neither, _ = rf.loglikelihood(ctx, " Neither")
ll_false, _ = rf.loglikelihood(ctx, " False")
ll_true, _ = rf.loglikelihood(ctx, " True")
ll_neither, _ = rf.loglikelihood(ctx, " Neither")
ll_false, _ = rf.loglikelihood(ctx, " False")
return ll_true, ll_neither, ll_false
def process_results(self, doc, results):
"""Take a single document and the LM results and evaluates, returning a
dict where keys are the names of submetrics and values are the values of
"""Take a single document and the LM results and evaluates, returning a
dict where keys are the names of submetrics and values are the values of
the metric for that one document
:param doc:
......@@ -106,29 +111,23 @@ class ANLIBase(Task):
"""
gold = doc["label"]
pred = np.argmax(results)
return {
"acc": pred == gold
}
return {"acc": pred == gold}
def aggregation(self):
"""
:returns: {str: [float] -> float}
A dictionary where keys are the names of submetrics and values are
A dictionary where keys are the names of submetrics and values are
functions that aggregate a list of metrics
"""
return {
"acc": mean
}
return {"acc": mean}
def higher_is_better(self):
"""
:returns: {str: bool}
A dictionary where keys are the names of submetrics and values are
A dictionary where keys are the names of submetrics and values are
whether a higher value of the submetric is better
"""
return {
"acc": True
}
return {"acc": True}
class ANLIRound1(ANLIBase):
......
lm_eval/tasks/arithmetic.py
View file @ a2cada5d
......@@ -49,7 +49,7 @@ class Arithmetic(Task):
def test_docs(self):
return NotImplemented
def doc_to_text(self, doc):
return doc["context"]
......@@ -67,10 +67,8 @@ class Arithmetic(Task):
return is_prediction
def process_results(self, doc, results):
is_prediction, = results
return {
"acc": is_prediction
}
(is_prediction,) = results
return {"acc": is_prediction}
def aggregation(self):
return {
......@@ -78,9 +76,7 @@ class Arithmetic(Task):
}
def higher_is_better(self):
return {
"acc": True
}
return {"acc": True}
class Arithmetic2DPlus(Arithmetic):
......
lm_eval/tasks/asdiv.py
View file @ a2cada5d
......@@ -54,48 +54,41 @@ class Asdiv(Task):
def test_docs(self):
raise NotImplementedError("This dataset has no test docs")
def fewshot_context(self, doc, num_fewshot, provide_description=None, rnd=None, description=None):
def fewshot_context(
self, doc, num_fewshot, provide_description=None, rnd=None, description=None
):
assert num_fewshot == 0, "ASDiv is intended only for the zero-shot setting."
return super().fewshot_context(
doc=doc,
num_fewshot=num_fewshot,
rnd=rnd,
description=description
doc=doc, num_fewshot=num_fewshot, rnd=rnd, description=description
)
def doc_to_text(self, doc):
# TODO: add solution-type
return doc['body'] + '\n' + 'Question:' + doc['question'] + '\n' + 'Answer:'
return doc["body"] + "\n" + "Question:" + doc["question"] + "\n" + "Answer:"
def should_decontaminate(self):
return True
def doc_to_decontamination_query(self, doc):
return doc['body'] + " " + doc['question']
return doc["body"] + " " + doc["question"]
def doc_to_target(self, doc):
# TODO: add formula
answer = doc['answer'].split(' (')[0]
answer = doc["answer"].split(" (")[0]
return " " + answer
def construct_requests(self, doc, ctx):
ll, is_greedy = rf.loglikelihood(ctx, self.doc_to_target(doc))
return ll, is_greedy
def process_results(self, doc, results):
ll, is_greedy = results
return {
'acc': int(is_greedy)
}
return {"acc": int(is_greedy)}
def aggregation(self):
return {
'acc': mean
}
return {"acc": mean}
def higher_is_better(self):
return {
'acc': True
}
return {"acc": True}
lm_eval/tasks/blimp.py
View file @ a2cada5d
......@@ -28,7 +28,7 @@ _CITATION = """
eprint = {https://doi.org/10.1162/tacl_a_00321},
abstract = { We introduce The Benchmark of Linguistic Minimal Pairs (BLiMP),1 a challenge set for evaluating the linguistic knowledge of language models (LMs) on major grammatical phenomena in English. BLiMP consists of 67 individual datasets, each containing 1,000 minimal pairs—that is, pairs of minimally different sentences that contrast in grammatical acceptability and isolate specific phenomenon in syntax, morphology, or semantics. We generate the data according to linguist-crafted grammar templates, and human aggregate agreement with the labels is 96.4\%. We evaluate n-gram, LSTM, and Transformer (GPT-2 and Transformer-XL) LMs by observing whether they assign a higher probability to the acceptable sentence in each minimal pair. We find that state-of-the-art models identify morphological contrasts related to agreement reliably, but they struggle with some subtle semantic and syntactic phenomena, such as negative polarity items and extraction islands. }
}
"""
""" # noqa: W605
class BlimpTask(Task):
......@@ -37,7 +37,7 @@ class BlimpTask(Task):
def has_training_docs(self):
return False
def has_validation_docs(self):
return True
......@@ -50,9 +50,13 @@ class BlimpTask(Task):
