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Error code: FeaturesError Exception: ArrowInvalid Message: JSON parse error: Column(/connections) changed from object to array in row 656 Traceback: Traceback (most recent call last): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 160, in _generate_tables df = pandas_read_json(f) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json return pd.read_json(path_or_buf, **kwargs) File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 815, in read_json return json_reader.read() File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1025, in read obj = self._get_object_parser(self.data) File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1051, in _get_object_parser obj = FrameParser(json, **kwargs).parse() File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1187, in parse self._parse() File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1403, in _parse ujson_loads(json, precise_float=self.precise_float), dtype=None ValueError: Trailing data During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 233, in compute_first_rows_from_streaming_response iterable_dataset = iterable_dataset._resolve_features() File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2998, in _resolve_features features = _infer_features_from_batch(self.with_format(None)._head()) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1918, in _head return _examples_to_batch(list(self.take(n))) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2093, in __iter__ for key, example in ex_iterable: File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1576, in __iter__ for key_example in islice(self.ex_iterable, self.n - ex_iterable_num_taken): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 279, in __iter__ for key, pa_table in self.generate_tables_fn(**gen_kwags): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 163, in _generate_tables raise e File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 137, in _generate_tables pa_table = paj.read_json( File "pyarrow/_json.pyx", line 308, in pyarrow._json.read_json File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: JSON parse error: Column(/connections) changed from object to array in row 656
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Dataset Card for Achieve the Core
This repository includes Common Core math standards, their descriptions, and metadata obtained from Achieve the Core.
Example of a math standard:
{
"id": "K.CC.B.4",
"description": "Understand the relationship between numbers and quantities; connect counting to cardinality.",
"source": "Achieve the Core",
"level": "Standard",
"cluster_type": "major cluster",
"aspects": [],
"parent": "K.CC.B",
"children": ["K.CC.B.4c", "K.CC.B.4b", "K.CC.B.4a"],
"connections": {"progress to": ["1.OA.C.5", "K.CC.B.5"], "progress from": [], "related": ["K.CC.A.2", "K.CC.C.6", "K.CC.A.1"]},
"modeling": false
}
See MathFish for more details on uses of this data.
This data can be used to evaluate language models' abilities to assess whether math problems enable students to learn specific skills/concepts. Code to support this can be found in this Github repository.
Dataset Details
Dataset Description
- Curated by: Lucy Li, Tal August, Rose E Wang, Luca Soldaini, Courtney Allison, Kyle Lo
- Funded by: The Gates Foundation
- Language(s) (NLP): English
- License: ODC-By 1.0
Dataset Sources
- Repository: Achieve the Core's Github
- Website: Achieve the Core's Coherence Map
Dataset Structure
This repository includes two key files: domain_groups.json
and standards.jsonl
.
We created domain_groups.json
because the "domains" we evaluate with for our tagging task do not have a one-to-one mapping to K-8 domains and high school (HS) categories in Common Core State Standards (CCSS). Some HS categories are equivalent or similar to a domain in K-8, and some differences in K-8 domains are difficult to explain a brief description at the domain-level. Thus, a "domain" in our paper sometimes groups multiple actual CCSS domains/categories. We mostly retain the original CCSS K-8 domains and HS categories, but make exceptions for the following: we group OA (Operations & Algebraic Thinking), EE (Expressions & Equations), and A (HS Algebra) into Operations & Algebra, S (HS Statistics & Probability) and SP (K-8 Statistics & Probability) to \textit{Statistics & Probability}, and finally NS (K-8 The Number System) and N (HS Number and Quantity) to Number Systems and Quantity. Since CCSS and Achieve the Core do not provide brief descriptions of domains, we worked with a curriculum specialist to write domains' descriptions.
Within standards.jsonl
, each line is a standard, sub-standard, cluster, domain, or grade level:
{
id: '', # e.g. 'K.OA.A.1'
description: 'description of standard from achieve the core',
source: 'Achieve the Core',
level: '', # one of Grade, HS Category, Domain, Cluster, Standard, Sub-standard
cluster_type: '', # e.g. major cluster, additional cluster, minor cluster
aspects: [], # a list containing items such as "Application", "conceptual understanding", "Procedural Skill and Fluency"
parent: '',
children: [],
connections: {''progress to': [], 'progress from': [], 'related': []} # standard-level Achieve the Core connections
modeling: # True or False depending on whether the standard is a "modeling" standard
}
After downloading each file, you can load them:
import json
with open('domain_groups.json', 'r') as infile:
domain_groups = json.load(infile)
print(domain_groups.keys()) # should print the keys of this dictionary
with open('standards.jsonl', 'r') as infile:
for line in infile:
this_standard = json.loads(line)
print(this_standard['id']) # should print the ID of the row in this file
Citation
@misc{lucy2024evaluatinglanguagemodelmath,
title={Evaluating Language Model Math Reasoning via Grounding in Educational Curricula},
author={Li Lucy and Tal August and Rose E. Wang and Luca Soldaini and Courtney Allison and Kyle Lo},
year={2024},
eprint={2408.04226},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2408.04226},
}
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