File size: 4,846 Bytes
2b2f744 70f4226 7e951ff 2b2f744 7e951ff 2b3c98b 2b2f744 535608c 70f4226 535608c 2b2f744 3dad049 2b2f744 70f4226 07dd735 70f4226 2b2f744 3dad049 2b2f744 d482c79 535608c d482c79 535608c 2b2f744 3dad049 d482c79 2b2f744 3dad049 2b2f744 535608c 2b2f744 d482c79 2b2f744 d482c79 7e951ff |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 |
# coding=utf-8
"""
SyntaxGym dataset as used in Hu et al. (2020).
"""
from collections import defaultdict
from copy import deepcopy
import json
from pathlib import Path
import re
from typing import List, Dict, Tuple
from typing_extensions import TypedDict
import datasets
from datasets import logging
import numpy as np
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
_CITATION = """
@inproceedings{Hu:et-al:2020,
author = {Hu, Jennifer and Gauthier, Jon and Qian, Peng and Wilcox, Ethan and Levy, Roger},
title = {A systematic assessment of syntactic generalization in neural language models},
booktitle = {Proceedings of the Association of Computational Linguistics},
year = {2020}
}
"""
_DESCRIPTION = "" # TODO
_PROJECT_URL = "https://syntaxgym.org"
_DOWNLOAD_URL = "https://raw.githubusercontent.com/cpllab/syntactic-generalization/nextflow/test_suites/json/"
def condition_to_string(cond):
ret = " ".join([region["content"].lstrip()
for region in cond["regions"]
if region["content"].strip() != ""])
ret = re.sub(r"\s+([.,])", r"\1", ret)
return ret
class SyntaxGymSuiteConfig(datasets.BuilderConfig):
def __init__(self, name, version=datasets.Version("1.0.0"), **kwargs):
description = f"SyntaxGym test suite {name}.\n" + _DESCRIPTION
super().__init__(name=name, description=description, version=version,
**kwargs)
class SyntaxGymAll2020SuitesConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super().__init__(
name="all-2020",
description="All SyntaxGym test suites from Hu et al. (2020).\n" + _DESCRIPTION)
SUITE_DATASET_CONDITION_SPEC = {
"condition_name": datasets.Value("string"),
"content": datasets.Value("string"),
"regions": datasets.Sequence({
"region_number": datasets.Value("int32"),
"content": datasets.Value("string")
})
}
SUITE_DATASET_SPEC = {
"suite_name": datasets.Value("string"),
"item_number": datasets.Value("int32"),
"conditions": datasets.Sequence(SUITE_DATASET_CONDITION_SPEC),
"predictions": datasets.Sequence(datasets.Value("string")),
}
class SyntaxGym(datasets.GeneratorBasedBuilder):
SUITES = [
"center_embed", "center_embed_mod",
"cleft", "cleft_modifier",
"fgd_hierarchy", "fgd_object",
"fgd_pp", "fgd_subject",
"mvrr", "mvrr_mod",
"npi_orc_any", "npi_orc_ever", "npi_src_any", "npi_src_ever",
"npz_ambig", "npz_ambig_mod", "npz_obj", "npz_obj_mod",
"number_orc", "number_prep", "number_src",
"reflexive_orc_fem", "reflexive_orc_masc",
"reflexive_prep_fem", "reflexive_prep_masc",
"reflexive_src_fem", "reflexive_src_masc",
"subordination", "subordination_orc-orc",
"subordination_pp-pp", "subordination_src-src",
]
BUILDER_CONFIGS = \
[SyntaxGymSuiteConfig(suite_name) for suite_name in SUITES] + \
[SyntaxGymAll2020SuitesConfig()]
DEFAULT_CONFIG_NAME = "all-2020"
def _info(self):
citation = ""
# print(self.BUILDER_CONFIGS)
# if self.config.meta["reference"]:
# citation = f"Test suite citation: {self.meta['reference']}\n"
citation += f"SyntaxGym citation:\n{_CITATION}"
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(SUITE_DATASET_SPEC),
homepage=_PROJECT_URL,
citation=citation,
)
def _download_suite(self, name, dl_manager: datasets.DownloadManager):
return dl_manager.download_and_extract(_DOWNLOAD_URL + f"{name}.json")
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
if isinstance(self.config, SyntaxGymAll2020SuitesConfig):
paths = [self._download_suite(suite_name, dl_manager) for suite_name in self.SUITES]
else:
paths = [self._download_suite(self.config.name, dl_manager)]
return [datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"paths": paths})]
def _generate_examples(self, paths):
for path in paths:
with open(path, "r", encoding="utf-8") as f:
suite_json = json.load(f)
suite_name = suite_json["meta"]["name"]
predictions = [p["formula"] for p in suite_json["predictions"]]
for item in suite_json["items"]:
# Convert to sentence input.
for cond in item["conditions"]:
cond["content"] = condition_to_string(cond)
item["suite_name"] = suite_name
item["predictions"] = predictions
yield f"{suite_name}/{item['item_number']}", item
|