# trained on this data.
return self.dataset["train"]
def fewshot_context(self, doc, num_fewshot, provide_description=None, rnd=None, description=None):
def fewshot_context(
self, doc, num_fewshot, provide_description=None, rnd=None, description=None
):
assert num_fewshot == 0
assert rnd is not None, "A `random.Random` generator argument must be provided to `rnd`"
assert (
rnd is not None
), "A `random.Random` generator argument must be provided to `rnd`"
assert not provide_description, (
"The `provide_description` arg will be removed in future versions. To prepend "
"a custom description to the context, supply the corresponding string via the "
......@@ -60,7 +64,9 @@ class BlimpTask(Task):
)
if provide_description is not None:
# nudge people to not specify it at all
print("WARNING: provide_description is deprecated and will be removed in a future version in favor of description_dict")
print(
"WARNING: provide_description is deprecated and will be removed in a future version in favor of description_dict"
)
return ""
......
lm_eval/tasks/cbt.py
View file @ a2cada5d
......@@ -2,7 +2,7 @@
The Children’s Book Test (CBT) from the paper:
https://research.fb.com/wp-content/uploads/2016/11/the_goldilocks_principle_reading_children_s_books_with_explicit_memory_representations.pdf
The Children's Book Test (CBT) is test of how well language models capture
The Children's Book Test (CBT) is test of how well language models capture
meaning in children's books. Unlike standard language modelling benchmarks,
it distinguishes the task of predicting syntactic function words from that
of predicting lower-frequency words, which carry greater semantic content.
......@@ -19,7 +19,7 @@ from lm_eval.metrics import mean
_CITATION = """
@misc{hill2016goldilocks,
title={The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations},
title={The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations},
author={Felix Hill and Antoine Bordes and Sumit Chopra and Jason Weston},
year={2016},
eprint={1511.02301},
......@@ -86,7 +86,9 @@ class CBTBase(Task):
return ""
def fewshot_examples(self, k, rnd):
assert k == 0, f"CBT is only implemented for the zero-shot setting. Given k={k}."
assert (
k == 0
), f"CBT is only implemented for the zero-shot setting. Given k={k}."
return super().fewshot_examples(k, rnd)
def construct_requests(self, doc, ctx):
......@@ -120,9 +122,7 @@ class CBTBase(Task):
"""
gold = doc["options"].index(doc["answer"])
pred = np.argmax(results)
return {
"acc": pred == gold
}
return {"acc": pred == gold}
def aggregation(self):
"""
......@@ -130,9 +130,7 @@ class CBTBase(Task):
A dictionary where keys are the names of submetrics and values are
functions that aggregate a list of metrics
"""
return {
"acc": mean
}
return {"acc": mean}
def higher_is_better(self):
"""
......@@ -140,9 +138,7 @@ class CBTBase(Task):
A dictionary where keys are the names of submetrics and values are
whether a higher value of the submetric is better
"""
return {
"acc": True
}
return {"acc": True}
class CBTCN(CBTBase):
......
lm_eval/tasks/coqa.py
View file @ a2cada5d
......@@ -2,9 +2,9 @@
CoQA: A Conversational Question Answering Challenge
https://arxiv.org/pdf/1808.07042.pdf
CoQA is a large-scale dataset for building Conversational Question Answering
systems. The goal of the CoQA challenge is to measure the ability of machines to
understand a text passage and answer a series of interconnected questions that
CoQA is a large-scale dataset for building Conversational Question Answering
systems. The goal of the CoQA challenge is to measure the ability of machines to
understand a text passage and answer a series of interconnected questions that
appear in a conversation.
