The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 159, in compute
compute_split_names_from_info_response(
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 131, in compute_split_names_from_info_response
config_info_response = get_previous_step_or_raise(kind="config-info", dataset=dataset, config=config)
File "/src/libs/libcommon/src/libcommon/simple_cache.py", line 567, in get_previous_step_or_raise
raise CachedArtifactError(
libcommon.simple_cache.CachedArtifactError: The previous step failed.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.9/tarfile.py", line 190, in nti
s = nts(s, "ascii", "strict")
File "/usr/local/lib/python3.9/tarfile.py", line 174, in nts
return s.decode(encoding, errors)
UnicodeDecodeError: 'ascii' codec can't decode byte 0xbb in position 1: ordinal not in range(128)
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.9/tarfile.py", line 2588, in next
tarinfo = self.tarinfo.fromtarfile(self)
File "/usr/local/lib/python3.9/tarfile.py", line 1292, in fromtarfile
obj = cls.frombuf(buf, tarfile.encoding, tarfile.errors)
File "/usr/local/lib/python3.9/tarfile.py", line 1234, in frombuf
chksum = nti(buf[148:156])
File "/usr/local/lib/python3.9/tarfile.py", line 193, in nti
raise InvalidHeaderError("invalid header")
tarfile.InvalidHeaderError: invalid header
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 499, in get_dataset_config_info
for split_generator in builder._split_generators(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 86, in _split_generators
first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 30, in _get_pipeline_from_tar
for filename, f in tar_iterator:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 1577, in __iter__
for x in self.generator(*self.args, **self.kwargs):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 1637, in _iter_from_urlpath
yield from cls._iter_tar(f)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 1588, in _iter_tar
stream = tarfile.open(fileobj=f, mode="r|*")
File "/usr/local/lib/python3.9/tarfile.py", line 1822, in open
t = cls(name, filemode, stream, **kwargs)
File "/usr/local/lib/python3.9/tarfile.py", line 1703, in __init__
self.firstmember = self.next()
File "/usr/local/lib/python3.9/tarfile.py", line 2600, in next
raise ReadError(str(e))
tarfile.ReadError: invalid header
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 75, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 572, in get_dataset_split_names
info = get_dataset_config_info(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 504, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
data-intermediate
If you are looking for our test ready version, please refer to mango-ttic/data
Find more about us at mango.ttic.edu
Folder Structure
Each folder inside data-intermediate contains
all intermediate files we used during data annotation and generation. Here is the tree structure from game data-intermediate/night .
data-intermediate/night/
├── night.all2all.json # all simple paths between any 2 nodes
├── night.all_pairs.json # all connectivity between any 2 nodes
├── night.anno2code.json # annotation to codename mapping
├── night.code2anno.json # codename to annotation mapping
├── night.edges.json # list of all edges
├── night.map.human # human map derived from human annotation
├── night.map.machine # machine map derived from exported action sequences
├── night.map.reversed # reverse map derived from human annotation map
├── night.moves # list of mentioned actions
├── night.nodes.json # list of all nodes
├── night.valid_moves.csv # human annotation
├── night.walkthrough # enriched walkthrough exported from Jericho simulator
└── night.walkthrough_acts # action sequences exported from Jericho simulator
Variations
70-step vs all-step version
In our paper, we benchmark using the first 70 steps of the walkthrough from each game. We also provide all-step versions of both data and data-intermediate collection.
70-step
data-intermediate-70steps.tar.zst: contains the first 70 steps of each walkthrough. If the complete walkthrough is shorter than 70 steps, then all steps are used.All-step
data-intermediate.tar.zst: contains all steps of each walkthrough.
Word-only & Word+ID
Word-only
data-intermediate.tar.zst: Nodes are annotated by additional descriptive text to distinguish different locations with similar names.Word + Object ID
data-intermediate-objid.tar.zst: variation of the word-only version, where nodes are labeled using minimaly fixed names with object id from Jericho simulator.Word + Random ID
data-intermediate-randid.tar.zst: variation of the Jericho ID version, where the Jericho object id replaced with randomly generated integer.
We primarily rely on the word-only version as benchmark, yet providing word+ID version for diverse benchmark settings.
How to use
We use data-intermediate.tar.zst as an example here.
1. download from Huggingface
by directly download
You can selectively download certain variation of your choice.
by git
Make sure you have git-lfs installed
git lfs install
git clone https://huggingface.co/datasets/mango-ttic/data-intermediate
# or, use hf-mirror if your connection to huggingface.co is slow
# git clone https://hf-mirror.com/datasets/mango-ttic/data-intermediate
If you want to clone without large files - just their pointers
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/mango-ttic/data-intermediate
# or, use hf-mirror if your connection to huggingface.co is slow
# GIT_LFS_SKIP_SMUDGE=1 git clone https://hf-mirror.com/datasets/mango-ttic/data-intermediate
2. decompress
Because some json files are huge, we use tar.zst to package the data efficiently.
silently decompress
tar -I 'zstd -d' -xf data-intermediate.tar.zst
or, verbosely decompress
zstd -d -c data-intermediate.tar.zst | tar -xvf -
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