Homepage: https://stanfordnlp.github.io/coqa/
......@@ -52,15 +52,17 @@ class CoQA(Task):
pass
def doc_to_text(self, doc):
# Given a passage p, the conversation history {q1, a1, . . . qi−1, ai−1}
# Given a passage p, the conversation history {q1, a1, . . . qi−1, ai−1}
# and a question qi, the task is to predict the answer ai
doc_text = doc["story"] + '\n\n'
for (q, a) in zip_longest(doc["questions"]["input_text"], doc["answers"]["input_text"][:-1]): # omit target answer ai
doc_text = doc["story"] + "\n\n"
for (q, a) in zip_longest(
doc["questions"]["input_text"], doc["answers"]["input_text"][:-1]
): # omit target answer ai
question = f"Q: {q}\n\n"
answer = f"A: {a}\n\n" if a is not None else "A:"
doc_text += question + answer
return doc_text
def should_decontaminate(self):
return True
......@@ -73,28 +75,30 @@ class CoQA(Task):
answers = []
answer_forturn = doc["answers"]["input_text"][turn_id - 1]
answers.append(answer_forturn)
additional_answers = doc.get("additional_answers")
if additional_answers:
for key in additional_answers:
additional_answer_for_turn = additional_answers[key]["input_text"][turn_id - 1]
additional_answer_for_turn = additional_answers[key]["input_text"][
turn_id - 1
]
if additional_answer_for_turn.lower() not in map(str.lower, answers):
answers.append(additional_answer_for_turn)
return answers
@classmethod
def get_answer_choice(self, raw_text):
# Function maps answers to CoQA answer categories
# ~ 1/5 of the CoQA answers are Yes/No
# ~ 1/5 of the CoQA answers are Yes/No
# ~ 2/3 of the CoQA answers are span-based
# (answers overlap with the passage ignoring punctuation and case mismatch)
if raw_text == "unknown":
return '0'
return "0"
if squad_metrics.normalize_answer(raw_text) == "yes":
return '1'
return "1"
if squad_metrics.normalize_answer(raw_text) == "no":
return '2'
return '3' # Not a yes/no question
return "2"
return "3" # Not a yes/no question
@staticmethod
def compute_scores(gold_list, pred):
......@@ -104,40 +108,45 @@ class CoQA(Task):
em_sum = 0.0
if len(gold_list) > 1:
for i in range(len(gold_list)):
gold_answers = gold_list[0:i] + gold_list[i + 1:]
gold_answers = gold_list[0:i] + gold_list[i + 1 :]
# predictions compared against (n) golds and take maximum
em_sum += max(squad_metrics.compute_exact(a, pred) for a in gold_answers)
em_sum += max(
squad_metrics.compute_exact(a, pred) for a in gold_answers
)
f1_sum += max(squad_metrics.compute_f1(a, pred) for a in gold_answers)
else:
em_sum += max(squad_metrics.compute_exact(a, pred) for a in gold_list)
f1_sum += max(squad_metrics.compute_f1(a, pred) for a in gold_list)
return {'em': em_sum / max(1, len(gold_list)), 'f1': f1_sum / max(1, len(gold_list))}
return {
"em": em_sum / max(1, len(gold_list)),
"f1": f1_sum / max(1, len(gold_list)),
}
def doc_to_target(self, doc, turnid=None):
# Default to prediction of last turn.
if turnid is None:
turnid = len(doc["questions"]["input_text"])
raw_text = doc['answers']["input_text"][turnid - 1]
raw_text = doc["answers"]["input_text"][turnid - 1]
return " " + raw_text
def construct_requests(self, doc, ctx):
""" Uses RequestFactory to construct Requests and returns an iterable of
"""Uses RequestFactory to construct Requests and returns an iterable of
Requests which will be sent to the LM.
:param doc:
The document as returned from training_docs, validation_docs, or test_docs.
:param ctx: str
The context string, generated by fewshot_context. This includes the natural
The context string, generated by fewshot_context. This includes the natural
language description, as well as the few shot examples, and the question
part of the document for `doc`.
part of the document for `doc`.
"""
cont_request = rf.greedy_until(ctx, ['\nQ:'])
cont_request = rf.greedy_until(ctx, ["\nQ:"])
return cont_request
def process_results(self, doc, results):
"""Take a single document and the LM results and evaluates, returning a
dict where keys are the names of submetrics and values are the values of
"""Take a single document and the LM results and evaluates, returning a
dict where keys are the names of submetrics and values are the values of
the metric for that one document
:param doc:
......@@ -147,13 +156,13 @@ class CoQA(Task):
"""
turn_id = len(doc["questions"]["input_text"])
gold_list = self.get_answers(doc, turn_id)
pred = results[0].strip().split('\n')[0]
pred = results[0].strip().split("\n")[0]
scores = self.compute_scores(gold_list, pred)
return {
"f1": scores['f1'],
"em": scores['em'],
"f1": scores["f1"],
"em": scores["em"],
}
def higher_is_better(self):
......
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